commit ddccf653d255b4d6b686471918f8aa8847eeeb70 Author: Rick McEwen Date: Wed Dec 31 10:42:36 2025 -0500 Initial commit: Logo detection test framework Add DETR+CLIP based logo detection library and test framework: - DetectLogosDETR class for logo detection and matching - Test script with margin-based and multi-ref matching methods - Data preparation script for test database - Documentation for API usage and test methodology diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..102342e --- /dev/null +++ b/.gitignore @@ -0,0 +1,38 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv + +# Image directories +reference_logos/ +test_images/ + +# Image files +*.jpg +*.jpeg +*.png +*.gif +*.bmp +*.webp + +# Database and data files +*.db +*.json +*.pkl + +# Cache files +.embedding_cache.pkl + +# IDE +.idea/ +.vscode/ + +# Results files +results*.txt +sample_results.txt diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..ab3f2d4 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,53 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project Overview + +Logo detection system using deep learning models: +- **DETR** (DEtection TRansformer) for logo region detection +- **CLIP** (Contrastive Language-Image Pre-training) for feature extraction and matching + +## Development Commands + +```bash +# Install dependencies (uses uv package manager) +uv sync + +# Run main script +uv run python main.py + +# Run logo detection module directly +uv run python logo_detection_detr.py +``` + +## Architecture + +### Core Module: `logo_detection_detr.py` + +The `DetectLogosDETR` class provides the main detection pipeline: + +1. **Detection Flow**: OpenCV image (BGR) → DETR detects bounding boxes → CLIP extracts embeddings for each region +2. **Matching Flow**: Compare detected embeddings against reference logo embeddings using cosine similarity + +**Key Methods:** +- `detect(image)` - Detect logos, returns boxes + CLIP embeddings +- `get_embedding(image)` - Get CLIP embedding for a reference logo +- `compare_embeddings(emb1, emb2)` - Cosine similarity between embeddings +- `detect_and_match(image, references, threshold)` - Combined detection and matching + +### Model Configuration + +Models are resolved in this order: +1. Absolute path if provided +2. Local directory from environment variables (`LOGO_DETR_MODEL_DIR`, `LOGO_CLIP_MODEL_DIR`) +3. Default local paths: `models/logo_detection/detr`, `models/logo_detection/clip` +4. HuggingFace download as fallback + +Default models: +- DETR: `Pravallika6/detr-finetuned-logo-detection_v2` +- CLIP: `openai/clip-vit-large-patch14` + +### Reference Dataset + +`LogoDet-3K/` contains logo images organized by category: Clothes, Electronic, Food, Leisure, Medical, Necessities, Others, Sports, Transportation. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..9ab8326 --- /dev/null +++ b/README.md @@ -0,0 +1,116 @@ +# Logo Detection Test Framework + +A testing framework for evaluating logo detection accuracy using DETR (DEtection TRansformer) and CLIP (Contrastive Language-Image Pre-training) models. + +## Overview + +This project provides tools to: +- Detect logos in images using a fine-tuned DETR model +- Match detected logos against reference images using CLIP embeddings +- Evaluate detection accuracy with precision, recall, and F1 metrics + +## Architecture + +The system uses a two-stage pipeline: + +1. **DETR** - Identifies potential logo regions (bounding boxes) in images +2. **CLIP** - Extracts feature embeddings for each detected region and compares against reference logos + +## Installation + +Requires Python 3.12+. Uses [uv](https://github.com/astral-sh/uv) for package management. + +```bash +# Install dependencies +uv sync + +# Or using pip +pip install -r requirements.txt +``` + +## Usage + +### Prepare Test Data + +First, prepare the test database with logo mappings: + +```bash +uv run python prepare_test_data.py +``` + +This creates `test_data_mapping.db` with ground truth mappings between test images and logos. + +### Run Detection Tests + +```bash +# Basic test with default settings (margin-based matching) +uv run python test_logo_detection.py + +# Test with more logos and custom threshold +uv run python test_logo_detection.py -n 20 --threshold 0.75 + +# Use multi-ref matching method +uv run python test_logo_detection.py --matching-method multi-ref \ + --refs-per-logo 5 --min-matching-refs 2 + +# Reproducible test with seed +uv run python test_logo_detection.py -n 50 --seed 42 +``` + +### Key Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `-n, --num-logos` | 10 | Number of reference logos to sample | +| `-t, --threshold` | 0.7 | CLIP similarity threshold | +| `-d, --detr-threshold` | 0.5 | DETR detection confidence threshold | +| `--matching-method` | margin | Matching method: `margin` or `multi-ref` | +| `--margin` | 0.05 | Margin over second-best match (margin method) | +| `--min-matching-refs` | 1 | Min refs that must match (multi-ref method) | +| `--refs-per-logo` | 3 | Reference images per logo | +| `-s, --seed` | None | Random seed for reproducibility | + +See `--help` for all options. + +## Project Structure + +``` +logo_test/ +├── logo_detection_detr.py # Core detection library (DetectLogosDETR class) +├── test_logo_detection.py # Test script for accuracy evaluation +├── prepare_test_data.py # Script to prepare test database +├── test_data_mapping.db # SQLite database with ground truth +├── reference_logos/ # Reference logo images (not in git) +├── test_images/ # Test images (not in git) +├── logo_detection_detr_usage.md # API usage guide +└── logo_detection_test_methodology.md # Test methodology documentation +``` + +## Accuracy Improvement Techniques + +The framework implements several techniques to improve detection accuracy: + +1. **Non-Maximum Suppression (NMS)** - Removes overlapping duplicate detections +2. **Minimum Box Size Filtering** - Filters out noise from tiny detections +3. **Confidence Threshold Filtering** - Removes low-confidence detections +4. **Multiple Reference Images** - Uses multiple refs per logo for robust matching +5. **Margin-Based Matching** - Requires confidence margin over second-best match +6. **Multi-Ref Matching** - Aggregates similarity scores across references +7. **Embedding Caching** - Caches embeddings to avoid recomputation + +## Models + +The framework uses: +- **DETR**: `Pravallika6/detr-finetuned-logo-detection_v2` +- **CLIP**: `openai/clip-vit-large-patch14` + +Models are automatically downloaded from HuggingFace on first run and cached in `~/.cache/huggingface/`. + +## Documentation + +- [API Usage Guide](logo_detection_detr_usage.md) - How to use the DetectLogosDETR class +- [Test Methodology](logo_detection_test_methodology.md) - Detailed explanation of test framework and tuning + +## License + +MIT \ No newline at end of file diff --git a/logo_detection_detr.py b/logo_detection_detr.py new file mode 100644 index 0000000..af8a9ac --- /dev/null +++ b/logo_detection_detr.py @@ -0,0 +1,556 @@ +""" +Logo detection using DETR for object detection and CLIP for feature matching. + +This module provides a class for detecting logos in images using: +1. DETR (DEtection TRansformer) for initial logo region detection +2. CLIP (Contrastive Language-Image Pre-training) for feature extraction and matching + +The class supports caching of embeddings for efficient reprocessing. +The class automatically uses local models if available, otherwise falls back to HuggingFace. +""" + +import os +import torch +import torch.nn.functional as F +from transformers import pipeline, CLIPProcessor, CLIPModel +from PIL import Image +import cv2 +import numpy as np +from pathlib import Path +from typing import List, Tuple, Dict, Optional, Any + + +class DetectLogosDETR: + """ + Logo detection class using DETR and CLIP models. + + This class detects logos in images by: + 1. Using DETR to find potential logo regions (bounding boxes) + 2. Extracting CLIP embeddings for each detected region + 3. Comparing embeddings with reference logos for identification + + The class automatically checks for local models before downloading from HuggingFace. + """ + + def __init__( + self, + logger, + detr_model: str = "Pravallika6/detr-finetuned-logo-detection_v2", + #clip_model: str = "openai/clip-vit-base-patch32", + clip_model: str = "openai/clip-vit-large-patch14", + detr_threshold: float = 0.5, + min_box_size: int = 20, + nms_iou_threshold: float = 0.5, + ): + """ + Initialize DETR and CLIP models. + + The class will automatically check for local models in the default directories + before downloading from HuggingFace. You can override this by providing absolute + paths to local models. + + Args: + logger: Logger instance for logging + detr_model: HuggingFace model name or local path for DETR object detection + clip_model: HuggingFace model name or local path for CLIP embeddings + detr_threshold: Confidence threshold for DETR detections (0-1) + min_box_size: Minimum width/height in pixels for detected boxes (filters noise) + nms_iou_threshold: IoU threshold for Non-Maximum Suppression + """ + self.logger = logger + self.detr_threshold = detr_threshold + self.min_box_size = min_box_size + self.nms_iou_threshold = nms_iou_threshold + + # Set device + self.device_str = "cuda:0" if torch.cuda.is_available() else "cpu" + self.device_index = 0 if torch.cuda.is_available() else -1 + self.device = torch.device(self.device_str) + + self.logger.info(f"Initializing DetectLogosDETR on device: {self.device_str}") + + # Get default model directories from environment variables + default_detr_dir = os.environ.get('LOGO_DETR_MODEL_DIR', 'models/logo_detection/detr') + default_clip_dir = os.environ.get('LOGO_CLIP_MODEL_DIR', 'models/logo_detection/clip') + + # Resolve DETR model path (check local first, then use HuggingFace name) + detr_model_path = self._resolve_model_path( + detr_model, default_detr_dir, "DETR" + ) + + # Initialize DETR pipeline for logo detection + self.logger.info(f"Loading DETR model: {detr_model_path}") + self.detr_pipe = pipeline( + task="object-detection", + model=detr_model_path, + device=self.device_index, + use_fast=True, + ) + + # Resolve CLIP model path (check local first, then use HuggingFace name) + clip_model_path = self._resolve_model_path( + clip_model, default_clip_dir, "CLIP" + ) + + # Initialize CLIP model for feature extraction + self.logger.info(f"Loading CLIP model: {clip_model_path}") + self.clip_model = CLIPModel.from_pretrained(clip_model_path).to(self.device) + self.clip_processor = CLIPProcessor.from_pretrained(clip_model_path) + + self.logger.info("DetectLogosDETR initialization complete") + + def _resolve_model_path( + self, model_name_or_path: str, default_local_dir: str, model_type: str + ) -> str: + """ + Resolve model path, checking for local models before using HuggingFace. + + Args: + model_name_or_path: HuggingFace model name or absolute path + default_local_dir: Default local directory to check + model_type: Type of model (for logging, e.g., "DETR" or "CLIP") + + Returns: + Resolved model path (local path or HuggingFace model name) + """ + # If it's an absolute path, use it directly + if os.path.isabs(model_name_or_path): + if os.path.exists(model_name_or_path): + self.logger.info( + f"{model_type} model: Using local model at {model_name_or_path}" + ) + return model_name_or_path + else: + self.logger.warning( + f"{model_type} model: Local path {model_name_or_path} does not exist, " + f"falling back to HuggingFace" + ) + return model_name_or_path + + # Check if default local directory exists + if os.path.exists(default_local_dir): + # Verify it's a valid model directory (has config.json) + config_file = os.path.join(default_local_dir, "config.json") + if os.path.exists(config_file): + abs_path = os.path.abspath(default_local_dir) + self.logger.info( + f"{model_type} model: Found local model at {abs_path}" + ) + return abs_path + else: + self.logger.warning( + f"{model_type} model: Local directory {default_local_dir} exists but " + f"is not a valid model (missing config.json)" + ) + + # Use HuggingFace model name + self.logger.info( + f"{model_type} model: No local model found, will download from HuggingFace: " + f"{model_name_or_path}" + ) + return model_name_or_path + + def detect(self, image: np.ndarray) -> List[Dict[str, Any]]: + """ + Detect logos in an image and return bounding boxes with CLIP embeddings. + + Args: + image: OpenCV image (BGR format, numpy array) + + Returns: + List of dictionaries, each containing: + - 'box': dict with 'xmin', 'ymin', 'xmax', 'ymax' (pixel coordinates) + - 'score': DETR confidence score (float 0-1) + - 'embedding': CLIP feature embedding (torch.Tensor) + - 'label': DETR predicted label (string) + """ + # Convert OpenCV BGR to RGB PIL Image + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + pil_image = Image.fromarray(image_rgb) + + # Run DETR detection + predictions = self.detr_pipe(pil_image) + + # Filter by threshold and size, then add CLIP embeddings + detections = [] + for pred in predictions: + score = pred.get("score", 0.0) + if score < self.detr_threshold: + continue + + box = pred.get("box", {}) + xmin = box.get("xmin", 0) + ymin = box.get("ymin", 0) + xmax = box.get("xmax", 0) + ymax = box.get("ymax", 0) + + # Filter by minimum box size + box_width = xmax - xmin + box_height = ymax - ymin + if box_width < self.min_box_size or box_height < self.min_box_size: + continue + + # Extract bounding box region + bbox_crop = pil_image.crop((xmin, ymin, xmax, ymax)) + + # Get CLIP embedding for this region + embedding = self._get_clip_embedding_pil(bbox_crop) + + detections.append( + { + "box": {"xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax}, + "score": score, + "embedding": embedding, + "label": pred.get("label", "logo"), + } + ) + + # Apply Non-Maximum Suppression to remove overlapping detections + detections = self._apply_nms(detections, self.nms_iou_threshold) + + self.logger.debug(f"Detected {len(detections)} logos (threshold: {self.detr_threshold})") + return detections + + def _apply_nms(self, predictions: List[Dict], iou_threshold: float) -> List[Dict]: + """ + Apply Non-Maximum Suppression to remove overlapping detections. + + Args: + predictions: List of prediction dictionaries with 'box' and 'score' + iou_threshold: IoU threshold for considering boxes as overlapping + + Returns: + Filtered list of predictions after NMS + """ + if len(predictions) == 0: + return [] + + # Extract boxes and scores + boxes = [] + scores = [] + for pred in predictions: + box = pred.get("box", {}) + boxes.append([ + box.get("xmin", 0), + box.get("ymin", 0), + box.get("xmax", 0), + box.get("ymax", 0) + ]) + scores.append(pred.get("score", 0.0)) + + # Convert to numpy arrays + boxes = np.array(boxes, dtype=np.float32) + scores = np.array(scores, dtype=np.float32) + + # Sort by scores (descending) + sorted_indices = np.argsort(scores)[::-1] + + keep_indices = [] + while len(sorted_indices) > 0: + # Keep the box with highest score + current_idx = sorted_indices[0] + keep_indices.append(current_idx) + + if len(sorted_indices) == 1: + break + + # Calculate IoU with remaining boxes + current_box = boxes[current_idx] + remaining_boxes = boxes[sorted_indices[1:]] + + ious = self._calculate_iou_batch(current_box, remaining_boxes) + + # Keep only boxes with IoU below threshold + mask = ious < iou_threshold + sorted_indices = sorted_indices[1:][mask] + + # Return predictions for kept indices + return [predictions[i] for i in keep_indices] + + def _calculate_iou_batch(self, box: np.ndarray, boxes: np.ndarray) -> np.ndarray: + """ + Calculate IoU between one box and multiple boxes. + + Args: + box: Single box [xmin, ymin, xmax, ymax] + boxes: Multiple boxes [[xmin, ymin, xmax, ymax], ...] + + Returns: + Array of IoU values + """ + # Calculate intersection coordinates + x1 = np.maximum(box[0], boxes[:, 0]) + y1 = np.maximum(box[1], boxes[:, 1]) + x2 = np.minimum(box[2], boxes[:, 2]) + y2 = np.minimum(box[3], boxes[:, 3]) + + # Calculate intersection area + intersection = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1) + + # Calculate union area + box_area = (box[2] - box[0]) * (box[3] - box[1]) + boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + union = box_area + boxes_area - intersection + + # Calculate IoU + iou = intersection / (union + 1e-6) # Add small epsilon to avoid division by zero + + return iou + + def get_embedding(self, image: np.ndarray) -> torch.Tensor: + """ + Get CLIP embedding for a reference logo image. + + This method is used to compute embeddings for reference logos + that will be compared against detected regions. + + Args: + image: OpenCV image (BGR format, numpy array) + + Returns: + Normalized CLIP feature embedding (torch.Tensor, shape: [1, 512]) + """ + # Convert OpenCV BGR to RGB PIL Image + image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + pil_image = Image.fromarray(image_rgb) + + return self._get_clip_embedding_pil(pil_image) + + def _get_clip_embedding_pil(self, pil_image: Image.Image) -> torch.Tensor: + """ + Internal method to get CLIP embedding from PIL image. + + Args: + pil_image: PIL Image (RGB format) + + Returns: + Normalized CLIP feature embedding (torch.Tensor) + """ + # Process image through CLIP + inputs = self.clip_processor(images=pil_image, return_tensors="pt").to(self.device) + + with torch.no_grad(): + features = self.clip_model.get_image_features(**inputs) + # Normalize for cosine similarity + features = F.normalize(features, dim=-1) + + return features + + def compare_embeddings( + self, embedding1: torch.Tensor, embedding2: torch.Tensor + ) -> float: + """ + Compute cosine similarity between two CLIP embeddings. + + Args: + embedding1: First CLIP embedding (torch.Tensor) + embedding2: Second CLIP embedding (torch.Tensor) + + Returns: + Cosine similarity score (float, range: -1 to 1, typically 0 to 1) + """ + # Ensure tensors are on the same device + if embedding1.device != embedding2.device: + embedding2 = embedding2.to(embedding1.device) + + # Compute cosine similarity + similarity = F.cosine_similarity(embedding1, embedding2, dim=-1) + + # Return as Python float + return similarity.item() + + def find_best_match( + self, + detected_embedding: torch.Tensor, + reference_embeddings: List[Tuple[str, torch.Tensor]], + similarity_threshold: float = 0.7, + ) -> Optional[Tuple[str, float]]: + """ + Find the best matching reference logo for a detected embedding. + + Args: + detected_embedding: CLIP embedding from detected logo region + reference_embeddings: List of (label, embedding) tuples for reference logos + similarity_threshold: Minimum similarity to consider a match (0-1) + + Returns: + Tuple of (label, similarity) for best match, or None if no match above threshold + """ + if not reference_embeddings: + return None + + best_similarity = -1.0 + best_label = None + + for label, ref_embedding in reference_embeddings: + similarity = self.compare_embeddings(detected_embedding, ref_embedding) + + if similarity > best_similarity: + best_similarity = similarity + best_label = label + + if best_similarity >= similarity_threshold: + return (best_label, best_similarity) + else: + return None + + def find_best_match_multi_ref( + self, + detected_embedding: torch.Tensor, + reference_embeddings: Dict[str, List[torch.Tensor]], + similarity_threshold: float = 0.85, + min_matching_refs: int = 1, + use_mean_similarity: bool = True, + ) -> Optional[Tuple[str, float, int]]: + """ + Find the best matching reference logo using multiple reference embeddings per logo. + + This method improves accuracy by using multiple reference images for each logo + and requiring consistency across references. + + Args: + detected_embedding: CLIP embedding from detected logo region + reference_embeddings: Dict mapping logo name to list of embeddings + similarity_threshold: Minimum similarity to consider a match (0-1) + min_matching_refs: Minimum number of references that must match above threshold + use_mean_similarity: If True, use mean similarity across all refs; if False, use max + + Returns: + Tuple of (label, similarity, num_matching_refs) for best match, + or None if no match meets criteria + """ + if not reference_embeddings: + return None + + best_score = -1.0 + best_label = None + best_num_matches = 0 + + for label, ref_embedding_list in reference_embeddings.items(): + if not ref_embedding_list: + continue + + # Calculate similarity to each reference embedding + similarities = [] + for ref_embedding in ref_embedding_list: + sim = self.compare_embeddings(detected_embedding, ref_embedding) + similarities.append(sim) + + # Count how many references match above threshold + num_matches = sum(1 for s in similarities if s >= similarity_threshold) + + # Calculate aggregate score + if use_mean_similarity: + score = sum(similarities) / len(similarities) + else: + score = max(similarities) + + # Check if this logo meets the minimum matching refs requirement + if num_matches >= min_matching_refs and score > best_score: + best_score = score + best_label = label + best_num_matches = num_matches + + if best_label is not None and best_score >= similarity_threshold: + return (best_label, best_score, best_num_matches) + else: + return None + + def find_best_match_with_margin( + self, + detected_embedding: torch.Tensor, + reference_embeddings: List[Tuple[str, torch.Tensor]], + similarity_threshold: float = 0.85, + margin: float = 0.05, + ) -> Optional[Tuple[str, float]]: + """ + Find best match with a confidence margin over the second-best match. + + This reduces false positives by requiring the best match to be + significantly better than alternatives. + + Args: + detected_embedding: CLIP embedding from detected logo region + reference_embeddings: List of (label, embedding) tuples for reference logos + similarity_threshold: Minimum similarity to consider a match (0-1) + margin: Required margin between best and second-best match + + Returns: + Tuple of (label, similarity) for best match, or None if no confident match + """ + if not reference_embeddings: + return None + + # Calculate all similarities + similarities = [] + for label, ref_embedding in reference_embeddings: + sim = self.compare_embeddings(detected_embedding, ref_embedding) + similarities.append((label, sim)) + + # Sort by similarity descending + similarities.sort(key=lambda x: x[1], reverse=True) + + best_label, best_sim = similarities[0] + + # Check if best is above threshold + if best_sim < similarity_threshold: + return None + + # Check margin against second best (if exists) + if len(similarities) > 1: + second_best_sim = similarities[1][1] + if best_sim - second_best_sim < margin: + return None # Not confident enough + + return (best_label, best_sim) + + def detect_and_match( + self, + image: np.ndarray, + reference_embeddings: List[Tuple[str, torch.Tensor]], + similarity_threshold: float = 0.7, + ) -> List[Dict[str, Any]]: + """ + Detect logos and match them against reference embeddings in one step. + + This is a convenience method that combines detection and matching. + + Args: + image: OpenCV image (BGR format, numpy array) + reference_embeddings: List of (label, embedding) tuples for reference logos + similarity_threshold: Minimum similarity to consider a match (0-1) + + Returns: + List of matched detections, each containing: + - 'box': bounding box coordinates + - 'detr_score': DETR confidence score + - 'clip_similarity': CLIP similarity score + - 'label': matched reference logo label + """ + # Detect all logos + detections = self.detect(image) + + # Match each detection against references + matched_detections = [] + for detection in detections: + match_result = self.find_best_match( + detection["embedding"], reference_embeddings, similarity_threshold + ) + + if match_result is not None: + label, similarity = match_result + matched_detections.append( + { + "box": detection["box"], + "detr_score": detection["score"], + "clip_similarity": similarity, + "label": label, + } + ) + + self.logger.debug( + f"Matched {len(matched_detections)}/{len(detections)} detections " + f"(threshold: {similarity_threshold})" + ) + + return matched_detections \ No newline at end of file diff --git a/logo_detection_detr_usage.md b/logo_detection_detr_usage.md new file mode 100644 index 0000000..da99362 --- /dev/null +++ b/logo_detection_detr_usage.md @@ -0,0 +1,301 @@ +# DetectLogosDETR Class Usage Guide + +## Overview + +The `DetectLogosDETR` class provides logo detection using: +- **DETR** (DEtection TRansformer) for initial logo region detection +- **CLIP** (Contrastive Language-Image Pre-training) for feature embeddings and matching + +## Key Features + +### 1. **Constructor** - Initializes models with CUDA support + +```python +from scan_utils.logo_detection_detr import DetectLogosDETR + +detector = DetectLogosDETR(logger, detr_threshold=0.5) +``` + +- Automatically detects and uses CUDA if available +- Loads DETR for logo region detection +- Loads CLIP for feature embeddings +- `detr_threshold`: Confidence threshold for DETR detections (0-1, default: 0.5) + +### 2. **Main Detection Methods** + +#### `detect(image)` - Detect logos and return embeddings + +```python +detections = detector.detect(opencv_image) +# Returns: [{'box': {...