






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
  SCREAMING_SNAKE_CASE							=    'platform'
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 				A_     :






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       PegasusConfig
 _SCREAMING_SNAKE_CASE				:      Optional[Any]			=       {}
 _SCREAMING_SNAKE_CASE				:      Optional[Any]			=       "gelu"


 def __init__(						self				,	_A				,	_A=13				,	_A=7				,	_A=True				,	_A=False				,	_A=99				,	_A=32				,	_A=5				,	_A=4				,	_A=37				,	_A=0.1				,	_A=0.1				,	_A=20				,	_A=2				,	_A=1				,	_A=0				,	)			->		Any:

     """simple docstring"""



     _UpperCAmelCase					:   Any      	=					parent
     _UpperCAmelCase					:   Optional[Any]      	=					batch_size
     _UpperCAmelCase					:   Dict      	=					seq_length
     _UpperCAmelCase					:   str      	=					is_training
     _UpperCAmelCase					:   int      	=					use_labels
     _UpperCAmelCase					:   int      	=					vocab_size
     _UpperCAmelCase					:   Optional[int]      	=					hidden_size
     _UpperCAmelCase					:   Tuple      	=					num_hidden_layers
     _UpperCAmelCase					:   Dict      	=					num_attention_heads
     _UpperCAmelCase					:   List[str]      	=					intermediate_size

     _UpperCAmelCase					:   Any      	=					hidden_dropout_prob
     _UpperCAmelCase					:   Any      	=					attention_probs_dropout_prob
     _UpperCAmelCase					:   Dict      	=					max_position_embeddings
     _UpperCAmelCase					:   int      	=					eos_token_id
     _UpperCAmelCase					:   List[str]      	=					pad_token_id
     _UpperCAmelCase					:   str      	=					bos_token_id


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

     """simple docstring"""



     _UpperCAmelCase					:   int      	=					ids_tensor([self.batch_size, self.seq_length - 1]				,	self.vocab_size).clip(3				,	self.vocab_size)
     _UpperCAmelCase					:   Any      	=					np.expand_dims(np.array([self.eos_token_id] * self.batch_size)				,	1)
     _UpperCAmelCase					:   Union[str, Any]      	=					np.concatenate([input_ids, eos_tensor]				,	axis=1)

     _UpperCAmelCase					:   str      	=					ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)

     _UpperCAmelCase					:   Union[str, Any]      	=					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				,	)
     _UpperCAmelCase					:   List[Any]      	=					prepare_pegasus_inputs_dict(__A				,	__A				,	__A)
     return config, inputs_dict


 def 			snake_case__					(						self				,	_A				,	_A				,	_A)			->		Tuple:

     """simple docstring"""



     _UpperCAmelCase					:   int      	=					20
     _UpperCAmelCase					:   Optional[int]      	=					model_class_name(__A)

     _UpperCAmelCase					:   Optional[int]      	=					model.encode(inputs_dict['''input_ids'''])

     _UpperCAmelCase					:   Optional[int]      	=					(
         inputs_dict['''decoder_input_ids'''],
         inputs_dict['''decoder_attention_mask'''],
     )

     _UpperCAmelCase					:   Tuple      	=					model.init_cache(decoder_input_ids.shape[0]				,	__A				,	__A)
     _UpperCAmelCase					:   Tuple      	=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,	dtype='''i4''')

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

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

     _UpperCAmelCase					:   Tuple      	=					model.decode(__A				,	__A)

     _UpperCAmelCase					:   Any      	=					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 			snake_case__					(						self				,	_A				,	_A				,	_A)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   Optional[int]      	=					20
     _UpperCAmelCase					:   List[str]      	=					model_class_name(__A)

     _UpperCAmelCase					:   Optional[int]      	=					model.encode(inputs_dict['''input_ids'''])

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

     _UpperCAmelCase					:   Tuple      	=					jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
         ]				,	axis=-1				,	)

     _UpperCAmelCase					:   Dict      	=					model.init_cache(decoder_input_ids.shape[0]				,	__A				,	__A)
     _UpperCAmelCase					:   List[str]      	=					jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]				,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,	)

     _UpperCAmelCase					:   List[str]      	=					model.decode(
         decoder_input_ids[:, :-1]				,	__A				,	decoder_attention_mask=__A				,	past_key_values=__A				,	decoder_position_ids=__A				,	)
     _UpperCAmelCase					:   Optional[int]      	=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,	dtype='''i4''')
     _UpperCAmelCase					:   Dict      	=					model.decode(
         decoder_input_ids[:, -1:]				,	__A				,	past_key_values=outputs_cache.past_key_values				,	decoder_attention_mask=__A				,	decoder_position_ids=__A				,	)

     _UpperCAmelCase					:   int      	=					model.decode(__A				,	__A				,	decoder_attention_mask=__A)

     _UpperCAmelCase					:   Union[str, Any]      	=					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						(    __A     :				List[str]		,		__A     :				Optional[Any]		,		__A     :				List[str]		,		__A     :				List[Any]=None		,		__A     :				int=None		,		)   ->       Optional[Any]:
    if attention_mask is None:
        _UpperCAmelCase					:   Tuple      	=					np.not_equal(UpperCAmelCase_		,		config.pad_token_id			).astype(np.inta			)
    if decoder_attention_mask is None:
        _UpperCAmelCase					:   List[Any]      	=					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 				A_     (    __lowercase			, unittest.TestCase      ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       (
     (
         FlaxPegasusForConditionalGeneration,
         FlaxPegasusModel,
     )
     if is_flax_available()
     else ()
 )
 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
 _SCREAMING_SNAKE_CASE				:      Union[str, Any]			=       True
 _SCREAMING_SNAKE_CASE				:      Dict			=       False
 _SCREAMING_SNAKE_CASE				:      Tuple			=       False
 _SCREAMING_SNAKE_CASE				:      Optional[int]			=       False


 def 			snake_case__					(						self)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					FlaxPegasusModelTester(self)
     _UpperCAmelCase					:   Dict      	=					ConfigTester(self				,	config_class=__A)


