feat✨: 更新数据集处理逻辑，添加Gigaspeech、TextVQA数据集支持，优化训练脚本，增加测试用例

.gitignore

@@ -1,3 +1,4 @@
 **/.venv/*
 **/__pycache__/*
 rsync.sh
+.pytest_cache/

pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
   "peft==0.14.0",
   "pip==24.3.1",
   "pre-commit>=4.0.1",
+  "pytest>=8.3.4",
   "requests==2.32.3",
   "rouge-score>=0.1.2",
   "safetensors>=0.5.2",
@@ -55,3 +56,11 @@ url = "https://download.pytorch.org/whl/cu124"
 [tool.black]
 line-length = 88
 exclude = "transformers_repo|peft_repo|.venv"
+
+[tool.pytest.ini_options]
+addopts = ["--color=yes", "--durations=0", "-v", "--capture=tee-sys"]
+norecursedirs = [
+  "src/transformers_repo",
+  "src/peft_repo",
+  ".venv",
+]

src/dataset_library/ChemDataset.py

@@ -4,7 +4,7 @@ import json
 import os
 
 
-class CHEMDataset(Dataset):
+class ChemDataset(Dataset):
     def __init__(
         self, vis_root, ann_path, vis_processor=None, text_processor=None, split="train"
     ):
@@ -112,7 +112,7 @@ class CHEMDataset(Dataset):
         }
 
 
-class CHEMDatasetForGeneration(CHEMDataset):
+class ChemDatasetForGeneration(ChemDataset):
     def __getitem__(self, index):
         sample = self.data[index]
         image = Image.open(os.path.join(self.vis_root, sample["image_path"])).convert(
@@ -147,29 +147,11 @@ class CHEMDatasetForGeneration(CHEMDataset):
                 ],
             },
         ]
-        from .format import create_generate
+        from .format import DatasetOutput
 
-        return create_generate(
+        return DatasetOutput(
             images=[image],
             chat=chat,
             answer=answer,
             original=sample["original"],
         )
-
-
-if __name__ == "__main__":
-    dataset = CHEMDataset(
-        "/home/zyy/research/accelerate/dataset/chem/images",
-        "/home/zyy/research/accelerate/dataset/chem/qwen_data",
-        split="train",
-    )
-    print(len(dataset))
-    print(dataset[0])
-    dataset = CHEMDatasetForGeneration(
-        "/home/zyy/research/accelerate/dataset/chem/images",
-        "/home/zyy/research/accelerate/dataset/chem/qwen_data",
-        split="train",
-    )
-    print(len(dataset))
-    print(dataset[0])
-    pass

src/dataset_library/GigaspeechDataset.py

@@ -1,7 +1,11 @@
-from PIL import Image
+from .format import (
+    Conversation,
+    ConverstationAudio,
+    ConverstationImage,
+    ConverstationText,
+    DatasetOutput,
+)
 from torch.utils.data import Dataset
-import json
-import os
 from datasets import load_dataset
 
 
@@ -32,22 +36,25 @@ class GigaspeechDataset(Dataset):
             text = self.text_processor(text)
 
         chat = [
-            {
+            Conversation(
-                "role": "user",
+                role="user",
-                "content": [
+                content=[
-                    {"type": "audio", "audio_url": ""},
+                    ConverstationAudio(type="audio", audio_url=""),
-                    {
+                    ConverstationText(
-                        "type": "text",
+                        type="text", text="Please convert the audio to text"
-                        "text": "Please convert the audio to text",
+                    ),
-                    },
                 ],
-            },
+            ),
-            {"role": "assistant", "content": [{"type": "text", "text": text}]},
+            Conversation(
+                role="assistant", content=[ConverstationText(type="text", text=text)]
+            ),
         ]
-        return {
+
-            "audio": (audio, sampling_rate),
+        return DatasetOutput(
-            "chat": chat,
+            audio=[(audio, sampling_rate)],
-        }
+            chat=chat,
+            original=sample,
+        )
 
 
 class GigaspeechDatasetForGeneration(GigaspeechDataset):
@@ -64,20 +71,18 @@ class GigaspeechDatasetForGeneration(GigaspeechDataset):
             text = self.text_processor(text)
 
         chat = [
-            {
+            Conversation(
-                "role": "user",
+                role="user",
-                "content": [
+                content=[
-                    {"type": "audio", "audio_url": ""},
+                    ConverstationAudio(type="audio", audio_url=""),
-                    {
+                    ConverstationText(
-                        "type": "text",
+                        type="text", text="Please convert the audio to text"
-                        "text": "Please convert the audio to text",
+                    ),
-                    },
                 ],
-            },
+            ),
         ]
-        from .format import create_generate
 
