

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  Any   	=						logging.get_logger(__name__)

lowerCAmelCase							:  Dict   	=						{
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

lowerCAmelCase							:  List[Any]   	=						{
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

lowerCAmelCase							:  Optional[Any]   	=						{
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       set()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       char

						SCREAMING_SNAKE_CASE_: Dict  		=       set(_UpperCAmelCase    )
						return pairs



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[Any]										=     VOCAB_FILES_NAMES
			_UpperCAmelCase   :							List[Any]										=     PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase   :							Optional[int]										=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Optional[Any]="<s>"   ,						lowerCAmelCase__  :     Optional[Any]="</s>"   ,						lowerCAmelCase__  :     Union[str, Any]="</s>"   ,						lowerCAmelCase__  :     Union[str, Any]="<s>"   ,						lowerCAmelCase__  :     List[str]="<unk>"   ,						lowerCAmelCase__  :     str="<pad>"   ,						lowerCAmelCase__  :     List[Any]="<mask>"   ,						**lowerCAmelCase__  :     List[Any]   ,						):
									super().__init__(
									    bos_token=lowerCAmelCase__   ,						eos_token=lowerCAmelCase__   ,						unk_token=lowerCAmelCase__   ,						sep_token=lowerCAmelCase__   ,						cls_token=lowerCAmelCase__   ,						pad_token=lowerCAmelCase__   ,						mask_token=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       vocab_file
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       merges_file

									SCREAMING_SNAKE_CASE_: List[str]  		=       {}
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       1
									SCREAMING_SNAKE_CASE_: Dict  		=       2
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       3

									self.add_from_file(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       {v: k for k, v in self.encoder.items()}

									with open(lowerCAmelCase__   ,						encoding="utf-8") as merges_handle:
															SCREAMING_SNAKE_CASE_: int  		=       merges_handle.read().split("\n")[:-1]
									SCREAMING_SNAKE_CASE_: int  		=       [tuple(merge.split()[:-1]) for merge in merges]
									SCREAMING_SNAKE_CASE_: Tuple  		=       dict(zip(lowerCAmelCase__   ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_: Dict  		=       {}



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):

									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [self.cls_token_id]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None   ,						lowerCAmelCase__  :     bool = False):

									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__   ,						token_ids_a=lowerCAmelCase__   ,						already_has_special_tokens=lowerCAmelCase__)

									if token_ids_a is None:
															return [1] + ([0] * len(lowerCAmelCase__)) + [1]
									return [1] + ([0] * len(lowerCAmelCase__)) + [1, 1] + ([0] * len(lowerCAmelCase__)) + [1]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     List[int]   ,						lowerCAmelCase__  :     Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_: Dict  		=       [self.sep_token_id]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [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 + sep + token_ids_a + sep) * [0]



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									return len(self.encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return dict(self.encoder   ,						**self.added_tokens_encoder)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Any):
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       tuple(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       tuple(list(word[:-1]) + [word[-1] + "</w>"])
									SCREAMING_SNAKE_CASE_: Tuple  		=       get_pairs(lowerCAmelCase__)

									if not pairs:
															return token

									while True:
															SCREAMING_SNAKE_CASE_: int  		=       min(lowerCAmelCase__   ,						key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__   ,						float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Dict  		=       bigram
															SCREAMING_SNAKE_CASE_: Dict  		=       []
															SCREAMING_SNAKE_CASE_: Tuple  		=       0
															while i < len(lowerCAmelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_: List[Any]  		=       word.index(lowerCAmelCase__   ,						lowerCAmelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_: Tuple  		=       j

																					if word[i] == first and i < len(lowerCAmelCase__) - 1 and word[i + 1] == second:
																											new_word.append(first + second)
																											i += 2
																					else:
																											new_word.append(word[i])
																											i += 1
															SCREAMING_SNAKE_CASE_: List[Any]  		=       tuple(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: int  		=       new_word
															if len(lowerCAmelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_: Dict  		=       get_pairs(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       "@@ ".join(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       word[:-4]
									SCREAMING_SNAKE_CASE_: int  		=       word
									return word



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       []

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.findall(R"\S+\n?"   ,						lowerCAmelCase__)

									for token in words:
															split_tokens.extend(list(self.bpe(lowerCAmelCase__).split(" ")))
									return split_tokens



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Optional[Any]):
									return self.encoder.get(lowerCAmelCase__   ,						self.encoder.get(self.unk_token))



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     List[str]):
									return self.decoder.get(lowerCAmelCase__   ,						self.unk_token)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       " ".join(lowerCAmelCase__).replace("@@ "   ,						"").strip()
									return out_string



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[str] = None):
									if not os.path.isdir(lowerCAmelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_: List[Any]  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       os.path.join(
									    lowerCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])

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

									if os.path.abspath(self.merges_file) != os.path.abspath(lowerCAmelCase__):
															copyfile(self.merges_file   ,						lowerCAmelCase__)

									return out_vocab_file, out_merge_file




			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]):
									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															try:
																					with open(lowerCAmelCase__   ,						"r"   ,						encoding="utf-8") as fd:
																											self.add_from_file(lowerCAmelCase__)
															except FileNotFoundError as fnfe:
																					raise fnfe
															except UnicodeError:
																					raise Exception(F"Incorrect encoding detected in {f}, please rebuild the dataset")
															return

									SCREAMING_SNAKE_CASE_: Any  		=       f.readlines()
									for lineTmp in lines:
															SCREAMING_SNAKE_CASE_: str  		=       lineTmp.strip()
															SCREAMING_SNAKE_CASE_: Any  		=       line.rfind(" ")
															if idx == -1:
																					raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       line[:idx]
															SCREAMING_SNAKE_CASE_: int  		=       len(self.encoder)












'''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 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


				def   A_ (		self    :					Any       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]					):
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				image_processor=lowercase_					)
									return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ,				threshold=0.0					)

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

									import datasets

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

									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'''],
									]
									snake_case_									=				object_detector(lowercase_       ,				threshold=0.0					)

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


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


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

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

									self.assertEqual(
									    nested_simplify(lowercase_       ,				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_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]       ,				threshold=0.0       ,				)

									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Optional[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Tuple					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					str					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,				threshold=lowercase_					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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   A_ (		self    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

									snake_case_									=				object_detector(
									    '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