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

     """simple docstring"""



     self.config_tester.run_common_tests()


 def 			snake_case__					(						self)			->		Dict:

     """simple docstring"""



     _UpperCAmelCase					:   Tuple      	=					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 			snake_case__					(						self)			->		str:

     """simple docstring"""



     _UpperCAmelCase					:   Dict      	=					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 			snake_case__					(						self)			->		Tuple:

     """simple docstring"""



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

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__):
             _UpperCAmelCase					:   List[str]      	=					self._prepare_for_class(__A				,	__A)
             _UpperCAmelCase					:   Optional[Any]      	=					model_class(__A)

             @jax.jit
             def encode_jitted(_A				,	_A=None				,	**_A):
                 return model.encode(input_ids=__A				,	attention_mask=__A)

             with self.subTest('''JIT Enabled'''):
                 _UpperCAmelCase					:   Union[str, Any]      	=					encode_jitted(**__A).to_tuple()

             with self.subTest('''JIT Disabled'''):
                 with jax.disable_jit():
                     _UpperCAmelCase					:   Any      	=					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 			snake_case__					(						self)			->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase					:   str      	=					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__):
             _UpperCAmelCase					:   Union[str, Any]      	=					model_class(__A)
             _UpperCAmelCase					:   str      	=					model.encode(inputs_dict['''input_ids''']				,	inputs_dict['''attention_mask'''])

             _UpperCAmelCase					:   str      	=					{
                 '''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(_A				,	_A				,	_A):
                 return model.decode(
                     decoder_input_ids=__A				,	decoder_attention_mask=__A				,	encoder_outputs=__A				,	)

             with self.subTest('''JIT Enabled'''):
                 _UpperCAmelCase					:   int      	=					decode_jitted(**__A).to_tuple()

             with self.subTest('''JIT Disabled'''):
                 with jax.disable_jit():
                     _UpperCAmelCase					:   Tuple      	=					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 			snake_case__					(						self)			->		Union[str, Any]:

     """simple docstring"""



     for model_class_name in self.all_model_classes:
         _UpperCAmelCase					:   Any      	=					model_class_name.from_pretrained('''google/pegasus-large'''				,	from_pt=__A)
         _UpperCAmelCase					:   Dict      	=					np.ones((1, 1))
         _UpperCAmelCase					:   Tuple      	=					model(__A)
         self.assertIsNotNone(__A)


 @slow
 def 			snake_case__					(						self)			->		Any:

     """simple docstring"""



     _UpperCAmelCase					:   List[Any]      	=					FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
     _UpperCAmelCase					:   List[str]      	=					PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

     _UpperCAmelCase					:   Dict      	=					[
         ''' 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!" ''',
     ]

     _UpperCAmelCase					:   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.''',
     ]

     _UpperCAmelCase					:   Tuple      	=					tokenizer(__A				,	return_tensors='''np'''				,	truncation=__A				,	max_length=512				,	padding=__A)
     _UpperCAmelCase					:   int      	=					model.generate(**__A				,	num_beams=2).sequences
     _UpperCAmelCase					:   Union[str, Any]      	=					tokenizer.batch_decode(__A				,	skip_special_tokens=__A)
     assert tgt_text == decoded






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


_lowercase											=    """▁"""

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


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BigBirdTokenizer
       __lowerCamelCase				       =						BigBirdTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Tuple:
              super().setUp()

              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		='''<s>'''
              SCREAMING_SNAKE_CASE_							:				Union[str, 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 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		=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				)						,       1_004				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)


       def 	_snake_case				(    self				)						->    str:
              if not self.test_rust_tokenizer:
                     return

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_tokenizer()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_rust_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''I was born in 92000, and this is falsé.'''

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rust_tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=rust_tokenizer.encode(__A						,       add_special_tokens=__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_rust_tokenizer()
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=rust_tokenizer.encode(__A				)
              self.assertListEqual(__A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BigBirdTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(__A						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

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

              SCREAMING_SNAKE_CASE_							:				List[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''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_tokens_to_ids(__A				)
              self.assertListEqual(
                  __A						,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,       )

              SCREAMING_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 	_snake_case				(    self				)						->    Union[str, Any]:
              return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		='''Hello World!'''
              SCREAMING_SNAKE_CASE_							:				Dict		=[65, 18_536, 2_260, 101, 66]

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


       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, 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
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
              # fmt: on
              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @require_torch
       @slow
       def 	_snake_case				(    self				)						->    int:
              import torch

              from transformers import BigBirdConfig, BigBirdModel

              # Build sequence
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(self.big_tokenizer.get_vocab().keys()				)[:10]
              SCREAMING_SNAKE_CASE_							:				Any		=''' '''.join(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=self.big_tokenizer.encode_plus(__A						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.big_tokenizer.batch_encode_plus(
                  [sequence + ''' ''' + sequence]						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BigBirdConfig(attention_type='''original_full'''				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=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 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Any		=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

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





       @slow
       def 	_snake_case				(    self				)						->    Any:
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 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, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 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=__A						,       model_name='''google/bigbird-roberta-base'''						,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,       )