}, 'score': 0.95, 'embedding': tensor, 'label': 'logo'}, ...] +``` + +Returns a list of dictionaries, each containing: +- `box`: Dictionary with `xmin`, `ymin`, `xmax`, `ymax` (pixel coordinates) +- `score`: DETR confidence score (float 0-1) +- `embedding`: CLIP feature embedding (torch.Tensor) +- `label`: DETR predicted label (string) + +#### `get_embedding(image)` - Get embedding for reference logos + +```python +embedding = detector.get_embedding(reference_logo_image) +# For caching reference logo embeddings +``` + +- Takes OpenCV image (BGR format) +- Returns normalized CLIP embedding (torch.Tensor, shape: [1, 512]) +- Used to compute embeddings for reference logos that will be cached + +#### `compare_embeddings(emb1, emb2)` - Compute cosine similarity + +```python +similarity = detector.compare_embeddings(detected_emb, reference_emb) +# Returns: float (0-1, higher = more similar) +``` + +- Compares two CLIP embeddings +- Returns cosine similarity score (float, range: -1 to 1, typically 0 to 1) + +### 3. **Convenience Methods** + +#### `find_best_match()` - Find best matching reference logo + +```python +match = detector.find_best_match( + detected_embedding, + reference_embeddings, + similarity_threshold=0.7 +) +# Returns: (label, similarity) or None +``` + +**Parameters:** +- `detected_embedding`: CLIP embedding from detected logo region +- `reference_embeddings`: List of (label, embedding) tuples for reference logos +- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.7) + +**Returns:** +- Tuple of (label, similarity) for best match, or None if no match above threshold + +#### `detect_and_match()` - One-step detection and matching + +```python +matches = detector.detect_and_match( + image, + reference_embeddings, + similarity_threshold=0.7 +) +``` + +Convenience method that combines detection and matching in one step. + +**Returns:** +- List of matched detections, each containing: + - `box`: Bounding box coordinates + - `detr_score`: DETR confidence score + - `clip_similarity`: CLIP similarity score + - `label`: Matched reference logo label + +### 4. **Advanced Matching Methods** + +These methods provide improved accuracy over basic matching. + +#### `find_best_match_with_margin()` - Margin-based matching + +Requires the best match to exceed the second-best by a minimum margin, reducing false positives from ambiguous matches. + +```python +match = detector.find_best_match_with_margin( + detected_embedding, + reference_embeddings, # List of (label, embedding) tuples + similarity_threshold=0.85, + margin=0.05 +) +# Returns: (label, similarity) or None +``` + +**Parameters:** +- `detected_embedding`: CLIP embedding from detected logo region +- `reference_embeddings`: List of (label, embedding) tuples for reference logos +- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.85) +- `margin`: Required difference between best and second-best match (default: 0.05) + +**Returns:** +- Tuple of (label, similarity) for best match, or None if: + - No match above threshold, OR + - Best match doesn't exceed second-best by the required margin + +**Example:** +```python +# Best match: Logo A (0.82), Second best: Logo B (0.79) +# With margin=0.05: No match returned (0.82 - 0.79 = 0.03 < 0.05) +# This prevents false positives when multiple logos look similar +``` + +#### `find_best_match_multi_ref()` - Multi-reference matching + +Uses multiple reference images per logo for more robust matching, aggregating similarity scores across references. + +```python +match = detector.find_best_match_multi_ref( + detected_embedding, + reference_embeddings, # Dict: logo_name -> list of embeddings + similarity_threshold=0.85, + min_matching_refs=1, + use_mean_similarity=True +) +# Returns: (label, similarity, num_matching_refs) or None +``` + +**Parameters:** +- `detected_embedding`: CLIP embedding from detected logo region +- `reference_embeddings`: Dict mapping logo name to list of embeddings +- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.85) +- `min_matching_refs`: Minimum number of references that must match above threshold (default: 1) +- `use_mean_similarity`: If True, use mean similarity; if False, use max (default: True) + +**Returns:** +- Tuple of (label, similarity, num_matching_refs) for best match, or None if no match meets criteria + +**Example:** +```python +# Build multi-ref embeddings dict +multi_ref_embeddings = { + "Nike": [embedding1, embedding2, embedding3], + "Adidas": [embedding4, embedding5], +} + +match = detector.find_best_match_multi_ref( + detected_embedding, + multi_ref_embeddings, + similarity_threshold=0.80, + min_matching_refs=2, # At least 2 refs must match + use_mean_similarity=True # Average across all refs +) + +if match: + label, avg_similarity, num_refs_matched = match + print(f"Matched {label} with {avg_similarity:.3f} ({num_refs_matched} refs matched)") +``` + +## Usage Pattern (Similar to Face Recognition) + +The class is designed to work with the caching pattern in scan.py: + +```python +from scan_utils.logo_detection_detr import DetectLogosDETR + +# Initialize detector +detector = DetectLogosDETR(logger, detr_threshold=0.5) + +# 1. Get embeddings for detected logos (cached per image) +detections = detector.detect(target_image) + +# 2. Get/cache reference logo embeddings +reference_embeddings = [] +for logo_file in reference_logos: + # Check cache first (kvstore) + logo_key = make_image_key("logo_reference", logo_file) + embedding = kv.get_torch(logo_key) + + if embedding is None: + # Load and compute embedding + logo_img = image_processor.load_image_safely(logo_file) + embedding = detector.get_embedding(logo_img) + + # Cache for future use + kv.put_torch(logo_key, embedding) + + reference_embeddings.append((logo_name, embedding)) + +# 3. Match detections against references +matched_logos = [] +for detection in detections: + match = detector.find_best_match( + detection['embedding'], + reference_embeddings, + similarity_threshold=0.7 + ) + + if match: + label, similarity = match + matched_logos.append({ + 'label': label, + 'box': detection['box'], + 'detr_score': detection['score'], + 'clip_similarity': similarity + }) + # Logo identified! +``` + +## Caching Strategy + +This follows the same caching pattern as facial recognition: + +1. **Target Image Embeddings**: Cache DETR detections and CLIP embeddings per image + - Key: `make_image_key("logo_detection", image_path)` + - Avoids re-running DETR on the same image + +2. **Reference Logo Embeddings**: Cache CLIP embeddings for reference logos + - Key: `make_image_key("logo_reference", logo_path)` + - Computed once and reused across all image scans + +3. **Benefits**: + - DETR only runs once per target image + - CLIP only runs once per reference logo + - Subsequent scans only perform embedding comparisons (very fast) + +## Integration Example + +```python +def detect_logos_with_caching( + detector, + img_file, + reference_logos, + max_size=1920 +): + # Load and resize image + im_in = image_processor.load_image_safely(img_file) + img = resize_if_needed_opt(im_in, max_size) + + # Check cache for detections + detection_key = make_image_key("logo_detection", img_file) + cached_data = kv.get(detection_key) + + if cached_data: + # Use cached detections + detections = json.loads(cached_data) + logger.debug("Logo detections loaded from cache") + else: + # Run detection and cache results + detections = detector.detect(img) + kv.put(detection_key, json.dumps(detections)) + + # Load reference embeddings (with caching) + reference_embeddings = [] + for logo_name, logo_path in reference_logos: + ref_key = make_image_key("logo_reference", logo_path) + embedding = kv.get_torch(ref_key) + + if embedding is None: + logo_img = image_processor.load_image_safely(logo_path) + embedding = detector.get_embedding(logo_img) + kv.put_torch(ref_key, embedding) + + reference_embeddings.append((logo_name, embedding)) + + # Match and return results + return detector.detect_and_match( + img, + reference_embeddings, + similarity_threshold=0.7 + ) +``` + +## Performance Considerations + +- **First Run**: Slower (DETR + CLIP inference) +- **Cached Runs**: Much faster (only embedding comparisons) +- **GPU Acceleration**: Automatically uses CUDA if available +- **Memory**: Models loaded once and reused across all images \ No newline at end of file diff --git a/logo_detection_test_methodology.md b/logo_detection_test_methodology.md new file mode 100644 index 0000000..dff29a5 --- /dev/null +++ b/logo_detection_test_methodology.md @@ -0,0 +1,308 @@ +# Logo Detection Test Methodology + +This document describes how the logo detection test framework works and the various techniques implemented to improve detection accuracy. + +## Overview + +The system uses a two-stage pipeline: +1. **DETR** (DEtection TRansformer) - Detects potential logo regions in images +2. **CLIP** (Contrastive Language-Image Pre-training) - Extracts feature embeddings for matching + +## Test Framework (`test_logo_detection.py`) + +### Test Flow + +1. **Sample Reference Logos**: Randomly select N logos from the database, with multiple reference images per logo +2. **Compute Reference Embeddings**: Generate CLIP embeddings for all reference logo images +3. **Build Test Set**: For each sampled logo, select: + - Positive samples: Images known to contain the logo + - Negative samples: Images known NOT to contain the logo +4. **Run Detection**: Process each test image through DETR to find logo regions +5. **Match Against References**: Compare detected regions against reference embeddings using margin-based matching +6. **Calculate Metrics**: Compute precision, recall, and F1 score + +### Configurable Parameters + +#### General Parameters + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `--num-logos` | 10 | Number of reference logos to sample | +| `--refs-per-logo` | 3 | Reference images per logo | +| `--positive-samples` | 5 | Positive test images per logo | +| `--negative-samples` | 20 | Negative test images per logo | +| `--threshold` | 0.7 | CLIP similarity threshold for matching | +| `--detr-threshold` | 0.5 | DETR detection confidence threshold | +| `--seed` | None | Random seed for reproducibility | + +#### Matching Method Selection + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `--matching-method` | margin | Matching method: `margin` or `multi-ref` | + +#### Margin Method Parameters (when `--matching-method margin`) + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `--margin` | 0.05 | Required margin between best and second-best match | + +#### Multi-Ref Method Parameters (when `--matching-method multi-ref`) + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `--min-matching-refs` | 1 | Minimum references that must match above threshold | +| `--use-max-similarity` | False | Use max similarity instead of mean across references | + +#### Cache Control + +| Parameter | Default | Description | +|-----------|---------|-------------| +| `--no-cache` | False | Disable embedding cache | +| `--clear-cache` | False | Clear cache before running | + +### Metrics + +- **True Positives**: Detected logo correctly matches expected logo +- **False Positives**: Detected logo matches wrong logo or image has no logo +- **False Negatives**: Expected logo not detected/matched +- **Precision**: TP / (TP + FP) - How many detections were correct +- **Recall**: TP / Total Expected - How many logos were found +- **F1 Score**: Harmonic mean of precision and recall + +--- + +## Accuracy Improvement Techniques + +### 1. Non-Maximum Suppression (NMS) + +**Location**: `logo_detection_detr.py:214-268` + +**Problem**: DETR may produce multiple overlapping bounding boxes for the same logo. + +**Solution**: NMS removes redundant detections by: +1. Sorting detections by confidence score (descending) +2. Keeping the highest-scoring box +3. Removing any remaining boxes with IoU > threshold (default 0.5) +4. Repeating until no boxes remain + +``` +IoU (Intersection over Union) = Area of Overlap / Area of Union +``` + +**Configuration**: `nms_iou_threshold` parameter (default: 0.5) + +--- + +### 2. Minimum Box Size Filtering + +**Location**: `logo_detection_detr.py:187-191` + +**Problem**: Very small detections are often noise or partial logo fragments. + +**Solution**: Filter out detections where width OR height is below a minimum threshold. + +**Configuration**: `min_box_size` parameter (default: 20 pixels) + +--- + +### 3. Confidence Threshold Filtering + +**Location**: `logo_detection_detr.py:177-179` + +**Problem**: Low-confidence DETR detections are unreliable. + +**Solution**: Only keep detections with confidence score >= threshold. + +**Configuration**: `detr_threshold` parameter (default: 0.5) + +--- + +### 4. Multiple Reference Images Per Logo + +**Location**: `logo_detection_detr.py:397-457` (`find_best_match_multi_ref`) + +**Problem**: A single reference image may not capture all variations of a logo (different angles, lighting, scales). + +**Solution**: Use multiple reference images per logo and aggregate their similarity scores: +- Calculate similarity to each reference embedding +- Count how many references match above threshold +- Use mean or max similarity as the aggregate score +- Require a minimum number of references to match + +**Configuration**: +- `refs_per_logo`: Number of reference images (default: 3) +- `min_matching_refs`: Minimum references that must match +- `use_mean_similarity`: Use mean vs max aggregation + +--- + +### 5. Margin-Based Matching + +**Location**: `logo_detection_detr.py:459-505` (`find_best_match_with_margin`) + +**Problem**: When multiple logos have similar embeddings, the best match may not be significantly better than alternatives, leading to false positives. + +**Solution**: Require the best match to exceed the second-best match by a minimum margin: + +``` +Match only if: best_similarity - second_best_similarity >= margin +``` + +This ensures confident matches and reduces ambiguous classifications. + +**Configuration**: `--margin` parameter (default: 0.05) + +**Example**: +- Best match: Logo A with similarity 0.82 +- Second best: Logo B with similarity 0.79 +- Margin required: 0.05 +- Result: **No match** (0.82 - 0.79 = 0.03 < 0.05) + +--- + +### 6. Embedding Caching + +**Location**: `test_logo_detection.py:49-82` (`EmbeddingCache` class) + +**Problem**: Computing CLIP embeddings is computationally expensive. Re-running tests would reprocess the same images. + +**Solution**: Cache embeddings to disk using pickle: +- Reference embeddings keyed by `ref:{filename}` +- Detection results keyed by `det:{filename}` +- Cache persists between runs (`.embedding_cache.pkl`) + +**Configuration**: +- `--no-cache`: Disable caching entirely +- `--clear-cache`: Clear cache before running + +--- + +### 7. Normalized Embeddings for Cosine Similarity + +**Location**: `logo_detection_detr.py:334-335` + +**Problem**: Raw CLIP embeddings have varying magnitudes, which can affect similarity calculations. + +**Solution**: L2-normalize all embeddings before comparison: + +```python +features = F.normalize(features, dim=-1) +``` + +This ensures cosine similarity is computed correctly and scores fall in the range [-1, 1]. + +--- + +## Matching Methods Summary + +| Method | Test Script Option | Key Feature | +|--------|-------------------|-------------| +| `find_best_match` | N/A (library only) | Returns highest similarity above threshold | +| `find_best_match_with_margin` | `--matching-method margin` | Requires margin over second-best match | +| `find_best_match_multi_ref` | `--matching-method multi-ref` | Aggregates scores across reference images | + +The test script supports both `margin` and `multi-ref` matching methods via the `--matching-method` parameter. + +--- + +## Detection Pipeline Summary + +``` +Input Image + │ + ▼ +┌─────────────────────────────────────┐ +│ DETR Object Detection │ +│ - Identifies potential logo regions│ +│ - Returns bounding boxes + scores │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ Confidence Filtering │ +│ - Remove detections < threshold │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ Size Filtering │ +│ - Remove boxes < min_box_size │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ CLIP Embedding Extraction │ +│ - Crop each detected region │ +│ - Generate normalized embedding │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ Non-Maximum Suppression │ +│ - Remove overlapping detections │ +│ - Keep highest confidence boxes │ +└─────────────────────────────────────┘ + │ + ▼ +┌─────────────────────────────────────┐ +│ Matching (selectable method) │ +│ ┌───────────────┬────────────────┐ │ +│ │ margin │ multi-ref │ │ +│ ├───────────────┼────────────────┤ │ +│ │ Require margin│ Aggregate │ │ +│ │ over 2nd best │ across refs │ │ +│ │ match │ (mean or max) │ │ +│ └───────────────┴────────────────┘ │ +└─────────────────────────────────────┘ + │ + ▼ +Matched Logo Labels +``` + +--- + +## Tuning Recommendations + +### For Margin-Based Matching (`--matching-method margin`) + +| Goal | Adjustments | +|------|-------------| +| **Reduce false positives** | Increase `--threshold`, increase `--margin` | +| **Reduce false negatives** | Decrease `--threshold`, decrease `--margin` | + +### For Multi-Ref Matching (`--matching-method multi-ref`) + +| Goal | Adjustments | +|------|-------------| +| **Reduce false positives** | Increase `--threshold`, increase `--min-matching-refs`, use mean similarity | +| **Reduce false negatives** | Decrease `--threshold`, decrease `--min-matching-refs`, use `--use-max-similarity` | + +### General Tuning + +| Goal | Adjustments | +|------|-------------| +| **Faster processing** | Decrease `--refs-per-logo`, use caching | +| **More robust detection** | Increase `--refs-per-logo`, decrease `--detr-threshold` | +| **Higher precision** | Increase `--detr-threshold`, use margin method with high margin | +| **Higher recall** | Decrease `--detr-threshold`, use multi-ref with low `--min-matching-refs` | + +--- + +## Example Usage + +```bash +# Default margin-based matching +python test_logo_detection.py -n 20 --threshold 0.75 --margin 0.05 + +# Multi-ref matching with mean similarity +python test_logo_detection.py -n 20 --matching-method multi-ref \ + --refs-per-logo 5 --min-matching-refs 2 --threshold 0.70 + +# Multi-ref matching with max similarity (more lenient) +python test_logo_detection.py -n 20 --matching-method multi-ref \ + --refs-per-logo 5 --min-matching-refs 1 --use-max-similarity + +# Reproducible test with seed +python test_logo_detection.py -n 50 --seed 42 --clear-cache +``` \ No newline at end of file diff --git a/main.py b/main.py new file mode 100644 index 0000000..8939cf4 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from logo-test!") + + +if __name__ == "__main__": + main() diff --git a/prepare_test_data.py b/prepare_test_data.py new file mode 100755 index 0000000..ba4a524 --- /dev/null +++ b/prepare_test_data.py @@ -0,0 +1,322 @@ +#!/usr/bin/env python3 +""" +Prepare test data from LogoDet-3K dataset. + +This script: +1. Scans LogoDet-3K for images and XML annotation files +2. Extracts cropped logos using bounding box data and saves to reference_logos/ +3. Copies full images to test_images/ with unique filenames +4. Creates a SQLite database for storing mappings and verification +""" + +import sqlite3 +import shutil +import xml.etree.ElementTree as ET +from pathlib import Path +from PIL import Image +from tqdm import tqdm + + +def parse_xml_annotation(xml_path: Path) -> dict: + """Parse Pascal VOC format XML annotation file.""" + tree = ET.parse(xml_path) + root = tree.getroot() + + annotation = { + "filename": root.find("filename").text, + "size": { + "width": int(root.find("size/width").text), + "height": int(root.find("size/height").text), + }, + "objects": [] + } + + for obj in root.findall("object"): + bbox = obj.find("bndbox") + annotation["objects"].append({ + "name": obj.find("name").text, + "xmin": int(bbox.find("xmin").text), + "ymin": int(bbox.find("ymin").text), + "xmax": int(bbox.find("xmax").text), + "ymax": int(bbox.find("ymax").text), + }) + + return annotation + + +def sanitize_filename(name: str) -> str: + """Convert logo name to a safe filename.""" + # Replace problematic characters + safe = name.replace("/", "_").replace("\\", "_").replace(" ", "_") + safe = safe.replace(":", "_").replace("*", "_").replace("?", "_") + safe = safe.replace('"', "_").replace("<", "_").replace(">", "_") + safe = safe.replace("|", "_") + return safe + + +def init_database(db_path: Path) -> sqlite3.Connection: + """Initialize SQLite database with schema.""" + # Remove existing database if present + if db_path.exists(): + db_path.unlink() + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Create tables + cursor.executescript(""" + -- Test images table + CREATE TABLE test_images ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + filename TEXT UNIQUE NOT NULL + ); + + -- Logo names table (unique brand/logo identifiers) + CREATE TABLE logo_names ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT UNIQUE NOT NULL + ); + + -- Reference logos table with foreign keys + CREATE TABLE reference_logos ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + filename TEXT UNIQUE NOT NULL, + test_image_id INTEGER NOT NULL, + logo_name_id INTEGER NOT NULL, + FOREIGN KEY (test_image_id) REFERENCES test_images(id), + FOREIGN KEY (logo_name_id) REFERENCES logo_names(id) + ); + + -- Statistics table for metadata + CREATE TABLE statistics ( + key TEXT PRIMARY KEY, + value INTEGER NOT NULL + ); + + -- Indexes for faster lookups + CREATE INDEX idx_reference_logos_test_image ON reference_logos(test_image_id); + CREATE INDEX idx_reference_logos_logo_name ON reference_logos(logo_name_id); + """) + + conn.commit() + return conn + + +def get_or_create_logo_name(cursor: sqlite3.Cursor, name: str) -> int: + """Get existing logo_name id or create new one.""" + cursor.execute("SELECT id FROM logo_names WHERE name = ?", (name,)) + row = cursor.fetchone() + if row: + return row[0] + cursor.execute("INSERT INTO logo_names (name) VALUES (?)", (name,)) + return cursor.lastrowid + + +def main(): + # Paths + dataset_dir = Path("/data/dev.python/logo_test/LogoDet-3K") + reference_dir = Path("/data/dev.python/logo_test/reference_logos") + test_images_dir = Path("/data/dev.python/logo_test/test_images") + db_path = Path("/data/dev.python/logo_test/test_data_mapping.db") + + # Ensure output directories exist + reference_dir.mkdir(exist_ok=True) + test_images_dir.mkdir(exist_ok=True) + + # Initialize database + print(f"Initializing database at {db_path}...") + conn = init_database(db_path) + cursor = conn.cursor() + + # Find all XML files + print("Scanning for XML annotation files...") + xml_files = list(dataset_dir.rglob("*.xml")) + print(f"Found {len(xml_files)} annotation files") + + # Track unique filenames to avoid conflicts (keyed by subdirectory tuple) + used_test_filenames = {} + used_ref_filenames = {} + + # Counters for progress + stats = { + "images_processed": 0, + "logos_extracted": 0, + "skipped_missing_image": 0, + "skipped_invalid_bbox": 0, + } + + # Process each XML file + print("\nProcessing annotations...") + for xml_path in tqdm(xml_files, desc="Processing", unit="file"): + try: + annotation = parse_xml_annotation(xml_path) + except Exception as e: + tqdm.write(f"Error parsing {xml_path}: {e}") + continue + + # Find corresponding image file + image_filename = annotation["filename"] + image_path = xml_path.parent / image_filename + + if not image_path.exists(): + # Try common extensions + for ext in [".jpg", ".jpeg", ".png", ".JPG", ".JPEG", ".PNG"]: + alt_path = xml_path.parent / (xml_path.stem + ext) + if alt_path.exists(): + image_path = alt_path + break + + if not image_path.exists(): + stats["skipped_missing_image"] += 1 + continue + + # Generate unique test image filename + # Use category/brand/original_name format to avoid conflicts + rel_path = xml_path.relative_to(dataset_dir) + category = rel_path.parts[0] if len(rel_path.parts) > 0 else "unknown" + brand = rel_path.parts[1] if len(rel_path.parts) > 1 else "unknown" + + safe_category = sanitize_filename(category) + safe_brand = sanitize_filename(brand) + base_name = image_path.stem + ext = image_path.suffix + + # Create subdirectory structure: category/brand/ + test_subdir = test_images_dir / safe_category / safe_brand + test_subdir.mkdir(parents=True, exist_ok=True) + + test_basename = f"{base_name}{ext}" + + # Handle duplicates within subdirectory + counter = 1 + while test_basename in used_test_filenames.get((safe_category, safe_brand), set()): + test_basename = f"{base_name}_{counter}{ext}" + counter += 1 + used_test_filenames.setdefault((safe_category, safe_brand), set()).add(test_basename) + + # Store relative path from test_images_dir for database + test_filename = f"{safe_category}/{safe_brand}/{test_basename}" + + # Copy full image to test_images + test_image_path = test_subdir / test_basename + shutil.copy2(image_path, test_image_path) + stats["images_processed"] += 1 + + # Insert test image into database + cursor.execute( + "INSERT INTO test_images (filename) VALUES (?)", + (test_filename,) + ) + test_image_id = cursor.lastrowid + + # Load image for cropping + try: + img = Image.open(image_path) + except Exception as e: + tqdm.write(f"Error loading {image_path}: {e}") + continue + + img_width, img_height = img.size + + # Process each object/logo in the image + for obj_idx, obj in enumerate(annotation["objects"]): + logo_name = obj["name"] + xmin, ymin = obj["xmin"], obj["ymin"] + xmax, ymax = obj["xmax"], obj["ymax"] + + # Validate bounding box + if xmin >= xmax or ymin >= ymax: + stats["skipped_invalid_bbox"] += 1 + continue + + # Clamp to image bounds + xmin = max(0, min(xmin, img_width - 1)) + ymin = max(0, min(ymin, img_height - 1)) + xmax = max(1, min(xmax, img_width)) + ymax = max(1, min(ymax, img_height)) + + if xmin >= xmax or ymin >= ymax: + stats["skipped_invalid_bbox"] += 1 + continue + + # Crop logo region + try: + logo_crop = img.crop((xmin, ymin, xmax, ymax)) + except Exception as e: + tqdm.write(f"Error cropping {image_path}: {e}") + stats["skipped_invalid_bbox"] += 1 + continue + + # Generate reference logo filename with subdirectory structure: category/logo_name/ + safe_logo_name = sanitize_filename(logo_name) + ref_subdir = reference_dir / safe_category / safe_logo_name + ref_subdir.mkdir(parents=True, exist_ok=True) + + ref_basename = f"{base_name}_{obj_idx}.png" + + # Handle duplicates within subdirectory + counter = 1 + while ref_basename in used_ref_filenames.get((safe_category, safe_logo_name), set()): + ref_basename = f"{base_name}_{obj_idx}_{counter}.png" + counter += 1 + used_ref_filenames.setdefault((safe_category, safe_logo_name), set()).add(ref_basename) + + # Store relative path from reference_dir for database + ref_filename = f"{safe_category}/{safe_logo_name}/{ref_basename}" + + # Save cropped logo + ref_path = ref_subdir / ref_basename + try: + logo_crop.save(ref_path, "PNG") + except Exception as e: + tqdm.write(f"Error saving {ref_path}: {e}") + continue + + stats["logos_extracted"] += 1 + + # Get or create logo_name entry + logo_name_id = get_or_create_logo_name(cursor, logo_name) + + # Insert reference logo into database + cursor.execute( + "INSERT INTO reference_logos (filename, test_image_id, logo_name_id) VALUES (?, ?, ?)", + (ref_filename, test_image_id, logo_name_id) + ) + + # Get unique logo names count + cursor.execute("SELECT COUNT(*) FROM logo_names") + unique_logo_names = cursor.fetchone()[0] + + # Save statistics to database + statistics_data = [ + ("total_test_images", stats["images_processed"]), + ("total_reference_logos", stats["logos_extracted"]), + ("unique_logo_names", unique_logo_names), + ("skipped_missing_image", stats["skipped_missing_image"]), + ("skipped_invalid_bbox", stats["skipped_invalid_bbox"]), + ] + cursor.executemany( + "INSERT INTO statistics (key, value) VALUES (?, ?)", + statistics_data + ) + + # Commit and close database + conn.commit() + conn.close() + + # Print summary + print("\n" + "=" * 60) + print("SUMMARY") + print("=" * 60) + print(f"Test images created: {stats['images_processed']:,}") + print(f"Reference logos created: {stats['logos_extracted']:,}") + print(f"Unique logo names: {unique_logo_names:,}") + print(f"Skipped (missing image): {stats['skipped_missing_image']:,}") + print(f"Skipped (invalid bbox): {stats['skipped_invalid_bbox']:,}") + print(f"\nDatabase saved to: {db_path}") + print(f"Reference logos: {reference_dir}") + print(f"Test images: {test_images_dir}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..c7a24be --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,15 @@ +[project] +name = "logo-test" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "numpy>=2.2.6", + "opencv-python>=4.12.0.88", + "pillow>=12.0.0", + "torch>=2.9.1", + "tqdm>=4.67.1", + "transformers>=4.57.3", + "typing>=3.10.0.0", +] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..d1bf43f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,44 @@ +certifi==2025.11.12 +charset-normalizer==3.4.4 +filelock==3.20.1 +fsspec==2025.12.0 +hf-xet==1.2.0 +huggingface-hub==0.36.0 +idna==3.11 +jinja2==3.1.6 +markupsafe==3.0.3 +mpmath==1.3.0 +networkx==3.6.1 +numpy==2.2.6 +nvidia-cublas-cu12==12.8.4.1 +nvidia-cuda-cupti-cu12==12.8.90 +nvidia-cuda-nvrtc-cu12==12.8.93 +nvidia-cuda-runtime-cu12==12.8.90 +nvidia-cudnn-cu12==9.10.2.21 +nvidia-cufft-cu12==11.3.3.83 +nvidia-cufile-cu12==1.13.1.3 +nvidia-curand-cu12==10.3.9.90 +nvidia-cusolver-cu12==11.7.3.90 +nvidia-cusparse-cu12==12.5.8.93 +nvidia-cusparselt-cu12==0.7.1 +nvidia-nccl-cu12==2.27.5 +nvidia-nvjitlink-cu12==12.8.93 +nvidia-nvshmem-cu12==3.3.20 +nvidia-nvtx-cu12==12.8.90 +opencv-python==4.12.0.88 +packaging==25.0 +pillow==12.0.0 +pyyaml==6.0.3 +regex==2025.11.3 +requests==2.32.5 +safetensors==0.7.0 +setuptools==80.9.0 +sympy==1.14.0 +tokenizers==0.22.1 +torch==2.9.1 +tqdm==4.67.1 +transformers==4.57.3 +triton==3.5.1 +typing==3.10.0.0 +typing-extensions==4.15.0 +urllib3==2.6.2 diff --git a/test_cuda_support.py b/test_cuda_support.py new file mode 100755 index 0000000..9dfd244 --- /dev/null +++ b/test_cuda_support.py @@ -0,0 +1,275 @@ +#!/usr/bin/env python3 +""" +CUDA Support Test for Nvidia Jetson Hardware + +This script verifies that OpenCV and PyTorch are properly configured +with CUDA support on Jetson devices. + +Usage: + python test_cuda_support.py + +Returns: + Exit code 0 if CUDA is properly configured + Exit code 1 if CUDA support is missing or misconfigured +""" + +import sys +import platform + + +def print_section(title): + """Print a section header.""" + print("\n" + "=" * 60) + print(f" {title}") + print("=" * 60) + + +def test_pytorch_cuda(): + """Test PyTorch CUDA support.""" + print_section("PyTorch CUDA Support") + + try: + import torch + print(f"✓ PyTorch imported successfully") + print(f" Version: {torch.__version__}") + + # Check CUDA availability + cuda_available = torch.cuda.is_available() + print(f"\nCUDA Available: {'✓ YES' if cuda_available else '✗ NO'}") + + if cuda_available: + print(f" CUDA Version: {torch.version.cuda}") + print(f" cuDNN Version: {torch.backends.cudnn.version()}") + print(f" cuDNN Enabled: {torch.backends.cudnn.enabled}") + + # Get device information + device_count = torch.cuda.device_count() + print(f"\n GPU Devices: {device_count}") + + for i in range(device_count): + props = torch.cuda.get_device_properties(i) + print(f"\n Device {i}: {props.name}") + print(f" Compute Capability: {props.major}.{props.minor}") + print(f" Total Memory: {props.total_memory / 1024**3:.2f} GB") + print(f" Multi-Processor Count: {props.multi_processor_count}") + + # Test tensor operations + print("\n Testing GPU tensor operations...") + try: + x = torch.randn(3, 3).cuda() + y = torch.randn(3, 3).cuda() + z = x @ y + print(f" ✓ GPU tensor operations successful") + + # Check current device + print(f" Current Device: {torch.cuda.current_device()}") + print(f" Device Name: {torch.cuda.get_device_name(0)}") + + except Exception as e: + print(f" ✗ GPU tensor operations failed: {e}") + return False + else: + print("\n ⚠ PyTorch CUDA is NOT available") + print(" Possible reasons:") + print(" - PyTorch not built with CUDA support") + print(" - CUDA drivers not installed") + print(" - Incompatible CUDA version") + return False + + return cuda_available + + except ImportError as e: + print(f"✗ Failed to import PyTorch: {e}") + return False + except Exception as e: + print(f"✗ Error testing PyTorch: {e}") + return False + + +def test_opencv_cuda(): + """Test OpenCV CUDA support.""" + print_section("OpenCV CUDA Support") + + try: + import cv2 + print(f"✓ OpenCV imported successfully") + print(f" Version: {cv2.__version__}") + + # Check build information + build_info = cv2.getBuildInformation() + + # Parse build info for CUDA + cuda_enabled = "CUDA:" in build_info and "YES" in build_info.split("CUDA:")[1].split("\n")[0] + + print(f"\nCUDA Support: {'✓ YES' if cuda_enabled else '✗ NO'}") + + if cuda_enabled: + # Extract CUDA-related information from build info + lines = build_info.split('\n') + cuda_section = False + + print("\n CUDA Build Configuration:") + for line in lines: + if 'CUDA' in line or cuda_section: + if 'CUDA' in line: + cuda_section = True + if cuda_section: + # Print relevant CUDA lines + if any(keyword in line for keyword in ['CUDA', 'cuDNN', 'NVIDIA', 'GPU']): + print(f" {line.strip()}") + # Stop at next major section + if line.strip() and not line.startswith(' ') and 'CUDA' not in line: + break + + # Check for CUDA device count + try: + cuda_device_count = cv2.cuda.getCudaEnabledDeviceCount() + print(f"\n CUDA Devices: {cuda_device_count}") + + if cuda_device_count > 0: + # Test CUDA operations + print("\n Testing CUDA operations...") + try: + # Create a simple GPU matrix + import numpy as np + test_img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8) + gpu_mat = cv2.cuda_GpuMat() + gpu_mat.upload(test_img) + result = gpu_mat.download() + print(f" ✓ CUDA operations successful") + except Exception as e: + print(f" ✗ CUDA operations failed: {e}") + return False + else: + print(" ⚠ No CUDA devices detected") + return False + + except AttributeError: + print(" ⚠ cv2.cuda module not available") + print(" OpenCV may not be built with CUDA support") + return False + else: + print("\n ⚠ OpenCV CUDA is NOT available") + print(" Possible reasons:") + print(" - OpenCV not built with CUDA support") + print(" - Need to install opencv-contrib-python with CUDA") + print(" - For Jetson, may need to build from source") + return False + + return cuda_enabled + + except ImportError as e: + print(f"✗ Failed to import OpenCV: {e}") + return False + except Exception as e: + print(f"✗ Error testing OpenCV: {e}") + return False + + +def print_system_info(): + """Print system information.""" + print_section("System Information") + + print(f"Platform: {platform.platform()}") + print(f"Python Version: {platform.python_version()}") + print(f"Architecture: {platform.machine()}") + print(f"Processor: {platform.processor()}") + + # Try to detect Jetson + try: + with open('/etc/nv_tegra_release', 'r') as f: + jetson_version = f.read().strip() + print(f"\n✓ Jetson Device Detected") + print(f" {jetson_version}") + except FileNotFoundError: + print("\n Not running on Jetson device (or /etc/nv_tegra_release not found)") + + # Check for CUDA toolkit + import subprocess + try: + result = subprocess.run(['nvcc', '--version'], + capture_output=True, + text=True, + timeout=5) + if result.returncode == 0: + print("\n✓ CUDA Toolkit detected:") + # Print version line + for line in result.stdout.split('\n'): + if 'release' in line.lower(): + print(f" {line.strip()}") + else: + print("\n⚠ nvcc not found - CUDA toolkit may not be installed") + except FileNotFoundError: + print("\n⚠ nvcc not found - CUDA toolkit may not be installed") + except Exception as e: + print(f"\n⚠ Error checking CUDA toolkit: {e}") + + +def print_memory_info(): + """Print GPU memory information.""" + print_section("GPU Memory Information") + + try: + import torch + if torch.cuda.is_available(): + device = torch.cuda.current_device() + total_mem = torch.cuda.get_device_properties(device).total_memory + allocated = torch.cuda.memory_allocated(device) + cached = torch.cuda.memory_reserved(device) + + print(f"Total GPU Memory: {total_mem / 1024**3:.2f} GB") + print(f"Allocated: {allocated / 1024**3:.2f} GB") + print(f"Cached: {cached / 1024**3:.2f} GB") + print(f"Free: {(total_mem - allocated) / 1024**3:.2f} GB") + else: + print("CUDA not available - cannot query GPU memory") + except Exception as e: + print(f"Error querying GPU memory: {e}") + + +def main(): + """Main test function.""" + print("\n" + "=" * 60) + print(" CUDA Support Verification for Nvidia Jetson") + print("=" * 60) + + # Print system info + print_system_info() + + # Test PyTorch + pytorch_cuda = test_pytorch_cuda() + + # Test OpenCV + opencv_cuda = test_opencv_cuda() + + # Print memory info + if pytorch_cuda: + print_memory_info() + + # Print summary + print_section("Summary") + + print(f"PyTorch CUDA Support: {'✓ ENABLED' if pytorch_cuda else '✗ DISABLED'}") + print(f"OpenCV CUDA Support: {'✓ ENABLED' if opencv_cuda else '✗ DISABLED'}") + + if pytorch_cuda and opencv_cuda: + print("\n✓ All CUDA checks passed - system ready for GPU-accelerated processing") + return 0 + elif pytorch_cuda: + print("\n⚠ PyTorch CUDA enabled, but OpenCV CUDA disabled") + print(" Some operations will use GPU, but OpenCV operations will use CPU") + return 1 + elif opencv_cuda: + print("\n⚠ OpenCV CUDA enabled, but PyTorch CUDA disabled") + print(" OpenCV operations will use GPU, but PyTorch models will use CPU") + return 1 + else: + print("\n✗ CUDA support not available - will run in CPU mode") + print(" Performance will be significantly slower") + return 1 + + +if __name__ == "__main__": + exit_code = main() + print("\n" + "=" * 60 + "\n") + sys.exit(exit_code) diff --git a/test_logo_detection.py b/test_logo_detection.py new file mode 100755 index 0000000..94d0581 --- /dev/null +++ b/test_logo_detection.py @@ -0,0 +1,553 @@ +#!/usr/bin/env python3 +""" +Test script for logo detection accuracy. + +This script: +1. Randomly samples N reference logos from the database +2. Processes all test images through the DETR+CLIP pipeline +3. Compares detected logos against reference embeddings +4. Reports accuracy metrics (correct matches, false positives, missed detections) + +Embeddings are cached to avoid reprocessing images. +""" + +import argparse +import logging +import pickle +import random +import sqlite3 +import sys +from pathlib import Path +from typing import Dict, List, Optional, Set, Tuple + +import cv2 +import torch +from tqdm import tqdm + +from logo_detection_detr import DetectLogosDETR + + +def setup_logging(verbose: bool = False) -> logging.Logger: + """Configure logging.""" + level = logging.DEBUG if verbose else logging.INFO + logging.basicConfig( + level=level, + format="%(asctime)s - %(levelname)s - %(message)s", + datefmt="%H:%M:%S", + ) + return logging.getLogger(__name__) + + +def load_image(image_path: Path) -> Optional[cv2.Mat]: + """Load an image using OpenCV.""" + img = cv2.imread(str(image_path)) + if img is None: + return None + return img + + +class EmbeddingCache: + """Simple file-based cache for embeddings.""" + + def __init__(self, cache_path: Path): + self.cache_path = cache_path + self.cache: Dict[str, torch.Tensor] = {} + self._load() + + def _load(self): + """Load cache from disk if it exists.""" + if self.cache_path.exists(): + try: + with open(self.cache_path, "rb") as f: + self.cache = pickle.load(f) + except Exception: + self.cache = {} + + def save(self): + """Save cache to disk.""" + self.cache_path.parent.mkdir(parents=True, exist_ok=True) + with open(self.cache_path, "wb") as f: + pickle.dump(self.cache, f) + + def get(self, key: str) -> Optional[torch.Tensor]: + """Get embedding from cache.""" + return self.cache.get(key) + + def put(self, key: str, embedding: torch.Tensor): + """Store embedding in cache.""" + # Store on CPU to save GPU memory + self.cache[key] = embedding.cpu() + + def __len__(self): + return len(self.cache) + + +def get_ground_truth(db_path: Path) -> Tuple[Dict[str, Set[str]], Dict[str, Set[str]]]: + """ + Load ground truth from database. + + Returns: + Tuple of: + - Dict mapping test image filename to set of logo names it contains + - Dict mapping logo name to set of test image filenames containing it + """ + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Query to get test image -> logo names mapping + cursor.execute(""" + SELECT ti.filename, ln.name + FROM test_images ti + JOIN reference_logos rl ON ti.id = rl.test_image_id + JOIN logo_names ln ON rl.logo_name_id = ln.id + """) + + image_to_logos: Dict[str, Set[str]] = {} + logo_to_images: Dict[str, Set[str]] = {} + + for row in cursor.fetchall(): + test_filename, logo_name = row + if test_filename not in image_to_logos: + image_to_logos[test_filename] = set() + image_to_logos[test_filename].add(logo_name) + + if logo_name not in logo_to_images: + logo_to_images[logo_name] = set() + logo_to_images[logo_name].add(test_filename) + + conn.close() + return image_to_logos, logo_to_images + + +def sample_reference_logos( + db_path: Path, num_logos: int, refs_per_logo: int = 1, seed: Optional[int] = None +) -> Dict[str, List[str]]: + """ + Randomly sample reference logos from database with multiple refs per logo. + + Args: + db_path: Path to database + num_logos: Number of logos to sample + refs_per_logo: Number of reference images per logo + seed: Random seed for reproducibility + + Returns: + Dict mapping logo_name to list of reference filenames + """ + if seed is not None: + random.seed(seed) + + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + + # Get all unique logo names + cursor.execute("SELECT id, name FROM logo_names") + all_logo_names = cursor.fetchall() + + # Sample logos + if num_logos >= len(all_logo_names): + sampled_logos = all_logo_names + else: + sampled_logos = random.sample(all_logo_names, num_logos) + + # For each sampled logo, get multiple reference files + result: Dict[str, List[str]] = {} + for logo_id, logo_name in sampled_logos: + cursor.execute( + "SELECT filename FROM reference_logos WHERE logo_name_id = ?", + (logo_id,) + ) + all_refs = [row[0] for row in cursor.fetchall()] + + # Sample refs_per_logo references (or all if fewer available) + if len(all_refs) > refs_per_logo: + selected_refs = random.sample(all_refs, refs_per_logo) + else: + selected_refs = all_refs + + result[logo_name] = selected_refs + + conn.close() + return result + + +def get_test_images(db_path: Path) -> List[str]: + """Get all test image filenames from database.""" + conn = sqlite3.connect(db_path) + cursor = conn.cursor() + cursor.execute("SELECT filename FROM test_images") + filenames = [row[0] for row in cursor.fetchall()] + conn.close() + return filenames + + +def main(): + parser = argparse.ArgumentParser( + description="Test logo detection accuracy against ground truth" + ) + parser.add_argument( + "-n", "--num-logos", + type=int, + default=10, + help="Number of reference logos to sample (default: 10)", + ) + parser.add_argument( + "-t", "--threshold", + type=float, + default=0.7, + help="CLIP similarity threshold for matching (default: 0.7)", + ) + parser.add_argument( + "-d", "--detr-threshold", + type=float, + default=0.5, + help="DETR detection confidence threshold (default: 0.5)", + ) + parser.add_argument( + "-s", "--seed", + type=int, + default=None, + help="Random seed for reproducibility", + ) + parser.add_argument( + "--positive-samples", + type=int, + default=5, + help="Number of positive test images per logo (images containing the logo) (default: 5)", + ) + parser.add_argument( + "--negative-samples", + type=int, + default=20, + help="Number of negative test images per logo (images NOT containing the logo) (default: 20)", + ) + parser.add_argument( + "--refs-per-logo", + type=int, + default=3, + help="Number of reference images per logo for multi-ref matching (default: 3)", + ) + parser.add_argument( + "--margin", + type=float, + default=0.05, + help="Required margin between best and second-best match for 'margin' method (default: 0.05)", + ) + parser.add_argument( + "--matching-method", + type=str, + choices=["margin", "multi-ref"], + default="margin", + help="Matching method: 'margin' requires confidence margin over 2nd best, " + "'multi-ref' aggregates scores across reference images (default: margin)", + ) + parser.add_argument( + "--min-matching-refs", + type=int, + default=1, + help="For 'multi-ref' method: minimum references that must match above threshold (default: 1)", + ) + parser.add_argument( + "--use-max-similarity", + action="store_true", + help="For 'multi-ref' method: use max similarity instead of mean across references", + ) + parser.add_argument( + "-v", "--verbose", + action="store_true", + help="Enable verbose logging", + ) + parser.add_argument( + "--no-cache", + action="store_true", + help="Disable embedding cache", + ) + parser.add_argument( + "--clear-cache", + action="store_true", + help="Clear embedding cache before running", + ) + + args = parser.parse_args() + logger = setup_logging(args.verbose) + + # Paths + base_dir = Path(__file__).resolve().parent + db_path = base_dir / "test_data_mapping.db" + reference_dir = base_dir / "reference_logos" + test_images_dir = base_dir / "test_images" + cache_path = base_dir / ".embedding_cache.pkl" + + # Verify database exists + if not db_path.exists(): + logger.error(f"Database not found: {db_path}") + logger.error("Run prepare_test_data.py first to create the database.") + sys.exit(1) + + # Handle cache clearing + if args.clear_cache and cache_path.exists(): + cache_path.unlink() + logger.info("Cleared embedding cache") + + # Initialize embedding cache + cache = EmbeddingCache(cache_path) if not args.no_cache else None + if cache: + logger.info(f"Loaded {len(cache)} cached embeddings") + + # Initialize detector + logger.info("Initializing logo detector...") + detector = DetectLogosDETR( + logger=logger, + detr_threshold=args.detr_threshold, + ) + + # Load ground truth (both mappings) + logger.info("Loading ground truth from database...") + image_to_logos, logo_to_images = get_ground_truth(db_path) + all_test_images = set(image_to_logos.keys()) + logger.info(f"Loaded ground truth for {len(image_to_logos)} test images") + + # Sample reference logos (with multiple refs per logo) + logger.info(f"Sampling {args.num_logos} reference logos with {args.refs_per_logo} refs each...") + sampled_logos = sample_reference_logos(db_path, args.num_logos, args.refs_per_logo, args.seed) + logger.info(f"Selected {len(sampled_logos)} reference logos") + + # Compute reference embeddings (multiple per logo for multi-ref matching) + logger.info("Computing reference logo embeddings...") + # Dict for multi-ref matching: logo_name -> list of embeddings + multi_ref_embeddings: Dict[str, List[torch.Tensor]] = {} + # List for margin-based matching: (logo_name, embedding) tuples + reference_embeddings: List[Tuple[str, torch.Tensor]] = [] + total_refs = 0 + + for logo_name, ref_filenames in tqdm(sampled_logos.items(), desc="Reference logos"): + multi_ref_embeddings[logo_name] = [] + + for ref_filename in ref_filenames: + ref_path = reference_dir / ref_filename + + if not ref_path.exists(): + logger.warning(f"Reference logo not found: {ref_path}") + continue + + # Check cache + cache_key = f"ref:{ref_filename}" + embedding = cache.get(cache_key) if cache else None + + if embedding is None: + img = load_image(ref_path) + if img is None: + logger.warning(f"Failed to load reference logo: {ref_path}") + continue + embedding = detector.get_embedding(img) + if cache: + cache.put(cache_key, embedding) + + multi_ref_embeddings[logo_name].append(embedding) + reference_embeddings.append((logo_name, embedding)) + total_refs += 1 + + logger.info(f"Computed {total_refs} embeddings for {len(sampled_logos)} logos") + + # Build test set: for each logo, sample positive and negative images + logger.info(f"Sampling test images: {args.positive_samples} positive, {args.negative_samples} negative per logo...") + test_image_set: Set[str] = set() + test_image_expected: Dict[str, Set[str]] = {} # image -> logos it should match + + # Use sampled_logos keys (unique logo names) instead of reference_embeddings + for logo_name in sampled_logos.keys(): + # Get positive images (contain this logo) + positive_images = list(logo_to_images.get(logo_name, set())) + if len(positive_images) > args.positive_samples: + positive_images = random.sample(positive_images, args.positive_samples) + + # Get negative images (do NOT contain this logo) + negative_pool = list(all_test_images - logo_to_images.get(logo_name, set())) + if len(negative_pool) > args.negative_samples: + negative_images = random.sample(negative_pool, args.negative_samples) + else: + negative_images = negative_pool + + # Add to test set + for img in positive_images: + test_image_set.add(img) + if img not in test_image_expected: + test_image_expected[img] = set() + test_image_expected[img].add(logo_name) + + for img in negative_images: + test_image_set.add(img) + if img not in test_image_expected: + test_image_expected[img] = set() + # Don't add logo_name - this is a negative sample + + test_images = list(test_image_set) + logger.info(f"Selected {len(test_images)} unique test images") + + # Get set of reference logo names for quick lookup + reference_logo_names = set(sampled_logos.keys()) + + # Metrics + true_positives = 0 # Correctly matched logos + false_positives = 0 # Matched but wrong logo or no logo present + false_negatives = 0 # Logo present but not detected/matched + total_expected = 0 # Total logos we should have found + + # Detailed results for analysis + results = [] + + # Process test images + for test_filename in tqdm(test_images, desc="Testing"): + test_path = test_images_dir / test_filename + + if not test_path.exists(): + logger.warning(f"Test image not found: {test_path}") + continue + + # Get expected logos for this image (from our sampled set) + expected_logos = test_image_expected.get(test_filename, set()) + total_expected += len(expected_logos) + + # Check cache for detections + cache_key = f"det:{test_filename}" + cached_detections = cache.get(cache_key) if cache else None + + if cached_detections is not None: + # Cached detections contain serialized box data and embeddings + detections = cached_detections + else: + # Load and detect + img = load_image(test_path) + if img is None: + logger.warning(f"Failed