-        return create_generate(
+        return DatasetOutput(
             audio=[(audio, sampling_rate)],
             chat=chat,
             answer=text,
@@ -85,15 +90,16 @@ class GigaspeechDatasetForGeneration(GigaspeechDataset):
         )
 
 
-if __name__ == "__main__":
+def test_gigaspeech():
     dataset = GigaspeechDataset(
         split="train",
     )
-    print(len(dataset))
     print(dataset[0])
+    assert len(dataset) > 0
+    assert len(dataset[0]["chat"]) > 0
     dataset = GigaspeechDatasetForGeneration(
         split="train",
     )
-    print(len(dataset))
     print(dataset[0])
-    pass
+    assert len(dataset) > 0
+    assert len(dataset[0]["chat"]) > 0

src/dataset_library/OCRVQA200KDataset.py

@@ -1,7 +1,15 @@
 from PIL import Image
+from .format import (
+    Conversation,
+    ConverstationAudio,
+    ConverstationImage,
+    ConverstationText,
+    DatasetOutput,
+)
 from torch.utils.data import Dataset
 import json
 import os
+from pathlib import Path
 
 
 class OCRVQADataset(Dataset):
@@ -19,9 +27,9 @@ class OCRVQADataset(Dataset):
         )
         self.text_processor = text_processor
         if split == "train":
-            self.data = self.create_data(ann_path, split=1)
+            self.data = self.create_data(Path(ann_path, "dataset.json"), split=1)
         elif split == "test":
-            self.data = self.create_data(ann_path, split=3)
+            self.data = self.create_data(Path(ann_path, "dataset.json"), split=3)
 
         # self.instruction_pool = [
         #     "[vqa] {}",
@@ -48,6 +56,7 @@ class OCRVQADataset(Dataset):
                             "image_id": k,
                             "title": data[k]["title"],
                             "genre": data[k]["genre"],
+                            "original": data[k],
                         }
                     )
         return processed_data
@@ -89,22 +98,26 @@ class OCRVQADataset(Dataset):
             answer = self.text_processor(answer)
 
         chat = [
-            {
+            Conversation(
-                "role": "user",
+                role="user",
-                "content": [
+                content=[
-                    {"type": "image"},
+                    ConverstationImage(type="image", image_url=""),
-                    {
+                    ConverstationText(
-                        "type": "text",
+                        type="text",
-                        "text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                        text=f"[vqa] Based on the image, respond to this question with a short answer: {question}",
-                    },
+                    ),
                 ],
-            },
+            ),
-            {"role": "assistant", "content": [{"type": "text", "text": answer}]},
+            Conversation(
+                role="assistant", content=[ConverstationText(type="text", text=answer)]
+            ),
         ]
-        return {
+
-            "image": image,
+        return DatasetOutput(
-            "chat": chat,
+            chat=chat,
-        }
+            original=sample["original"],
+            images=[image],
+        )
 
 
 class OCRVQADatasetForGeneration(OCRVQADataset):
@@ -124,20 +137,20 @@ class OCRVQADatasetForGeneration(OCRVQADataset):
             answer = self.text_processor(answer)
 
         chat = [
-            {
+            Conversation(
-                "role": "user",
+                role="user",
-                "content": [
+                content=[
-                    {"type": "image"},
+                    ConverstationImage(type="image", image_url=""),
-                    {
+                    ConverstationText(
-                        "type": "text",
+                        type="text",
-                        "text": f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                        text=f"[vqa] Based on the image, respond to this question with a short answer: {question}",
-                    },
+                    ),
                 ],
-            }
+            ),
-            # {"role": "assistant", "content": answer},
         ]
-        return {
+        return DatasetOutput(
-            "image": image,
+            images=[image],
-            "chat": chat,
+            chat=chat,
-            "answer": answer,
+            answer=answer,
-        }
+            original=sample["original"],
+        )

