'''simple docstring'''





import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

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


if is_torch_available():
    import torch

    from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )




class        __A      :



    def __init__(self    :    str   ,			__a    :    Union[str, Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    Optional[Any]=7   ,			__a    :    Dict=False   ,			__a    :    List[str]=True   ,			__a    :    List[str]=False   ,			__a    :    Any=True   ,			__a    :    Optional[Any]=33   ,			__a    :    Union[str, Any]=32   ,			__a    :    str=5   ,			__a    :    List[Any]=4   ,			__a    :    str=37   ,			__a    :    Dict="gelu"   ,			__a    :    Optional[Any]=0.1   ,			__a    :    List[Any]=0.1   ,			__a    :    Tuple=512   ,			__a    :    int=16   ,			__a    :    str=2   ,			__a    :    Any=0.02   ,			__a    :    Union[str, Any]=3   ,			__a    :    List[Any]=4   ,			__a    :    Optional[Any]=None   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   seq_length
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_input_mask
         UpperCAmelCase_           =   use_token_type_ids
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   type_vocab_size
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   num_labels
         UpperCAmelCase_           =   num_choices
         UpperCAmelCase_           =   scope



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size )

         UpperCAmelCase_           =   None
         if self.use_input_mask:
              UpperCAmelCase_           =   random_attention_mask([self.batch_size, self.seq_length] )

         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None
         if self.use_labels:
              UpperCAmelCase_           =   ids_tensor([self.batch_size]   ,			self.type_sequence_label_size )
              UpperCAmelCase_           =   ids_tensor([self.batch_size, self.seq_length]   ,			self.num_labels )
              UpperCAmelCase_           =   ids_tensor([self.batch_size]   ,			self.num_choices )

         UpperCAmelCase_           =   self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



    def 					_lowercase						(self    :    Any ):
         return EsmConfig(
             vocab_size=self.vocab_size   ,			hidden_size=self.hidden_size   ,			pad_token_id=1   ,			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   ,			max_position_embeddings=self.max_position_embeddings   ,			type_vocab_size=self.type_vocab_size   ,			initializer_range=self.initializer_range   ,			)



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    Tuple   ,			__a    :    List[str]   ,			__a    :    Optional[int]   ,			__a    :    List[Any]   ,			__a    :    List[str]   ,			__a    :    int ):
         UpperCAmelCase_           =   EsmModel(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a )
         UpperCAmelCase_           =   model(__a )
         UpperCAmelCase_           =   model(__a )

         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size) )
         self.parent.assertEqual(result.pooler_output.shape   ,			(self.batch_size, self.hidden_size) )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    int   ,			__a    :    Optional[Any]   ,			__a    :    List[str]   ,			__a    :    Dict   ,			__a    :    Tuple   ,			__a    :    str ):
         UpperCAmelCase_           =   EsmForMaskedLM(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.vocab_size) )



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    Dict   ,			__a    :    Dict   ,			__a    :    List[str]   ,			__a    :    List[str]   ,			__a    :    Any   ,			__a    :    int ):
         UpperCAmelCase_           =   self.num_labels
         UpperCAmelCase_           =   EsmForTokenClassification(config=__a )
         model.to(__a )
         model.eval()
         UpperCAmelCase_           =   model(__a   ,			attention_mask=__a   ,			labels=__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.num_labels) )






    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class        __A      ( UpperCamelCase__		,	UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Union[str, Any]          =  False

    a__       :						Tuple          =  (
        (
            EsmForMaskedLM,
            EsmModel,
            EsmForSequenceClassification,
            EsmForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    a__       :						Any          =  ()
    a__       :						Union[str, Any]          =  (
        {
            """feature-extraction""": EsmModel,
            """fill-mask""": EsmForMaskedLM,
            """text-classification""": EsmForSequenceClassification,
            """token-classification""": EsmForTokenClassification,
            """zero-shot""": EsmForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    a__       :						List[Any]          =  True



    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   EsmModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    List[Any] ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_           =   type
              self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*__a )



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__a )



    @slow
    def 					_lowercase						(self    :    Dict ):
         for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_           =   EsmModel.from_pretrained(__a )
              self.assertIsNotNone(__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_           =   EsmEmbeddings(config=__a )

         UpperCAmelCase_           =   torch.as_tensor([[12, 31, 13, model.padding_idx]] )
         UpperCAmelCase_           =   torch.as_tensor(
             [
                 [
                     0 + model.padding_idx + 1,
                     1 + model.padding_idx + 1,
                     2 + model.padding_idx + 1,
                     model.padding_idx,
                 ]
             ] )
         UpperCAmelCase_           =   create_position_ids_from_input_ids(__a   ,			model.padding_idx )
         self.assertEqual(position_ids.shape   ,			expected_positions.shape )
         self.assertTrue(torch.all(torch.eq(__a   ,			__a ) ) )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()[0]
         UpperCAmelCase_           =   EsmEmbeddings(config=__a )

