



"""simple docstring"""






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
	    MODEL_MAPPING,
	    LevitForImageClassification,
	    LevitForImageClassificationWithTeacher,
	    LevitModel,
	)
	from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor







class       snake_case__  ( _A     ):







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(lowercase			,  "hidden_sizes"  )  )
								self.parent.assertTrue(hasattr(lowercase			,  "num_attention_heads"  )  )







class       snake_case__  :







			def __init__(			self			: List[Any]			,  lowercase			: Dict			,  lowercase			: Optional[int]=13			,  lowercase			: str=64			,  lowercase			: str=3			,  lowercase			: Optional[Any]=3			,  lowercase			: List[Any]=2			,  lowercase			: Union[str, Any]=1			,  lowercase			: List[str]=16			,  lowercase			: str=[1_28, 2_56, 3_84]			,  lowercase			: Optional[int]=[4, 6, 8]			,  lowercase			: Tuple=[2, 3, 4]			,  lowercase			: Optional[Any]=[16, 16, 16]			,  lowercase			: Tuple=0			,  lowercase			: Any=[2, 2, 2]			,  lowercase			: Optional[Any]=[2, 2, 2]			,  lowercase			: str=0.0_2			,  lowercase			: Optional[int]=True			,  lowercase			: Dict=True			,  lowercase			: int=2			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				parent
								UpperCAmelCase			:   str      =				batch_size
								UpperCAmelCase			:   Optional[Any]      =				image_size
								UpperCAmelCase			:   str      =				num_channels
								UpperCAmelCase			:   Tuple      =				kernel_size
								UpperCAmelCase			:   str      =				stride
								UpperCAmelCase			:   Optional[int]      =				padding
								UpperCAmelCase			:   List[Any]      =				hidden_sizes
								UpperCAmelCase			:   List[str]      =				num_attention_heads
								UpperCAmelCase			:   Any      =				depths
								UpperCAmelCase			:   Union[str, Any]      =				key_dim
								UpperCAmelCase			:   List[Any]      =				drop_path_rate
								UpperCAmelCase			:   List[Any]      =				patch_size
								UpperCAmelCase			:   List[str]      =				attention_ratio
								UpperCAmelCase			:   List[Any]      =				mlp_ratio
								UpperCAmelCase			:   Optional[Any]      =				initializer_range
								UpperCAmelCase			:   Union[str, Any]      =				[
								    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								UpperCAmelCase			:   List[Any]      =				is_training
								UpperCAmelCase			:   Any      =				use_labels
								UpperCAmelCase			:   str      =				num_labels
								UpperCAmelCase			:   Union[str, Any]      =				initializer_range







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



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

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

								UpperCAmelCase			:   str      =				self.get_config()
								return config, pixel_values, labels







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return LevitConfig(
								    image_size=self.image_size			,  num_channels=self.num_channels			,  kernel_size=self.kernel_size			,  stride=self.stride			,  padding=self.padding			,  patch_size=self.patch_size			,  hidden_sizes=self.hidden_sizes			,  num_attention_heads=self.num_attention_heads			,  depths=self.depths			,  key_dim=self.key_dim			,  drop_path_rate=self.drop_path_rate			,  mlp_ratio=self.mlp_ratio			,  attention_ratio=self.attention_ratio			,  initializer_range=self.initializer_range			,  down_ops=self.down_ops			,  )







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





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				LevitModel(config=lowercase  )
								model.to(lowercase  )
								model.eval()
								UpperCAmelCase			:   int      =				model(lowercase  )
								UpperCAmelCase			:   Tuple      =				(self.image_size, self.image_size)
								UpperCAmelCase			:   int      =				image_size[0], image_size[1]
								for _ in range(4  ):
													UpperCAmelCase			:   int      =				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
													UpperCAmelCase			:   Optional[int]      =				floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape			,  (self.batch_size, ceil(height / 4  ) * ceil(width / 4  ), self.hidden_sizes[-1])			,  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: str			,  lowercase			: Optional[int]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.num_labels
								UpperCAmelCase			:   Any      =				LevitForImageClassification(lowercase  )
								model.to(lowercase  )
								model.eval()
								UpperCAmelCase			:   str      =				model(lowercase			,  labels=lowercase  )
								self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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







@require_torch
class       snake_case__  ( _A					,  _A					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(
			    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
			    if is_torch_available()
			    else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(
			    {
			        '''feature-extraction''': LevitModel,
			        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

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







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				LevitModelTester(self  )
								UpperCAmelCase			:   Tuple      =				ConfigTester(self			,  config_class=lowercase			,  has_text_modality=lowercase			,  hidden_size=37  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return







			@unittest.skip(reason="Levit does not use inputs_embeds"  )
			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								pass







			@unittest.skip(reason="Levit does not support input and output embeddings"  )
			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								pass







			@unittest.skip(reason="Levit does not output attentions"  )
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								pass







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



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

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

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







			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								def check_hidden_states_output(lowercase			: Tuple			,  lowercase			: Any			,  lowercase			: Optional[int]  ):
													UpperCAmelCase			:   Union[str, Any]      =				model_class(lowercase  )
													model.to(lowercase  )
													model.eval()

													with torch.no_grad():
																		UpperCAmelCase			:   int      =				model(**self._prepare_for_class(lowercase			,  lowercase  )  )

