import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__				= get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

					'''simple docstring'''

					A_										=  ReformerTokenizer
					A_										=  ReformerTokenizerFast
					A_										=  True
					A_										=  False
					A_										=  True



					def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






											"""simple docstring"""
											super().setUp()

											_lowerCamelCase			    =				ReformerTokenizer(A_				,				keep_accents=A_						)
											tokenizer.save_pretrained(self.tmpdirname						)



					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""
											_lowerCamelCase			    =				'''<s>'''
											_lowerCamelCase			    =				1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_						)				,				A_						)
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_						)				,				A_						)



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											_lowerCamelCase			    =				list(self.get_tokenizer().get_vocab().keys()						)

											self.assertEqual(vocab_keys[0]				,				'''<unk>'''						)
											self.assertEqual(vocab_keys[1]				,				'''<s>'''						)
											self.assertEqual(vocab_keys[-1]				,				'''j'''						)
											self.assertEqual(len(A_						)				,				10_00						)



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											self.assertEqual(self.get_tokenizer().vocab_size				,				10_00						)



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											if not self.test_rust_tokenizer:
																	return

											_lowerCamelCase			    =				self.get_tokenizer()
											_lowerCamelCase			    =				self.get_rust_tokenizer()

											_lowerCamelCase			    =				'''I was born in 92000, and this is falsé.'''

											_lowerCamelCase			    =				tokenizer.tokenize(A_						)
											_lowerCamelCase			    =				rust_tokenizer.tokenize(A_						)
											self.assertListEqual(A_				,				A_						)

											_lowerCamelCase			    =				tokenizer.encode(A_				,				add_special_tokens=A_						)
											_lowerCamelCase			    =				rust_tokenizer.encode(A_				,				add_special_tokens=A_						)
											self.assertListEqual(A_				,				A_						)

											_lowerCamelCase			    =				self.get_rust_tokenizer()
											_lowerCamelCase			    =				tokenizer.encode(A_						)
											_lowerCamelCase			    =				rust_tokenizer.encode(A_						)
											self.assertListEqual(A_				,				A_						)



					def 		UpperCamelCase_						(					self				,				A_=15						) ->				Any:






											"""simple docstring"""
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'						):
																							_lowerCamelCase			    =				self.rust_tokenizer_class.from_pretrained(A_				,				**A_						)

																							# Simple input
																							_lowerCamelCase			    =				'''This is a simple input'''
																							_lowerCamelCase			    =				['''This is a simple input 1''', '''This is a simple input 2''']
																							_lowerCamelCase			    =				('''This is a simple input''', '''This is a pair''')
																							_lowerCamelCase			    =				[
																							    ('''This is a simple input 1''', '''This is a simple input 2'''),
																							    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																							]

																							# Simple input tests
																							self.assertRaises(A_				,				tokenizer_r.encode				,				A_				,				max_length=A_				,				padding='''max_length'''						)

																							# Simple input
																							self.assertRaises(A_				,				tokenizer_r.encode_plus				,				A_				,				max_length=A_				,				padding='''max_length'''						)

																							# Simple input
																							self.assertRaises(
																							    A_				,				tokenizer_r.batch_encode_plus				,				A_				,				max_length=A_				,				padding='''max_length'''				,				)

																							# Pair input
																							self.assertRaises(A_				,				tokenizer_r.encode				,				A_				,				max_length=A_				,				padding='''max_length'''						)

																							# Pair input
																							self.assertRaises(A_				,				tokenizer_r.encode_plus				,				A_				,				max_length=A_				,				padding='''max_length'''						)

																							# Pair input
																							self.assertRaises(
																							    A_				,				tokenizer_r.batch_encode_plus				,				A_				,				max_length=A_				,				padding='''max_length'''				,				)



					def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






											"""simple docstring"""
											pass



					def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






											"""simple docstring"""
											_lowerCamelCase			    =				ReformerTokenizer(A_				,				keep_accents=A_						)

											_lowerCamelCase			    =				tokenizer.tokenize('''This is a test'''						)
											self.assertListEqual(A_				,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']						)

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(A_						)				,				[2_85, 46, 10, 1_70, 3_82]				,				)

