

'''simple docstring'''





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
							import os

							# The slow tests are often failing with OOM error on GPU
							# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
							# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
							UpperCamelCase_							=					"""platform"""
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel





@require_flax
class 		__SCREAMING_SNAKE_CASE       :
				lowerCamelCase_							=						PegasusConfig
				lowerCamelCase_							=						{}
				lowerCamelCase_							=						'gelu'







				def __init__(					self    :		Tuple		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		List[str]=13		,				UpperCAmelCase__    :		Union[str, Any]=7		,				UpperCAmelCase__    :		int=True		,				UpperCAmelCase__    :		int=False		,				UpperCAmelCase__    :		List[str]=99		,				UpperCAmelCase__    :		Any=32		,				UpperCAmelCase__    :		Tuple=5		,				UpperCAmelCase__    :		List[Any]=4		,				UpperCAmelCase__    :		Optional[int]=37		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		Optional[int]=0.1		,				UpperCAmelCase__    :		List[Any]=20		,				UpperCAmelCase__    :		List[str]=2		,				UpperCAmelCase__    :		List[Any]=1		,				UpperCAmelCase__    :		List[Any]=0		,				):
										'''simple docstring'''
										lowercase   :       Dict								=parent
										lowercase   :       List[Any]								=batch_size
										lowercase   :       Union[str, Any]								=seq_length
										lowercase   :       List[str]								=is_training
										lowercase   :       Tuple								=use_labels
										lowercase   :       Optional[int]								=vocab_size
										lowercase   :       Optional[int]								=hidden_size
										lowercase   :       Dict								=num_hidden_layers
										lowercase   :       Optional[Any]								=num_attention_heads
										lowercase   :       Optional[Any]								=intermediate_size

										lowercase   :       List[str]								=hidden_dropout_prob
										lowercase   :       Dict								=attention_probs_dropout_prob
										lowercase   :       Tuple								=max_position_embeddings
										lowercase   :       str								=eos_token_id
										lowercase   :       Any								=pad_token_id
										lowercase   :       List[str]								=bos_token_id







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=ids_tensor([self.batch_size, self.seq_length - 1]		,				self.vocab_size      ).clip(3		,				self.vocab_size      )
										lowercase   :       Optional[int]								=np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )		,				1      )
										lowercase   :       Any								=np.concatenate([input_ids, eos_tensor]		,				axis=1      )

										lowercase   :       Dict								=ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

										lowercase   :       str								=self.config_cls(
										    vocab_size=self.vocab_size		,				d_model=self.hidden_size		,				encoder_layers=self.num_hidden_layers		,				decoder_layers=self.num_hidden_layers		,				encoder_attention_heads=self.num_attention_heads		,				decoder_attention_heads=self.num_attention_heads		,				encoder_ffn_dim=self.intermediate_size		,				decoder_ffn_dim=self.intermediate_size		,				dropout=self.hidden_dropout_prob		,				attention_dropout=self.attention_probs_dropout_prob		,				max_position_embeddings=self.max_position_embeddings		,				eos_token_ids=[2]		,				bos_token_id=self.bos_token_id		,				pad_token_id=self.pad_token_id		,				decoder_start_token_id=self.pad_token_id		,				**self.config_updates		,				)
										lowercase   :       Any								=prepare_pegasus_inputs_dict(A__		,				A__		,				A__      )
										return config, inputs_dict







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=20
										lowercase   :       Optional[Any]								=model_class_name(A__      )

										lowercase   :       Optional[Any]								=model.encode(inputs_dict['''input_ids''']      )

										lowercase		,  lowercase   :       Union[str, Any]								=(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										lowercase   :       Optional[Any]								=model.init_cache(decoder_input_ids.shape[0]		,				A__		,				A__      )
										lowercase   :       str								=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,				dtype='''i4'''      )

										lowercase   :       Optional[Any]								=jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)
										lowercase   :       int								=model.decode(
										    decoder_input_ids[:, :-1]		,				A__		,				decoder_attention_mask=A__		,				past_key_values=A__		,				decoder_position_ids=A__		,				)

										lowercase   :       Optional[Any]								=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype='''i4'''      )
										lowercase   :       Any								=model.decode(
										    decoder_input_ids[:, -1:]		,				A__		,				decoder_attention_mask=A__		,				past_key_values=outputs_cache.past_key_values		,				decoder_position_ids=A__		,				)

										lowercase   :       Any								=model.decode(A__		,				A__      )

