

import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:





    class 			_SCREAMING_SNAKE_CASE :







       '''simple docstring'''




       @staticmethod
       def      _snake_case		(       *__lowerCamelCase					:			List[str]     , **__lowerCamelCase					:			Any			):
           pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      MODEL_FOR_OBJECT_DETECTION_MAPPING





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   ObjectDetectionPipeline(model=__lowerCamelCase     , image_processor=__lowerCamelCase			)
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"     , threshold=0.0			)

       self.assertGreater(len(__lowerCamelCase			)     , 0			)
       for detected_object in outputs:
           self.assertEqual(
               __lowerCamelCase     , {
                   "score": ANY(__lowerCamelCase			),
                   "label": ANY(__lowerCamelCase			),
                   "box": {"xmin": ANY(__lowerCamelCase			), "ymin": ANY(__lowerCamelCase			), "xmax": ANY(__lowerCamelCase			), "ymax": ANY(__lowerCamelCase			)},
               }     , )

       import datasets

       SCREAMING_SNAKE_CASE										=   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"     , "image"     , split="test"			)

       SCREAMING_SNAKE_CASE										=   [
           Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			),
           "http://images.cocodataset.org/val2017/000000039769.jpg",
           # RGBA
           dataset[0]["file"],
           # LA
           dataset[1]["file"],
           # L
           dataset[2]["file"],
       ]
       SCREAMING_SNAKE_CASE										=   object_detector(__lowerCamelCase     , threshold=0.0			)

       self.assertEqual(len(__lowerCamelCase			)     , len(__lowerCamelCase			)			)
       for outputs in batch_outputs:
           self.assertGreater(len(__lowerCamelCase			)     , 0			)
           for detected_object in outputs:
               self.assertEqual(
                   __lowerCamelCase     , {
                       "score": ANY(__lowerCamelCase			),
                       "label": ANY(__lowerCamelCase			),
                       "box": {"xmin": ANY(__lowerCamelCase			), "ymin": ANY(__lowerCamelCase			), "xmax": ANY(__lowerCamelCase			), "ymax": ANY(__lowerCamelCase			)},
                   }     , )





   @require_tf
   @unittest.skip("Object detection not implemented in TF"			)
   def      _snake_case		(       self					:			int			):
       pass





   @require_torch
   def      _snake_case		(       self					:			List[str]			):
       SCREAMING_SNAKE_CASE										=   "hf-internal-testing/tiny-detr-mobilenetsv3"

       SCREAMING_SNAKE_CASE										=   AutoModelForObjectDetection.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoFeatureExtractor.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   ObjectDetectionPipeline(model=__lowerCamelCase     , feature_extractor=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     , threshold=0.0			)

       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
               {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
           ]     , )

       SCREAMING_SNAKE_CASE										=   object_detector(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]     , threshold=0.0     , )

       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               [
                   {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                   {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
               ],
               [
                   {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                   {"score": 0.3_376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
               ],
           ]     , )





   @require_torch
   @slow
   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   "facebook/detr-resnet-50"

       SCREAMING_SNAKE_CASE										=   AutoModelForObjectDetection.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   AutoFeatureExtractor.from_pretrained(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   ObjectDetectionPipeline(model=__lowerCamelCase     , feature_extractor=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
               {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
               {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
               {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
               {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
           ]     , )

       SCREAMING_SNAKE_CASE										=   object_detector(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               [
                   {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                   {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                   {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                   {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                   {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
               ],
               [
                   {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                   {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                   {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                   {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                   {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
               ],
           ]     , )





   @require_torch
   @slow
   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   "facebook/detr-resnet-50"

       SCREAMING_SNAKE_CASE										=   pipeline("object-detection"     , model=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
               {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
               {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
               {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
               {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
           ]     , )

       SCREAMING_SNAKE_CASE										=   object_detector(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               [
                   {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                   {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                   {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                   {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                   {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
               ],
               [
                   {"score": 0.9_982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                   {"score": 0.9_960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                   {"score": 0.9_955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                   {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                   {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
               ],
           ]     , )





   @require_torch
   @slow
   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   0.9_985
       SCREAMING_SNAKE_CASE										=   "facebook/detr-resnet-50"

       SCREAMING_SNAKE_CASE										=   pipeline("object-detection"     , model=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     , threshold=__lowerCamelCase			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               {"score": 0.9_988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
               {"score": 0.9_987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
           ]     , )





   @require_torch
   @require_pytesseract
   @slow
   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   "Narsil/layoutlmv3-finetuned-funsd"
       SCREAMING_SNAKE_CASE										=   0.9_993

       SCREAMING_SNAKE_CASE										=   pipeline("object-detection"     , model=__lowerCamelCase     , threshold=__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   object_detector(
           "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"			)
       self.assertEqual(
           nested_simplify(__lowerCamelCase     , decimals=4			)     , [
               {"score": 0.9_993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
               {"score": 0.9_993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
           ]     , )


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )