

"""simple docstring"""






import inspect
import math
import tempfile
import unittest

import numpy as np

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

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


if is_torch_available():
	import torch
	from torch import nn

	from transformers import ViTMAEForPreTraining, ViTMAEModel
	from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor







class       snake_case__  :







			def __init__(			self			: Dict			,  lowercase			: Optional[Any]			,  lowercase			: List[str]=13			,  lowercase			: List[str]=30			,  lowercase			: str=2			,  lowercase			: Any=3			,  lowercase			: Dict=True			,  lowercase			: Tuple=True			,  lowercase			: Optional[int]=32			,  lowercase			: List[Any]=5			,  lowercase			: Union[str, Any]=4			,  lowercase			: List[str]=37			,  lowercase			: Optional[int]="gelu"			,  lowercase			: Union[str, Any]=0.1			,  lowercase			: Optional[Any]=0.1			,  lowercase			: Union[str, Any]=10			,  lowercase			: Optional[int]=0.0_2			,  lowercase			: Optional[Any]=3			,  lowercase			: List[Any]=0.6			,  lowercase			: List[Any]=None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				parent
								UpperCAmelCase			:   Optional[int]      =				batch_size
								UpperCAmelCase			:   int      =				image_size
								UpperCAmelCase			:   List[str]      =				patch_size
								UpperCAmelCase			:   Dict      =				num_channels
								UpperCAmelCase			:   Tuple      =				is_training
								UpperCAmelCase			:   str      =				use_labels
								UpperCAmelCase			:   Tuple      =				hidden_size
								UpperCAmelCase			:   Tuple      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				num_attention_heads
								UpperCAmelCase			:   Union[str, Any]      =				intermediate_size
								UpperCAmelCase			:   Tuple      =				hidden_act
								UpperCAmelCase			:   List[str]      =				hidden_dropout_prob
								UpperCAmelCase			:   Optional[Any]      =				attention_probs_dropout_prob
								UpperCAmelCase			:   Optional[int]      =				type_sequence_label_size
								UpperCAmelCase			:   Tuple      =				initializer_range
								UpperCAmelCase			:   List[Any]      =				mask_ratio
								UpperCAmelCase			:   Union[str, Any]      =				scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								UpperCAmelCase			:   Any      =				(image_size // patch_size) ** 2
								UpperCAmelCase			:   str      =				int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								UpperCAmelCase			:   Union[str, Any]      =				None
								if self.use_labels:
													UpperCAmelCase			:   Union[str, Any]      =				ids_tensor([self.batch_size]			,  self.type_sequence_label_size  )

								UpperCAmelCase			:   Dict      =				self.get_config()

								return config, pixel_values, labels







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return ViTMAEConfig(
								    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			,  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=lowercase			,  initializer_range=self.initializer_range			,  mask_ratio=self.mask_ratio			,  )







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[int]			,  lowercase			: List[Any]			,  lowercase			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				ViTMAEModel(config=lowercase  )
								model.to(lowercase  )
								model.eval()
								UpperCAmelCase			:   List[Any]      =				model(lowercase  )
								self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: List[Any]			,  lowercase			: Union[str, Any]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				ViTMAEForPreTraining(lowercase  )
								model.to(lowercase  )
								model.eval()
								UpperCAmelCase			:   Any      =				model(lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				(self.image_size // self.patch_size) ** 2
								UpperCAmelCase			:   Any      =				self.patch_size**2 * self.num_channels
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, num_patches, expected_num_channels)  )

								# test greyscale images
								UpperCAmelCase			:   Optional[Any]      =				1
								UpperCAmelCase			:   Any      =				ViTMAEForPreTraining(lowercase  )
								model.to(lowercase  )
								model.eval()
								UpperCAmelCase			:   List[Any]      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								UpperCAmelCase			:   Optional[Any]      =				model(lowercase  )
								UpperCAmelCase			:   Union[str, Any]      =				self.patch_size**2
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, num_patches, expected_num_channels)  )







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.prepare_config_and_inputs()
								UpperCAmelCase      ,							UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				config_and_inputs
								UpperCAmelCase			:   int      =				{"pixel_values": pixel_values}
								return config, inputs_dict