										lowercase   :       str								=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
										self.parent.assertTrue(diff < 1E-3		,				msg=F'''Max diff is {diff}'''      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=20
										lowercase   :       List[Any]								=model_class_name(A__      )

										lowercase   :       Optional[Any]								=model.encode(inputs_dict['''input_ids''']      )

										lowercase		,  lowercase   :       List[str]								=(
										    inputs_dict['''decoder_input_ids'''],
										    inputs_dict['''decoder_attention_mask'''],
										)

										lowercase   :       Optional[int]								=jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])      ),
										    ]		,				axis=-1		,				)

										lowercase   :       int								=model.init_cache(decoder_input_ids.shape[0]		,				A__		,				A__      )
										lowercase   :       Optional[Any]								=jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]		,				(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,				)

										lowercase   :       List[Any]								=model.decode(
										    decoder_input_ids[:, :-1]		,				A__		,				decoder_attention_mask=A__		,				past_key_values=A__		,				decoder_position_ids=A__		,				)
										lowercase   :       Dict								=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,				dtype='''i4'''      )
										lowercase   :       Union[str, Any]								=model.decode(
										    decoder_input_ids[:, -1:]		,				A__		,				past_key_values=outputs_cache.past_key_values		,				decoder_attention_mask=A__		,				decoder_position_ids=A__		,				)

										lowercase   :       Union[str, Any]								=model.decode(A__		,				A__		,				decoder_attention_mask=A__      )

										lowercase   :       int								=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
										self.parent.assertTrue(diff < 1E-3		,				msg=F'''Max diff is {diff}'''      )






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    str      ,   __magic_name__	:    Union[str, Any]      ,   __magic_name__	:    Optional[int]=None      ,   __magic_name__	:    int=None      ,   ):
						if attention_mask is None:
												lowercase   :       Any								=np.not_equal(__magic_name__      ,   config.pad_token_id	).astype(np.inta	)
						if decoder_attention_mask is None:
												lowercase   :       Tuple								=np.concatenate(
												    [
												        np.ones(decoder_input_ids[:, :1].shape      ,   dtype=np.inta	),
												        np.not_equal(decoder_input_ids[:, 1:]      ,   config.pad_token_id	).astype(np.inta	),
												    ]      ,   axis=-1      ,   )
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}







@require_flax
class 		__SCREAMING_SNAKE_CASE       ( __a	,       unittest.TestCase			):
				lowerCamelCase_							=						(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				lowerCamelCase_							=						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=FlaxPegasusModelTester(self      )
										lowercase   :       Dict								=ConfigTester(self		,				config_class=A__      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       str								=self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(A__		,				A__		,				A__      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(A__		,				A__		,				A__      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__      ):
																						lowercase   :       Dict								=self._prepare_for_class(A__		,				A__      )
																						lowercase   :       List[str]								=model_class(A__      )

																						@jax.jit
																						def encode_jitted(UpperCAmelCase__    :		Dict		,				UpperCAmelCase__    :		Dict=None		,				**UpperCAmelCase__    :		List[str]      ):
																												return model.encode(input_ids=A__		,				attention_mask=A__      )

																						with self.subTest('''JIT Enabled'''      ):
																												lowercase   :       List[Any]								=encode_jitted(**A__      ).to_tuple()

																						with self.subTest('''JIT Disabled'''      ):
																												with jax.disable_jit():
																																		lowercase   :       Dict								=encode_jitted(**A__      ).to_tuple()

																						self.assertEqual(len(A__      )		,				len(A__      )      )
																						for jitted_output, output in zip(A__		,				A__      ):
																												self.assertEqual(jitted_output.shape		,				output.shape      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Tuple								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__      ):
																						lowercase   :       Optional[int]								=model_class(A__      )
																						lowercase   :       Optional[Any]								=model.encode(inputs_dict['''input_ids''']		,				inputs_dict['''attention_mask''']      )

																						lowercase   :       Optional[int]								={
																						    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
																						    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
																						    '''encoder_outputs''': encoder_outputs,
																						}