src/dataset_library/TextVQADataset.py

@@ -0,0 +1,173 @@
+from PIL import Image
+from .format import (
+    Conversation,
+    ConverstationAudio,
+    ConverstationImage,
+    ConverstationText,
+    DatasetOutput,
+)
+from torch.utils.data import Dataset
+import json
+import os.path as osp
+from pathlib import Path
+
+
+class TextVQADataset(Dataset):
+    def __init__(
+        self, vis_root, ann_path, vis_processor=None, text_processor=None, split="train"
+    ):
+        """
+        vis_root (string): Root directory of images (e.g. coco/images/)
+        ann_root (string): directory to store the annotation file
+        """
+
+        self.vis_processor = (
+            vis_processor if vis_processor is not None else self._vis_processor
+        )
+        self.text_processor = text_processor
+        if split == "train":
+            self.data = self.create_data(
+                Path(ann_path, "TextVQA_0.5.1_train.json"),
+                vis_root=Path(vis_root, "train_images"),
+            )
+        elif split == "test":
+            self.data = self.create_data(
+                Path(ann_path, "TextVQA_0.5.1_val.json"),
+                vis_root=Path(vis_root, "train_images"),
+            )
+
+        # self.instruction_pool = [
+        #     "[vqa] {}",
+        #     "[vqa] Based on the image, respond to this question with a short answer: {}",
+        # ]
+
+    def create_data(self, ann_path, vis_root):
+        processed_data = []
+        with open(ann_path, "r") as f:
+            data = json.load(f)
+        data = data["data"]
+        for i in range(len(data)):
+            # print(data[0])
+            # {'question': 'what is the brand of phone?', 'image_id': '0054c91397f2fe05', 'image_classes': ['Belt', 'Headphones', 'Goggles', 'Scale', 'Bottle opener', 'Mobile phone', 'Mirror', 'Digital clock', 'Television', 'Telephone', 'Tool', 'Wheel', 'Camera', 'Watch', 'Glasses', 'Aircraft'], 'flickr_original_url': 'https://farm6.staticflickr.com/2891/9134076951_f65b421097_o.jpg', 'flickr_300k_url': 'https://c4.staticflickr.com/3/2891/9134076951_9db89d3e0f_z.jpg', 'image_width': 1024, 'image_height': 730, 'answers': ['nokia', 'nokia', 'nokia', 'nokia', 'toshiba', 'nokia', 'nokia', 'nokia', 'nokia', 'nokia'], 'question_tokens': ['what', 'is', 'the', 'brand', 'of', 'phone'], 'question_id': 0, 'set_name': 'train'}
+            try:
+                imageFile = data[i]["image_id"] + ".jpg"
+                question = data[i]["question"]
+                answer = data[i]["answers"][0]
+                processed_data.append(
+                    {
+                        "question": question,
+                        "answer": answer,
+                        "image_path": Path(vis_root, imageFile),
+                        "image_id": data[i]["image_id"],
+                        "title": data[i]["image_id"],
+                        "genre": data[i]["image_classes"],
+                        "original": data[i],
+                    }
+                )
+            except:
+                print(data[i])
+                pass
+
+        return processed_data
+
+    def __len__(self):
+        return len(self.data)
+
+    def _vis_processor(self, image: Image.Image):
+        width, height = image.size
+        if width > 500 or height > 500:
+            max_size = max(width, height)
+            ratio = 500 / max_size
+            new_width = int(width * ratio)
+            new_height = int(height * ratio)
+            image = image.resize((new_width, new_height), Image.Resampling.BILINEAR)
+
+        if width < 28 or height < 28:
+            min_size = min(width, height)
+            ratio = 28 / min_size + 1
+            new_width = int(width * ratio)
+            new_height = int(height * ratio)
+            image = image.resize((new_width, new_height), Image.Resampling.BILINEAR)
+
+        return image
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        image: Image.Image = Image.open(sample["image_path"]).convert("RGB")
+        # resize image
+
+        question = sample["question"]
+        answer = sample["answer"]
+        if self.vis_processor is not None:
+            image = self.vis_processor(image)
+        if self.text_processor is not None:
+            question = self.text_processor(question)
+            answer = self.text_processor(answer)
+
+        chat = [
+            Conversation(
+                role="user",
+                content=[
+                    ConverstationImage(type="image", image_url=""),
+                    ConverstationText(
+                        type="text",
+                        text=f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                    ),
+                ],
+            ),
+            Conversation(
+                role="assistant", content=[ConverstationText(type="text", text=answer)]
+            ),
+        ]
+
+        return DatasetOutput(
+            chat=chat,
+            original=sample["original"],
+            images=[image],
+        )
+
+
+class TextVQADatasetForGeneration(TextVQADataset):
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        image = Image.open(sample["image_path"]).convert("RGB")
+        # resize image
+        question = sample["question"]
+        answer = sample["answer"]
+        if self.vis_processor is not None:
+            image = self.vis_processor(image)
+        if self.text_processor is not None:
+            question = self.text_processor(question)
+            answer = self.text_processor(answer)
+
+        chat = [
+            Conversation(
+                role="user",
+                content=[
+                    ConverstationImage(type="image", image_url=""),
+                    ConverstationText(
+                        type="text",
+                        text=f"[vqa] Based on the image, respond to this question with a short answer: {question}",
+                    ),
+                ],
+            ),
+        ]
+        return DatasetOutput(
+            images=[image],
+            chat=chat,
+            answer=answer,
+            original=sample["original"],
+        )
+
+
+def test_dataset():
+    vis_root = "/home/zyy/dataset/TextVQA/images"
+    ann_path = "/home/zyy/dataset/TextVQA"
+    dataset = TextVQADataset(vis_root, ann_path)
+    for i in range(10):
+        print(dataset[i])
+
+
+if __name__ == "__main__":
+    test_dataset()

