


"""simple docstring"""
import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
			from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import DPTImageProcessor



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_				,       a_=2				,       a_=32				,       a_=16				,       a_=3				,       a_=True				,       a_=True				,       a_=32				,       a_=4				,       a_=[0, 1, 2, 3]				,       a_=4				,       a_=37				,       a_="gelu"				,       a_=0.1				,       a_=0.1				,       a_=0.02				,       a_=3				,       a_=[1, 3_84, 24, 24]				,       a_=True				,       a_=None				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				parent
			__snake_case	:						Optional[int]						=				batch_size
			__snake_case	:						Optional[int]						=				image_size
			__snake_case	:						Union[str, Any]						=				patch_size
			__snake_case	:						List[Any]						=				num_channels
			__snake_case	:						Union[str, Any]						=				is_training
			__snake_case	:						Optional[Any]						=				use_labels
			__snake_case	:						List[Any]						=				hidden_size
			__snake_case	:						str						=				num_hidden_layers
			__snake_case	:						int						=				backbone_out_indices
			__snake_case	:						Optional[Any]						=				num_attention_heads
			__snake_case	:						Any						=				intermediate_size
			__snake_case	:						Union[str, Any]						=				hidden_act
			__snake_case	:						Optional[Any]						=				hidden_dropout_prob
			__snake_case	:						Optional[int]						=				attention_probs_dropout_prob
			__snake_case	:						List[str]						=				initializer_range
			__snake_case	:						int						=				num_labels
			__snake_case	:						Optional[int]						=				backbone_featmap_shape
			__snake_case	:						Optional[int]						=				scope
			__snake_case	:						Optional[int]						=				is_hybrid
			# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
			__snake_case	:						Dict						=				(image_size // patch_size) ** 2
			__snake_case	:						List[str]						=				num_patches + 1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			__snake_case	:						Tuple						=				None
			if self.use_labels:
				__snake_case	:						int						=				ids_tensor([self.batch_size, self.image_size, self.image_size]				,       self.num_labels					)

			__snake_case	:						Any						=				self.get_config()

			return config, pixel_values, labels



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				{
			    '''global_padding''': '''same''',
			    '''layer_type''': '''bottleneck''',
			    '''depths''': [3, 4, 9],
			    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
			    '''embedding_dynamic_padding''': True,
			    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
			    '''num_groups''': 2,
			}

			return DPTConfig(
			    image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       backbone_out_indices=self.backbone_out_indices				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=a_				,       initializer_range=self.initializer_range				,       is_hybrid=self.is_hybrid				,       backbone_config=a_				,       backbone_featmap_shape=self.backbone_featmap_shape				,       )



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





			'''simple docstring'''

			__snake_case	:						Dict						=				DPTModel(config=a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Optional[Any]						=				model(a_					)
			self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)



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





			'''simple docstring'''

			__snake_case	:						str						=				self.num_labels
			__snake_case	:						Any						=				DPTForDepthEstimation(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						Any						=				model(a_					)
			self.parent.assertEqual(result.predicted_depth.shape				,       (self.batch_size, self.image_size, self.image_size)					)



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





			'''simple docstring'''

			__snake_case	:						Tuple						=				self.num_labels
			__snake_case	:						Optional[Any]						=				DPTForSemanticSegmentation(a_					)
			model.to(a_					)
			model.eval()
			__snake_case	:						List[Any]						=				model(a_				,       labels=a_					)
			self.parent.assertEqual(
			    result.logits.shape				,       (self.batch_size, self.num_labels, self.image_size, self.image_size)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				self.prepare_config_and_inputs()
			__snake_case	,							__snake_case	,							__snake_case	:						Tuple						=				config_and_inputs
			__snake_case	:						Optional[int]						=				{'''pixel_values''': pixel_values}
			return config, inputs_dict



@require_torch
class 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		lowerCamelCase__				=(
		    {
		        'depth-estimation': DPTForDepthEstimation,
		        'feature-extraction': DPTModel,
		        'image-segmentation': DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase__				=False
		lowerCamelCase__				=False
		lowerCamelCase__				=False