@require_torch
class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			SCREAMING_SNAKE_CASE__     				=						{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				ViTMAEModelTester(self  )
								UpperCAmelCase			:   Tuple      =				ConfigTester(self			,  config_class=lowercase			,  has_text_modality=lowercase			,  hidden_size=37  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								self.config_tester.run_common_tests()







			@unittest.skip(reason="ViTMAE does not use inputs_embeds"  )
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								pass







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   Union[str, Any]      =				self.model_tester.prepare_config_and_inputs_for_common()

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







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



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

								for model_class in self.all_model_classes:
													UpperCAmelCase			:   Union[str, Any]      =				model_class(lowercase  )
													UpperCAmelCase			:   Any      =				inspect.signature(model.forward  )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													UpperCAmelCase			:   Tuple      =				[*signature.parameters.keys()]

													UpperCAmelCase			:   Optional[Any]      =				["pixel_values"]
													self.assertListEqual(arg_names[:1]			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: Optional[int]			,  lowercase			: List[Any]			,  lowercase			: List[Any]  ):





								'''simple docstring'''



								np.random.seed(2  )

								UpperCAmelCase			:   str      =				int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
								UpperCAmelCase			:   Any      =				np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
								UpperCAmelCase			:   Optional[Any]      =				torch.from_numpy(lowercase  )

								# Add `noise` argument.
								# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
								UpperCAmelCase			:   Tuple      =				pt_noise

								super().check_pt_tf_models(lowercase			,  lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													UpperCAmelCase			:   Union[str, Any]      =				model_class(lowercase  )
													model.to(lowercase  )
													model.eval()
													# make random mask reproducible
													torch.manual_seed(2  )
													with torch.no_grad():
																		UpperCAmelCase			:   Any      =				model(**self._prepare_for_class(lowercase			,  lowercase  )  )

													UpperCAmelCase			:   Optional[int]      =				outputs[0].cpu().numpy()
													UpperCAmelCase			:   str      =				0

													with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(lowercase  )
																		UpperCAmelCase			:   Dict      =				model_class.from_pretrained(lowercase  )
																		model.to(lowercase  )
																		# make random mask reproducible
																		torch.manual_seed(2  )
																		with torch.no_grad():
																							UpperCAmelCase			:   str      =				model(**self._prepare_for_class(lowercase			,  lowercase  )  )

																		# Make sure we don't have nans
																		UpperCAmelCase			:   int      =				after_outputs[0].cpu().numpy()
																		UpperCAmelCase			:   int      =				0
																		UpperCAmelCase			:   List[str]      =				np.amax(np.abs(out_a - out_a  )  )
																		self.assertLessEqual(lowercase			,  1E-5  )







			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								pass







			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								pass







			@unittest.skip(
			    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."  )
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								pass







			@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"  )
			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								pass







			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								pass







			@slow
			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase			:   List[str]      =				ViTMAEModel.from_pretrained(lowercase  )
													self.assertIsNotNone(lowercase  )



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
					return image







@require_torch
@require_vision
class       snake_case__  ( unittest.TestCase     ):







			@cached_property
			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"  ) if is_vision_available() else None







			@slow
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								np.random.seed(2  )

								UpperCAmelCase			:   Tuple      =				ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"  ).to(lowercase  )

								UpperCAmelCase			:   int      =				self.default_image_processor
								UpperCAmelCase			:   Optional[Any]      =				prepare_img()
								UpperCAmelCase			:   Tuple      =				image_processor(images=lowercase			,  return_tensors="pt"  ).to(lowercase  )

								# prepare a noise vector that will be also used for testing the TF model
								# (this way we can ensure that the PT and TF models operate on the same inputs)
								UpperCAmelCase			:   List[Any]      =				ViTMAEConfig()
								UpperCAmelCase			:   List[str]      =				int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
								UpperCAmelCase			:   Optional[int]      =				np.random.uniform(size=(1, num_patches)  )

								# forward pass
								with torch.no_grad():
													UpperCAmelCase			:   str      =				model(**lowercase			,  noise=torch.from_numpy(lowercase  ).to(device=lowercase  )  )

								# verify the logits
								UpperCAmelCase			:   Dict      =				torch.Size((1, 1_96, 7_68)  )
								self.assertEqual(outputs.logits.shape			,  lowercase  )