											_lowerCamelCase			    =				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''						)
											self.assertListEqual(
											    A_				,				[
											        SPIECE_UNDERLINE + '''I''',
											        SPIECE_UNDERLINE + '''was''',
											        SPIECE_UNDERLINE + '''b''',
											        '''or''',
											        '''n''',
											        SPIECE_UNDERLINE + '''in''',
											        SPIECE_UNDERLINE + '''''',
											        '''9''',
											        '''2''',
											        '''0''',
											        '''0''',
											        '''0''',
											        ''',''',
											        SPIECE_UNDERLINE + '''and''',
											        SPIECE_UNDERLINE + '''this''',
											        SPIECE_UNDERLINE + '''is''',
											        SPIECE_UNDERLINE + '''f''',
											        '''al''',
											        '''s''',
											        '''é''',
											        '''.''',
											    ]				,				)
											_lowerCamelCase			    =				tokenizer.convert_tokens_to_ids(A_						)
											self.assertListEqual(
											    A_				,				[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,				)

											_lowerCamelCase			    =				tokenizer.convert_ids_to_tokens(A_						)
											self.assertListEqual(
											    A_				,				[
											        SPIECE_UNDERLINE + '''I''',
											        SPIECE_UNDERLINE + '''was''',
											        SPIECE_UNDERLINE + '''b''',
											        '''or''',
											        '''n''',
											        SPIECE_UNDERLINE + '''in''',
											        SPIECE_UNDERLINE + '''''',
											        '''<unk>''',
											        '''2''',
											        '''0''',
											        '''0''',
											        '''0''',
											        ''',''',
											        SPIECE_UNDERLINE + '''and''',
											        SPIECE_UNDERLINE + '''this''',
											        SPIECE_UNDERLINE + '''is''',
											        SPIECE_UNDERLINE + '''f''',
											        '''al''',
											        '''s''',
											        '''<unk>''',
											        '''.''',
											    ]				,				)



					@cached_property
					def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






											"""simple docstring"""
											return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''						)



					@slow
					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""
											_lowerCamelCase			    =				'''Hello World!'''
											_lowerCamelCase			    =				[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

											self.assertListEqual(A_				,				self.big_tokenizer.encode(A_						)						)



					@slow
					def 		UpperCamelCase_						(					self						) ->				int:






											"""simple docstring"""
											_lowerCamelCase			    =				(
											    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
											    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
											)
											_lowerCamelCase			    =				[
											    1_08,
											    2_65,
											    24,
											    1_11,
											    4,
											    2_58,
											    1_56,
											    35,
											    28,
											    2_75,
											    3,
											    2_59,
											    2_97,
											    2_60,
											    84,
											    4,
											    35,
											    1_10,
											    44,
											    8,
											    2_59,
											    91,
											    2_68,
											    21,
											    11,
											    2_09,
											    2_74,
											    1_09,
											    2_66,
											    2_77,
											    1_17,
											    86,
											    93,
											    3_15,
											    2_58,
											    2_78,
											    2_58,
											    2_77,
											    2_58,
											    0,
											    2_58,
											    2_88,
											    2_58,
											    3_19,
											    2_58,
											    0,
											    2_58,
											    0,
											    2_58,
											    0,
											    2_58,
											    0,
											    2_58,
											    2_87,
											    2_58,
											    3_15,
											    2_58,
											    2_89,
											    2_58,
											    2_78,
											    99,
											    2_69,
											    2_66,
											    2_62,
											    8,
											    2_59,
											    2_41,
											    4,
											    2_17,
											    2_30,
											    2_68,
											    2_66,
											    55,
											    1_68,
											    1_06,
											    75,
											    1_93,
											    2_66,
											    2_23,
											    27,
											    49,
											    26,
											    2_82,
											    25,
											    2_64,
											    2_99,
											    19,
											    26,
											    0,
											    2_58,
											    2_77,
											    1_17,
											    86,
											    93,
											    1_76,
											    1_83,
											    2_70,
											    11,
											    2_62,
											    42,
											    61,
											    2_65,
											]

											self.assertListEqual(A_				,				self.big_tokenizer.encode(A_						)						)



					@require_torch
					@slow
					def 		UpperCamelCase_						(					self						) ->				Tuple:






											"""simple docstring"""
											import torch

											from transformers import ReformerConfig, ReformerModel

											# Build sequence
											_lowerCamelCase			    =				list(self.big_tokenizer.get_vocab().keys()						)[:10]
											_lowerCamelCase			    =				''' '''.join(A_						)
											_lowerCamelCase			    =				self.big_tokenizer.encode_plus(A_				,				return_tensors='''pt'''						)
											_lowerCamelCase			    =				self.big_tokenizer.batch_encode_plus([sequence, sequence]				,				return_tensors='''pt'''						)

