


"""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 :






					'''simple docstring'''

					@staticmethod
					def 				SCREAMING_SNAKE_CASE  (*a_				,       **a_					):





						'''simple docstring'''

						pass



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






		'''simple docstring'''

		lowerCamelCase__				=MODEL_FOR_OBJECT_DETECTION_MAPPING



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				ObjectDetectionPipeline(model=a_				,       image_processor=a_					)
			return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				,       threshold=0.0					)

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

			import datasets

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

			__snake_case	:						Tuple						=				[
			    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	:						List[Any]						=				object_detector(a_				,       threshold=0.0					)

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



		@require_tf
		@unittest.skip('''Object detection not implemented in TF'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

			__snake_case	:						Union[str, Any]						=				AutoModelForObjectDetection.from_pretrained(a_					)
			__snake_case	:						Dict						=				AutoFeatureExtractor.from_pretrained(a_					)
			__snake_case	:						Optional[Any]						=				ObjectDetectionPipeline(model=a_				,       feature_extractor=a_					)

			__snake_case	:						Dict						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''				,       threshold=0.0					)

			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			    ]				,       )

			__snake_case	:						Dict						=				object_detector(
			    [
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			    ]				,       threshold=0.0				,       )

			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        [
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        ],
			        [
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				'''facebook/detr-resnet-50'''

			__snake_case	:						int						=				AutoModelForObjectDetection.from_pretrained(a_					)
			__snake_case	:						int						=				AutoFeatureExtractor.from_pretrained(a_					)
			__snake_case	:						List[Any]						=				ObjectDetectionPipeline(model=a_				,       feature_extractor=a_					)

			__snake_case	:						Union[str, Any]						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )

			__snake_case	:						Optional[Any]						=				object_detector(
			    [
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			    ]					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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

			__snake_case	:						Any						=				pipeline('''object-detection'''				,       model=a_					)

			__snake_case	:						Dict						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )

			__snake_case	:						Optional[Any]						=				object_detector(
			    [
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
			    ]					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				0.9985
			__snake_case	:						Optional[int]						=				'''facebook/detr-resnet-50'''

			__snake_case	:						Any						=				pipeline('''object-detection'''				,       model=a_					)

			__snake_case	:						Optional[Any]						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''				,       threshold=a_					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )



		@require_torch
		@require_pytesseract
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''Narsil/layoutlmv3-finetuned-funsd'''
			__snake_case	:						Optional[Any]						=				0.9993

			__snake_case	:						int						=				pipeline('''object-detection'''				,       model=a_				,       threshold=a_					)

			__snake_case	:						Dict						=				object_detector(
			    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
			        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
			    ]				,       )







'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_rembert import RemBertTokenizer
else:
      lowercase			:				Optional[Any]    		=			None

lowercase			:				Tuple    		=			logging.get_logger(__name__)
lowercase			:				Dict    		=			{'vocab_file': 'sentencepiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				Tuple    		=			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
    'tokenizer_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/tokenizer.json',
    },
}

lowercase			:				List[str]    		=			{
    'google/rembert': 2_56,
}

lowercase			:				Dict    		=			'▁'





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		RemBertTokenizer





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<unk>"						,			SCREAMING_SNAKE_CASE="[SEP]"						,			SCREAMING_SNAKE_CASE="<pad>"						,			SCREAMING_SNAKE_CASE="[CLS]"						,			SCREAMING_SNAKE_CASE="[MASK]"						,			**SCREAMING_SNAKE_CASE						,			) ->				List[str]:






         """simple docstring"""






         A       :   Optional[Any]					=						AddedToken(SCREAMING_SNAKE_CASE						,			lstrip=SCREAMING_SNAKE_CASE						,			rstrip=SCREAMING_SNAKE_CASE	) if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) else mask_token

         super().__init__(
             SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			do_lower_case=SCREAMING_SNAKE_CASE						,			remove_space=SCREAMING_SNAKE_CASE						,			keep_accents=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			sep_token=SCREAMING_SNAKE_CASE						,			pad_token=SCREAMING_SNAKE_CASE						,			cls_token=SCREAMING_SNAKE_CASE						,			mask_token=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   List[Any]					=						do_lower_case
         A       :   str					=						remove_space
         A       :   int					=						keep_accents
         A       :   Union[str, Any]					=						vocab_file
         A       :   List[Any]					=						False if not self.vocab_file else True





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   List[Any]					=						[self.sep_token_id]
         A       :   Tuple					=						[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = False	) ->				List[int]:






         """simple docstring"""






         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.'''	)
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE	)) + [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				List[int]:






         """simple docstring"""






         A       :   Tuple					=						[self.sep_token_id]
         A       :   Dict					=						[self.cls_token_id]

         if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE	):
             logger.error('''Vocabulary path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE	)	)
             return
         A       :   Any					=						os.path.join(
             SCREAMING_SNAKE_CASE						,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

         if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE	):
             copyfile(self.vocab_file						,			SCREAMING_SNAKE_CASE	)

         return (out_vocab_file,)