													UpperCAmelCase			:   Tuple      =				outputs.hidden_states

													UpperCAmelCase			:   str      =				len(self.model_tester.depths  ) + 1
													self.assertEqual(len(lowercase  )			,  lowercase  )

													UpperCAmelCase			:   Optional[Any]      =				(self.model_tester.image_size, self.model_tester.image_size)
													UpperCAmelCase			:   List[str]      =				image_size[0], image_size[1]
													for _ in range(4  ):
																		UpperCAmelCase			:   str      =				floor(
																		    (
																		        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																		        / self.model_tester.stride
																		    )
																		    + 1  )
																		UpperCAmelCase			:   List[str]      =				floor(
																		    (
																		        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																		        / self.model_tester.stride
																		    )
																		    + 1  )
													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]  )			,  [
													        height * width,
													        self.model_tester.hidden_sizes[0],
													    ]			,  )

								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													UpperCAmelCase			:   Dict      =				True
													check_hidden_states_output(lowercase			,  lowercase			,  lowercase  )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													UpperCAmelCase			:   Dict      =				True

													check_hidden_states_output(lowercase			,  lowercase			,  lowercase  )







			@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







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Union[str, Any]			,  lowercase			: Tuple			,  lowercase			: Tuple=False  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				super()._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase  )

								if return_labels:
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																		del inputs_dict["labels"]

								return inputs_dict







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								if not self.model_tester.is_training:
													return

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

								for model_class in self.all_model_classes:
													# LevitForImageClassificationWithTeacher supports inference-only
													if (
													    model_class in get_values(lowercase  )
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																		continue
													UpperCAmelCase			:   Optional[int]      =				model_class(lowercase  )
													model.to(lowercase  )
													model.train()
													UpperCAmelCase			:   Dict      =				self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase  )
													UpperCAmelCase			:   List[Any]      =				model(**lowercase  ).loss
													loss.backward()







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[int]      =				self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
													return

								UpperCAmelCase			:   int      =				False
								UpperCAmelCase			:   Any      =				True

								for model_class in self.all_model_classes:
													if model_class in get_values(lowercase  ) or not model_class.supports_gradient_checkpointing:
																		continue
													# LevitForImageClassificationWithTeacher supports inference-only
													if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																		continue
													UpperCAmelCase			:   str      =				model_class(lowercase  )
													model.gradient_checkpointing_enable()
													model.to(lowercase  )
													model.train()
													UpperCAmelCase			:   Any      =				self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase  )
													UpperCAmelCase			:   List[Any]      =				model(**lowercase  ).loss
													loss.backward()







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase			:   Dict      =				[
								    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
								    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
								    {"title": "regression", "num_labels": 1, "dtype": torch.float},
								]

								for model_class in self.all_model_classes:
													if (
													    model_class
													    not in [
													        *get_values(lowercase  ),
													    ]
													    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
													):
																		continue

													for problem_type in problem_types:
																		with self.subTest(msg=f"""Testing {model_class} with {problem_type['title']}"""  ):
																							UpperCAmelCase			:   int      =				problem_type["title"]
																							UpperCAmelCase			:   Union[str, Any]      =				problem_type["num_labels"]

																							UpperCAmelCase			:   Any      =				model_class(lowercase  )
																							model.to(lowercase  )
																							model.train()

																							UpperCAmelCase			:   Optional[int]      =				self._prepare_for_class(lowercase			,  lowercase			,  return_labels=lowercase  )

																							if problem_type["num_labels"] > 1:
																												UpperCAmelCase			:   Optional[int]      =				inputs["labels"].unsqueeze(1  ).repeat(1			,  problem_type["num_labels"]  )

																							UpperCAmelCase			:   Dict      =				inputs["labels"].to(problem_type["dtype"]  )

																							# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																							# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																							# they have the same size." which is a symptom something in wrong for the regression problem.
																							# See https://github.com/huggingface/transformers/issues/11780
																							with warnings.catch_warnings(record=lowercase  ) as warning_list:
																												UpperCAmelCase			:   Optional[int]      =				model(**lowercase  ).loss
																							for w in warning_list:
																												if "Using a target size that is different to the input size" in str(w.message  ):
																																	raise ValueError(
																																	    f"""Something is going wrong in the regression problem: intercepted {w.message}"""  )

																							loss.backward()







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





								'''simple docstring'''



								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase			:   List[Any]      =				LevitModel.from_pretrained(lowercase  )
													self.assertIsNotNone(lowercase  )



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, 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			: Optional[int]  ):





								'''simple docstring'''



								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )







			@slow
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(
								    lowercase  )

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

								# forward pass
								with torch.no_grad():
													UpperCAmelCase			:   Optional[int]      =				model(**lowercase  )

								# verify the logits
								UpperCAmelCase			:   Union[str, Any]      =				torch.Size((1, 10_00)  )
								self.assertEqual(outputs.logits.shape			,  lowercase  )

								UpperCAmelCase			:   str      =				torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7]  ).to(lowercase  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3]			,  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"			,  )