src/dataset_library/factory.py

@@ -1,50 +1,49 @@
 from torch.utils.data import Dataset
 from typing import Literal
+from pathlib import Path
 
 
 def get_dataset(
-    dataset_name, base_path="/home/zyy/research/accelerate/dataset"
+    dataset_name, base_path="/home/zyy/dataset"
 ) -> dict[Literal["train", "test", "generation"], Dataset]:
     dataset: dict[Literal["train", "test", "generation"], Dataset] = {}
-    if dataset_name == "OCR-VQA-200K":
+    if dataset_name == "ocrvqa200k":
-        import os.path as osp
+        from .OCRVQA200KDataset import OCRVQADataset, OCRVQADatasetForGeneration
-        from .OCRVQADataset import OCRVQADataset, OCRVQADatasetForGeneration
 
         dataset = {
             "train": OCRVQADataset(
-                osp.join(base_path, "OCR-VQA-200K/images"),
+                vis_root=Path(base_path, "OCR-VQA-200K", "images"),
-                osp.join(base_path, "OCR-VQA-200K/dataset.json"),
+                ann_path=Path(base_path, "OCR-VQA-200K"),
                 split="train",
             ),
             "test": OCRVQADataset(
-                osp.join(base_path, "OCR-VQA-200K/images"),
+                vis_root=Path(base_path, "OCR-VQA-200K", "images"),
-                osp.join(base_path, "OCR-VQA-200K/dataset.json"),
+                ann_path=Path(base_path, "OCR-VQA-200K"),
                 split="test",
             ),
             "generation": OCRVQADatasetForGeneration(
-                osp.join(base_path, "OCR-VQA-200K/images"),
+                vis_root=Path(base_path, "OCR-VQA-200K", "images"),
-                osp.join(base_path, "OCR-VQA-200K/dataset.json"),
+                ann_path=Path(base_path, "OCR-VQA-200K"),
                 split="test",
             ),
         }
-    if dataset_name == "CHEM":
+    if dataset_name == "chem":
-        import os.path as osp
+        from .ChemDataset import ChemDataset, ChemDatasetForGeneration
-        from .CHEM import CHEMDataset, CHEMDatasetForGeneration
 
         dataset = {
-            "train": CHEMDataset(
+            "train": ChemDataset(
-                osp.join(base_path, "chem/images"),
+                vis_root=Path(base_path, "chem", "images"),
-                osp.join(base_path, "chem"),
+                ann_path=Path(base_path, "chem"),
                 split="train",
             ),
-            "test": CHEMDataset(
+            "test": ChemDataset(
-                osp.join(base_path, "chem/images"),
+                vis_root=Path(base_path, "chem", "images"),
-                osp.join(base_path, "chem"),
+                ann_path=Path(base_path, "chem"),
                 split="test",
             ),
-            "generation": CHEMDatasetForGeneration(
+            "generation": ChemDatasetForGeneration(
-                osp.join(base_path, "chem/images"),
+                vis_root=Path(base_path, "chem", "images"),
-                osp.join(base_path, "chem"),
+                ann_path=Path(base_path, "chem"),
                 split="test",
             ),
         }
@@ -57,4 +56,26 @@ def get_dataset(
             "test": GigaspeechDataset(split="test"),
             "generation": GigaspeechDatasetForGeneration(split="test"),
         }
+
+    if dataset_name == "textvqa":
+        from .TextVQADataset import TextVQADataset, TextVQADatasetForGeneration
+
+        dataset = {
+            "train": TextVQADataset(
+                vis_root=Path(base_path, "TextVQA", "images"),
+                ann_path=Path(base_path, "TextVQA"),
+                split="train",
+            ),
+            "test": TextVQADataset(
+                vis_root=Path(base_path, "TextVQA", "images"),
+                ann_path=Path(base_path, "TextVQA"),
+                split="test",
+            ),
+            "generation": TextVQADatasetForGeneration(
+                vis_root=Path(base_path, "TextVQA", "images"),
+                ann_path=Path(base_path, "TextVQA"),
+                split="test",
+            ),
+        }
+
     return dataset