to load test image: {test_path}") + continue + + detections = detector.detect(img) + + # Cache the detections + if cache: + cache.put(cache_key, detections) + + # Match detections against references using selected method + matched_logos: Set[str] = set() + for detection in detections: + match = None + similarity = None + + if args.matching_method == "margin": + # Margin-based matching: requires margin over second-best + match_result = detector.find_best_match_with_margin( + detection["embedding"], + reference_embeddings, + similarity_threshold=args.threshold, + margin=args.margin, + ) + if match_result: + label, similarity = match_result + match = label + else: # multi-ref + # Multi-ref matching: aggregates scores across reference images + match_result = detector.find_best_match_multi_ref( + detection["embedding"], + multi_ref_embeddings, + similarity_threshold=args.threshold, + min_matching_refs=args.min_matching_refs, + use_mean_similarity=not args.use_max_similarity, + ) + if match_result: + label, similarity, num_matching = match_result + match = label + + if match: + matched_logos.add(match) + + # Check if this is a correct match + if match in expected_logos: + true_positives += 1 + else: + false_positives += 1 + + results.append({ + "test_image": test_filename, + "matched_logo": match, + "similarity": similarity, + "correct": match in expected_logos, + }) + + # Count missed detections (false negatives) + missed = expected_logos - matched_logos + false_negatives += len(missed) + + for missed_logo in missed: + results.append({ + "test_image": test_filename, + "matched_logo": None, + "expected_logo": missed_logo, + "similarity": None, + "correct": False, + }) + + # Save cache + if cache: + cache.save() + logger.info(f"Saved {len(cache)} embeddings to cache") + + # Calculate metrics + precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0 + recall = true_positives / total_expected if total_expected > 0 else 0 + f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0 + + # Print results + print("\n" + "=" * 60) + print("LOGO DETECTION TEST RESULTS") + print("=" * 60) + print(f"\nConfiguration:") + print(f" Reference logos sampled: {len(sampled_logos)}") + print(f" Refs per logo: {args.refs_per_logo}") + print(f" Total reference embeddings:{total_refs}") + print(f" Positive samples per logo: {args.positive_samples}") + print(f" Negative samples per logo: {args.negative_samples}") + print(f" Test images processed: {len(test_images)}") + print(f" CLIP similarity threshold: {args.threshold}") + print(f" DETR confidence threshold: {args.detr_threshold}") + print(f" Matching method: {args.matching_method}") + if args.matching_method == "margin": + print(f" Matching margin: {args.margin}") + else: # multi-ref + print(f" Min matching refs: {args.min_matching_refs}") + print(f" Similarity aggregation: {'max' if args.use_max_similarity else 'mean'}") + if args.seed is not None: + print(f" Random seed: {args.seed}") + + print(f"\nMetrics:") + print(f" True Positives (correct matches): {true_positives}") + print(f" False Positives (wrong matches): {false_positives}") + print(f" False Negatives (missed logos): {false_negatives}") + print(f" Total expected matches: {total_expected}") + + print(f"\nScores:") + print(f" Precision: {precision:.4f} ({precision*100:.1f}%)") + print(f" Recall: {recall:.4f} ({recall*100:.1f}%)") + print(f" F1 Score: {f1:.4f} ({f1*100:.1f}%)") + + # Show some example false positives + false_positive_examples = [r for r in results if r.get("matched_logo") and not r["correct"]] + if false_positive_examples: + print(f"\nExample False Positives (first 5):") + for r in false_positive_examples[:5]: + print(f" - Image: {r['test_image']}") + print(f" Matched: {r['matched_logo']} (similarity: {r['similarity']:.3f})") + + # Show reference logos used (unique names) + unique_logos = sorted(sampled_logos.keys()) + print(f"\nReference logos used ({len(unique_logos)}):") + for name in unique_logos[:20]: + print(f" - {name}") + if len(unique_logos) > 20: + print(f" ... and {len(unique_logos) - 20} more") + + print("=" * 60) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..c37370f --- /dev/null +++ b/uv.lock @@ -0,0 +1,869 @@ +version = 1 +revision = 3 +requires-python = ">=3.12" + +[[package]] +name = "certifi" +version = "2025.11.12" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a2/8c/58f469717fa48465e4a50c014a0400602d3c437d7c0c468e17ada824da3a/certifi-2025.11.12.tar.gz", hash = "sha256:d8ab5478f2ecd78af242878415affce761ca6bc54a22a27e026d7c25357c3316", size = 160538, upload-time = "2025-11-12T02:54:51.517Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/70/7d/9bc192684cea499815ff478dfcdc13835ddf401365057044fb721ec6bddb/certifi-2025.11.12-py3-none-any.whl", hash = "sha256:97de8790030bbd5c2d96b7ec782fc2f7820ef8dba6db909ccf95449f2d062d4b", size = 159438, upload-time = "2025-11-12T02:54:49.735Z" }, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.4" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/13/69/33ddede1939fdd074bce5434295f38fae7136463422fe4fd3e0e89b98062/charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a", size = 129418, upload-time = "2025-10-14T04:42:32.879Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f3/85/1637cd4af66fa687396e757dec650f28025f2a2f5a5531a3208dc0ec43f2/charset_normalizer-3.4.4-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:0a98e6759f854bd25a58a73fa88833fba3b7c491169f86ce1180c948ab3fd394", size = 208425, upload-time = "2025-10-14T04:40:53.353Z" }, + { url = "https://files.pythonhosted.org/packages/9d/6a/04130023fef2a0d9c62d0bae2649b69f7b7d8d24ea5536feef50551029df/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b5b290ccc2a263e8d185130284f8501e3e36c5e02750fc6b6bdeb2e9e96f1e25", size = 148162, upload-time = "2025-10-14T04:40:54.558Z" }, + { url = "https://files.pythonhosted.org/packages/78/29/62328d79aa60da22c9e0b9a66539feae06ca0f5a4171ac4f7dc285b83688/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74bb723680f9f7a6234dcf67aea57e708ec1fbdf5699fb91dfd6f511b0a320ef", size = 144558, upload-time = "2025-10-14T04:40:55.677Z" }, + { url = "https://files.pythonhosted.org/packages/86/bb/b32194a4bf15b88403537c2e120b817c61cd4ecffa9b6876e941c3ee38fe/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:f1e34719c6ed0b92f418c7c780480b26b5d9c50349e9a9af7d76bf757530350d", size = 161497, upload-time = "2025-10-14T04:40:57.217Z" }, + { url = "https://files.pythonhosted.org/packages/19/89/a54c82b253d5b9b111dc74aca196ba5ccfcca8242d0fb64146d4d3183ff1/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:2437418e20515acec67d86e12bf70056a33abdacb5cb1655042f6538d6b085a8", size = 159240, upload-time = "2025-10-14T04:40:58.358Z" }, + { url = "https://files.pythonhosted.org/packages/c0/10/d20b513afe03acc89ec33948320a5544d31f21b05368436d580dec4e234d/charset_normalizer-3.4.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:11d694519d7f29d6cd09f6ac70028dba10f92f6cdd059096db198c283794ac86", size = 153471, upload-time = "2025-10-14T04:40:59.468Z" }, + { url = "https://files.pythonhosted.org/packages/61/fa/fbf177b55bdd727010f9c0a3c49eefa1d10f960e5f09d1d887bf93c2e698/charset_normalizer-3.4.4-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:ac1c4a689edcc530fc9d9aa11f5774b9e2f33f9a0c6a57864e90908f5208d30a", size = 150864, upload-time = "2025-10-14T04:41:00.623Z" }, + { url = "https://files.pythonhosted.org/packages/05/12/9fbc6a4d39c0198adeebbde20b619790e9236557ca59fc40e0e3cebe6f40/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:21d142cc6c0ec30d2efee5068ca36c128a30b0f2c53c1c07bd78cb6bc1d3be5f", size = 150647, upload-time = "2025-10-14T04:41:01.754Z" }, + { url = "https://files.pythonhosted.org/packages/ad/1f/6a9a593d52e3e8c5d2b167daf8c6b968808efb57ef4c210acb907c365bc4/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:5dbe56a36425d26d6cfb40ce79c314a2e4dd6211d51d6d2191c00bed34f354cc", size = 145110, upload-time = "2025-10-14T04:41:03.231Z" }, + { url = "https://files.pythonhosted.org/packages/30/42/9a52c609e72471b0fc54386dc63c3781a387bb4fe61c20231a4ebcd58bdd/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:5bfbb1b9acf3334612667b61bd3002196fe2a1eb4dd74d247e0f2a4d50ec9bbf", size = 162839, upload-time = "2025-10-14T04:41:04.715Z" }, + { url = "https://files.pythonhosted.org/packages/c4/5b/c0682bbf9f11597073052628ddd38344a3d673fda35a36773f7d19344b23/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:d055ec1e26e441f6187acf818b73564e6e6282709e9bcb5b63f5b23068356a15", size = 150667, upload-time = "2025-10-14T04:41:05.827Z" }, + { url = "https://files.pythonhosted.org/packages/e4/24/a41afeab6f990cf2daf6cb8c67419b63b48cf518e4f56022230840c9bfb2/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:af2d8c67d8e573d6de5bc30cdb27e9b95e49115cd9baad5ddbd1a6207aaa82a9", size = 160535, upload-time = "2025-10-14T04:41:06.938Z" }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6a4ce77ed243c4a50a1fecca6aaaab419628c818a49434be428fe24c9957/charset_normalizer-3.4.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:780236ac706e66881f3b7f2f32dfe90507a09e67d1d454c762cf642e6e1586e0", size = 154816, upload-time = "2025-10-14T04:41:08.101Z" }, + { url = "https://files.pythonhosted.org/packages/a8/ef/89297262b8092b312d29cdb2517cb1237e51db8ecef2e9af5edbe7b683b1/charset_normalizer-3.4.4-cp312-cp312-win32.whl", hash = "sha256:5833d2c39d8896e4e19b689ffc198f08ea58116bee26dea51e362ecc7cd3ed26", size = 99694, upload-time = "2025-10-14T04:41:09.23Z" }, + { url = "https://files.pythonhosted.org/packages/3d/2d/1e5ed9dd3b3803994c155cd9aacb60c82c331bad84daf75bcb9c91b3295e/charset_normalizer-3.4.4-cp312-cp312-win_amd64.whl", hash = "sha256:a79cfe37875f822425b89a82333404539ae63dbdddf97f84dcbc3d339aae9525", size = 107131, upload-time = "2025-10-14T04:41:10.467Z" }, + { url = "https://files.pythonhosted.org/packages/d0/d9/0ed4c7098a861482a7b6a95603edce4c0d9db2311af23da1fb2b75ec26fc/charset_normalizer-3.4.4-cp312-cp312-win_arm64.whl", hash = "sha256:376bec83a63b8021bb5c8ea75e21c4ccb86e7e45ca4eb81146091b56599b80c3", size = 100390, upload-time = "2025-10-14T04:41:11.915Z" }, + { url = "https://files.pythonhosted.org/packages/97/45/4b3a1239bbacd321068ea6e7ac28875b03ab8bc0aa0966452db17cd36714/charset_normalizer-3.4.4-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794", size = 208091, upload-time = "2025-10-14T04:41:13.346Z" }, + { url = "https://files.pythonhosted.org/packages/7d/62/73a6d7450829655a35bb88a88fca7d736f9882a27eacdca2c6d505b57e2e/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed", size = 147936, upload-time = "2025-10-14T04:41:14.461Z" }, + { url = "https://files.pythonhosted.org/packages/89/c5/adb8c8b3d6625bef6d88b251bbb0d95f8205831b987631ab0c8bb5d937c2/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72", size = 144180, upload-time = "2025-10-14T04:41:15.588Z" }, + { url = "https://files.pythonhosted.org/packages/91/ed/9706e4070682d1cc219050b6048bfd293ccf67b3d4f5a4f39207453d4b99/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328", size = 161346, upload-time = "2025-10-14T04:41:16.738Z" }, + { url = "https://files.pythonhosted.org/packages/d5/0d/031f0d95e4972901a2f6f09ef055751805ff541511dc1252ba3ca1f80cf5/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede", size = 158874, upload-time = "2025-10-14T04:41:17.923Z" }, + { url = "https://files.pythonhosted.org/packages/f5/83/6ab5883f57c9c801ce5e5677242328aa45592be8a00644310a008d04f922/charset_normalizer-3.4.4-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894", size = 153076, upload-time = "2025-10-14T04:41:19.106Z" }, + { url = "https://files.pythonhosted.org/packages/75/1e/5ff781ddf5260e387d6419959ee89ef13878229732732ee73cdae01800f2/charset_normalizer-3.4.4-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1", size = 150601, upload-time = "2025-10-14T04:41:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/d7/57/71be810965493d3510a6ca79b90c19e48696fb1ff964da319334b12677f0/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490", size = 150376, upload-time = "2025-10-14T04:41:21.398Z" }, + { url = "https://files.pythonhosted.org/packages/e5/d5/c3d057a78c181d007014feb7e9f2e65905a6c4ef182c0ddf0de2924edd65/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_armv7l.whl", hash = "sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44", size = 144825, upload-time = "2025-10-14T04:41:22.583Z" }, + { url = "https://files.pythonhosted.org/packages/e6/8c/d0406294828d4976f275ffbe66f00266c4b3136b7506941d87c00cab5272/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133", size = 162583, upload-time = "2025-10-14T04:41:23.754Z" }, + { url = "https://files.pythonhosted.org/packages/d7/24/e2aa1f18c8f15c4c0e932d9287b8609dd30ad56dbe41d926bd846e22fb8d/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3", size = 150366, upload-time = "2025-10-14T04:41:25.27Z" }, + { url = "https://files.pythonhosted.org/packages/e4/5b/1e6160c7739aad1e2df054300cc618b06bf784a7a164b0f238360721ab86/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e", size = 160300, upload-time = "2025-10-14T04:41:26.725Z" }, + { url = "https://files.pythonhosted.org/packages/7a/10/f882167cd207fbdd743e55534d5d9620e095089d176d55cb22d5322f2afd/charset_normalizer-3.4.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc", size = 154465, upload-time = "2025-10-14T04:41:28.322Z" }, + { url = "https://files.pythonhosted.org/packages/89/66/c7a9e1b7429be72123441bfdbaf2bc13faab3f90b933f664db506dea5915/charset_normalizer-3.4.4-cp313-cp313-win32.whl", hash = "sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac", size = 99404, upload-time = "2025-10-14T04:41:29.95Z" }, + { url = "https://files.pythonhosted.org/packages/c4/26/b9924fa27db384bdcd97ab83b4f0a8058d96ad9626ead570674d5e737d90/charset_normalizer-3.4.4-cp313-cp313-win_amd64.whl", hash = "sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14", size = 107092, upload-time = "2025-10-14T04:41:31.188Z" }, + { url = "https://files.pythonhosted.org/packages/af/8f/3ed4bfa0c0c72a7ca17f0380cd9e4dd842b09f664e780c13cff1dcf2ef1b/charset_normalizer-3.4.4-cp313-cp313-win_arm64.whl", hash = "sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2", size = 100408, upload-time = "2025-10-14T04:41:32.624Z" }, + { url = "https://files.pythonhosted.org/packages/2a/35/7051599bd493e62411d6ede36fd5af83a38f37c4767b92884df7301db25d/charset_normalizer-3.4.4-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd", size = 207746, upload-time = "2025-10-14T04:41:33.773Z" }, + { url = "https://files.pythonhosted.org/packages/10/9a/97c8d48ef10d6cd4fcead2415523221624bf58bcf68a802721a6bc807c8f/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb", size = 147889, upload-time = "2025-10-14T04:41:34.897Z" }, + { url = "https://files.pythonhosted.org/packages/10/bf/979224a919a1b606c82bd2c5fa49b5c6d5727aa47b4312bb27b1734f53cd/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e", size = 143641, upload-time = "2025-10-14T04:41:36.116Z" }, + { url = "https://files.pythonhosted.org/packages/ba/33/0ad65587441fc730dc7bd90e9716b30b4702dc7b617e6ba4997dc8651495/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14", size = 160779, upload-time = "2025-10-14T04:41:37.229Z" }, + { url = "https://files.pythonhosted.org/packages/67/ed/331d6b249259ee71ddea93f6f2f0a56cfebd46938bde6fcc6f7b9a3d0e09/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191", size = 159035, upload-time = "2025-10-14T04:41:38.368Z" }, + { url = "https://files.pythonhosted.org/packages/67/ff/f6b948ca32e4f2a4576aa129d8bed61f2e0543bf9f5f2b7fc3758ed005c9/charset_normalizer-3.4.4-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838", size = 152542, upload-time = "2025-10-14T04:41:39.862Z" }, + { url = "https://files.pythonhosted.org/packages/16/85/276033dcbcc369eb176594de22728541a925b2632f9716428c851b149e83/charset_normalizer-3.4.4-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6", size = 149524, upload-time = "2025-10-14T04:41:41.319Z" }, + { url = "https://files.pythonhosted.org/packages/9e/f2/6a2a1f722b6aba37050e626530a46a68f74e63683947a8acff92569f979a/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e", size = 150395, upload-time = "2025-10-14T04:41:42.539Z" }, + { url = "https://files.pythonhosted.org/packages/60/bb/2186cb2f2bbaea6338cad15ce23a67f9b0672929744381e28b0592676824/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_armv7l.whl", hash = "sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c", size = 143680, upload-time = "2025-10-14T04:41:43.661Z" }, + { url = "https://files.pythonhosted.org/packages/7d/a5/bf6f13b772fbb2a90360eb620d52ed8f796f3c5caee8398c3b2eb7b1c60d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090", size = 162045, upload-time = "2025-10-14T04:41:44.821Z" }, + { url = "https://files.pythonhosted.org/packages/df/c5/d1be898bf0dc3ef9030c3825e5d3b83f2c528d207d246cbabe245966808d/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152", size = 149687, upload-time = "2025-10-14T04:41:46.442Z" }, + { url = "https://files.pythonhosted.org/packages/a5/42/90c1f7b9341eef50c8a1cb3f098ac43b0508413f33affd762855f67a410e/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828", size = 160014, upload-time = "2025-10-14T04:41:47.631Z" }, + { url = "https://files.pythonhosted.org/packages/76/be/4d3ee471e8145d12795ab655ece37baed0929462a86e72372fd25859047c/charset_normalizer-3.4.4-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec", size = 154044, upload-time = "2025-10-14T04:41:48.81Z" }, + { url = "https://files.pythonhosted.org/packages/b0/6f/8f7af07237c34a1defe7defc565a9bc1807762f672c0fde711a4b22bf9c0/charset_normalizer-3.4.4-cp314-cp314-win32.whl", hash = "sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9", size = 99940, upload-time = "2025-10-14T04:41:49.946Z" }, + { url = "https://files.pythonhosted.org/packages/4b/51/8ade005e5ca5b0d80fb4aff72a3775b325bdc3d27408c8113811a7cbe640/charset_normalizer-3.4.4-cp314-cp314-win_amd64.whl", hash = "sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c", size = 107104, upload-time = "2025-10-14T04:41:51.051Z" }, + { url = "https://files.pythonhosted.org/packages/da/5f/6b8f83a55bb8278772c5ae54a577f3099025f9ade59d0136ac24a0df4bde/charset_normalizer-3.4.4-cp314-cp314-win_arm64.whl", hash = "sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2", size = 100743, upload-time = "2025-10-14T04:41:52.122Z" }, + { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" }, +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/d8/53/6f443c9a4a8358a93a6792e2acffb9d9d5cb0a5cfd8802644b7b1c9a02e4/colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44", size = 27697, upload-time = "2022-10-25T02:36:22.414Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, +] + +[[package]] +name = "filelock" +version = "3.20.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a7/23/ce7a1126827cedeb958fc043d61745754464eb56c5937c35bbf2b8e26f34/filelock-3.20.1.tar.gz", hash = "sha256:b8360948b351b80f420878d8516519a2204b07aefcdcfd24912a5d33127f188c", size = 19476, upload-time = "2025-12-15T23:54:28.027Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e3/7f/a1a97644e39e7316d850784c642093c99df1290a460df4ede27659056834/filelock-3.20.1-py3-none-any.whl", hash = "sha256:15d9e9a67306188a44baa72f569d2bfd803076269365fdea0934385da4dc361a", size = 16666, upload-time = "2025-12-15T23:54:26.874Z" }, +] + +[[package]] +name = "fsspec" +version = "2025.12.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b6/27/954057b0d1f53f086f681755207dda6de6c660ce133c829158e8e8fe7895/fsspec-2025.12.0.tar.gz", hash = "sha256:c505de011584597b1060ff778bb664c1bc022e87921b0e4f10cc9c44f9635973", size = 309748, upload-time = "2025-12-03T15:23:42.687Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/51/c7/b64cae5dba3a1b138d7123ec36bb5ccd39d39939f18454407e5468f4763f/fsspec-2025.12.0-py3-none-any.whl", hash = "sha256:8bf1fe301b7d8acfa6e8571e3b1c3d158f909666642431cc78a1b7b4dbc5ec5b", size = 201422, upload-time = "2025-12-03T15:23:41.434Z" }, +] + +[[package]] +name = "hf-xet" +version = "1.2.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5e/6e/0f11bacf08a67f7fb5ee09740f2ca54163863b07b70d579356e9222ce5d8/hf_xet-1.2.0.tar.gz", hash = "sha256:a8c27070ca547293b6890c4bf389f713f80e8c478631432962bb7f4bc0bd7d7f", size = 506020, upload-time = "2025-10-24T19:04:32.129Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/a5/85ef910a0aa034a2abcfadc360ab5ac6f6bc4e9112349bd40ca97551cff0/hf_xet-1.2.0-cp313-cp313t-macosx_10_12_x86_64.whl", hash = "sha256:ceeefcd1b7aed4956ae8499e2199607765fbd1c60510752003b6cc0b8413b649", size = 2861870, upload-time = "2025-10-24T19:04:11.422Z" }, + { url = "https://files.pythonhosted.org/packages/ea/40/e2e0a7eb9a51fe8828ba2d47fe22a7e74914ea8a0db68a18c3aa7449c767/hf_xet-1.2.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:b70218dd548e9840224df5638fdc94bd033552963cfa97f9170829381179c813", size = 2717584, upload-time = "2025-10-24T19:04:09.586Z" }, + { url = "https://files.pythonhosted.org/packages/a5/7d/daf7f8bc4594fdd59a8a596f9e3886133fdc68e675292218a5e4c1b7e834/hf_xet-1.2.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7d40b18769bb9a8bc82a9ede575ce1a44c75eb80e7375a01d76259089529b5dc", size = 3315004, upload-time = "2025-10-24T19:04:00.314Z" }, + { url = "https://files.pythonhosted.org/packages/b1/ba/45ea2f605fbf6d81c8b21e4d970b168b18a53515923010c312c06cd83164/hf_xet-1.2.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:cd3a6027d59cfb60177c12d6424e31f4b5ff13d8e3a1247b3a584bf8977e6df5", size = 3222636, upload-time = "2025-10-24T19:03:58.111Z" }, + { url = "https://files.pythonhosted.org/packages/4a/1d/04513e3cab8f29ab8c109d309ddd21a2705afab9d52f2ba1151e0c14f086/hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:6de1fc44f58f6dd937956c8d304d8c2dea264c80680bcfa61ca4a15e7b76780f", size = 3408448, upload-time = "2025-10-24T19:04:20.951Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7c/60a2756d7feec7387db3a1176c632357632fbe7849fce576c5559d4520c7/hf_xet-1.2.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f182f264ed2acd566c514e45da9f2119110e48a87a327ca271027904c70c5832", size = 3503401, upload-time = "2025-10-24T19:04:22.549Z" }, + { url = "https://files.pythonhosted.org/packages/4e/64/48fffbd67fb418ab07451e4ce641a70de1c40c10a13e25325e24858ebe5a/hf_xet-1.2.0-cp313-cp313t-win_amd64.whl", hash = "sha256:293a7a3787e5c95d7be1857358a9130694a9c6021de3f27fa233f37267174382", size = 2900866, upload-time = "2025-10-24T19:04:33.461Z" }, + { url = "https://files.pythonhosted.org/packages/e2/51/f7e2caae42f80af886db414d4e9885fac959330509089f97cccb339c6b87/hf_xet-1.2.0-cp314-cp314t-macosx_10_12_x86_64.whl", hash = "sha256:10bfab528b968c70e062607f663e21e34e2bba349e8038db546646875495179e", size = 2861861, upload-time = "2025-10-24T19:04:19.01Z" }, + { url = "https://files.pythonhosted.org/packages/6e/1d/a641a88b69994f9371bd347f1dd35e5d1e2e2460a2e350c8d5165fc62005/hf_xet-1.2.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:2a212e842647b02eb6a911187dc878e79c4aa0aa397e88dd3b26761676e8c1f8", size = 2717699, upload-time = "2025-10-24T19:04:17.306Z" }, + { url = "https://files.pythonhosted.org/packages/df/e0/e5e9bba7d15f0318955f7ec3f4af13f92e773fbb368c0b8008a5acbcb12f/hf_xet-1.2.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:30e06daccb3a7d4c065f34fc26c14c74f4653069bb2b194e7f18f17cbe9939c0", size = 3314885, upload-time = "2025-10-24T19:04:07.642Z" }, + { url = "https://files.pythonhosted.org/packages/21/90/b7fe5ff6f2b7b8cbdf1bd56145f863c90a5807d9758a549bf3d916aa4dec/hf_xet-1.2.0-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:29c8fc913a529ec0a91867ce3d119ac1aac966e098cf49501800c870328cc090", size = 3221550, upload-time = "2025-10-24T19:04:05.55Z" }, + { url = "https://files.pythonhosted.org/packages/6f/cb/73f276f0a7ce46cc6a6ec7d6c7d61cbfe5f2e107123d9bbd0193c355f106/hf_xet-1.2.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e159cbfcfbb29f920db2c09ed8b660eb894640d284f102ada929b6e3dc410a", size = 3408010, upload-time = "2025-10-24T19:04:28.598Z" }, + { url = "https://files.pythonhosted.org/packages/b8/1e/d642a12caa78171f4be64f7cd9c40e3ca5279d055d0873188a58c0f5fbb9/hf_xet-1.2.