											_lowerCamelCase			    =				ReformerConfig()
											# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
											_lowerCamelCase			    =				encoded_sequence['''input_ids'''].shape
											_lowerCamelCase			    =				ReformerModel(A_						)

											# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
											assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

											with torch.no_grad():
																	model(**A_						)
																	model(**A_						)





					@slow
					def 		UpperCamelCase_						(					self						) ->				Optional[int]:






											"""simple docstring"""
											_lowerCamelCase			    =				{'''input_ids''': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
											# fmt: on

											# This tokenizer does not know some characters like ")".
											# That is the reason why we use very simple texts here.
											# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
											_lowerCamelCase			    =				[
											    '''This is a very simple sentence.''',
											    '''The quick brown fox jumps over the lazy dog.''',
											]

											self.tokenizer_integration_test_util(
											    expected_encoding=A_				,				model_name='''google/reformer-crime-and-punishment'''				,				revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''				,				padding=A_				,				sequences=A_				,				)
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import YolosImageProcessor



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=7				,				A_=3				,				A_=30				,				A_=4_00				,				A_=True				,				A_=None				,				A_=True				,				A_=[0.5, 0.5, 0.5]				,				A_=[0.5, 0.5, 0.5]				,				A_=True				,				A_=1 / 2_55				,				A_=True				,				) ->				List[Any]:






           """simple docstring"""
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_resolution
           _lowerCamelCase			    =				max_resolution
           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size
           _lowerCamelCase			    =				do_normalize
           _lowerCamelCase			    =				image_mean
           _lowerCamelCase			    =				image_std
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_pad



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }





     def 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				List[str]:






           """simple docstring"""
           if not batched:
                 _lowerCamelCase			    =				image_inputs[0]
                 if isinstance(A_				,				Image.Image						):
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.size
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.shape[1], image.shape[2]
                 if w < h:
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * h / w						)
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                 elif w > h:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * w / h						)
                 else:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				self.size['''shortest_edge''']

           else:
                 _lowerCamelCase			    =				[]
                 for image in image_inputs:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				self.get_expected_values([image]						)
                       expected_values.append((expected_height, expected_width)						)
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[0]						)[0]
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[1]						)[1]

           return expected_height, expected_width





@require_torch
@require_vision
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  YolosImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				YolosImageProcessingTester(self						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



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






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           self.assertTrue(hasattr(A_				,				'''image_mean'''						)						)
           self.assertTrue(hasattr(A_				,				'''image_std'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_normalize'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_resize'''						)						)
           self.assertTrue(hasattr(A_				,				'''size'''						)						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class.from_dict(self.image_processor_dict						)
           self.assertEqual(image_processor.size				,				{'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
           self.assertEqual(image_processor.do_pad				,				A_						)

           _lowerCamelCase			    =				self.image_processing_class.from_dict(
               self.image_processor_dict				,				size=42				,				max_size=84				,				pad_and_return_pixel_mask=A_						)
           self.assertEqual(image_processor.size				,				{'''shortest_edge''': 42, '''longest_edge''': 84}						)
           self.assertEqual(image_processor.do_pad				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           pass



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






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				Image.Image						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values
           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				numpify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				np.ndarray						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



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






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				torchify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				torch.Tensor						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # Initialize image_processings
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           _lowerCamelCase			    =				self.image_processing_class(do_resize=A_				,				do_normalize=A_				,				do_rescale=A_						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				torchify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				torch.Tensor						)

           # Test whether the method "pad" and calling the image processor return the same tensors
           _lowerCamelCase			    =				image_processing_a.pad(A_				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				image_processing_a(A_				,				return_tensors='''pt'''						)

           self.assertTrue(
               torch.allclose(encoded_images_with_method['''pixel_values''']				,				encoded_images['''pixel_values''']				,				atol=1E-4						)						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           # prepare image and target
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''image_id''': 3_97_69, '''annotations''': target}

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([75, 75, 63, 65, 17, 17]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # prepare image, target and masks_path
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

           _lowerCamelCase			    =				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor(format='''coco_panoptic'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				masks_path=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([17, 17, 63, 75, 75, 93]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify masks
           _lowerCamelCase			    =				82_28_73
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,				A_						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)