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				DPTModelTester(self					)
			__snake_case	:						Tuple						=				ConfigTester(self				,       config_class=a_				,       has_text_modality=a_				,       hidden_size=37					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.config_tester.run_common_tests()



		@unittest.skip(reason='''DPT does not use inputs_embeds'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__snake_case	:						str						=				model_class(a_					)
				self.assertIsInstance(model.get_input_embeddings()				,       (nn.Module)					)
				__snake_case	:						Union[str, Any]						=				model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(a_				,       nn.Linear					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						int						=				self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				__snake_case	:						List[Any]						=				model_class(a_					)
				__snake_case	:						str						=				inspect.signature(model.forward					)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				__snake_case	:						Dict						=				[*signature.parameters.keys()]

				__snake_case	:						Union[str, Any]						=				['''pixel_values''']
				self.assertListEqual(arg_names[:1]				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_depth_estimation(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_class in self.all_model_classes:
				if model_class.__name__ == "DPTForDepthEstimation":
					continue

				__snake_case	,							__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs_for_common()
				__snake_case	:						List[str]						=				True

				if model_class in get_values(a_					):
					continue

				__snake_case	:						Any						=				model_class(a_					)
				model.to(a_					)
				model.train()
				__snake_case	:						Any						=				self._prepare_for_class(a_				,       a_				,       return_labels=a_					)
				__snake_case	:						str						=				model(**a_					).loss
				loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_class in self.all_model_classes:
				if model_class.__name__ == "DPTForDepthEstimation":
					continue

				__snake_case	,							__snake_case	:						Union[str, Any]						=				self.model_tester.prepare_config_and_inputs_for_common()
				__snake_case	:						str						=				False
				__snake_case	:						str						=				True

				if model_class in get_values(a_					) or not model_class.supports_gradient_checkpointing:
					continue
				__snake_case	:						List[Any]						=				model_class(a_					)
				model.to(a_					)
				model.gradient_checkpointing_enable()
				model.train()
				__snake_case	:						Optional[int]						=				self._prepare_for_class(a_				,       a_				,       return_labels=a_					)
				__snake_case	:						Tuple						=				model(**a_					).loss
				loss.backward()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Optional[int]						=				self.model_tester.prepare_config_and_inputs_for_common()

			__snake_case	:						Dict						=				_config_zero_init(a_					)
			for model_class in self.all_model_classes:
				__snake_case	:						Optional[Any]						=				model_class(config=a_					)
				# Skip the check for the backbone
				__snake_case	:						Any						=				[]
				for name, module in model.named_modules():
					if module.__class__.__name__ == "DPTViTHybridEmbeddings":
						__snake_case	:						Optional[Any]						=				[f"""{name}.{key}""" for key in module.state_dict().keys()]
						break

				for name, param in model.named_parameters():
					if param.requires_grad:
						if name in backbone_params:
							continue
						self.assertIn(
						    ((param.data.mean() * 1E9).round() / 1E9).item()				,       [0.0, 1.0]				,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""				,       )



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
				__snake_case	:						Optional[Any]						=				DPTModel.from_pretrained(a_					)
				self.assertIsNotNone(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	,							__snake_case	:						Any						=				self.model_tester.prepare_config_and_inputs_for_common()
			__snake_case	:						str						=				'''add'''
			with self.assertRaises(a_					):
				__snake_case	:						List[Any]						=				DPTForDepthEstimation(a_					)






def 		lowercase							(     )     ->Tuple:
	"""simple docstring"""





	__snake_case	:						Tuple						=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
	return image



@require_torch
@require_vision
@slow
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''					)
			__snake_case	:						Dict						=				DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''					).to(a_					)

			__snake_case	:						str						=				prepare_img()
			__snake_case	:						Optional[Any]						=				image_processor(images=a_				,       return_tensors='''pt'''					).to(a_					)

			# forward pass
			with torch.no_grad():
				__snake_case	:						int						=				model(**a_					)
				__snake_case	:						int						=				outputs.predicted_depth

			# verify the predicted depth
			__snake_case	:						Any						=				torch.Size((1, 3_84, 3_84)					)
			self.assertEqual(predicted_depth.shape				,       a_					)