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9c91d5ae931510107f148874e9e2de8a16052b6f1b3ca3c1b12f15ccb491390f", size = 3503264, upload-time = "2025-10-24T19:04:30.397Z" }, + { url = "https://files.pythonhosted.org/packages/17/b5/33764714923fa1ff922770f7ed18c2daae034d21ae6e10dbf4347c854154/hf_xet-1.2.0-cp314-cp314t-win_amd64.whl", hash = "sha256:210d577732b519ac6ede149d2f2f34049d44e8622bf14eb3d63bbcd2d4b332dc", size = 2901071, upload-time = "2025-10-24T19:04:37.463Z" }, + { url = "https://files.pythonhosted.org/packages/96/2d/22338486473df5923a9ab7107d375dbef9173c338ebef5098ef593d2b560/hf_xet-1.2.0-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:46740d4ac024a7ca9b22bebf77460ff43332868b661186a8e46c227fdae01848", size = 2866099, upload-time = "2025-10-24T19:04:15.366Z" }, + { url = "https://files.pythonhosted.org/packages/7f/8c/c5becfa53234299bc2210ba314eaaae36c2875e0045809b82e40a9544f0c/hf_xet-1.2.0-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:27df617a076420d8845bea087f59303da8be17ed7ec0cd7ee3b9b9f579dff0e4", size = 2722178, upload-time = "2025-10-24T19:04:13.695Z" }, + { url = "https://files.pythonhosted.org/packages/9a/92/cf3ab0b652b082e66876d08da57fcc6fa2f0e6c70dfbbafbd470bb73eb47/hf_xet-1.2.0-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3651fd5bfe0281951b988c0facbe726aa5e347b103a675f49a3fa8144c7968fd", size = 3320214, upload-time = "2025-10-24T19:04:03.596Z" }, + { url = "https://files.pythonhosted.org/packages/46/92/3f7ec4a1b6a65bf45b059b6d4a5d38988f63e193056de2f420137e3c3244/hf_xet-1.2.0-cp37-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:d06fa97c8562fb3ee7a378dd9b51e343bc5bc8190254202c9771029152f5e08c", size = 3229054, upload-time = "2025-10-24T19:04:01.949Z" }, + { url = "https://files.pythonhosted.org/packages/0b/dd/7ac658d54b9fb7999a0ccb07ad863b413cbaf5cf172f48ebcd9497ec7263/hf_xet-1.2.0-cp37-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:4c1428c9ae73ec0939410ec73023c4f842927f39db09b063b9482dac5a3bb737", size = 3413812, upload-time = "2025-10-24T19:04:24.585Z" }, + { url = "https://files.pythonhosted.org/packages/92/68/89ac4e5b12a9ff6286a12174c8538a5930e2ed662091dd2572bbe0a18c8a/hf_xet-1.2.0-cp37-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a55558084c16b09b5ed32ab9ed38421e2d87cf3f1f89815764d1177081b99865", size = 3508920, upload-time = "2025-10-24T19:04:26.927Z" }, + { url = "https://files.pythonhosted.org/packages/cb/44/870d44b30e1dcfb6a65932e3e1506c103a8a5aea9103c337e7a53180322c/hf_xet-1.2.0-cp37-abi3-win_amd64.whl", hash = "sha256:e6584a52253f72c9f52f9e549d5895ca7a471608495c4ecaa6cc73dba2b24d69", size = 2905735, upload-time = "2025-10-24T19:04:35.928Z" }, +] + +[[package]] +name = "huggingface-hub" +version = "0.36.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "filelock" }, + { name = "fsspec" }, + { name = "hf-xet", marker = "platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64'" }, + { name = "packaging" }, + { name = "pyyaml" }, + { name = "requests" }, + { name = "tqdm" }, + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/98/63/4910c5fa9128fdadf6a9c5ac138e8b1b6cee4ca44bf7915bbfbce4e355ee/huggingface_hub-0.36.0.tar.gz", hash = "sha256:47b3f0e2539c39bf5cde015d63b72ec49baff67b6931c3d97f3f84532e2b8d25", size = 463358, upload-time = "2025-10-23T12:12:01.413Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/cb/bd/1a875e0d592d447cbc02805fd3fe0f497714d6a2583f59d14fa9ebad96eb/huggingface_hub-0.36.0-py3-none-any.whl", hash = "sha256:7bcc9ad17d5b3f07b57c78e79d527102d08313caa278a641993acddcb894548d", size = 566094, upload-time = "2025-10-23T12:11:59.557Z" }, +] + +[[package]] +name = "idna" +version = "3.11" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6f/6d/0703ccc57f3a7233505399edb88de3cbd678da106337b9fcde432b65ed60/idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902", size = 194582, upload-time = "2025-10-12T14:55:20.501Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/61/66938bbb5fc52dbdf84594873d5b51fb1f7c7794e9c0f5bd885f30bc507b/idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea", size = 71008, upload-time = "2025-10-12T14:55:18.883Z" }, +] + +[[package]] +name = "jinja2" +version = "3.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/bf/f7da0350254c0ed7c72f3e33cef02e048281fec7ecec5f032d4aac52226b/jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d", size = 245115, upload-time = "2025-03-05T20:05:02.478Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, +] + +[[package]] +name = "logo-test" +version = "0.1.0" +source = { virtual = "." } +dependencies = [ + { name = "numpy" }, + { name = "opencv-python" }, + { name = "pillow" }, + { name = "torch" }, + { name = "tqdm" }, + { name = "transformers" }, + { name = "typing" }, +] + +[package.metadata] +requires-dist = [ + { name = "numpy", specifier = ">=2.2.6" }, + { name = "opencv-python", specifier = ">=4.12.0.88" }, + { name = "pillow", specifier = ">=12.0.0" }, + { name = "torch", specifier = ">=2.9.1" }, + { name = "tqdm", specifier = ">=4.67.1" }, + { name = "transformers", specifier = ">=4.57.3" }, + { name = "typing", specifier = ">=3.10.0.0" }, +] + +[[package]] +name = "markupsafe" +version = "3.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/7e/99/7690b6d4034fffd95959cbe0c02de8deb3098cc577c67bb6a24fe5d7caa7/markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698", size = 80313, upload-time = "2025-09-27T18:37:40.426Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/5a/72/147da192e38635ada20e0a2e1a51cf8823d2119ce8883f7053879c2199b5/markupsafe-3.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d53197da72cc091b024dd97249dfc7794d6a56530370992a5e1a08983ad9230e", size = 11615, upload-time = "2025-09-27T18:36:30.854Z" }, + { url = "https://files.pythonhosted.org/packages/9a/81/7e4e08678a1f98521201c3079f77db69fb552acd56067661f8c2f534a718/markupsafe-3.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1872df69a4de6aead3491198eaf13810b565bdbeec3ae2dc8780f14458ec73ce", size = 12020, upload-time = "2025-09-27T18:36:31.971Z" }, + { url = "https://files.pythonhosted.org/packages/1e/2c/799f4742efc39633a1b54a92eec4082e4f815314869865d876824c257c1e/markupsafe-3.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3a7e8ae81ae39e62a41ec302f972ba6ae23a5c5396c8e60113e9066ef893da0d", size = 24332, upload-time = "2025-09-27T18:36:32.813Z" }, + { url = "https://files.pythonhosted.org/packages/3c/2e/8d0c2ab90a8c1d9a24f0399058ab8519a3279d1bd4289511d74e909f060e/markupsafe-3.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d6dd0be5b5b189d31db7cda48b91d7e0a9795f31430b7f271219ab30f1d3ac9d", size = 22947, upload-time = "2025-09-27T18:36:33.86Z" }, + { url = "https://files.pythonhosted.org/packages/2c/54/887f3092a85238093a0b2154bd629c89444f395618842e8b0c41783898ea/markupsafe-3.0.3-cp312-cp312-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:94c6f0bb423f739146aec64595853541634bde58b2135f27f61c1ffd1cd4d16a", size = 21962, upload-time = "2025-09-27T18:36:35.099Z" }, + { url = "https://files.pythonhosted.org/packages/c9/2f/336b8c7b6f4a4d95e91119dc8521402461b74a485558d8f238a68312f11c/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:be8813b57049a7dc738189df53d69395eba14fb99345e0a5994914a3864c8a4b", size = 23760, upload-time = "2025-09-27T18:36:36.001Z" }, + { url = "https://files.pythonhosted.org/packages/32/43/67935f2b7e4982ffb50a4d169b724d74b62a3964bc1a9a527f5ac4f1ee2b/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:83891d0e9fb81a825d9a6d61e3f07550ca70a076484292a70fde82c4b807286f", size = 21529, upload-time = "2025-09-27T18:36:36.906Z" }, + { url = "https://files.pythonhosted.org/packages/89/e0/4486f11e51bbba8b0c041098859e869e304d1c261e59244baa3d295d47b7/markupsafe-3.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:77f0643abe7495da77fb436f50f8dab76dbc6e5fd25d39589a0f1fe6548bfa2b", size = 23015, upload-time = "2025-09-27T18:36:37.868Z" }, + { url = "https://files.pythonhosted.org/packages/2f/e1/78ee7a023dac597a5825441ebd17170785a9dab23de95d2c7508ade94e0e/markupsafe-3.0.3-cp312-cp312-win32.whl", hash = "sha256:d88b440e37a16e651bda4c7c2b930eb586fd15ca7406cb39e211fcff3bf3017d", size = 14540, upload-time = "2025-09-27T18:36:38.761Z" }, + { url = "https://files.pythonhosted.org/packages/aa/5b/bec5aa9bbbb2c946ca2733ef9c4ca91c91b6a24580193e891b5f7dbe8e1e/markupsafe-3.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:26a5784ded40c9e318cfc2bdb30fe164bdb8665ded9cd64d500a34fb42067b1c", size = 15105, upload-time = "2025-09-27T18:36:39.701Z" }, + { url = "https://files.pythonhosted.org/packages/e5/f1/216fc1bbfd74011693a4fd837e7026152e89c4bcf3e77b6692fba9923123/markupsafe-3.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:35add3b638a5d900e807944a078b51922212fb3dedb01633a8defc4b01a3c85f", size = 13906, upload-time = "2025-09-27T18:36:40.689Z" }, + { url = "https://files.pythonhosted.org/packages/38/2f/907b9c7bbba283e68f20259574b13d005c121a0fa4c175f9bed27c4597ff/markupsafe-3.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795", size = 11622, upload-time = "2025-09-27T18:36:41.777Z" }, + { url = "https://files.pythonhosted.org/packages/9c/d9/5f7756922cdd676869eca1c4e3c0cd0df60ed30199ffd775e319089cb3ed/markupsafe-3.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219", size = 12029, upload-time = "2025-09-27T18:36:43.257Z" }, + { url = "https://files.pythonhosted.org/packages/00/07/575a68c754943058c78f30db02ee03a64b3c638586fba6a6dd56830b30a3/markupsafe-3.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6", size = 24374, upload-time = "2025-09-27T18:36:44.508Z" }, + { url = "https://files.pythonhosted.org/packages/a9/21/9b05698b46f218fc0e118e1f8168395c65c8a2c750ae2bab54fc4bd4e0e8/markupsafe-3.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676", size = 22980, upload-time = "2025-09-27T18:36:45.385Z" }, + { url = "https://files.pythonhosted.org/packages/7f/71/544260864f893f18b6827315b988c146b559391e6e7e8f7252839b1b846a/markupsafe-3.0.3-cp313-cp313-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9", size = 21990, upload-time = "2025-09-27T18:36:46.916Z" }, + { url = "https://files.pythonhosted.org/packages/c2/28/b50fc2f74d1ad761af2f5dcce7492648b983d00a65b8c0e0cb457c82ebbe/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1", size = 23784, upload-time = "2025-09-27T18:36:47.884Z" }, + { url = "https://files.pythonhosted.org/packages/ed/76/104b2aa106a208da8b17a2fb72e033a5a9d7073c68f7e508b94916ed47a9/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc", size = 21588, upload-time = "2025-09-27T18:36:48.82Z" }, + { url = "https://files.pythonhosted.org/packages/b5/99/16a5eb2d140087ebd97180d95249b00a03aa87e29cc224056274f2e45fd6/markupsafe-3.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12", size = 23041, upload-time = "2025-09-27T18:36:49.797Z" }, + { url = "https://files.pythonhosted.org/packages/19/bc/e7140ed90c5d61d77cea142eed9f9c303f4c4806f60a1044c13e3f1471d0/markupsafe-3.0.3-cp313-cp313-win32.whl", hash = "sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed", size = 14543, upload-time = "2025-09-27T18:36:51.584Z" }, + { url = "https://files.pythonhosted.org/packages/05/73/c4abe620b841b6b791f2edc248f556900667a5a1cf023a6646967ae98335/markupsafe-3.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5", size = 15113, upload-time = "2025-09-27T18:36:52.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3a/fa34a0f7cfef23cf9500d68cb7c32dd64ffd58a12b09225fb03dd37d5b80/markupsafe-3.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485", size = 13911, upload-time = "2025-09-27T18:36:53.513Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/e05cd7efe43a88a17a37b3ae96e79a19e846f3f456fe79c57ca61356ef01/markupsafe-3.0.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73", size = 11658, upload-time = "2025-09-27T18:36:54.819Z" }, + { url = "https://files.pythonhosted.org/packages/99/9e/e412117548182ce2148bdeacdda3bb494260c0b0184360fe0d56389b523b/markupsafe-3.0.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37", size = 12066, upload-time = "2025-09-27T18:36:55.714Z" }, + { url = "https://files.pythonhosted.org/packages/bc/e6/fa0ffcda717ef64a5108eaa7b4f5ed28d56122c9a6d70ab8b72f9f715c80/markupsafe-3.0.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19", size = 25639, upload-time = "2025-09-27T18:36:56.908Z" }, + { url = "https://files.pythonhosted.org/packages/96/ec/2102e881fe9d25fc16cb4b25d5f5cde50970967ffa5dddafdb771237062d/markupsafe-3.0.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025", size = 23569, upload-time = "2025-09-27T18:36:57.913Z" }, + { url = "https://files.pythonhosted.org/packages/4b/30/6f2fce1f1f205fc9323255b216ca8a235b15860c34b6798f810f05828e32/markupsafe-3.0.3-cp313-cp313t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6", size = 23284, upload-time = "2025-09-27T18:36:58.833Z" }, + { url = "https://files.pythonhosted.org/packages/58/47/4a0ccea4ab9f5dcb6f79c0236d954acb382202721e704223a8aafa38b5c8/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f", size = 24801, upload-time = "2025-09-27T18:36:59.739Z" }, + { url = "https://files.pythonhosted.org/packages/6a/70/3780e9b72180b6fecb83a4814d84c3bf4b4ae4bf0b19c27196104149734c/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb", size = 22769, upload-time = "2025-09-27T18:37:00.719Z" }, + { url = "https://files.pythonhosted.org/packages/98/c5/c03c7f4125180fc215220c035beac6b9cb684bc7a067c84fc69414d315f5/markupsafe-3.0.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009", size = 23642, upload-time = "2025-09-27T18:37:01.673Z" }, + { url = "https://files.pythonhosted.org/packages/80/d6/2d1b89f6ca4bff1036499b1e29a1d02d282259f3681540e16563f27ebc23/markupsafe-3.0.3-cp313-cp313t-win32.whl", hash = "sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354", size = 14612, upload-time = "2025-09-27T18:37:02.639Z" }, + { url = "https://files.pythonhosted.org/packages/2b/98/e48a4bfba0a0ffcf9925fe2d69240bfaa19c6f7507b8cd09c70684a53c1e/markupsafe-3.0.3-cp313-cp313t-win_amd64.whl", hash = "sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218", size = 15200, upload-time = "2025-09-27T18:37:03.582Z" }, + { url = "https://files.pythonhosted.org/packages/0e/72/e3cc540f351f316e9ed0f092757459afbc595824ca724cbc5a5d4263713f/markupsafe-3.0.3-cp313-cp313t-win_arm64.whl", hash = "sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287", size = 13973, upload-time = "2025-09-27T18:37:04.929Z" }, + { url = "https://files.pythonhosted.org/packages/33/8a/8e42d4838cd89b7dde187011e97fe6c3af66d8c044997d2183fbd6d31352/markupsafe-3.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe", size = 11619, upload-time = "2025-09-27T18:37:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/b5/64/7660f8a4a8e53c924d0fa05dc3a55c9cee10bbd82b11c5afb27d44b096ce/markupsafe-3.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026", size = 12029, upload-time = "2025-09-27T18:37:07.213Z" }, + { url = "https://files.pythonhosted.org/packages/da/ef/e648bfd021127bef5fa12e1720ffed0c6cbb8310c8d9bea7266337ff06de/markupsafe-3.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737", size = 24408, upload-time = "2025-09-27T18:37:09.572Z" }, + { url = "https://files.pythonhosted.org/packages/41/3c/a36c2450754618e62008bf7435ccb0f88053e07592e6028a34776213d877/markupsafe-3.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97", size = 23005, upload-time = "2025-09-27T18:37:10.58Z" }, + { url = "https://files.pythonhosted.org/packages/bc/20/b7fdf89a8456b099837cd1dc21974632a02a999ec9bf7ca3e490aacd98e7/markupsafe-3.0.3-cp314-cp314-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d", size = 22048, upload-time = "2025-09-27T18:37:11.547Z" }, + { url = "https://files.pythonhosted.org/packages/9a/a7/591f592afdc734f47db08a75793a55d7fbcc6902a723ae4cfbab61010cc5/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda", size = 23821, upload-time = "2025-09-27T18:37:12.48Z" }, + { url = "https://files.pythonhosted.org/packages/7d/33/45b24e4f44195b26521bc6f1a82197118f74df348556594bd2262bda1038/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf", size = 21606, upload-time = "2025-09-27T18:37:13.485Z" }, + { url = "https://files.pythonhosted.org/packages/ff/0e/53dfaca23a69fbfbbf17a4b64072090e70717344c52eaaaa9c5ddff1e5f0/markupsafe-3.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe", size = 23043, upload-time = "2025-09-27T18:37:14.408Z" }, + { url = "https://files.pythonhosted.org/packages/46/11/f333a06fc16236d5238bfe74daccbca41459dcd8d1fa952e8fbd5dccfb70/markupsafe-3.0.3-cp314-cp314-win32.whl", hash = "sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9", size = 14747, upload-time = "2025-09-27T18:37:15.36Z" }, + { url = "https://files.pythonhosted.org/packages/28/52/182836104b33b444e400b14f797212f720cbc9ed6ba34c800639d154e821/markupsafe-3.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581", size = 15341, upload-time = "2025-09-27T18:37:16.496Z" }, + { url = "https://files.pythonhosted.org/packages/6f/18/acf23e91bd94fd7b3031558b1f013adfa21a8e407a3fdb32745538730382/markupsafe-3.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4", size = 14073, upload-time = "2025-09-27T18:37:17.476Z" }, + { url = "https://files.pythonhosted.org/packages/3c/f0/57689aa4076e1b43b15fdfa646b04653969d50cf30c32a102762be2485da/markupsafe-3.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab", size = 11661, upload-time = "2025-09-27T18:37:18.453Z" }, + { url = "https://files.pythonhosted.org/packages/89/c3/2e67a7ca217c6912985ec766c6393b636fb0c2344443ff9d91404dc4c79f/markupsafe-3.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175", size = 12069, upload-time = "2025-09-27T18:37:19.332Z" }, + { url = "https://files.pythonhosted.org/packages/f0/00/be561dce4e6ca66b15276e184ce4b8aec61fe83662cce2f7d72bd3249d28/markupsafe-3.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634", size = 25670, upload-time = "2025-09-27T18:37:20.245Z" }, + { url = "https://files.pythonhosted.org/packages/50/09/c419f6f5a92e5fadde27efd190eca90f05e1261b10dbd8cbcb39cd8ea1dc/markupsafe-3.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50", size = 23598, upload-time = "2025-09-27T18:37:21.177Z" }, + { url = "https://files.pythonhosted.org/packages/22/44/a0681611106e0b2921b3033fc19bc53323e0b50bc70cffdd19f7d679bb66/markupsafe-3.0.3-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e", size = 23261, upload-time = "2025-09-27T18:37:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/5f/57/1b0b3f100259dc9fffe780cfb60d4be71375510e435efec3d116b6436d43/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5", size = 24835, upload-time = "2025-09-27T18:37:23.296Z" }, + { url = "https://files.pythonhosted.org/packages/26/6a/4bf6d0c97c4920f1597cc14dd720705eca0bf7c787aebc6bb4d1bead5388/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523", size = 22733, upload-time = "2025-09-27T18:37:24.237Z" }, + { url = "https://files.pythonhosted.org/packages/14/c7/ca723101509b518797fedc2fdf79ba57f886b4aca8a7d31857ba3ee8281f/markupsafe-3.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc", size = 23672, upload-time = "2025-09-27T18:37:25.271Z" }, + { url = "https://files.pythonhosted.org/packages/fb/df/5bd7a48c256faecd1d36edc13133e51397e41b73bb77e1a69deab746ebac/markupsafe-3.0.3-cp314-cp314t-win32.whl", hash = "sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d", size = 14819, upload-time = "2025-09-27T18:37:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/1a/8a/0402ba61a2f16038b48b39bccca271134be00c5c9f0f623208399333c448/markupsafe-3.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9", size = 15426, upload-time = "2025-09-27T18:37:27.316Z" }, + { url = "https://files.pythonhosted.org/packages/70/bc/6f1c2f612465f5fa89b95bead1f44dcb607670fd42891d8fdcd5d039f4f4/markupsafe-3.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa", size = 14146, upload-time = "2025-09-27T18:37:28.327Z" }, +] + +[[package]] +name = "mpmath" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e0/47/dd32fa426cc72114383ac549964eecb20ecfd886d1e5ccf5340b55b02f57/mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f", size = 508106, upload-time = "2023-03-07T16:47:11.061Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c", size = 536198, upload-time = "2023-03-07T16:47:09.197Z" }, +] + +[[package]] +name = "networkx" +version = "3.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/6a/51/63fe664f3908c97be9d2e4f1158eb633317598cfa6e1fc14af5383f17512/networkx-3.6.1.tar.gz", hash = "sha256:26b7c357accc0c8cde558ad486283728b65b6a95d85ee1cd66bafab4c8168509", size = 2517025, upload-time = "2025-12-08T17:02:39.908Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9e/c9/b2622292ea83fbb4ec318f5b9ab867d0a28ab43c5717bb85b0a5f6b3b0a4/networkx-3.6.1-py3-none-any.whl", hash = "sha256:d47fbf302e7d9cbbb9e2555a0d267983d2aa476bac30e90dfbe5669bd57f3762", size = 2068504, upload-time = "2025-12-08T17:02:38.159Z" }, +] + +[[package]] +name = "numpy" +version = "2.2.6" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" }, + { url = "https://files.pythonhosted.org/packages/66/ee/560deadcdde6c2f90200450d5938f63a34b37e27ebff162810f716f6a230/numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c", size = 14119362, upload-time = "2025-05-17T21:35:01.241Z" }, + { url = "https://files.pythonhosted.org/packages/3c/65/4baa99f1c53b30adf0acd9a5519078871ddde8d2339dc5a7fde80d9d87da/numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3", size = 5084103, upload-time = "2025-05-17T21:35:10.622Z" }, + { url = "https://files.pythonhosted.org/packages/cc/89/e5a34c071a0570cc40c9a54eb472d113eea6d002e9ae12bb3a8407fb912e/numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282", size = 6625382, upload-time = "2025-05-17T21:35:21.414Z" }, + { url = "https://files.pythonhosted.org/packages/f8/35/8c80729f1ff76b3921d5c9487c7ac3de9b2a103b1cd05e905b3090513510/numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87", size = 14018462, upload-time = "2025-05-17T21:35:42.174Z" }, + { url = "https://files.pythonhosted.org/packages/8c/3d/1e1db36cfd41f895d266b103df00ca5b3cbe965184df824dec5c08c6b803/numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249", size = 16527618, upload-time = "2025-05-17T21:36:06.711Z" }, + { url = "https://files.pythonhosted.org/packages/61/c6/03ed30992602c85aa3cd95b9070a514f8b3c33e31124694438d88809ae36/numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49", size = 15505511, upload-time = "2025-05-17T21:36:29.965Z" }, + { url = "https://files.pythonhosted.org/packages/b7/25/5761d832a81df431e260719ec45de696414266613c9ee268394dd5ad8236/numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de", size = 18313783, upload-time = "2025-05-17T21:36:56.883Z" }, + { url = "https://files.pythonhosted.org/packages/57/0a/72d5a3527c5ebffcd47bde9162c39fae1f90138c961e5296491ce778e682/numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4", size = 6246506, upload-time = "2025-05-17T21:37:07.368Z" }, + { url = "https://files.pythonhosted.org/packages/36/fa/8c9210162ca1b88529ab76b41ba02d433fd54fecaf6feb70ef9f124683f1/numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2", size = 12614190, upload-time = "2025-05-17T21:37:26.213Z" }, + { url = "https://files.pythonhosted.org/packages/f9/5c/6657823f4f594f72b5471f1db1ab12e26e890bb2e41897522d134d2a3e81/numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84", size = 20867828, upload-time = "2025-05-17T21:37:56.699Z" }, + { url = "https://files.pythonhosted.org/packages/dc/9e/14520dc3dadf3c803473bd07e9b2bd1b69bc583cb2497b47000fed2fa92f/numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b", size = 14143006, upload-time = "2025-05-17T21:38:18.291Z" }, + { url = "https://files.pythonhosted.org/packages/4f/06/7e96c57d90bebdce9918412087fc22ca9851cceaf5567a45c1f404480e9e/numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d", size = 5076765, upload-time = "2025-05-17T21:38:27.319Z" }, + { url = "https://files.pythonhosted.org/packages/73/ed/63d920c23b4289fdac96ddbdd6132e9427790977d5457cd132f18e76eae0/numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566", size = 6617736, upload-time = "2025-05-17T21:38:38.141Z" }, + { url = "https://files.pythonhosted.org/packages/85/c5/e19c8f99d83fd377ec8c7e0cf627a8049746da54afc24ef0a0cb73d5dfb5/numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f", size = 14010719, upload-time = "2025-05-17T21:38:58.433Z" }, + { url = "https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f", size = 16526072, upload-time = "2025-05-17T21:39:22.638Z" }, + { url = "https://files.pythonhosted.org/packages/b2/6c/04b5f47f4f32f7c2b0e7260442a8cbcf8168b0e1a41ff1495da42f42a14f/numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868", size = 15503213, upload-time = "2025-05-17T21:39:45.865Z" }, + { url = "https://files.pythonhosted.org/packages/17/0a/5cd92e352c1307640d5b6fec1b2ffb06cd0dabe7d7b8227f97933d378422/numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d", size = 18316632, upload-time = "2025-05-17T21:40:13.331Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3b/5cba2b1d88760ef86596ad0f3d484b1cbff7c115ae2429678465057c5155/numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd", size = 6244532, upload-time = "2025-05-17T21:43:46.099Z" }, + { url = "https://files.pythonhosted.org/packages/cb/3b/d58c12eafcb298d4e6d0d40216866ab15f59e55d148a5658bb3132311fcf/numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c", size = 12610885, upload-time = "2025-05-17T21:44:05.145Z" }, + { url = "https://files.pythonhosted.org/packages/6b/9e/4bf918b818e516322db999ac25d00c75788ddfd2d2ade4fa66f1f38097e1/numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6", size = 20963467, upload-time = "2025-05-17T21:40:44Z" }, + { url = "https://files.pythonhosted.org/packages/61/66/d2de6b291507517ff2e438e13ff7b1e2cdbdb7cb40b3ed475377aece69f9/numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda", size = 14225144, upload-time = "2025-05-17T21:41:05.695Z" }, + { url = "https://files.pythonhosted.org/packages/e4/25/480387655407ead912e28ba3a820bc69af9adf13bcbe40b299d454ec011f/numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40", size = 5200217, upload-time = "2025-05-17T21:41:15.903Z" }, + { url = "https://files.pythonhosted.org/packages/aa/4a/6e313b5108f53dcbf3aca0c0f3e9c92f4c10ce57a0a721851f9785872895/numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8", size = 6712014, upload-time = "2025-05-17T21:41:27.321Z" }, + { url = "https://files.pythonhosted.org/packages/b7/30/172c2d5c4be71fdf476e9de553443cf8e25feddbe185e0bd88b096915bcc/numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f", size = 14077935, upload-time = "2025-05-17T21:41:49.738Z" }, + { url = "https://files.pythonhosted.org/packages/12/fb/9e743f8d4e4d3c710902cf87af3512082ae3d43b945d5d16563f26ec251d/numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa", size = 16600122, upload-time = "2025-05-17T21:42:14.046Z" }, + { url = "https://files.pythonhosted.org/packages/12/75/ee20da0e58d3a66f204f38916757e01e33a9737d0b22373b3eb5a27358f9/numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571", size = 15586143, upload-time = "2025-05-17T21:42:37.464Z" }, + { url = "https://files.pythonhosted.org/packages/76/95/bef5b37f29fc5e739947e9ce5179ad402875633308504a52d188302319c8/numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1", size = 18385260, upload-time = "2025-05-17T21:43:05.189Z" }, + { url = "https://files.pythonhosted.org/packages/09/04/f2f83279d287407cf36a7a8053a5abe7be3622a4363337338f2585e4afda/numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff", size = 6377225, upload-time = "2025-05-17T21:43:16.254Z" }, + { url = "https://files.pythonhosted.org/packages/67/0e/35082d13c09c02c011cf21570543d202ad929d961c02a147493cb0c2bdf5/numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06", size = 12771374, upload-time = "2025-05-17T21:43:35.479Z" }, +] + +[[package]] +name = "nvidia-cublas-cu12" +version = "12.8.4.