src/dataset_library/format.py

@@ -0,0 +1,32 @@
+from typing import Any, Tuple, TypedDict, Literal, Optional
+import numpy as np
+from PIL import Image
+
+
+class ConverstationText(TypedDict):
+    type: Literal["text"]
+    text: str
+
+
+class ConverstationAudio(TypedDict):
+    type: Literal["audio"]
+    audio_url: str
+
+
+class ConverstationImage(TypedDict):
+    type: Literal["image"]
+    image_url: str
+
+
+class Conversation(TypedDict):
+
+    role: Literal["user", "assistant", "system"]
+    content: list[ConverstationText | ConverstationAudio | ConverstationImage]
+
+
+class DatasetOutput(TypedDict):
+    audios: Optional[list[Tuple[np.ndarray, int]]]
+    chat: list[Conversation]
+    answer: Optional[str]
+    original: Any
+    images: Optional[list[Image.Image]]

src/dataset_library/test_dataset.py

@@ -0,0 +1,37 @@
+from .factory import get_dataset
+
+
+def test_gigaspeech():
+    dataset = get_dataset("gigaspeech")
+    assert len(dataset["train"]) > 0
+    assert len(dataset["train"][0]["chat"]) > 0
+
+    assert len(dataset["test"]) > 0
+    assert len(dataset["test"][0]["chat"]) > 0
+
+
+def test_chem():
+    dataset = get_dataset("chem")
+    assert len(dataset["train"]) > 0
+    assert len(dataset["train"][0]["chat"]) > 0
+
+    assert len(dataset["test"]) > 0
+    assert len(dataset["test"][0]["chat"]) > 0
+
+
+def test_ocrvqa200k():
+    dataset = get_dataset("ocrvqa200k")
+    assert len(dataset["train"]) > 0
+    assert len(dataset["train"][0]["chat"]) > 0
+
+    assert len(dataset["test"]) > 0
+    assert len(dataset["test"][0]["chat"]) > 0
+
+
+def test_textvqa():
+    dataset = get_dataset("textvqa")
+    assert len(dataset["train"]) > 0
+    assert len(dataset["train"][0]["chat"]) > 0
+
+    assert len(dataset["test"]) > 0
+    assert len(dataset["test"][0]["chat"]) > 0

src/model_library/factory.py

@@ -5,8 +5,6 @@ from trl import (
     get_quantization_config,
 )
 from utils.args import ContinualModelConfig
-import transformers
-print(transformers.__version__)
 
 
 def get_model(model_args: ContinualModelConfig):
@@ -26,7 +24,8 @@ def get_model(model_args: ContinualModelConfig):
 
     if model_args.model_name_or_path == "Qwen/Qwen2-VL-7B-Instruct":
         from transformers import Qwen2VLProcessor, Qwen2VLForConditionalGeneration
-        from model_library.qwen2vl import Qwen2VLForConditionalGeneration_modified
+
+        # from .qwen2vl import Qwen2VLForConditionalGeneration_modified
 
         model = Qwen2VLForConditionalGeneration.from_pretrained(
             model_args.model_name_or_path,
@@ -38,7 +37,7 @@ def get_model(model_args: ContinualModelConfig):
             trust_remote_code=model_args.trust_remote_code,
             padding_side="left",
         )
-        from model_library.qwen2vl import (
+        from .qwen2vl import (
             collate_fn_for_train,
             collate_fn_for_evaluate,
         )
@@ -60,7 +59,7 @@ def get_model(model_args: ContinualModelConfig):
             trust_remote_code=model_args.trust_remote_code,
             padding_side="left",
         )
-        from model_library.qwen2audio import (
+        from .qwen2audio import (
             collate_fn_for_train,
             collate_fn_for_evaluate,
         )

src/model_library/qwen2vl/collate_fn.py

@@ -1,15 +1,16 @@
 from transformers import Qwen2VLProcessor
+from dataset_library.format import DatasetOutput
 import torch
 
