"""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        UpperCamelCase		:
          @staticmethod
          def 			_UpperCAmelCase   (			*__UpperCamelCase     ,**__UpperCamelCase			)	->      int:


               '''simple docstring'''


               pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  MODEL_FOR_OBJECT_DETECTION_MAPPING
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	ObjectDetectionPipeline(model=__UpperCamelCase     ,image_processor=__UpperCamelCase			)
        return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	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

        lowercase_	:				int													=	datasets.load_dataset('hf-internal-testing/fixtures_image_utils'     ,'image'     ,split='test'			)

        lowercase_	:				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'''],
        ]
        lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        pass
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	'''hf-internal-testing/tiny-detr-mobilenetsv3'''

        lowercase_	:				Any													=	AutoModelForObjectDetection.from_pretrained(__UpperCamelCase			)
        lowercase_	:				str													=	AutoFeatureExtractor.from_pretrained(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	ObjectDetectionPipeline(model=__UpperCamelCase     ,feature_extractor=__UpperCamelCase			)

        lowercase_	:				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}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	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 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	'''facebook/detr-resnet-50'''

        lowercase_	:				str													=	AutoModelForObjectDetection.from_pretrained(__UpperCamelCase			)
        lowercase_	:				Any													=	AutoFeatureExtractor.from_pretrained(__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	ObjectDetectionPipeline(model=__UpperCamelCase     ,feature_extractor=__UpperCamelCase			)

        lowercase_	:				str													=	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}},
            ]     ,)

        lowercase_	:				List[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 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	'''facebook/detr-resnet-50'''

        lowercase_	:				Tuple													=	pipeline('object-detection'     ,model=__UpperCamelCase			)

        lowercase_	:				str													=	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}},
            ]     ,)

        lowercase_	:				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 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Dict													=	0.9985
        lowercase_	:				Dict													=	'''facebook/detr-resnet-50'''

        lowercase_	:				Dict													=	pipeline('object-detection'     ,model=__UpperCamelCase			)

        lowercase_	:				str													=	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 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	'''Narsil/layoutlmv3-finetuned-funsd'''
        lowercase_	:				List[str]													=	0.9993

        lowercase_	:				Dict													=	pipeline('object-detection'     ,model=__UpperCamelCase     ,threshold=__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	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
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     int       			=      ''''''
			lowerCamelCase_     :     str       			=      (
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			lowerCamelCase_     :     str       			=      None  # compression type in fsspec. ex: "gzip"
			lowerCamelCase_     :     str       			=      None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz






			def __init__(self					,							__magic_name__ = ""					,							__magic_name__ = None					,							__magic_name__ = None					,							**__magic_name__    )     ->   Any:


							'''simple docstring'''



							super().__init__(self					,							**__magic_name__    )
							# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
							snake_case_						:    Union[str, Any]				=  fsspec.open(
							    __magic_name__					,							mode='''rb'''					,							protocol=__magic_name__					,							compression=self.compression					,							client_kwargs={
							        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
							        '''trust_env''': True,  # Enable reading proxy env variables.
							        **(target_options or {}).pop('''client_kwargs'''					,							{}    ),  # To avoid issues if it was already passed.
							    }					,							**(target_options or {})					,							)
							snake_case_						:    Tuple				=  os.path.basename(self.file.path.split('''::'''    )[0]    )
							snake_case_						:    Optional[Any]				=  (
							    self.compressed_name[: self.compressed_name.rindex('''.'''    )]
							    if '''.''' in self.compressed_name
							    else self.compressed_name
							)
							snake_case_						:    Dict				=  None






			@classmethod
			def 			lowerCamelCase  (cls					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							return super()._strip_protocol(__magic_name__    ).lstrip('''/'''    )






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							if self.dir_cache is None:
											snake_case_						:    Optional[int]				=  {**self.file.fs.info(self.file.path    ), '''name''': self.uncompressed_name}
											snake_case_						:    List[str]				=  {f['''name''']: f}






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[Any]:


							'''simple docstring'''



							return self.file.open().read()






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__=None					,							__magic_name__=True					,							__magic_name__=None					,							**__magic_name__					,							)     ->   int:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  self._strip_protocol(__magic_name__    )
							if mode != "rb":
											raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''    )
							return self.file.open()




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''bz2'''
			lowerCamelCase_     :     Any       			=      '''bz2'''
			lowerCamelCase_     :     int       			=      '''.bz2'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''gzip'''
			lowerCamelCase_     :     Dict       			=      '''gzip'''
			lowerCamelCase_     :     int       			=      '''.gz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Any       			=      '''lz4'''
			lowerCamelCase_     :     Optional[Any]       			=      '''.lz4'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Tuple       			=      '''xz'''
			lowerCamelCase_     :     Any       			=      '''xz'''
			lowerCamelCase_     :     int       			=      '''.xz'''




class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Union[str, Any]       			=      '''zstd'''
			lowerCamelCase_     :     Tuple       			=      '''zstd'''
			lowerCamelCase_     :     Any       			=      '''.zst'''






			def __init__(self					,							__magic_name__					,							__magic_name__ = "rb"					,							__magic_name__ = None					,							__magic_name__ = None					,							__magic_name__ = DEFAULT_BLOCK_SIZE					,							**__magic_name__					,							)     ->   Tuple:


							'''simple docstring'''



							super().__init__(
							    fo=__magic_name__					,							mode=__magic_name__					,							target_protocol=__magic_name__					,							target_options=__magic_name__					,							block_size=__magic_name__					,							**__magic_name__					,							)
							# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
							#
							# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
							#     out.close = close
							# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
							#
							# see https://github.com/intake/filesystem_spec/issues/725
							snake_case_						:    Dict				=  self.file.__enter__



							class 					__lowerCAmelCase       :






										def __init__(self					,							__magic_name__    )     ->   List[Any]:


														'''simple docstring'''



														snake_case_						:    str				=  file_






										def __enter__(self    )     ->   List[Any]:


														'''simple docstring'''



														self._file.__enter__()
														return self






										def __exit__(self					,							*__magic_name__					,							**__magic_name__    )     ->   int:


														'''simple docstring'''



														self._file.__exit__(*__magic_name__					,							**__magic_name__    )






										def __iter__(self    )     ->   Optional[int]:


														'''simple docstring'''



														return iter(self._file    )






										def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


														'''simple docstring'''



														return next(self._file    )






										def __getattr__(self					,							__magic_name__    )     ->   str:


														'''simple docstring'''



														return getattr(self._file					,							__magic_name__    )

							def fixed_enter(*__magic_name__					,							**__magic_name__    ):
											return WrappedFile(_enter(*__magic_name__					,							**__magic_name__    )    )

							snake_case_						:    Tuple				=  fixed_enter