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dc/61/e24b560ab2e2eaeb3c839129175fb330dfcfc29e5203196e5541a4c44682/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142", size = 594346921, upload-time = "2025-03-07T01:44:31.254Z" }, +] + +[[package]] +name = "nvidia-cuda-cupti-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/02/2adcaa145158bf1a8295d83591d22e4103dbfd821bcaf6f3f53151ca4ffa/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182", size = 10248621, upload-time = "2025-03-07T01:40:21.213Z" }, +] + +[[package]] +name = "nvidia-cuda-nvrtc-cu12" +version = "12.8.93" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/6b/32f747947df2da6994e999492ab306a903659555dddc0fbdeb9d71f75e52/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994", size = 88040029, upload-time = "2025-03-07T01:42:13.562Z" }, +] + +[[package]] +name = "nvidia-cuda-runtime-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0d/9b/a997b638fcd068ad6e4d53b8551a7d30fe8b404d6f1804abf1df69838932/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90", size = 954765, upload-time = "2025-03-07T01:40:01.615Z" }, +] + +[[package]] +name = "nvidia-cudnn-cu12" +version = "9.10.2.21" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/ba/51/e123d997aa098c61d029f76663dedbfb9bc8dcf8c60cbd6adbe42f76d049/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8", size = 706758467, upload-time = "2025-06-06T21:54:08.597Z" }, +] + +[[package]] +name = "nvidia-cufft-cu12" +version = "11.3.3.83" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/1f/13/ee4e00f30e676b66ae65b4f08cb5bcbb8392c03f54f2d5413ea99a5d1c80/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74", size = 193118695, upload-time = "2025-03-07T01:45:27.821Z" }, +] + +[[package]] +name = "nvidia-cufile-cu12" +version = "1.13.1.3" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bb/fe/1bcba1dfbfb8d01be8d93f07bfc502c93fa23afa6fd5ab3fc7c1df71038a/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc", size = 1197834, upload-time = "2025-03-07T01:45:50.723Z" }, +] + +[[package]] +name = "nvidia-curand-cu12" +version = "10.3.9.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/aa/6584b56dc84ebe9cf93226a5cde4d99080c8e90ab40f0c27bda7a0f29aa1/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9", size = 63619976, upload-time = "2025-03-07T01:46:23.323Z" }, +] + +[[package]] +name = "nvidia-cusolver-cu12" +version = "11.7.3.90" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-cublas-cu12" }, + { name = "nvidia-cusparse-cu12" }, + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/85/48/9a13d2975803e8cf2777d5ed57b87a0b6ca2cc795f9a4f59796a910bfb80/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450", size = 267506905, upload-time = "2025-03-07T01:47:16.273Z" }, +] + +[[package]] +name = "nvidia-cusparse-cu12" +version = "12.5.8.93" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "nvidia-nvjitlink-cu12" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/c2/f5/e1854cb2f2bcd4280c44736c93550cc300ff4b8c95ebe370d0aa7d2b473d/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b", size = 288216466, upload-time = "2025-03-07T01:48:13.779Z" }, +] + +[[package]] +name = "nvidia-cusparselt-cu12" +version = "0.7.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/56/79/12978b96bd44274fe38b5dde5cfb660b1d114f70a65ef962bcbbed99b549/nvidia_cusparselt_cu12-0.7.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623", size = 287193691, upload-time = "2025-02-26T00:15:44.104Z" }, +] + +[[package]] +name = "nvidia-nccl-cu12" +version = "2.27.5" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6e/89/f7a07dc961b60645dbbf42e80f2bc85ade7feb9a491b11a1e973aa00071f/nvidia_nccl_cu12-2.27.5-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457", size = 322348229, upload-time = "2025-06-26T04:11:28.385Z" }, +] + +[[package]] +name = "nvidia-nvjitlink-cu12" +version = "12.8.93" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f6/74/86a07f1d0f42998ca31312f998bd3b9a7eff7f52378f4f270c8679c77fb9/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88", size = 39254836, upload-time = "2025-03-07T01:49:55.661Z" }, +] + +[[package]] +name = "nvidia-nvshmem-cu12" +version = "3.3.20" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/3b/6c/99acb2f9eb85c29fc6f3a7ac4dccfd992e22666dd08a642b303311326a97/nvidia_nvshmem_cu12-3.3.20-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5", size = 124657145, upload-time = "2025-08-04T20:25:19.995Z" }, +] + +[[package]] +name = "nvidia-nvtx-cu12" +version = "12.8.90" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a2/eb/86626c1bbc2edb86323022371c39aa48df6fd8b0a1647bc274577f72e90b/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f", size = 89954, upload-time = "2025-03-07T01:42:44.131Z" }, +] + +[[package]] +name = "opencv-python" +version = "4.12.0.88" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "numpy" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/ac/71/25c98e634b6bdeca4727c7f6d6927b056080668c5008ad3c8fc9e7f8f6ec/opencv-python-4.12.0.88.tar.gz", hash = "sha256:8b738389cede219405f6f3880b851efa3415ccd674752219377353f017d2994d", size = 95373294, upload-time = "2025-07-07T09:20:52.389Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/85/68/3da40142e7c21e9b1d4e7ddd6c58738feb013203e6e4b803d62cdd9eb96b/opencv_python-4.12.0.88-cp37-abi3-macosx_13_0_arm64.whl", hash = "sha256:f9a1f08883257b95a5764bf517a32d75aec325319c8ed0f89739a57fae9e92a5", size = 37877727, upload-time = "2025-07-07T09:13:31.47Z" }, + { url = "https://files.pythonhosted.org/packages/33/7c/042abe49f58d6ee7e1028eefc3334d98ca69b030e3b567fe245a2b28ea6f/opencv_python-4.12.0.88-cp37-abi3-macosx_13_0_x86_64.whl", hash = "sha256:812eb116ad2b4de43ee116fcd8991c3a687f099ada0b04e68f64899c09448e81", size = 57326471, upload-time = "2025-07-07T09:13:41.26Z" }, + { url = "https://files.pythonhosted.org/packages/62/3a/440bd64736cf8116f01f3b7f9f2e111afb2e02beb2ccc08a6458114a6b5d/opencv_python-4.12.0.88-cp37-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:51fd981c7df6af3e8f70b1556696b05224c4e6b6777bdd2a46b3d4fb09de1a92", size = 45887139, upload-time = "2025-07-07T09:13:50.761Z" }, + { url = "https://files.pythonhosted.org/packages/68/1f/795e7f4aa2eacc59afa4fb61a2e35e510d06414dd5a802b51a012d691b37/opencv_python-4.12.0.88-cp37-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:092c16da4c5a163a818f120c22c5e4a2f96e0db4f24e659c701f1fe629a690f9", size = 67041680, upload-time = "2025-07-07T09:14:01.995Z" }, + { url = "https://files.pythonhosted.org/packages/02/96/213fea371d3cb2f1d537612a105792aa0a6659fb2665b22cad709a75bd94/opencv_python-4.12.0.88-cp37-abi3-win32.whl", hash = "sha256:ff554d3f725b39878ac6a2e1fa232ec509c36130927afc18a1719ebf4fbf4357", size = 30284131, upload-time = "2025-07-07T09:14:08.819Z" }, + { url = "https://files.pythonhosted.org/packages/fa/80/eb88edc2e2b11cd2dd2e56f1c80b5784d11d6e6b7f04a1145df64df40065/opencv_python-4.12.0.88-cp37-abi3-win_amd64.whl", hash = "sha256:d98edb20aa932fd8ebd276a72627dad9dc097695b3d435a4257557bbb49a79d2", size = 39000307, upload-time = "2025-07-07T09:14:16.641Z" }, +] + +[[package]] +name = "packaging" +version = "25.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a1/d4/1fc4078c65507b51b96ca8f8c3ba19e6a61c8253c72794544580a7b6c24d/packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f", size = 165727, upload-time = "2025-04-19T11:48:59.673Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/20/12/38679034af332785aac8774540895e234f4d07f7545804097de4b666afd8/packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484", size = 66469, upload-time = "2025-04-19T11:48:57.875Z" }, +] + +[[package]] +name = "pillow" +version = "12.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/5a/b0/cace85a1b0c9775a9f8f5d5423c8261c858760e2466c79b2dd184638b056/pillow-12.0.0.tar.gz", hash = "sha256:87d4f8125c9988bfbed67af47dd7a953e2fc7b0cc1e7800ec6d2080d490bb353", size = 47008828, upload-time = "2025-10-15T18:24:14.008Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/2c/90/4fcce2c22caf044e660a198d740e7fbc14395619e3cb1abad12192c0826c/pillow-12.0.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:53561a4ddc36facb432fae7a9d8afbfaf94795414f5cdc5fc52f28c1dca90371", size = 5249377, upload-time = "2025-10-15T18:22:05.993Z" }, + { url = "https://files.pythonhosted.org/packages/fd/e0/ed960067543d080691d47d6938ebccbf3976a931c9567ab2fbfab983a5dd/pillow-12.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:71db6b4c1653045dacc1585c1b0d184004f0d7e694c7b34ac165ca70c0838082", size = 4650343, upload-time = "2025-10-15T18:22:07.718Z" }, + { url = "https://files.pythonhosted.org/packages/e7/a1/f81fdeddcb99c044bf7d6faa47e12850f13cee0849537a7d27eeab5534d4/pillow-12.0.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2fa5f0b6716fc88f11380b88b31fe591a06c6315e955c096c35715788b339e3f", size = 6232981, upload-time = "2025-10-15T18:22:09.287Z" }, + { url = "https://files.pythonhosted.org/packages/88/e1/9098d3ce341a8750b55b0e00c03f1630d6178f38ac191c81c97a3b047b44/pillow-12.0.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82240051c6ca513c616f7f9da06e871f61bfd7805f566275841af15015b8f98d", size = 8041399, upload-time = "2025-10-15T18:22:10.872Z" }, + { url = "https://files.pythonhosted.org/packages/a7/62/a22e8d3b602ae8cc01446d0c57a54e982737f44b6f2e1e019a925143771d/pillow-12.0.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:55f818bd74fe2f11d4d7cbc65880a843c4075e0ac7226bc1a23261dbea531953", size = 6347740, upload-time = "2025-10-15T18:22:12.769Z" }, + { url = "https://files.pythonhosted.org/packages/4f/87/424511bdcd02c8d7acf9f65caa09f291a519b16bd83c3fb3374b3d4ae951/pillow-12.0.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b87843e225e74576437fd5b6a4c2205d422754f84a06942cfaf1dc32243e45a8", size = 7040201, upload-time = "2025-10-15T18:22:14.813Z" }, + { url = "https://files.pythonhosted.org/packages/dc/4d/435c8ac688c54d11755aedfdd9f29c9eeddf68d150fe42d1d3dbd2365149/pillow-12.0.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c607c90ba67533e1b2355b821fef6764d1dd2cbe26b8c1005ae84f7aea25ff79", size = 6462334, upload-time = "2025-10-15T18:22:16.375Z" }, + { url = "https://files.pythonhosted.org/packages/2b/f2/ad34167a8059a59b8ad10bc5c72d4d9b35acc6b7c0877af8ac885b5f2044/pillow-12.0.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:21f241bdd5080a15bc86d3466a9f6074a9c2c2b314100dd896ac81ee6db2f1ba", size = 7134162, upload-time = "2025-10-15T18:22:17.996Z" }, + { url = "https://files.pythonhosted.org/packages/0c/b1/a7391df6adacf0a5c2cf6ac1cf1fcc1369e7d439d28f637a847f8803beb3/pillow-12.0.0-cp312-cp312-win32.whl", hash = "sha256:dd333073e0cacdc3089525c7df7d39b211bcdf31fc2824e49d01c6b6187b07d0", size = 6298769, upload-time = "2025-10-15T18:22:19.923Z" }, + { url = "https://files.pythonhosted.org/packages/a2/0b/d87733741526541c909bbf159e338dcace4f982daac6e5a8d6be225ca32d/pillow-12.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:9fe611163f6303d1619bbcb653540a4d60f9e55e622d60a3108be0d5b441017a", size = 7001107, upload-time = "2025-10-15T18:22:21.644Z" }, + { url = "https://files.pythonhosted.org/packages/bc/96/aaa61ce33cc98421fb6088af2a03be4157b1e7e0e87087c888e2370a7f45/pillow-12.0.0-cp312-cp312-win_arm64.whl", hash = "sha256:7dfb439562f234f7d57b1ac6bc8fe7f838a4bd49c79230e0f6a1da93e82f1fad", size = 2436012, upload-time = "2025-10-15T18:22:23.621Z" }, + { url = "https://files.pythonhosted.org/packages/62/f2/de993bb2d21b33a98d031ecf6a978e4b61da207bef02f7b43093774c480d/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0869154a2d0546545cde61d1789a6524319fc1897d9ee31218eae7a60ccc5643", size = 4045493, upload-time = "2025-10-15T18:22:25.758Z" }, + { url = "https://files.pythonhosted.org/packages/0e/b6/bc8d0c4c9f6f111a783d045310945deb769b806d7574764234ffd50bc5ea/pillow-12.0.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:a7921c5a6d31b3d756ec980f2f47c0cfdbce0fc48c22a39347a895f41f4a6ea4", size = 4120461, upload-time = "2025-10-15T18:22:27.286Z" }, + { url = "https://files.pythonhosted.org/packages/5d/57/d60d343709366a353dc56adb4ee1e7d8a2cc34e3fbc22905f4167cfec119/pillow-12.0.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:1ee80a59f6ce048ae13cda1abf7fbd2a34ab9ee7d401c46be3ca685d1999a399", size = 3576912, upload-time = "2025-10-15T18:22:28.751Z" }, + { url = "https://files.pythonhosted.org/packages/a4/a4/a0a31467e3f83b94d37568294b01d22b43ae3c5d85f2811769b9c66389dd/pillow-12.0.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c50f36a62a22d350c96e49ad02d0da41dbd17ddc2e29750dbdba4323f85eb4a5", size = 5249132, upload-time = "2025-10-15T18:22:30.641Z" }, + { url = "https://files.pythonhosted.org/packages/83/06/48eab21dd561de2914242711434c0c0eb992ed08ff3f6107a5f44527f5e9/pillow-12.0.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:5193fde9a5f23c331ea26d0cf171fbf67e3f247585f50c08b3e205c7aeb4589b", size = 4650099, upload-time = "2025-10-15T18:22:32.73Z" }, + { url = "https://files.pythonhosted.org/packages/fc/bd/69ed99fd46a8dba7c1887156d3572fe4484e3f031405fcc5a92e31c04035/pillow-12.0.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bde737cff1a975b70652b62d626f7785e0480918dece11e8fef3c0cf057351c3", size = 6230808, upload-time = "2025-10-15T18:22:34.337Z" }, + { url = "https://files.pythonhosted.org/packages/ea/94/8fad659bcdbf86ed70099cb60ae40be6acca434bbc8c4c0d4ef356d7e0de/pillow-12.0.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a6597ff2b61d121172f5844b53f21467f7082f5fb385a9a29c01414463f93b07", size = 8037804, upload-time = "2025-10-15T18:22:36.402Z" }, + { url = "https://files.pythonhosted.org/packages/20/39/c685d05c06deecfd4e2d1950e9a908aa2ca8bc4e6c3b12d93b9cafbd7837/pillow-12.0.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0b817e7035ea7f6b942c13aa03bb554fc44fea70838ea21f8eb31c638326584e", size = 6345553, upload-time = "2025-10-15T18:22:38.066Z" }, + { url = "https://files.pythonhosted.org/packages/38/57/755dbd06530a27a5ed74f8cb0a7a44a21722ebf318edbe67ddbd7fb28f88/pillow-12.0.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f4f1231b7dec408e8670264ce63e9c71409d9583dd21d32c163e25213ee2a344", size = 7037729, upload-time = "2025-10-15T18:22:39.769Z" }, + { url = "https://files.pythonhosted.org/packages/ca/b6/7e94f4c41d238615674d06ed677c14883103dce1c52e4af16f000338cfd7/pillow-12.0.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:6e51b71417049ad6ab14c49608b4a24d8fb3fe605e5dfabfe523b58064dc3d27", size = 6459789, upload-time = "2025-10-15T18:22:41.437Z" }, + { url = "https://files.pythonhosted.org/packages/9c/14/4448bb0b5e0f22dd865290536d20ec8a23b64e2d04280b89139f09a36bb6/pillow-12.0.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d120c38a42c234dc9a8c5de7ceaaf899cf33561956acb4941653f8bdc657aa79", size = 7130917, upload-time = "2025-10-15T18:22:43.152Z" }, + { url = "https://files.pythonhosted.org/packages/dd/ca/16c6926cc1c015845745d5c16c9358e24282f1e588237a4c36d2b30f182f/pillow-12.0.0-cp313-cp313-win32.whl", hash = "sha256:4cc6b3b2efff105c6a1656cfe59da4fdde2cda9af1c5e0b58529b24525d0a098", size = 6302391, upload-time = "2025-10-15T18:22:44.753Z" }, + { url = "https://files.pythonhosted.org/packages/6d/2a/dd43dcfd6dae9b6a49ee28a8eedb98c7d5ff2de94a5d834565164667b97b/pillow-12.0.0-cp313-cp313-win_amd64.whl", hash = "sha256:4cf7fed4b4580601c4345ceb5d4cbf5a980d030fd5ad07c4d2ec589f95f09905", size = 7007477, upload-time = "2025-10-15T18:22:46.838Z" }, + { url = "https://files.pythonhosted.org/packages/77/f0/72ea067f4b5ae5ead653053212af05ce3705807906ba3f3e8f58ddf617e6/pillow-12.0.0-cp313-cp313-win_arm64.whl", hash = "sha256:9f0b04c6b8584c2c193babcccc908b38ed29524b29dd464bc8801bf10d746a3a", size = 2435918, upload-time = "2025-10-15T18:22:48.399Z" }, + { url = "https://files.pythonhosted.org/packages/f5/5e/9046b423735c21f0487ea6cb5b10f89ea8f8dfbe32576fe052b5ba9d4e5b/pillow-12.0.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:7fa22993bac7b77b78cae22bad1e2a987ddf0d9015c63358032f84a53f23cdc3", size = 5251406, upload-time = "2025-10-15T18:22:49.905Z" }, + { url = "https://files.pythonhosted.org/packages/12/66/982ceebcdb13c97270ef7a56c3969635b4ee7cd45227fa707c94719229c5/pillow-12.0.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:f135c702ac42262573fe9714dfe99c944b4ba307af5eb507abef1667e2cbbced", size = 4653218, upload-time = "2025-10-15T18:22:51.587Z" }, + { url = "https://files.pythonhosted.org/packages/16/b3/81e625524688c31859450119bf12674619429cab3119eec0e30a7a1029cb/pillow-12.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c85de1136429c524e55cfa4e033b4a7940ac5c8ee4d9401cc2d1bf48154bbc7b", size = 6266564, upload-time = "2025-10-15T18:22:53.215Z" }, + { url = "https://files.pythonhosted.org/packages/98/59/dfb38f2a41240d2408096e1a76c671d0a105a4a8471b1871c6902719450c/pillow-12.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38df9b4bfd3db902c9c2bd369bcacaf9d935b2fff73709429d95cc41554f7b3d", size = 8069260, upload-time = "2025-10-15T18:22:54.933Z" }, + { url = "https://files.pythonhosted.org/packages/dc/3d/378dbea5cd1874b94c312425ca77b0f47776c78e0df2df751b820c8c1d6c/pillow-12.0.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7d87ef5795da03d742bf49439f9ca4d027cde49c82c5371ba52464aee266699a", size = 6379248, upload-time = "2025-10-15T18:22:56.605Z" }, + { url = "https://files.pythonhosted.org/packages/84/b0/d525ef47d71590f1621510327acec75ae58c721dc071b17d8d652ca494d8/pillow-12.0.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aff9e4d82d082ff9513bdd6acd4f5bd359f5b2c870907d2b0a9c5e10d40c88fe", size = 7066043, upload-time = "2025-10-15T18:22:58.53Z" }, + { url = "https://files.pythonhosted.org/packages/61/2c/aced60e9cf9d0cde341d54bf7932c9ffc33ddb4a1595798b3a5150c7ec4e/pillow-12.0.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:8d8ca2b210ada074d57fcee40c30446c9562e542fc46aedc19baf758a93532ee", size = 6490915, upload-time = "2025-10-15T18:23:00.582Z" }, + { url = "https://files.pythonhosted.org/packages/ef/26/69dcb9b91f4e59f8f34b2332a4a0a951b44f547c4ed39d3e4dcfcff48f89/pillow-12.0.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:99a7f72fb6249302aa62245680754862a44179b545ded638cf1fef59befb57ef", size = 7157998, upload-time = "2025-10-15T18:23:02.627Z" }, + { url = "https://files.pythonhosted.org/packages/61/2b/726235842220ca95fa441ddf55dd2382b52ab5b8d9c0596fe6b3f23dafe8/pillow-12.0.0-cp313-cp313t-win32.whl", hash = "sha256:4078242472387600b2ce8d93ade8899c12bf33fa89e55ec89fe126e9d6d5d9e9", size = 6306201, upload-time = "2025-10-15T18:23:04.709Z" }, + { url = "https://files.pythonhosted.org/packages/c0/3d/2afaf4e840b2df71344ababf2f8edd75a705ce500e5dc1e7227808312ae1/pillow-12.0.0-cp313-cp313t-win_amd64.whl", hash = "sha256:2c54c1a783d6d60595d3514f0efe9b37c8808746a66920315bfd34a938d7994b", size = 7013165, upload-time = "2025-10-15T18:23:06.46Z" }, + { url = "https://files.pythonhosted.org/packages/6f/75/3fa09aa5cf6ed04bee3fa575798ddf1ce0bace8edb47249c798077a81f7f/pillow-12.0.0-cp313-cp313t-win_arm64.whl", hash = "sha256:26d9f7d2b604cd23aba3e9faf795787456ac25634d82cd060556998e39c6fa47", size = 2437834, upload-time = "2025-10-15T18:23:08.194Z" }, + { url = "https://files.pythonhosted.org/packages/54/2a/9a8c6ba2c2c07b71bec92cf63e03370ca5e5f5c5b119b742bcc0cde3f9c5/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:beeae3f27f62308f1ddbcfb0690bf44b10732f2ef43758f169d5e9303165d3f9", size = 4045531, upload-time = "2025-10-15T18:23:10.121Z" }, + { url = "https://files.pythonhosted.org/packages/84/54/836fdbf1bfb3d66a59f0189ff0b9f5f666cee09c6188309300df04ad71fa/pillow-12.0.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:d4827615da15cd59784ce39d3388275ec093ae3ee8d7f0c089b76fa87af756c2", size = 4120554, upload-time = "2025-10-15T18:23:12.14Z" }, + { url = "https://files.pythonhosted.org/packages/0d/cd/16aec9f0da4793e98e6b54778a5fbce4f375c6646fe662e80600b8797379/pillow-12.0.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:3e42edad50b6909089750e65c91aa09aaf1e0a71310d383f11321b27c224ed8a", size = 3576812, upload-time = "2025-10-15T18:23:13.962Z" }, + { url = "https://files.pythonhosted.org/packages/f6/b7/13957fda356dc46339298b351cae0d327704986337c3c69bb54628c88155/pillow-12.0.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:e5d8efac84c9afcb40914ab49ba063d94f5dbdf5066db4482c66a992f47a3a3b", size = 5252689, upload-time = "2025-10-15T18:23:15.562Z" }, + { url = "https://files.pythonhosted.org/packages/fc/f5/eae31a306341d8f331f43edb2e9122c7661b975433de5e447939ae61c5da/pillow-12.0.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:266cd5f2b63ff316d5a1bba46268e603c9caf5606d44f38c2873c380950576ad", size = 4650186, upload-time = "2025-10-15T18:23:17.379Z" }, + { url = "https://files.pythonhosted.org/packages/86/62/2a88339aa40c4c77e79108facbd307d6091e2c0eb5b8d3cf4977cfca2fe6/pillow-12.0.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:58eea5ebe51504057dd95c5b77d21700b77615ab0243d8152793dc00eb4faf01", size = 6230308, upload-time = "2025-10-15T18:23:18.971Z" }, + { url = "https://files.pythonhosted.org/packages/c7/33/5425a8992bcb32d1cb9fa3dd39a89e613d09a22f2c8083b7bf43c455f760/pillow-12.0.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f13711b1a5ba512d647a0e4ba79280d3a9a045aaf7e0cc6fbe96b91d4cdf6b0c", size = 8039222, upload-time = "2025-10-15T18:23:20.909Z" }, + { url = "https://files.pythonhosted.org/packages/d8/61/3f5d3b35c5728f37953d3eec5b5f3e77111949523bd2dd7f31a851e50690/pillow-12.0.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6846bd2d116ff42cba6b646edf5bf61d37e5cbd256425fa089fee4ff5c07a99e", size = 6346657, upload-time = "2025-10-15T18:23:23.077Z" }, + { url = "https://files.pythonhosted.org/packages/3a/be/ee90a3d79271227e0f0a33c453531efd6ed14b2e708596ba5dd9be948da3/pillow-12.0.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c98fa880d695de164b4135a52fd2e9cd7b7c90a9d8ac5e9e443a24a95ef9248e", size = 7038482, upload-time = "2025-10-15T18:23:25.005Z" }, + { url = "https://files.pythonhosted.org/packages/44/34/a16b6a4d1ad727de390e9bd9f19f5f669e079e5826ec0f329010ddea492f/pillow-12.0.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fa3ed2a29a9e9d2d488b4da81dcb54720ac3104a20bf0bd273f1e4648aff5af9", size = 6461416, upload-time = "2025-10-15T18:23:27.009Z" }, + { url = "https://files.pythonhosted.org/packages/b6/39/1aa5850d2ade7d7ba9f54e4e4c17077244ff7a2d9e25998c38a29749eb3f/pillow-12.0.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:d034140032870024e6b9892c692fe2968493790dd57208b2c37e3fb35f6df3ab", size = 7131584, upload-time = "2025-10-15T18:23:29.752Z" }, + { url = "https://files.pythonhosted.org/packages/bf/db/4fae862f8fad0167073a7733973bfa955f47e2cac3dc3e3e6257d10fab4a/pillow-12.0.0-cp314-cp314-win32.whl", hash = "sha256:1b1b133e6e16105f524a8dec491e0586d072948ce15c9b914e41cdadd209052b", size = 6400621, upload-time = "2025-10-15T18:23:32.06Z" }, + { url = "https://files.pythonhosted.org/packages/2b/24/b350c31543fb0107ab2599464d7e28e6f856027aadda995022e695313d94/pillow-12.0.0-cp314-cp314-win_amd64.whl", hash = "sha256:8dc232e39d409036af549c86f24aed8273a40ffa459981146829a324e0848b4b", size = 7142916, upload-time = "2025-10-15T18:23:34.71Z" }, + { url = "https://files.pythonhosted.org/packages/0f/9b/0ba5a6fd9351793996ef7487c4fdbde8d3f5f75dbedc093bb598648fddf0/pillow-12.0.0-cp314-cp314-win_arm64.whl", hash = "sha256:d52610d51e265a51518692045e372a4c363056130d922a7351429ac9f27e70b0", size = 2523836, upload-time = "2025-10-15T18:23:36.967Z" }, + { url = "https://files.pythonhosted.org/packages/f5/7a/ceee0840aebc579af529b523d530840338ecf63992395842e54edc805987/pillow-12.0.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1979f4566bb96c1e50a62d9831e2ea2d1211761e5662afc545fa766f996632f6", size = 5255092, upload-time = "2025-10-15T18:23:38.573Z" }, + { url = "https://files.pythonhosted.org/packages/44/76/20776057b4bfd1aef4eeca992ebde0f53a4dce874f3ae693d0ec90a4f79b/pillow-12.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b2e4b27a6e15b04832fe9bf292b94b5ca156016bbc1ea9c2c20098a0320d6cf6", size = 4653158, upload-time = "2025-10-15T18:23:40.238Z" }, + { url = "https://files.pythonhosted.org/packages/82/3f/d9ff92ace07be8836b4e7e87e6a4c7a8318d47c2f1463ffcf121fc57d9cb/pillow-12.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fb3096c30df99fd01c7bf8e544f392103d0795b9f98ba71a8054bcbf56b255f1", size = 6267882, upload-time = "2025-10-15T18:23:42.434Z" }, + { url = "https://files.pythonhosted.org/packages/9f/7a/4f7ff87f00d3ad33ba21af78bfcd2f032107710baf8280e3722ceec28cda/pillow-12.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7438839e9e053ef79f7112c881cef684013855016f928b168b81ed5835f3e75e", size = 8071001, upload-time = "2025-10-15T18:23:44.29Z" }, + { url = "https://files.pythonhosted.org/packages/75/87/fcea108944a52dad8cca0715ae6247e271eb80459364a98518f1e4f480c1/pillow-12.0.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d5c411a8eaa2299322b647cd932586b1427367fd3184ffbb8f7a219ea2041ca", size = 6380146, upload-time = "2025-10-15T18:23:46.065Z" }, + { url = "https://files.pythonhosted.org/packages/91/52/0d31b5e571ef5fd111d2978b84603fce26aba1b6092f28e941cb46570745/pillow-12.0.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7e091d464ac59d2c7ad8e7e08105eaf9dafbc3883fd7265ffccc2baad6ac925", size = 7067344, upload-time = "2025-10-15T18:23:47.898Z" }, + { url = "https://files.pythonhosted.org/packages/7b/f4/2dd3d721f875f928d48e83bb30a434dee75a2531bca839bb996bb0aa5a91/pillow-12.0.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:792a2c0be4dcc18af9d4a2dfd8a11a17d5e25274a1062b0ec1c2d79c76f3e7f8", size = 6491864, upload-time = "2025-10-15T18:23:49.607Z" }, + { url = "https://files.pythonhosted.org/packages/30/4b/667dfcf3d61fc309ba5a15b141845cece5915e39b99c1ceab0f34bf1d124/pillow-12.0.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:afbefa430092f71a9593a99ab6a4e7538bc9eabbf7bf94f91510d3503943edc4", size = 7158911, upload-time = "2025-10-15T18:23:51.351Z" }, + { url = "https://files.pythonhosted.org/packages/a2/2f/16cabcc6426c32218ace36bf0d55955e813f2958afddbf1d391849fee9d1/pillow-12.0.0-cp314-cp314t-win32.whl", hash = "sha256:3830c769decf88f1289680a59d4f4c46c72573446352e2befec9a8512104fa52", size = 6408045, upload-time = "2025-10-15T18:23:53.177Z" }, + { url = "https://files.pythonhosted.org/packages/35/73/e29aa0c9c666cf787628d3f0dcf379f4791fba79f4936d02f8b37165bdf8/pillow-12.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:905b0365b210c73afb0ebe9101a32572152dfd1c144c7e28968a331b9217b94a", size = 7148282, upload-time = "2025-10-15T18:23:55.316Z" }, + { url = "https://files.pythonhosted.org/packages/c1/70/6b41bdcddf541b437bbb9f47f94d2db5d9ddef6c37ccab8c9107743748a4/pillow-12.0.0-cp314-cp314t-win_arm64.whl", hash = "sha256:99353a06902c2e43b43e8ff74ee65a7d90307d82370604746738a1e0661ccca7", size = 2525630, upload-time = "2025-10-15T18:23:57.149Z" }, +] + +[[package]] +name = "pyyaml" +version = "6.0.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d1/33/422b98d2195232ca1826284a76852ad5a86fe23e31b009c9886b2d0fb8b2/pyyaml-6.0.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196", size = 182063, upload-time = "2025-09-25T21:32:11.445Z" }, + { url = "https://files.pythonhosted.org/packages/89/a0/6cf41a19a1f2f3feab0e9c0b74134aa2ce6849093d5517a0c550fe37a648/pyyaml-6.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0", size = 173973, upload-time = "2025-09-25T21:32:12.492Z" }, + { url = "https://files.pythonhosted.org/packages/ed/23/7a778b6bd0b9a8039df8b1b1d80e2e2ad78aa04171592c8a5c43a56a6af4/pyyaml-6.0.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28", size = 775116, upload-time = "2025-09-25T21:32:13.652Z" }, + { url = "https://files.pythonhosted.org/packages/65/30/d7353c338e12baef4ecc1b09e877c1970bd3382789c159b4f89d6a70dc09/pyyaml-6.0.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c", size = 844011, upload-time = "2025-09-25T21:32:15.21Z" }, + { url = "https://files.pythonhosted.org/packages/8b/9d/b3589d3877982d4f2329302ef98a8026e7f4443c765c46cfecc8858c6b4b/pyyaml-6.0.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc", size = 807870, upload-time = "2025-09-25T21:32:16.431Z" }, + { url = "https://files.pythonhosted.org/packages/05/c0/b3be26a015601b822b97d9149ff8cb5ead58c66f981e04fedf4e762f4bd4/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e", size = 761089, upload-time = "2025-09-25T21:32:17.56Z" }, + { url = "https://files.pythonhosted.org/packages/be/8e/98435a21d1d4b46590d5459a22d88128103f8da4c2d4cb8f14f2a96504e1/pyyaml-6.0.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea", size = 790181, upload-time = "2025-09-25T21:32:18.834Z" }, + { url = "https://files.pythonhosted.org/packages/74/93/7baea19427dcfbe1e5a372d81473250b379f04b1bd3c4c5ff825e2327202/pyyaml-6.0.3-cp312-cp312-win32.whl", hash = "sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5", size = 137658, upload-time = "2025-09-25T21:32:20.209Z" }, + { url = "https://files.pythonhosted.org/packages/86/bf/899e81e4cce32febab4fb42bb97dcdf66bc135272882d1987881a4b519e9/pyyaml-6.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b", size = 154003, upload-time = "2025-09-25T21:32:21.167Z" }, + { url = "https://files.pythonhosted.org/packages/1a/08/67bd04656199bbb51dbed1439b7f27601dfb576fb864099c7ef0c3e55531/pyyaml-6.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd", size = 140344, upload-time = "2025-09-25T21:32:22.617Z" }, + { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" }, + { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" }, + { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" }, + { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" }, + { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" }, + { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" }, + { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" }, + { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" }, + { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" }, + { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" }, + { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" }, + { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" }, + { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" }, + { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" }, + { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" }, + { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" }, + { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" }, + { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" }, + { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" }, + { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" }, + { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" }, + { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" }, + { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" }, +] + +[[package]] +name = "regex" +version = "2025.11.3" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/cc/a9/546676f25e573a4cf00fe8e119b78a37b6a8fe2dc95cda877b30889c9c45/regex-2025.11.3.tar.gz", hash = "sha256:1fedc720f9bb2494ce31a58a1631f9c82df6a09b49c19517ea5cc280b4541e01", size = 414669, upload-time = "2025-11-03T21:34:22.089Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e8/74/18f04cb53e58e3fb107439699bd8375cf5a835eec81084e0bddbd122e4c2/regex-2025.11.3-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:bc8ab71e2e31b16e40868a40a69007bc305e1109bd4658eb6cad007e0bf67c41", size = 489312, upload-time = "2025-11-03T21:31:34.343Z" }, + { url = "https://files.pythonhosted.org/packages/78/3f/37fcdd0d2b1e78909108a876580485ea37c91e1acf66d3bb8e736348f441/regex-2025.11.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:22b29dda7e1f7062a52359fca6e58e548e28c6686f205e780b02ad8ef710de36", size = 291256, upload-time = "2025-11-03T21:31:35.675Z" }, + { url = "https://files.pythonhosted.org/packages/bf/26/0a575f58eb23b7ebd67a45fccbc02ac030b737b896b7e7a909ffe43ffd6a/regex-2025.11.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3a91e4a29938bc1a082cc28fdea44be420bf2bebe2665343029723892eb073e1", size = 288921, upload-time = "2025-11-03T21:31:37.07Z" }, + { url = "https://files.pythonhosted.org/packages/ea/98/6a8dff667d1af907150432cf5abc05a17ccd32c72a3615410d5365ac167a/regex-2025.11.3-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b884f4226602ad40c5d55f52bf91a9df30f513864e0054bad40c0e9cf1afb7", size = 798568, upload-time = "2025-11-03T21:31:38.784Z" }, + { url = "https://files.pythonhosted.org/packages/64/15/92c1db4fa4e12733dd5a526c2dd2b6edcbfe13257e135fc0f6c57f34c173/regex-2025.11.3-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3e0b11b2b2433d1c39c7c7a30e3f3d0aeeea44c2a8d0bae28f6b95f639927a69", size = 864165, upload-time = "2025-11-03T21:31:40.559Z" }, + { url = "https://files.pythonhosted.org/packages/f9/e7/3ad7da8cdee1ce66c7cd37ab5ab05c463a86ffeb52b1a25fe7bd9293b36c/regex-2025.11.3-cp312-cp312-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:87eb52a81ef58c7ba4d45c3ca74e12aa4b4e77816f72ca25258a85b3ea96cb48", size = 912182, upload-time = "2025-11-03T21:31:42.002Z" }, + { url = "https://files.pythonhosted.org/packages/84/bd/9ce9f629fcb714ffc2c3faf62b6766ecb7a585e1e885eb699bcf130a5209/regex-2025.11.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a12ab1f5c29b4e93db518f5e3872116b7e9b1646c9f9f426f777b50d44a09e8c", size = 803501, upload-time = "2025-11-03T21:31:43.815Z" }, + { url = "https://files.pythonhosted.org/packages/7c/0f/8dc2e4349d8e877283e6edd6c12bdcebc20f03744e86f197ab6e4492bf08/regex-2025.11.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7521684c8c7c4f6e88e35ec89680ee1aa8358d3f09d27dfbdf62c446f5d4c695", size = 787842, upload-time = "2025-11-03T21:31:45.353Z" }, + { url = "https://files.pythonhosted.org/packages/f9/73/cff02702960bc185164d5619c0c62a2f598a6abff6695d391b096237d4ab/regex-2025.11.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:7fe6e5440584e94cc4b3f5f4d98a25e29ca12dccf8873679a635638349831b98", size = 858519, upload-time = "2025-11-03T21:31:46.814Z" }, + { url = "https://files.pythonhosted.org/packages/61/83/0e8d1ae71e15bc1dc36231c90b46ee35f9d52fab2e226b0e039e7ea9c10a/regex-2025.11.3-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:8e026094aa12b43f4fd74576714e987803a315c76edb6b098b9809db5de58f74", size = 850611, upload-time = "2025-11-03T21:31:48.289Z" }, + { url = "https://files.pythonhosted.org/packages/c8/f5/70a5cdd781dcfaa12556f2955bf170cd603cb1c96a1827479f8faea2df97/regex-2025.11.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:435bbad13e57eb5606a68443af62bed3556de2f46deb9f7d4237bc2f1c9fb3a0", size = 789759, upload-time = "2025-11-03T21:31:49.759Z" }, + { url = "https://files.pythonhosted.org/packages/59/9b/7c29be7903c318488983e7d97abcf8ebd3830e4c956c4c540005fcfb0462/regex-2025.11.3-cp312-cp312-win32.whl", hash = "sha256:3839967cf4dc4b985e1570fd8d91078f0c519f30491c60f9ac42a8db039be204", size = 266194, upload-time = "2025-11-03T21:31:51.53Z" }, + { url = "https://files.pythonhosted.org/packages/1a/67/3b92df89f179d7c367be654ab5626ae311cb28f7d5c237b6bb976cd5fbbb/regex-2025.11.3-cp312-cp312-win_amd64.whl", hash = "sha256:e721d1b46e25c481dc5ded6f4b3f66c897c58d2e8cfdf77bbced84339108b0b9", size = 277069, upload-time = "2025-11-03T21:31:53.151Z" }, + { url = "https://files.pythonhosted.org/packages/d7/55/85ba4c066fe5094d35b249c3ce8df0ba623cfd35afb22d6764f23a52a1c5/regex-2025.11.3-cp312-cp312-win_arm64.whl", hash = "sha256:64350685ff08b1d3a6fff33f45a9ca183dc1d58bbfe4981604e70ec9801bbc26", size = 270330, upload-time = "2025-11-03T21:31:54.514Z" }, + { url = "https://files.pythonhosted.org/packages/e1/a7/dda24ebd49da46a197436ad96378f17df30ceb40e52e859fc42cac45b850/regex-2025.11.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:c1e448051717a334891f2b9a620fe36776ebf3dd8ec46a0b877c8ae69575feb4", size = 489081, upload-time = "2025-11-03T21:31:55.9Z" }, + { url = "https://files.pythonhosted.org/packages/19/22/af2dc751aacf88089836aa088a1a11c4f21a04707eb1b0478e8e8fb32847/regex-2025.11.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:9b5aca4d5dfd7fbfbfbdaf44850fcc7709a01146a797536a8f84952e940cca76", size = 291123, upload-time = "2025-11-03T21:31:57.758Z" }, + { url = "https://files.pythonhosted.org/packages/a3/88/1a3ea5672f4b0a84802ee9891b86743438e7c04eb0b8f8c4e16a42375327/regex-2025.11.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:04d2765516395cf7dda331a244a3282c0f5ae96075f728629287dfa6f76ba70a", size = 288814, upload-time = "2025-11-03T21:32:01.12Z" }, + { url = "https://files.pythonhosted.org/packages/fb/8c/f5987895bf42b8ddeea1b315c9fedcfe07cadee28b9c98cf50d00adcb14d/regex-2025.11.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5d9903ca42bfeec4cebedba8022a7c97ad2aab22e09573ce9976ba01b65e4361", size = 798592, upload-time = "2025-11-03T21:32:03.006Z" }, + { url = "https://files.pythonhosted.org/packages/99/2a/6591ebeede78203fa77ee46a1c36649e02df9eaa77a033d1ccdf2fcd5d4e/regex-2025.11.3-cp313-cp313-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:639431bdc89d6429f6721625e8129413980ccd62e9d3f496be618a41d205f160", size = 864122, upload-time = "2025-11-03T21:32:04.553Z" }, + { url = "https://files.pythonhosted.org/packages/94/d6/be32a87cf28cf8ed064ff281cfbd49aefd90242a83e4b08b5a86b38e8eb4/regex-2025.11.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f117efad42068f9715677c8523ed2be1518116d1c49b1dd17987716695181efe", size = 912272, upload-time = "2025-11-03T21:32:06.148Z" }, + { url = "https://files.pythonhosted.org/packages/62/11/9bcef2d1445665b180ac7f230406ad80671f0fc2a6ffb93493b5dd8cd64c/regex-2025.11.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4aecb6f461316adf9f1f0f6a4a1a3d79e045f9b71ec76055a791affa3b285850", size = 803497, upload-time = "2025-11-03T21:32:08.162Z" }, + { url = "https://files.pythonhosted.org/packages/e5/a7/da0dc273d57f560399aa16d8a68ae7f9b57679476fc7ace46501d455fe84/regex-2025.11.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3b3a5f320136873cc5561098dfab677eea139521cb9a9e8db98b7e64aef44cbc", size = 787892, upload-time = "2025-11-03T21:32:09.769Z" }, + { url = "https://files.pythonhosted.org/packages/da/4b/732a0c5a9736a0b8d6d720d4945a2f1e6f38f87f48f3173559f53e8d5d82/regex-2025.11.3-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:75fa6f0056e7efb1f42a1c34e58be24072cb9e61a601340cc1196ae92326a4f9", size = 858462, upload-time = "2025-11-03T21:32:11.769Z" }, + { url = "https://files.pythonhosted.org/packages/0c/f5/a2a03df27dc4c2d0c769220f5110ba8c4084b0bfa9ab0f9b4fcfa3d2b0fc/regex-2025.11.3-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:dbe6095001465294f13f1adcd3311e50dd84e5a71525f20a10bd16689c61ce0b", size = 850528, upload-time = "2025-11-03T21:32:13.906Z" }, + { url = "https://files.pythonhosted.org/packages/d6/09/e1cd5bee3841c7f6eb37d95ca91cdee7100b8f88b81e41c2ef426910891a/regex-2025.11.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:454d9b4ae7881afbc25015b8627c16d88a597479b9dea82b8c6e7e2e07240dc7", size = 789866, upload-time = "2025-11-03T21:32:15.748Z" }, + { url = "https://files.pythonhosted.org/packages/eb/51/702f5ea74e2a9c13d855a6a85b7f80c30f9e72a95493260193c07f3f8d74/regex-2025.11.3-cp313-cp313-win32.whl", hash = "sha256:28ba4d69171fc6e9896337d4fc63a43660002b7da53fc15ac992abcf3410917c", size = 266189, upload-time = "2025-11-03T21:32:17.493Z" }, + { url = "https://files.pythonhosted.org/packages/8b/00/6e29bb314e271a743170e53649db0fdb8e8ff0b64b4f425f5602f4eb9014/regex-2025.11.3-cp313-cp313-win_amd64.whl", hash = "sha256:bac4200befe50c670c405dc33af26dad5a3b6b255dd6c000d92fe4629f9ed6a5", size = 277054, upload-time = "2025-11-03T21:32:19.042Z" }, + { url = "https://files.pythonhosted.org/packages/25/f1/b156ff9f2ec9ac441710764dda95e4edaf5f36aca48246d1eea3f1fd96ec/regex-2025.11.3-cp313-cp313-win_arm64.whl", hash = "sha256:2292cd5a90dab247f9abe892ac584cb24f0f54680c73fcb4a7493c66c2bf2467", size = 270325, upload-time = "2025-11-03T21:32:21.338Z" }, + { url = "https://files.pythonhosted.org/packages/20/28/fd0c63357caefe5680b8ea052131acbd7f456893b69cc2a90cc3e0dc90d4/regex-2025.11.3-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:1eb1ebf6822b756c723e09f5186473d93236c06c579d2cc0671a722d2ab14281", size = 491984, upload-time = "2025-11-03T21:32:23.466Z" }, + { url = "https://files.pythonhosted.org/packages/df/ec/7014c15626ab46b902b3bcc4b28a7bae46d8f281fc7ea9c95e22fcaaa917/regex-2025.11.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:1e00ec2970aab10dc5db34af535f21fcf32b4a31d99e34963419636e2f85ae39", size = 292673, upload-time = "2025-11-03T21:32:25.034Z" }, + { url = "https://files.pythonhosted.org/packages/23/ab/3b952ff7239f20d05f1f99e9e20188513905f218c81d52fb5e78d2bf7634/regex-2025.11.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:a4cb042b615245d5ff9b3794f56be4138b5adc35a4166014d31d1814744148c7", size = 291029, upload-time = "2025-11-03T21:32:26.528Z" }, + { url = "https://files.pythonhosted.org/packages/21/7e/3dc2749fc684f455f162dcafb8a187b559e2614f3826877d3844a131f37b/regex-2025.11.3-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:44f264d4bf02f3176467d90b294d59bf1db9fe53c141ff772f27a8b456b2a9ed", size = 807437, upload-time = "2025-11-03T21:32:28.363Z" }, + { url = "https://files.pythonhosted.org/packages/1b/0b/d529a85ab349c6a25d1ca783235b6e3eedf187247eab536797021f7126c6/regex-2025.11.3-cp313-cp313t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:7be0277469bf3bd7a34a9c57c1b6a724532a0d235cd0dc4e7f4316f982c28b19", size = 873368, upload-time = "2025-11-03T21:32:30.4Z" }, + { url = "https://files.pythonhosted.org/packages/7d/18/2d868155f8c9e3e9d8f9e10c64e9a9f496bb8f7e037a88a8bed26b435af6/regex-2025.11.3-cp313-cp313t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0d31e08426ff4b5b650f68839f5af51a92a5b51abd8554a60c2fbc7c71f25d0b", size = 914921, upload-time = "2025-11-03T21:32:32.123Z" }, + { url = "https://files.pythonhosted.org/packages/2d/71/9d72ff0f354fa783fe2ba913c8734c3b433b86406117a8db4ea2bf1c7a2f/regex-2025.11.3-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e43586ce5bd28f9f285a6e729466841368c4a0353f6fd08d4ce4630843d3648a", size = 812708, upload-time = "2025-11-03T21:32:34.305Z" }, + { url = "https://files.pythonhosted.org/packages/e7/19/ce4bf7f5575c97f82b6e804ffb5c4e940c62609ab2a0d9538d47a7fdf7d4/regex-2025.11.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:0f9397d561a4c16829d4e6ff75202c1c08b68a3bdbfe29dbfcdb31c9830907c6", size = 795472, upload-time = "2025-11-03T21:32:36.364Z" }, + { url = "https://files.pythonhosted.org/packages/03/86/fd1063a176ffb7b2315f9a1b08d17b18118b28d9df163132615b835a26ee/regex-2025.11.3-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:dd16e78eb18ffdb25ee33a0682d17912e8cc8a770e885aeee95020046128f1ce", size = 868341, upload-time = "2025-11-03T21:32:38.042Z" }, + { url = "https://files.pythonhosted.org/packages/12/43/103fb2e9811205e7386366501bc866a164a0430c79dd59eac886a2822950/regex-2025.11.3-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:ffcca5b9efe948ba0661e9df0fa50d2bc4b097c70b9810212d6b62f05d83b2dd", size = 854666, upload-time = "2025-11-03T21:32:40.079Z" }, + { url = "https://files.pythonhosted.org/packages/7d/22/e392e53f3869b75804762c7c848bd2dd2abf2b70fb0e526f58724638bd35/regex-2025.11.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:c56b4d162ca2b43318ac671c65bd4d563e841a694ac70e1a976ac38fcf4ca1d2", size = 799473, upload-time = "2025-11-03T21:32:42.148Z" }, + { url = "https://files.pythonhosted.org/packages/4f/f9/8bd6b656592f925b6845fcbb4d57603a3ac2fb2373344ffa1ed70aa6820a/regex-2025.11.3-cp313-cp313t-win32.whl", hash = "sha256:9ddc42e68114e161e51e272f667d640f97e84a2b9ef14b7477c53aac20c2d59a", size = 268792, upload-time = "2025-11-03T21:32:44.13Z" }, + { url = "https://files.pythonhosted.org/packages/e5/87/0e7d603467775ff65cd2aeabf1b5b50cc1c3708556a8b849a2fa4dd1542b/regex-2025.11.3-cp313-cp313t-win_amd64.whl", hash = "sha256:7a7c7fdf755032ffdd72c77e3d8096bdcb0eb92e89e17571a196f03d88b11b3c", size = 280214, upload-time = "2025-11-03T21:32:45.853Z" }, + { url = "https://files.pythonhosted.org/packages/8d/d0/2afc6f8e94e2b64bfb738a7c2b6387ac1699f09f032d363ed9447fd2bb57/regex-2025.11.3-cp313-cp313t-win_arm64.whl", hash = "sha256:df9eb838c44f570283712e7cff14c16329a9f0fb19ca492d21d4b7528ee6821e", size = 271469, upload-time = "2025-11-03T21:32:48.026Z" }, + { url = "https://files.pythonhosted.org/packages/31/e9/f6e13de7e0983837f7b6d238ad9458800a874bf37c264f7923e63409944c/regex-2025.11.3-cp314-cp314-macosx_10_13_universal2.whl", hash = "sha256:9697a52e57576c83139d7c6f213d64485d3df5bf84807c35fa409e6c970801c6", size = 489089, upload-time = "2025-11-03T21:32:50.027Z" }, + { url = "https://files.pythonhosted.org/packages/a3/5c/261f4a262f1fa65141c1b74b255988bd2fa020cc599e53b080667d591cfc/regex-2025.11.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:e18bc3f73bd41243c9b38a6d9f2366cd0e0137a9aebe2d8ff76c5b67d4c0a3f4", size = 291059, upload-time = "2025-11-03T21:32:51.682Z" }, + { url = "https://files.pythonhosted.org/packages/8e/57/f14eeb7f072b0e9a5a090d1712741fd8f214ec193dba773cf5410108bb7d/regex-2025.11.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:61a08bcb0ec14ff4e0ed2044aad948d0659604f824cbd50b55e30b0ec6f09c73", size = 288900, upload-time = "2025-11-03T21:32:53.569Z" }, + { url = "https://files.pythonhosted.org/packages/3c/6b/1d650c45e99a9b327586739d926a1cd4e94666b1bd4af90428b36af66dc7/regex-2025.11.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c9c30003b9347c24bcc210958c5d167b9e4f9be786cb380a7d32f14f9b84674f", size = 799010, upload-time = "2025-11-03T21:32:55.222Z" }, + { url = "https://files.pythonhosted.org/packages/99/ee/d66dcbc6b628ce4e3f7f0cbbb84603aa2fc0ffc878babc857726b8aab2e9/regex-2025.11.3-cp314-cp314-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4e1e592789704459900728d88d41a46fe3969b82ab62945560a31732ffc19a6d", size = 864893, upload-time = "2025-11-03T21:32:57.239Z" }, + { url = "https://files.pythonhosted.org/packages/bf/2d/f238229f1caba7ac87a6c4153d79947fb0261415827ae0f77c304260c7d3/regex-2025.11.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6538241f45eb5a25aa575dbba1069ad786f68a4f2773a29a2bd3dd1f9de787be", size = 911522, upload-time = "2025-11-03T21:32:59.274Z" }, + { url = "https://files.pythonhosted.org/packages/bd/3d/22a4eaba214a917c80e04f6025d26143690f0419511e0116508e24b11c9b/regex-2025.11.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bce22519c989bb72a7e6b36a199384c53db7722fe669ba891da75907fe3587db", size = 803272, upload-time = "2025-11-03T21:33:01.393Z" }, + { url = "https://files.pythonhosted.org/packages/84/b1/03188f634a409353a84b5ef49754b97dbcc0c0f6fd6c8ede505a8960a0a4/regex-2025.11.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:66d559b21d3640203ab9075797a55165d79017520685fb407b9234d72ab63c62", size = 787958, upload-time = "2025-11-03T21:33:03.379Z" }, + { url = "https://files.pythonhosted.org/packages/99/6a/27d072f7fbf6fadd59c64d210305e1ff865cc3b78b526fd147db768c553b/regex-2025.11.3-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:669dcfb2e38f9e8c69507bace46f4889e3abbfd9b0c29719202883c0a603598f", size = 859289, upload-time = "2025-11-03T21:33:05.374Z" }, + { url = "https://files.pythonhosted.org/packages/9a/70/1b3878f648e0b6abe023172dacb02157e685564853cc363d9961bcccde4e/regex-2025.11.3-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:32f74f35ff0f25a5021373ac61442edcb150731fbaa28286bbc8bb1582c89d02", size = 850026, upload-time = "2025-11-03T21:33:07.131Z" }, + { url = "https://files.pythonhosted.org/packages/dd/d5/68e25559b526b8baab8e66839304ede68ff6727237a47727d240006bd0ff/regex-2025.11.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e6c7a21dffba883234baefe91bc3388e629779582038f75d2a5be918e250f0ed", size = 789499, upload-time = "2025-11-03T21:33:09.141Z" }, + { url = "https://files.pythonhosted.org/packages/fc/df/43971264857140a350910d4e33df725e8c94dd9dee8d2e4729fa0d63d49e/regex-2025.11.3-cp314-cp314-win32.whl", hash = "sha256:795ea137b1d809eb6836b43748b12634291c0ed55ad50a7d72d21edf1cd565c4", size = 271604, upload-time = "2025-11-03T21:33:10.9Z" }, + { url = "https://files.pythonhosted.org/packages/01/6f/9711b57dc6894a55faf80a4c1b5aa4f8649805cb9c7aef46f7d27e2b9206/regex-2025.11.3-cp314-cp314-win_amd64.whl", hash = "sha256:9f95fbaa0ee1610ec0fc6b26668e9917a582ba80c52cc6d9ada15e30aa9ab9ad", size = 280320, upload-time = "2025-11-03T21:33:12.572Z" }, + { url = "https://files.pythonhosted.org/packages/f1/7e/f6eaa207d4377481f5e1775cdeb5a443b5a59b392d0065f3417d31d80f87/regex-2025.11.3-cp314-cp314-win_arm64.whl", hash = "sha256:dfec44d532be4c07088c3de2876130ff0fbeeacaa89a137decbbb5f665855a0f", size = 273372, upload-time = "2025-11-03T21:33:14.219Z" }, + { url = "https://files.pythonhosted.org/packages/c3/06/49b198550ee0f5e4184271cee87ba4dfd9692c91ec55289e6282f0f86ccf/regex-2025.11.3-cp314-cp314t-macosx_10_13_universal2.whl", hash = "sha256:ba0d8a5d7f04f73ee7d01d974d47c5834f8a1b0224390e4fe7c12a3a92a78ecc", size = 491985, upload-time = "2025-11-03T21:33:16.555Z" }, + { url = "https://files.pythonhosted.org/packages/ce/bf/abdafade008f0b1c9da10d934034cb670432d6cf6cbe38bbb53a1cfd6cf8/regex-2025.11.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:442d86cf1cfe4faabf97db7d901ef58347efd004934da045c745e7b5bd57ac49", size = 292669, upload-time = "2025-11-03T21:33:18.32Z" }, + { url = "https://files.pythonhosted.org/packages/f9/ef/0c357bb8edbd2ad8e273fcb9e1761bc37b8acbc6e1be050bebd6475f19c1/regex-2025.11.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:fd0a5e563c756de210bb964789b5abe4f114dacae9104a47e1a649b910361536", size = 291030, upload-time = "2025-11-03T21:33:20.048Z" }, + { url = "https://files.pythonhosted.org/packages/79/06/edbb67257596649b8fb088d6aeacbcb248ac195714b18a65e018bf4c0b50/regex-2025.11.