         UpperCAmelCase_           =   torch.empty(2   ,			4   ,			30 )
         UpperCAmelCase_           =   [
             0 + embeddings.padding_idx + 1,
             1 + embeddings.padding_idx + 1,
             2 + embeddings.padding_idx + 1,
             3 + embeddings.padding_idx + 1,
         ]
         UpperCAmelCase_           =   torch.as_tensor([expected_single_positions, expected_single_positions] )
         UpperCAmelCase_           =   embeddings.create_position_ids_from_inputs_embeds(__a )
         self.assertEqual(position_ids.shape   ,			expected_positions.shape )
         self.assertTrue(torch.all(torch.eq(__a   ,			__a ) ) )



    @unittest.skip("Esm does not support embedding resizing" )
    def 					_lowercase						(self    :    List[str] ):
         pass



    @unittest.skip("Esm does not support embedding resizing" )
    def 					_lowercase						(self    :    str ):
         pass






    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
    def 					_lowercase						(self    :    Tuple ):
         pass




@require_torch
class        __A      ( UpperCamelCase__	):



    @slow
    def 					_lowercase						(self    :    Any ):
         with torch.no_grad():
              UpperCAmelCase_           =   EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
              model.eval()
              UpperCAmelCase_           =   torch.tensor([[0, 1, 2, 3, 4, 5]] )
              UpperCAmelCase_           =   model(__a )[0]

              UpperCAmelCase_           =   33

              UpperCAmelCase_           =   torch.Size((1, 6, vocab_size) )
              self.assertEqual(output.shape   ,			__a )

              UpperCAmelCase_           =   torch.tensor(
                  [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]] )
              self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=1E-4 ) )






    @slow
    def 					_lowercase						(self    :    List[Any] ):
         with torch.no_grad():
              UpperCAmelCase_           =   EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
              model.eval()

              UpperCAmelCase_           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
              UpperCAmelCase_           =   model(__a )[0]
              # compare the actual values for a slice.
              UpperCAmelCase_           =   torch.tensor(
                  [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]] )
              self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__a   ,			atol=1E-4 ) )








import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


__lowerCAmelCase		       =							get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Optional[int]        = True
 __lowercase :							Tuple        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   List[str]  			=   PegasusTokenizer(lowerCAmelCase__ )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/pegasus-large' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   '</s>'
        lowercase__:   Union[str, Any]  			=   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ )				, lowerCAmelCase__ )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ )				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   list(self.get_tokenizer().get_vocab().keys() )

        self.assertEqual(vocab_keys[0]				, '<pad>' )
        self.assertEqual(vocab_keys[1]				, '</s>' )
        self.assertEqual(vocab_keys[-1]				, 'v' )
        self.assertEqual(len(lowerCAmelCase__ )				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        self.assertEqual(self.get_tokenizer().vocab_size				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Dict  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[int]  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[Any]  			=   (
            'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
            ' </s> <pad> <pad> <pad>'
        )
        lowercase__:   Dict  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Tuple  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   int  			=   self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        lowercase__:   Any  			=   '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
        lowercase__:   Union[str, Any]  			=   [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   int  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96_103
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 103
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1_024
        lowercase__:   int  			=   'To ensure a smooth flow of bank resolutions.'
        lowercase__:   Any  			=   [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   str  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Any  			=   ['This is going to be way too long.' * 150, 'short example']
        lowercase__:   Tuple  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Dict  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Any  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 1_024)
        assert batch.attention_mask.shape == (2, 1_024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        # fmt: off
        lowercase__:   List[str]  			=   {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=lowerCAmelCase__				, model_name='google/bigbird-pegasus-large-arxiv'				, revision='ba85d0851d708441f91440d509690f1ab6353415'				, )





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Any        = True
 __lowercase :							Dict        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   Union[str, Any]  			=   PegasusTokenizer(lowerCAmelCase__				, offset=0				, mask_token_sent=lowerCAmelCase__				, mask_token='[MASK]' )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   str  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Tuple  			=   (
            'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
            ' <pad> <pad> <pad>'
        )
        lowercase__:   List[Any]  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Any  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   ['This is going to be way too long.' * 1_000, 'short example']
        lowercase__:   str  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Tuple  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Dict  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 4_096)
        assert batch.attention_mask.shape == (2, 4_096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   str  			=   (
            'This is an example string that is used to test the original TF implementation against the HF'
            ' implementation'
        )

        lowercase__:   Optional[int]  			=   self._large_tokenizer(lowerCAmelCase__ ).input_ids

        self.assertListEqual(
            lowerCAmelCase__				, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				, )