			__snake_case	:						str						=				torch.tensor(
			    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]					).to(a_					)

			self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				,       a_				,       atol=1E-4					)					)









"""simple docstring"""
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


SCREAMING_SNAKE_CASE :    str									=				"""▁"""

SCREAMING_SNAKE_CASE :    List[str]									=				get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=BigBirdTokenizer
		lowerCamelCase__				=BigBirdTokenizerFast
		lowerCamelCase__				=True
		lowerCamelCase__				=True



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().setUp()

			__snake_case	:						List[Any]						=				self.tokenizer_class(a_				,       keep_accents=a_					)
			tokenizer.save_pretrained(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				'''<s>'''
			__snake_case	:						Optional[Any]						=				1

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				list(self.get_tokenizer().get_vocab().keys()					)

			self.assertEqual(vocab_keys[0]				,       '''<unk>'''					)
			self.assertEqual(vocab_keys[1]				,       '''<s>'''					)
			self.assertEqual(vocab_keys[-1]				,       '''[MASK]'''					)
			self.assertEqual(len(a_					)				,       10_04					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.assertEqual(self.get_tokenizer().vocab_size				,       10_00					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.test_rust_tokenizer:
				return

			__snake_case	:						str						=				self.get_tokenizer()
			__snake_case	:						Dict						=				self.get_rust_tokenizer()

			__snake_case	:						Dict						=				'''I was born in 92000, and this is falsé.'''

			__snake_case	:						int						=				tokenizer.tokenize(a_					)
			__snake_case	:						str						=				rust_tokenizer.tokenize(a_					)
			self.assertListEqual(a_				,       a_					)

			__snake_case	:						Tuple						=				tokenizer.encode(a_				,       add_special_tokens=a_					)
			__snake_case	:						Tuple						=				rust_tokenizer.encode(a_				,       add_special_tokens=a_					)
			self.assertListEqual(a_				,       a_					)

			__snake_case	:						Optional[Any]						=				self.get_rust_tokenizer()
			__snake_case	:						Optional[int]						=				tokenizer.encode(a_					)
			__snake_case	:						Dict						=				rust_tokenizer.encode(a_					)
			self.assertListEqual(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				BigBirdTokenizer(a_				,       keep_accents=a_					)

			__snake_case	:						Optional[int]						=				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]				,       )

			__snake_case	:						Union[str, Any]						=				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''',
			        '''.''',
			    ]				,       )
			__snake_case	:						Tuple						=				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]				,       )

			__snake_case	:						Optional[Any]						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				'''Hello World!'''
			__snake_case	:						List[Any]						=				[65, 1_85_36, 22_60, 1_01, 66]

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				(
			    '''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'''
			)
			# fmt: off
			__snake_case	:						Optional[int]						=				[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
			# fmt: on
			self.assertListEqual(a_				,       self.big_tokenizer.encode(a_					)					)



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			import torch

			from transformers import BigBirdConfig, BigBirdModel

			# Build sequence
			__snake_case	:						str						=				list(self.big_tokenizer.get_vocab().keys()					)[:10]
			__snake_case	:						Tuple						=				''' '''.join(a_					)
			__snake_case	:						Tuple						=				self.big_tokenizer.encode_plus(a_				,       return_tensors='''pt'''				,       return_token_type_ids=a_					)
			__snake_case	:						List[Any]						=				self.big_tokenizer.batch_encode_plus(
			    [sequence + ''' ''' + sequence]				,       return_tensors='''pt'''				,       return_token_type_ids=a_					)

			__snake_case	:						Optional[int]						=				BigBirdConfig(attention_type='''original_full'''					)
			__snake_case	:						str						=				BigBirdModel(a_					)

			assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''					)
			__snake_case	:						Any						=				tokenizer.decode(tokenizer('''Paris is the [MASK].'''					).input_ids					)

			self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				{'''input_ids''': [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 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, 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, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
			# fmt: on

			self.tokenizer_integration_test_util(
			    expected_encoding=a_				,       model_name='''google/bigbird-roberta-base'''				,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''				,       )