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf3490bcbb985a1ae97b2ce9ad1c0f06a852d5b19dde9b07bdf25bf224248c95", size = 807674, upload-time = "2025-11-03T21:33:21.797Z" }, + { url = "https://files.pythonhosted.org/packages/f4/d9/ad4deccfce0ea336296bd087f1a191543bb99ee1c53093dcd4c64d951d00/regex-2025.11.3-cp314-cp314t-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:3809988f0a8b8c9dcc0f92478d6501fac7200b9ec56aecf0ec21f4a2ec4b6009", size = 873451, upload-time = "2025-11-03T21:33:23.741Z" }, + { url = "https://files.pythonhosted.org/packages/13/75/a55a4724c56ef13e3e04acaab29df26582f6978c000ac9cd6810ad1f341f/regex-2025.11.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:f4ff94e58e84aedb9c9fce66d4ef9f27a190285b451420f297c9a09f2b9abee9", size = 914980, upload-time = "2025-11-03T21:33:25.999Z" }, + { url = "https://files.pythonhosted.org/packages/67/1e/a1657ee15bd9116f70d4a530c736983eed997b361e20ecd8f5ca3759d5c5/regex-2025.11.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7eb542fd347ce61e1321b0a6b945d5701528dca0cd9759c2e3bb8bd57e47964d", size = 812852, upload-time = "2025-11-03T21:33:27.852Z" }, + { url = "https://files.pythonhosted.org/packages/b8/6f/f7516dde5506a588a561d296b2d0044839de06035bb486b326065b4c101e/regex-2025.11.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:d6c2d5919075a1f2e413c00b056ea0c2f065b3f5fe83c3d07d325ab92dce51d6", size = 795566, upload-time = "2025-11-03T21:33:32.364Z" }, + { url = "https://files.pythonhosted.org/packages/d9/dd/3d10b9e170cc16fb34cb2cef91513cf3df65f440b3366030631b2984a264/regex-2025.11.3-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:3f8bf11a4827cc7ce5a53d4ef6cddd5ad25595d3c1435ef08f76825851343154", size = 868463, upload-time = "2025-11-03T21:33:34.459Z" }, + { url = "https://files.pythonhosted.org/packages/f5/8e/935e6beff1695aa9085ff83195daccd72acc82c81793df480f34569330de/regex-2025.11.3-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:22c12d837298651e5550ac1d964e4ff57c3f56965fc1812c90c9fb2028eaf267", size = 854694, upload-time = "2025-11-03T21:33:36.793Z" }, + { url = "https://files.pythonhosted.org/packages/92/12/10650181a040978b2f5720a6a74d44f841371a3d984c2083fc1752e4acf6/regex-2025.11.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:62ba394a3dda9ad41c7c780f60f6e4a70988741415ae96f6d1bf6c239cf01379", size = 799691, upload-time = "2025-11-03T21:33:39.079Z" }, + { url = "https://files.pythonhosted.org/packages/67/90/8f37138181c9a7690e7e4cb388debbd389342db3c7381d636d2875940752/regex-2025.11.3-cp314-cp314t-win32.whl", hash = "sha256:4bf146dca15cdd53224a1bf46d628bd7590e4a07fbb69e720d561aea43a32b38", size = 274583, upload-time = "2025-11-03T21:33:41.302Z" }, + { url = "https://files.pythonhosted.org/packages/8f/cd/867f5ec442d56beb56f5f854f40abcfc75e11d10b11fdb1869dd39c63aaf/regex-2025.11.3-cp314-cp314t-win_amd64.whl", hash = "sha256:adad1a1bcf1c9e76346e091d22d23ac54ef28e1365117d99521631078dfec9de", size = 284286, upload-time = "2025-11-03T21:33:43.324Z" }, + { url = "https://files.pythonhosted.org/packages/20/31/32c0c4610cbc070362bf1d2e4ea86d1ea29014d400a6d6c2486fcfd57766/regex-2025.11.3-cp314-cp314t-win_arm64.whl", hash = "sha256:c54f768482cef41e219720013cd05933b6f971d9562544d691c68699bf2b6801", size = 274741, upload-time = "2025-11-03T21:33:45.557Z" }, +] + +[[package]] +name = "requests" +version = "2.32.5" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "certifi" }, + { name = "charset-normalizer" }, + { name = "idna" }, + { name = "urllib3" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/c9/74/b3ff8e6c8446842c3f5c837e9c3dfcfe2018ea6ecef224c710c85ef728f4/requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf", size = 134517, upload-time = "2025-08-18T20:46:02.573Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/1e/db/4254e3eabe8020b458f1a747140d32277ec7a271daf1d235b70dc0b4e6e3/requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6", size = 64738, upload-time = "2025-08-18T20:46:00.542Z" }, +] + +[[package]] +name = "safetensors" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/29/9c/6e74567782559a63bd040a236edca26fd71bc7ba88de2ef35d75df3bca5e/safetensors-0.7.0.tar.gz", hash = "sha256:07663963b67e8bd9f0b8ad15bb9163606cd27cc5a1b96235a50d8369803b96b0", size = 200878, upload-time = "2025-11-19T15:18:43.199Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fa/47/aef6c06649039accf914afef490268e1067ed82be62bcfa5b7e886ad15e8/safetensors-0.7.0-cp38-abi3-macosx_10_12_x86_64.whl", hash = "sha256:c82f4d474cf725255d9e6acf17252991c3c8aac038d6ef363a4bf8be2f6db517", size = 467781, upload-time = "2025-11-19T15:18:35.84Z" }, + { url = "https://files.pythonhosted.org/packages/e8/00/374c0c068e30cd31f1e1b46b4b5738168ec79e7689ca82ee93ddfea05109/safetensors-0.7.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:94fd4858284736bb67a897a41608b5b0c2496c9bdb3bf2af1fa3409127f20d57", size = 447058, upload-time = "2025-11-19T15:18:34.416Z" }, + { url = "https://files.pythonhosted.org/packages/f1/06/578ffed52c2296f93d7fd2d844cabfa92be51a587c38c8afbb8ae449ca89/safetensors-0.7.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e07d91d0c92a31200f25351f4acb2bc6aff7f48094e13ebb1d0fb995b54b6542", size = 491748, upload-time = "2025-11-19T15:18:09.79Z" }, + { url = "https://files.pythonhosted.org/packages/ae/33/1debbbb70e4791dde185edb9413d1fe01619255abb64b300157d7f15dddd/safetensors-0.7.0-cp38-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8469155f4cb518bafb4acf4865e8bb9d6804110d2d9bdcaa78564b9fd841e104", size = 503881, upload-time = "2025-11-19T15:18:16.145Z" }, + { url = "https://files.pythonhosted.org/packages/8e/1c/40c2ca924d60792c3be509833df711b553c60effbd91da6f5284a83f7122/safetensors-0.7.0-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:54bef08bf00a2bff599982f6b08e8770e09cc012d7bba00783fc7ea38f1fb37d", size = 623463, upload-time = "2025-11-19T15:18:21.11Z" }, + { url = "https://files.pythonhosted.org/packages/9b/3a/13784a9364bd43b0d61eef4bea2845039bc2030458b16594a1bd787ae26e/safetensors-0.7.0-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:42cb091236206bb2016d245c377ed383aa7f78691748f3bb6ee1bfa51ae2ce6a", size = 532855, upload-time = "2025-11-19T15:18:25.719Z" }, + { url = "https://files.pythonhosted.org/packages/a0/60/429e9b1cb3fc651937727befe258ea24122d9663e4d5709a48c9cbfceecb/safetensors-0.7.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dac7252938f0696ddea46f5e855dd3138444e82236e3be475f54929f0c510d48", size = 507152, upload-time = "2025-11-19T15:18:33.023Z" }, + { url = "https://files.pythonhosted.org/packages/3c/a8/4b45e4e059270d17af60359713ffd83f97900d45a6afa73aaa0d737d48b6/safetensors-0.7.0-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1d060c70284127fa805085d8f10fbd0962792aed71879d00864acda69dbab981", size = 541856, upload-time = "2025-11-19T15:18:31.075Z" }, + { url = "https://files.pythonhosted.org/packages/06/87/d26d8407c44175d8ae164a95b5a62707fcc445f3c0c56108e37d98070a3d/safetensors-0.7.0-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:cdab83a366799fa730f90a4ebb563e494f28e9e92c4819e556152ad55e43591b", size = 674060, upload-time = "2025-11-19T15:18:37.211Z" }, + { url = "https://files.pythonhosted.org/packages/11/f5/57644a2ff08dc6325816ba7217e5095f17269dada2554b658442c66aed51/safetensors-0.7.0-cp38-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:672132907fcad9f2aedcb705b2d7b3b93354a2aec1b2f706c4db852abe338f85", size = 771715, upload-time = "2025-11-19T15:18:38.689Z" }, + { url = "https://files.pythonhosted.org/packages/86/31/17883e13a814bd278ae6e266b13282a01049b0c81341da7fd0e3e71a80a3/safetensors-0.7.0-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:5d72abdb8a4d56d4020713724ba81dac065fedb7f3667151c4a637f1d3fb26c0", size = 714377, upload-time = "2025-11-19T15:18:40.162Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d8/0c8a7dc9b41dcac53c4cbf9df2b9c83e0e0097203de8b37a712b345c0be5/safetensors-0.7.0-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:b0f6d66c1c538d5a94a73aa9ddca8ccc4227e6c9ff555322ea40bdd142391dd4", size = 677368, upload-time = "2025-11-19T15:18:41.627Z" }, + { url = "https://files.pythonhosted.org/packages/05/e5/cb4b713c8a93469e3c5be7c3f8d77d307e65fe89673e731f5c2bfd0a9237/safetensors-0.7.0-cp38-abi3-win32.whl", hash = "sha256:c74af94bf3ac15ac4d0f2a7c7b4663a15f8c2ab15ed0fc7531ca61d0835eccba", size = 326423, upload-time = "2025-11-19T15:18:45.74Z" }, + { url = "https://files.pythonhosted.org/packages/5d/e6/ec8471c8072382cb91233ba7267fd931219753bb43814cbc71757bfd4dab/safetensors-0.7.0-cp38-abi3-win_amd64.whl", hash = "sha256:d1239932053f56f3456f32eb9625590cc7582e905021f94636202a864d470755", size = 341380, upload-time = "2025-11-19T15:18:44.427Z" }, +] + +[[package]] +name = "setuptools" +version = "80.9.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/18/5d/3bf57dcd21979b887f014ea83c24ae194cfcd12b9e0fda66b957c69d1fca/setuptools-80.9.0.tar.gz", hash = "sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c", size = 1319958, upload-time = "2025-05-27T00:56:51.443Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a3/dc/17031897dae0efacfea57dfd3a82fdd2a2aeb58e0ff71b77b87e44edc772/setuptools-80.9.0-py3-none-any.whl", hash = "sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922", size = 1201486, upload-time = "2025-05-27T00:56:49.664Z" }, +] + +[[package]] +name = "sympy" +version = "1.14.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "mpmath" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/83/d3/803453b36afefb7c2bb238361cd4ae6125a569b4db67cd9e79846ba2d68c/sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517", size = 7793921, upload-time = "2025-04-27T18:05:01.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" }, +] + +[[package]] +name = "tokenizers" +version = "0.22.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/1c/46/fb6854cec3278fbfa4a75b50232c77622bc517ac886156e6afbfa4d8fc6e/tokenizers-0.22.1.tar.gz", hash = "sha256:61de6522785310a309b3407bac22d99c4db5dba349935e99e4d15ea2226af2d9", size = 363123, upload-time = "2025-09-19T09:49:23.424Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/bf/33/f4b2d94ada7ab297328fc671fed209368ddb82f965ec2224eb1892674c3a/tokenizers-0.22.1-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:59fdb013df17455e5f950b4b834a7b3ee2e0271e6378ccb33aa74d178b513c73", size = 3069318, upload-time = "2025-09-19T09:49:11.848Z" }, + { url = "https://files.pythonhosted.org/packages/1c/58/2aa8c874d02b974990e89ff95826a4852a8b2a273c7d1b4411cdd45a4565/tokenizers-0.22.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:8d4e484f7b0827021ac5f9f71d4794aaef62b979ab7608593da22b1d2e3c4edc", size = 2926478, upload-time = "2025-09-19T09:49:09.759Z" }, + { url = "https://files.pythonhosted.org/packages/1e/3b/55e64befa1e7bfea963cf4b787b2cea1011362c4193f5477047532ce127e/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:19d2962dd28bc67c1f205ab180578a78eef89ac60ca7ef7cbe9635a46a56422a", size = 3256994, upload-time = "2025-09-19T09:48:56.701Z" }, + { url = "https://files.pythonhosted.org/packages/71/0b/fbfecf42f67d9b7b80fde4aabb2b3110a97fac6585c9470b5bff103a80cb/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:38201f15cdb1f8a6843e6563e6e79f4abd053394992b9bbdf5213ea3469b4ae7", size = 3153141, upload-time = "2025-09-19T09:48:59.749Z" }, + { url = "https://files.pythonhosted.org/packages/17/a9/b38f4e74e0817af8f8ef925507c63c6ae8171e3c4cb2d5d4624bf58fca69/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d1cbe5454c9a15df1b3443c726063d930c16f047a3cc724b9e6e1a91140e5a21", size = 3508049, upload-time = "2025-09-19T09:49:05.868Z" }, + { url = "https://files.pythonhosted.org/packages/d2/48/dd2b3dac46bb9134a88e35d72e1aa4869579eacc1a27238f1577270773ff/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e7d094ae6312d69cc2a872b54b91b309f4f6fbce871ef28eb27b52a98e4d0214", size = 3710730, upload-time = "2025-09-19T09:49:01.832Z" }, + { url = "https://files.pythonhosted.org/packages/93/0e/ccabc8d16ae4ba84a55d41345207c1e2ea88784651a5a487547d80851398/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:afd7594a56656ace95cdd6df4cca2e4059d294c5cfb1679c57824b605556cb2f", size = 3412560, upload-time = "2025-09-19T09:49:03.867Z" }, + { url = "https://files.pythonhosted.org/packages/d0/c6/dc3a0db5a6766416c32c034286d7c2d406da1f498e4de04ab1b8959edd00/tokenizers-0.22.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e2ef6063d7a84994129732b47e7915e8710f27f99f3a3260b8a38fc7ccd083f4", size = 3250221, upload-time = "2025-09-19T09:49:07.664Z" }, + { url = "https://files.pythonhosted.org/packages/d7/a6/2c8486eef79671601ff57b093889a345dd3d576713ef047776015dc66de7/tokenizers-0.22.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ba0a64f450b9ef412c98f6bcd2a50c6df6e2443b560024a09fa6a03189726879", size = 9345569, upload-time = "2025-09-19T09:49:14.214Z" }, + { url = "https://files.pythonhosted.org/packages/6b/16/32ce667f14c35537f5f605fe9bea3e415ea1b0a646389d2295ec348d5657/tokenizers-0.22.1-cp39-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:331d6d149fa9c7d632cde4490fb8bbb12337fa3a0232e77892be656464f4b446", size = 9271599, upload-time = "2025-09-19T09:49:16.639Z" }, + { url = "https://files.pythonhosted.org/packages/51/7c/a5f7898a3f6baa3fc2685c705e04c98c1094c523051c805cdd9306b8f87e/tokenizers-0.22.1-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:607989f2ea68a46cb1dfbaf3e3aabdf3f21d8748312dbeb6263d1b3b66c5010a", size = 9533862, upload-time = "2025-09-19T09:49:19.146Z" }, + { url = "https://files.pythonhosted.org/packages/36/65/7e75caea90bc73c1dd8d40438adf1a7bc26af3b8d0a6705ea190462506e1/tokenizers-0.22.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a0f307d490295717726598ef6fa4f24af9d484809223bbc253b201c740a06390", size = 9681250, upload-time = "2025-09-19T09:49:21.501Z" }, + { url = "https://files.pythonhosted.org/packages/30/2c/959dddef581b46e6209da82df3b78471e96260e2bc463f89d23b1bf0e52a/tokenizers-0.22.1-cp39-abi3-win32.whl", hash = "sha256:b5120eed1442765cd90b903bb6cfef781fd8fe64e34ccaecbae4c619b7b12a82", size = 2472003, upload-time = "2025-09-19T09:49:27.089Z" }, + { url = "https://files.pythonhosted.org/packages/b3/46/e33a8c93907b631a99377ef4c5f817ab453d0b34f93529421f42ff559671/tokenizers-0.22.1-cp39-abi3-win_amd64.whl", hash = "sha256:65fd6e3fb11ca1e78a6a93602490f134d1fdeb13bcef99389d5102ea318ed138", size = 2674684, upload-time = "2025-09-19T09:49:24.953Z" }, +] + +[[package]] +name = "torch" +version = "2.9.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "filelock" }, + { name = "fsspec" }, + { name = "jinja2" }, + { name = "networkx" }, + { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cufile-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvshmem-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "setuptools" }, + { name = "sympy" }, + { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "typing-extensions" }, +] +wheels = [ + { url = "https://files.pythonhosted.org/packages/0f/27/07c645c7673e73e53ded71705045d6cb5bae94c4b021b03aa8d03eee90ab/torch-2.9.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:da5f6f4d7f4940a173e5572791af238cb0b9e21b1aab592bd8b26da4c99f1cd6", size = 104126592, upload-time = "2025-11-12T15:20:41.62Z" }, + { url = "https://files.pythonhosted.org/packages/19/17/e377a460603132b00760511299fceba4102bd95db1a0ee788da21298ccff/torch-2.9.1-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:27331cd902fb4322252657f3902adf1c4f6acad9dcad81d8df3ae14c7c4f07c4", size = 899742281, upload-time = "2025-11-12T15:22:17.602Z" }, + { url = "https://files.pythonhosted.org/packages/b1/1a/64f5769025db846a82567fa5b7d21dba4558a7234ee631712ee4771c436c/torch-2.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:81a285002d7b8cfd3fdf1b98aa8df138d41f1a8334fd9ea37511517cedf43083", size = 110940568, upload-time = "2025-11-12T15:21:18.689Z" }, + { url = "https://files.pythonhosted.org/packages/6e/ab/07739fd776618e5882661d04c43f5b5586323e2f6a2d7d84aac20d8f20bd/torch-2.9.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:c0d25d1d8e531b8343bea0ed811d5d528958f1dcbd37e7245bc686273177ad7e", size = 74479191, upload-time = "2025-11-12T15:21:25.816Z" }, + { url = "https://files.pythonhosted.org/packages/20/60/8fc5e828d050bddfab469b3fe78e5ab9a7e53dda9c3bdc6a43d17ce99e63/torch-2.9.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:c29455d2b910b98738131990394da3e50eea8291dfeb4b12de71ecf1fdeb21cb", size = 104135743, upload-time = "2025-11-12T15:21:34.936Z" }, + { url = "https://files.pythonhosted.org/packages/f2/b7/6d3f80e6918213babddb2a37b46dbb14c15b14c5f473e347869a51f40e1f/torch-2.9.1-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:524de44cd13931208ba2c4bde9ec7741fd4ae6bfd06409a604fc32f6520c2bc9", size = 899749493, upload-time = "2025-11-12T15:24:36.356Z" }, + { url = "https://files.pythonhosted.org/packages/a6/47/c7843d69d6de8938c1cbb1eba426b1d48ddf375f101473d3e31a5fc52b74/torch-2.9.1-cp313-cp313-win_amd64.whl", hash = "sha256:545844cc16b3f91e08ce3b40e9c2d77012dd33a48d505aed34b7740ed627a1b2", size = 110944162, upload-time = "2025-11-12T15:21:53.151Z" }, + { url = "https://files.pythonhosted.org/packages/28/0e/2a37247957e72c12151b33a01e4df651d9d155dd74d8cfcbfad15a79b44a/torch-2.9.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5be4bf7496f1e3ffb1dd44b672adb1ac3f081f204c5ca81eba6442f5f634df8e", size = 74830751, upload-time = "2025-11-12T15:21:43.792Z" }, + { url = "https://files.pythonhosted.org/packages/4b/f7/7a18745edcd7b9ca2381aa03353647bca8aace91683c4975f19ac233809d/torch-2.9.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:30a3e170a84894f3652434b56d59a64a2c11366b0ed5776fab33c2439396bf9a", size = 104142929, upload-time = "2025-11-12T15:21:48.319Z" }, + { url = "https://files.pythonhosted.org/packages/f4/dd/f1c0d879f2863ef209e18823a988dc7a1bf40470750e3ebe927efdb9407f/torch-2.9.1-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:8301a7b431e51764629208d0edaa4f9e4c33e6df0f2f90b90e261d623df6a4e2", size = 899748978, upload-time = "2025-11-12T15:23:04.568Z" }, + { url = "https://files.pythonhosted.org/packages/1f/9f/6986b83a53b4d043e36f3f898b798ab51f7f20fdf1a9b01a2720f445043d/torch-2.9.1-cp313-cp313t-win_amd64.whl", hash = "sha256:2e1c42c0ae92bf803a4b2409fdfed85e30f9027a66887f5e7dcdbc014c7531db", size = 111176995, upload-time = "2025-11-12T15:22:01.618Z" }, + { url = "https://files.pythonhosted.org/packages/40/60/71c698b466dd01e65d0e9514b5405faae200c52a76901baf6906856f17e4/torch-2.9.1-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:2c14b3da5df416cf9cb5efab83aa3056f5b8cd8620b8fde81b4987ecab730587", size = 74480347, upload-time = "2025-11-12T15:21:57.648Z" }, + { url = "https://files.pythonhosted.org/packages/48/50/c4b5112546d0d13cc9eaa1c732b823d676a9f49ae8b6f97772f795874a03/torch-2.9.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:1edee27a7c9897f4e0b7c14cfc2f3008c571921134522d5b9b5ec4ebbc69041a", size = 74433245, upload-time = "2025-11-12T15:22:39.027Z" }, + { url = "https://files.pythonhosted.org/packages/81/c9/2628f408f0518b3bae49c95f5af3728b6ab498c8624ab1e03a43dd53d650/torch-2.9.1-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:19d144d6b3e29921f1fc70503e9f2fc572cde6a5115c0c0de2f7ca8b1483e8b6", size = 104134804, upload-time = "2025-11-12T15:22:35.222Z" }, + { url = "https://files.pythonhosted.org/packages/28/fc/5bc91d6d831ae41bf6e9e6da6468f25330522e92347c9156eb3f1cb95956/torch-2.9.1-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:c432d04376f6d9767a9852ea0def7b47a7bbc8e7af3b16ac9cf9ce02b12851c9", size = 899747132, upload-time = "2025-11-12T15:23:36.068Z" }, + { url = "https://files.pythonhosted.org/packages/63/5d/e8d4e009e52b6b2cf1684bde2a6be157b96fb873732542fb2a9a99e85a83/torch-2.9.1-cp314-cp314-win_amd64.whl", hash = "sha256:d187566a2cdc726fc80138c3cdb260970fab1c27e99f85452721f7759bbd554d", size = 110934845, upload-time = "2025-11-12T15:22:48.367Z" }, + { url = "https://files.pythonhosted.org/packages/bd/b2/2d15a52516b2ea3f414643b8de68fa4cb220d3877ac8b1028c83dc8ca1c4/torch-2.9.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:cb10896a1f7fedaddbccc2017ce6ca9ecaaf990f0973bdfcf405439750118d2c", size = 74823558, upload-time = "2025-11-12T15:22:43.392Z" }, + { url = "https://files.pythonhosted.org/packages/86/5c/5b2e5d84f5b9850cd1e71af07524d8cbb74cba19379800f1f9f7c997fc70/torch-2.9.1-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:0a2bd769944991c74acf0c4ef23603b9c777fdf7637f115605a4b2d8023110c7", size = 104145788, upload-time = "2025-11-12T15:23:52.109Z" }, + { url = "https://files.pythonhosted.org/packages/a9/8c/3da60787bcf70add986c4ad485993026ac0ca74f2fc21410bc4eb1bb7695/torch-2.9.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:07c8a9660bc9414c39cac530ac83b1fb1b679d7155824144a40a54f4a47bfa73", size = 899735500, upload-time = "2025-11-12T15:24:08.788Z" }, + { url = "https://files.pythonhosted.org/packages/db/2b/f7818f6ec88758dfd21da46b6cd46af9d1b3433e53ddbb19ad1e0da17f9b/torch-2.9.1-cp314-cp314t-win_amd64.whl", hash = "sha256:c88d3299ddeb2b35dcc31753305612db485ab6f1823e37fb29451c8b2732b87e", size = 111163659, upload-time = "2025-11-12T15:23:20.009Z" }, +] + +[[package]] +name = "tqdm" +version = "4.67.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "colorama", marker = "sys_platform == 'win32'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/a8/4b/29b4ef32e036bb34e4ab51796dd745cdba7ed47ad142a9f4a1eb8e0c744d/tqdm-4.67.1.tar.gz", hash = "sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2", size = 169737, upload-time = "2024-11-24T20:12:22.481Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" }, +] + +[[package]] +name = "transformers" +version = "4.57.3" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "filelock" }, + { name = "huggingface-hub" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "pyyaml" }, + { name = "regex" }, + { name = "requests" }, + { name = "safetensors" }, + { name = "tokenizers" }, + { name = "tqdm" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/dd/70/d42a739e8dfde3d92bb2fff5819cbf331fe9657323221e79415cd5eb65ee/transformers-4.57.3.tar.gz", hash = "sha256:df4945029aaddd7c09eec5cad851f30662f8bd1746721b34cc031d70c65afebc", size = 10139680, upload-time = "2025-11-25T15:51:30.139Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6a/6b/2f416568b3c4c91c96e5a365d164f8a4a4a88030aa8ab4644181fdadce97/transformers-4.57.3-py3-none-any.whl", hash = "sha256:c77d353a4851b1880191603d36acb313411d3577f6e2897814f333841f7003f4", size = 11993463, upload-time = "2025-11-25T15:51:26.493Z" }, +] + +[[package]] +name = "triton" +version = "3.5.1" +source = { registry = "https://pypi.org/simple" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f2/50/9a8358d3ef58162c0a415d173cfb45b67de60176e1024f71fbc4d24c0b6d/triton-3.5.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d2c6b915a03888ab931a9fd3e55ba36785e1fe70cbea0b40c6ef93b20fc85232", size = 170470207, upload-time = "2025-11-11T17:41:00.253Z" }, + { url = "https://files.pythonhosted.org/packages/27/46/8c3bbb5b0a19313f50edcaa363b599e5a1a5ac9683ead82b9b80fe497c8d/triton-3.5.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f3f4346b6ebbd4fad18773f5ba839114f4826037c9f2f34e0148894cd5dd3dba", size = 170470410, upload-time = "2025-11-11T17:41:06.319Z" }, + { url = "https://files.pythonhosted.org/packages/37/92/e97fcc6b2c27cdb87ce5ee063d77f8f26f19f06916aa680464c8104ef0f6/triton-3.5.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0b4d2c70127fca6a23e247f9348b8adde979d2e7a20391bfbabaac6aebc7e6a8", size = 170579924, upload-time = "2025-11-11T17:41:12.455Z" }, + { url = "https://files.pythonhosted.org/packages/a4/e6/c595c35e5c50c4bc56a7bac96493dad321e9e29b953b526bbbe20f9911d0/triton-3.5.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d0637b1efb1db599a8e9dc960d53ab6e4637db7d4ab6630a0974705d77b14b60", size = 170480488, upload-time = "2025-11-11T17:41:18.222Z" }, + { url = "https://files.pythonhosted.org/packages/16/b5/b0d3d8b901b6a04ca38df5e24c27e53afb15b93624d7fd7d658c7cd9352a/triton-3.5.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bac7f7d959ad0f48c0e97d6643a1cc0fd5786fe61cb1f83b537c6b2d54776478", size = 170582192, upload-time = "2025-11-11T17:41:23.963Z" }, +] + +[[package]] +name = "typing" +version = "3.10.0.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/b0/1b/835d4431805939d2996f8772aca1d2313a57e8860fec0e48e8e7dfe3a477/typing-3.10.0.0.tar.gz", hash = "sha256:13b4ad211f54ddbf93e5901a9967b1e07720c1d1b78d596ac6a439641aa1b130", size = 78962, upload-time = "2021-05-01T18:03:58.186Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f2/5d/865e17349564eb1772688d8afc5e3081a5964c640d64d1d2880ebaed002d/typing-3.10.0.0-py3-none-any.whl", hash = "sha256:12fbdfbe7d6cca1a42e485229afcb0b0c8259258cfb919b8a5e2a5c953742f89", size = 26320, upload-time = "2021-05-01T18:03:56.398Z" }, +] + +[[package]] +name = "typing-extensions" +version = "4.15.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" }, +] + +[[package]] +name = "urllib3" +version = "2.6.2" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/1e/24/a2a2ed9addd907787d7aa0355ba36a6cadf1768b934c652ea78acbd59dcd/urllib3-2.6.2.tar.gz", hash = "sha256:016f9c98bb7e98085cb2b4b17b87d2c702975664e4f060c6532e64d1c1a5e797", size = 432930, upload-time = "2025-12-11T15:56:40.252Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6d/b9/4095b668ea3678bf6a0af005527f39de12fb026516fb3df17495a733b7f8/urllib3-2.6.2-py3-none-any.whl", hash = "sha256:ec21cddfe7724fc7cb4ba4bea7aa8e2ef36f607a4bab81aa6ce42a13dc3f03dd", size = 131182, upload-time = "2025-12-11T15:56:38.584Z" }, +]