								UpperCAmelCase			:   Dict      =				torch.tensor(
								    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,  expected_slice.to(lowercase  )			,  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


snake_case_   :   Union[str, Any]        =       """▁"""

snake_case_   :   List[Any]        =       get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class       snake_case__  ( lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						BigBirdTokenizer
			SCREAMING_SNAKE_CASE__     				=						BigBirdTokenizerFast
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						True







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								super().setUp()

								UpperCAmelCase			:   str      =				self.tokenizer_class(lowercase			,  keep_accents=lowercase  )
								tokenizer.save_pretrained(self.tmpdirname  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				"<s>"
								UpperCAmelCase			:   Union[str, Any]      =				1

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







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				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(lowercase  )			,  10_04  )







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



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







			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								if not self.test_rust_tokenizer:
													return

								UpperCAmelCase			:   Tuple      =				self.get_tokenizer()
								UpperCAmelCase			:   Tuple      =				self.get_rust_tokenizer()

								UpperCAmelCase			:   Dict      =				"I was born in 92000, and this is falsé."

								UpperCAmelCase			:   Optional[int]      =				tokenizer.tokenize(lowercase  )
								UpperCAmelCase			:   Tuple      =				rust_tokenizer.tokenize(lowercase  )
								self.assertListEqual(lowercase			,  lowercase  )

								UpperCAmelCase			:   List[str]      =				tokenizer.encode(lowercase			,  add_special_tokens=lowercase  )
								UpperCAmelCase			:   Optional[int]      =				rust_tokenizer.encode(lowercase			,  add_special_tokens=lowercase  )
								self.assertListEqual(lowercase			,  lowercase  )

								UpperCAmelCase			:   Tuple      =				self.get_rust_tokenizer()
								UpperCAmelCase			:   Dict      =				tokenizer.encode(lowercase  )
								UpperCAmelCase			:   List[str]      =				rust_tokenizer.encode(lowercase  )
								self.assertListEqual(lowercase			,  lowercase  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				BigBirdTokenizer(lowercase			,  keep_accents=lowercase  )

								UpperCAmelCase			:   int      =				tokenizer.tokenize("This is a test"  )
								self.assertListEqual(lowercase			,  ["▁This", "▁is", "▁a", "▁t", "est"]  )

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

								UpperCAmelCase			:   Optional[int]      =				tokenizer.tokenize("I was born in 92000, and this is falsé."  )
								self.assertListEqual(
								    lowercase			,  [
								        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",
								        ".",
								    ]			,  )
								UpperCAmelCase			:   Optional[Any]      =				tokenizer.convert_tokens_to_ids(lowercase  )
								self.assertListEqual(
								    lowercase			,  [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]			,  )

								UpperCAmelCase			:   Dict      =				tokenizer.convert_ids_to_tokens(lowercase  )
								self.assertListEqual(
								    lowercase			,  [
								        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 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



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







			@slow
			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   str      =				"Hello World!"
								UpperCAmelCase			:   Union[str, Any]      =				[65, 1_85_36, 22_60, 1_01, 66]

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







			@slow
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				(
								    "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
								UpperCAmelCase			:   Tuple      =				[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(lowercase			,  self.big_tokenizer.encode(lowercase  )  )







			@require_torch
			@slow
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								import torch

								from transformers import BigBirdConfig, BigBirdModel

								# Build sequence
								UpperCAmelCase			:   List[Any]      =				list(self.big_tokenizer.get_vocab().keys()  )[:10]
								UpperCAmelCase			:   Optional[Any]      =				" ".join(lowercase  )
								UpperCAmelCase			:   List[Any]      =				self.big_tokenizer.encode_plus(lowercase			,  return_tensors="pt"			,  return_token_type_ids=lowercase  )
								UpperCAmelCase			:   Tuple      =				self.big_tokenizer.batch_encode_plus(
								    [sequence + " " + sequence]			,  return_tensors="pt"			,  return_token_type_ids=lowercase  )

								UpperCAmelCase			:   Optional[Any]      =				BigBirdConfig(attention_type="original_full"  )
								UpperCAmelCase			:   List[Any]      =				BigBirdModel(lowercase  )

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

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







			@slow
			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"  )
								UpperCAmelCase			:   Any      =				tokenizer.decode(tokenizer("Paris is the [MASK]."  ).input_ids  )

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







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				{"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=lowercase			,  model_name="google/bigbird-roberta-base"			,  revision="215c99f1600e06f83acce68422f2035b2b5c3510"			,  )