 
-def collate_fn_for_train(examples, processor: Qwen2VLProcessor):
+def collate_fn_for_train(examples: list[DatasetOutput], processor: Qwen2VLProcessor):
     # Get the texts and images, and apply the chat template
     texts = [
         processor.apply_chat_template(example["chat"], tokenize=False)
         for example in examples
     ]
     # print(texts)
-    images = [example["image"] for example in examples]
+    images = [example["images"] for example in examples]
     # Tokenize the texts and process the images
     batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
 
@@ -65,7 +66,7 @@ def collate_fn_for_evaluate(examples, processor: Qwen2VLProcessor):
         for example in examples
     ]
     # print(texts)
-    images = [example["image"] for example in examples]
+    images = [example["images"] for example in examples]
 
     # Tokenize the texts and process the images
     batch = processor(text=texts, images=images, return_tensors="pt", padding=True)

src/train.sh

@@ -1,7 +1,7 @@
 #!/bin/bash
 
 accelerate launch --config_file configs/accelerate_configs/deepspeed_zero2.yaml train.py \
-    --dataset_name CHEM \
+    --dataset_name gigaspeech \
     --use_peft \
     --peft_type LORA \
     --model_name_or_path Qwen/Qwen2-VL-7B-Instruct \

uv.lock

@@ -270,6 +270,7 @@ dependencies = [
     { name = "peft" },
     { name = "pip" },
     { name = "pre-commit" },
+    { name = "pytest" },
     { name = "requests" },
     { name = "rouge-score" },
     { name = "safetensors" },
@@ -303,6 +304,7 @@ requires-dist = [
     { name = "peft", specifier = "==0.14.0" },
     { name = "pip", specifier = "==24.3.1" },
     { name = "pre-commit", specifier = ">=4.0.1" },
+    { name = "pytest", specifier = ">=8.3.4" },
     { name = "requests", specifier = "==2.32.3", index = "https://pypi.org/simple" },
     { name = "rouge-score", specifier = ">=0.1.2" },
     { name = "safetensors", specifier = ">=0.5.2" },
@@ -571,6 +573,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/c6/c88e154df9c4e1a2a66ccf0005a88dfb2650c1dffb6f5ce603dfbd452ce3/idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3", size = 70442 },
 ]
 
+[[package]]
+name = "iniconfig"
+version = "2.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/4b/cbd8e699e64a6f16ca3a8220661b5f83792b3017d0f79807cb8708d33913/iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3", size = 4646 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374", size = 5892 },
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.5"
@@ -1171,6 +1182,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/3c/a6/bc1012356d8ece4d66dd75c4b9fc6c1f6650ddd5991e421177d9f8f671be/platformdirs-4.3.6-py3-none-any.whl", hash = "sha256:73e575e1408ab8103900836b97580d5307456908a03e92031bab39e4554cc3fb", size = 18439 },
 ]
 
+[[package]]
+name = "pluggy"
+version = "1.5.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/2d/02d4312c973c6050a18b314a5ad0b3210edb65a906f868e31c111dede4a6/pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1", size = 67955 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669", size = 20556 },
+]
+
 [[package]]
 name = "pooch"
 version = "1.8.2"
@@ -1402,6 +1422,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8a/0b/9fcc47d19c48b59121088dd6da2488a49d5f72dacf8262e2790a1d2c7d15/pygments-2.19.1-py3-none-any.whl", hash = "sha256:9ea1544ad55cecf4b8242fab6dd35a93bbce657034b0611ee383099054ab6d8c", size = 1225293 },
 ]
 
+[[package]]
+name = "pytest"
+version = "8.3.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "iniconfig" },
+    { name = "packaging" },
+    { name = "pluggy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/05/35/30e0d83068951d90a01852cb1cef56e5d8a09d20c7f511634cc2f7e0372a/pytest-8.3.4.tar.gz", hash = "sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761", size = 1445919 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/11/92/76a1c94d3afee238333bc0a42b82935dd8f9cf8ce9e336ff87ee14d9e1cf/pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6", size = 343083 },
+]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"