																						@jax.jit
																						def decode_jitted(UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[Any]      ):
																												return model.decode(
																												    decoder_input_ids=A__		,				decoder_attention_mask=A__		,				encoder_outputs=A__		,				)

																						with self.subTest('''JIT Enabled'''      ):
																												lowercase   :       Dict								=decode_jitted(**A__      ).to_tuple()

																						with self.subTest('''JIT Disabled'''      ):
																												with jax.disable_jit():
																																		lowercase   :       List[str]								=decode_jitted(**A__      ).to_tuple()

																						self.assertEqual(len(A__      )		,				len(A__      )      )
																						for jitted_output, output in zip(A__		,				A__      ):
																												self.assertEqual(jitted_output.shape		,				output.shape      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																lowercase   :       int								=model_class_name.from_pretrained('''google/pegasus-large'''		,				from_pt=A__      )
																lowercase   :       int								=np.ones((1, 1)      )
																lowercase   :       List[str]								=model(A__      )
																self.assertIsNotNone(A__      )







				@slow
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Any								=FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''      )
										lowercase   :       str								=PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''      )

										lowercase   :       int								=[
										    ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
										    ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
										]

										lowercase   :       List[Any]								=[
										    '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
										    '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
										]

										lowercase   :       int								=tokenizer(A__		,				return_tensors='''np'''		,				truncation=A__		,				max_length=512		,				padding=A__      )
										lowercase   :       Optional[int]								=model.generate(**A__		,				num_beams=2      ).sequences
										lowercase   :       List[Any]								=tokenizer.batch_decode(A__		,				skip_special_tokens=A__      )
										assert tgt_text == decoded








'''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


UpperCamelCase_							=					"""▁"""

UpperCamelCase_							=					get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						BigBirdTokenizer
				lowerCamelCase_							=						BigBirdTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						True







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										super().setUp()

										lowercase   :       Optional[int]								=self.tokenizer_class(UpperCAmelCase__		,				keep_accents=UpperCAmelCase__      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								='''<s>'''
										lowercase   :       int								=1

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







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       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(UpperCAmelCase__      )		,				1004      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										self.assertEqual(self.get_tokenizer().vocab_size		,				1000      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										if not self.test_rust_tokenizer:
																return

										lowercase   :       Optional[int]								=self.get_tokenizer()
										lowercase   :       Any								=self.get_rust_tokenizer()

										lowercase   :       int								='''I was born in 92000, and this is falsé.'''

										lowercase   :       List[str]								=tokenizer.tokenize(UpperCAmelCase__      )
										lowercase   :       Dict								=rust_tokenizer.tokenize(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       str								=tokenizer.encode(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=rust_tokenizer.encode(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       Optional[Any]								=self.get_rust_tokenizer()
										lowercase   :       Optional[Any]								=tokenizer.encode(UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=rust_tokenizer.encode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Tuple								=BigBirdTokenizer(UpperCAmelCase__		,				keep_accents=UpperCAmelCase__      )

										lowercase   :       Tuple								=tokenizer.tokenize('''This is a test'''      )
										self.assertListEqual(UpperCAmelCase__		,				['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )		,				[285, 46, 10, 170, 382]		,				)

										lowercase   :       Tuple								=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
										self.assertListEqual(
										    UpperCAmelCase__		,				[
										        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''',
										        '''.''',
										    ]		,				)
										lowercase   :       Any								=tokenizer.convert_tokens_to_ids(UpperCAmelCase__      )
										self.assertListEqual(
										    UpperCAmelCase__		,				[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,				)

										lowercase   :       List[Any]								=tokenizer.convert_ids_to_tokens(UpperCAmelCase__      )
										self.assertListEqual(
										    UpperCAmelCase__		,				[
										        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    :		str      ):
										'''simple docstring'''
										return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''      )







				@slow
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       str								='''Hello World!'''
										lowercase   :       Union[str, Any]								=[65, 18536, 2260, 101, 66]

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







				@slow
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								=(
										    '''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
										lowercase   :       Tuple								=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
										# fmt: on
										self.assertListEqual(UpperCAmelCase__		,				self.big_tokenizer.encode(UpperCAmelCase__      )      )







				@require_torch
				@slow
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										import torch

										from transformers import BigBirdConfig, BigBirdModel

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

										lowercase   :       Optional[int]								=BigBirdConfig(attention_type='''original_full'''      )
										lowercase   :       Dict								=BigBirdModel(UpperCAmelCase__      )

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

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







				@slow
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''      )
										lowercase   :       Dict								=tokenizer.decode(tokenizer('''Paris is the [MASK].'''      ).input_ids      )

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







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										# fmt: off
										lowercase   :       str								={'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 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, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 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=UpperCAmelCase__		,				model_name='''google/bigbird-roberta-base'''		,				revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''		,				)





