






'''simple docstring'''





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 					_snake_case						:






		@staticmethod
		def    lowerCAmelCase_							(		*UpperCamelCase					,**UpperCamelCase       )						->    Union[str, Any]:
						pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					_snake_case						(			unittest.TestCase ):
	_A          =   MODEL_FOR_OBJECT_DETECTION_MAPPING






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[Any]:
					snake_case__      :int         =   ObjectDetectionPipeline(model=UpperCamelCase					,image_processor=UpperCamelCase       )
					return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Any:
					snake_case__      :int         =   object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"					,threshold=0.0       )

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

					import datasets

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

					snake_case__      :str         =   [
					    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"""],
					]
					snake_case__      :Any         =   object_detector(UpperCamelCase					,threshold=0.0       )

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






	@require_tf
	@unittest.skip("Object detection not implemented in TF"       )
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Tuple         =   """hf-internal-testing/tiny-detr-mobilenetsv3"""

					snake_case__      :Union[str, Any]         =   AutoModelForObjectDetection.from_pretrained(UpperCamelCase       )
					snake_case__      :Any         =   AutoFeatureExtractor.from_pretrained(UpperCamelCase       )
					snake_case__      :int         =   ObjectDetectionPipeline(model=UpperCamelCase					,feature_extractor=UpperCamelCase       )

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

					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					    ]					,)

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

					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        [
					            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					        ],
					        [
					            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
					        ],
					    ]					,)






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Union[str, Any]         =   """facebook/detr-resnet-50"""

					snake_case__      :Dict         =   AutoModelForObjectDetection.from_pretrained(UpperCamelCase       )
					snake_case__      :Any         =   AutoFeatureExtractor.from_pretrained(UpperCamelCase       )
					snake_case__      :Tuple         =   ObjectDetectionPipeline(model=UpperCamelCase					,feature_extractor=UpperCamelCase       )

					snake_case__      :Optional[Any]         =   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					    ]					,)

					snake_case__      :str         =   object_detector(
					    [
					        "http://images.cocodataset.org/val2017/000000039769.jpg",
					        "http://images.cocodataset.org/val2017/000000039769.jpg",
					    ]       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        [
					            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					        ],
					        [
					            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					        ],
					    ]					,)






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Any         =   """facebook/detr-resnet-50"""

					snake_case__      :int         =   pipeline("object-detection"					,model=UpperCamelCase       )

					snake_case__      :Optional[int]         =   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					    ]					,)

					snake_case__      :Any         =   object_detector(
					    [
					        "http://images.cocodataset.org/val2017/000000039769.jpg",
					        "http://images.cocodataset.org/val2017/000000039769.jpg",
					    ]       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        [
					            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					        ],
					        [
					            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
					            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
					            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
					            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					        ],
					    ]					,)






	@require_torch
	@slow
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :List[Any]         =   0.9985
					snake_case__      :Tuple         =   """facebook/detr-resnet-50"""

					snake_case__      :str         =   pipeline("object-detection"					,model=UpperCamelCase       )

					snake_case__      :Optional[int]         =   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"					,threshold=UpperCamelCase       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
					        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
					    ]					,)






	@require_torch
	@require_pytesseract
	@slow
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Union[str, Any]         =   """Narsil/layoutlmv3-finetuned-funsd"""
					snake_case__      :Union[str, Any]         =   0.9993

					snake_case__      :Any         =   pipeline("object-detection"					,model=UpperCamelCase					,threshold=UpperCamelCase       )

					snake_case__      :Optional[int]         =   object_detector(
					    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"       )
					self.assertEqual(
					    nested_simplify(UpperCamelCase					,decimals=4       )					,[
					        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
					        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
					    ]					,)







import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [file for file in filepaths if file != file.lower()]
if upper_files:
	print(F'''{len(upper_files)} files contain uppercase characters:''')
	print("\n".join(upper_files) + "\n")

__UpperCAmelCase							:   Any							=  [file for file in filepaths if " " in file]
if space_files:
	print(F'''{len(space_files)} files contain space characters:''')
	print("\n".join(space_files) + "\n")

__UpperCAmelCase							:   str							=  [file for file in filepaths if "-" in file]
if hyphen_files:
	print(F'''{len(hyphen_files)} files contain hyphen characters:''')
	print("\n".join(hyphen_files) + "\n")

__UpperCAmelCase							:   Dict							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(F'''{len(nodir_files)} files are not in a directory:''')
	print("\n".join(nodir_files) + "\n")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)