





from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import numpy
      import tensorflow as tf

      from transformers.models.esm.modeling_tf_esm import (
          TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
          TFEsmModel,
      )





class 				A_				:







       """simple docstring"""





       def __init__(    self	:	Optional[int]						,__A	:	List[str]						,)						->					int:
         _lowercase							= parent
         _lowercase							= 13
         _lowercase							= 7
         _lowercase							= True
         _lowercase							= True
         _lowercase							= True
         _lowercase							= 99
         _lowercase							= 32
         _lowercase							= 2
         _lowercase							= 4
         _lowercase							= 37
         _lowercase							= 'gelu'
         _lowercase							= 0.1
         _lowercase							= 0.1
         _lowercase							= 512
         _lowercase							= 16
         _lowercase							= 2
         _lowercase							= 0.02
         _lowercase							= 3
         _lowercase							= 4
         _lowercase							= None







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Union[str, Any]:
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size  )

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

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

         _lowercase							= EsmConfig(
             vocab_size=self.vocab_size						,hidden_size=self.hidden_size						,num_hidden_layers=self.num_hidden_layers						,pad_token_id=1						,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						,)

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







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[str]:
         (
             (
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		      
         )							= self.prepare_config_and_inputs()

         _lowercase							= True
         _lowercase							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
         _lowercase							= ids_tensor([self.batch_size, self.seq_length]						,vocab_size=2  )

         return (
             config,
             input_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Optional[int]						,__A	:	Union[str, Any]						,__A	:	Dict						,__A	:	Tuple						,__A	:	int						,__A	:	Tuple  )						->					int:
         _lowercase							= TFEsmModel(config=_SCREAMING_SNAKE_CASE  )
         _lowercase							= {'input_ids': input_ids, 'attention_mask': input_mask}
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )

         _lowercase							= [input_ids, input_mask]
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )

         _lowercase							= model(_SCREAMING_SNAKE_CASE  )

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







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[str]						,__A	:	Tuple						,__A	:	Union[str, Any]						,__A	:	Any						,__A	:	Optional[Any]						,__A	:	Tuple						,__A	:	List[Any]						,__A	:	List[str]						,)						->					Tuple:
         _lowercase							= True

         _lowercase							= TFEsmModel(config=_SCREAMING_SNAKE_CASE  )
         _lowercase							= {
             'input_ids': input_ids,
             'attention_mask': input_mask,
             'encoder_hidden_states': encoder_hidden_states,
             'encoder_attention_mask': encoder_attention_mask,
         }
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )

         _lowercase							= [input_ids, input_mask]
         _lowercase							= model(_SCREAMING_SNAKE_CASE						,encoder_hidden_states=_SCREAMING_SNAKE_CASE  )

         # Also check the case where encoder outputs are not passed
         _lowercase							= model(_SCREAMING_SNAKE_CASE						,attention_mask=_SCREAMING_SNAKE_CASE  )

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







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	List[Any]						,__A	:	Optional[int]						,__A	:	int						,__A	:	int						,__A	:	List[Any]						,__A	:	str  )						->					Any:
         _lowercase							= TFEsmForMaskedLM(config=_SCREAMING_SNAKE_CASE  )
         _lowercase							= model([input_ids, input_mask]  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.vocab_size)  )







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	Tuple						,__A	:	Tuple						,__A	:	Union[str, Any]						,__A	:	str						,__A	:	Union[str, Any]						,__A	:	List[str]  )						->					Optional[int]:
         _lowercase							= self.num_labels
         _lowercase							= TFEsmForTokenClassification(config=_SCREAMING_SNAKE_CASE  )
         _lowercase							= {'input_ids': input_ids, 'attention_mask': input_mask}
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length, self.num_labels)  )







       def    __UpperCAmelCase (    self	:	Any  )						->					List[Any]:
         _lowercase							= self.prepare_config_and_inputs()
         (
             (
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		(
             _lowercase      
         )						,		      
         )							= config_and_inputs
         _lowercase							= {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict





@require_tf
class 				A_				(      lowerCAmelCase__					,	lowerCAmelCase__					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  (
           (
               TFEsmModel,
               TFEsmForMaskedLM,
               TFEsmForSequenceClassification,
               TFEsmForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_      : Tuple      =  (
           {
               "feature-extraction": TFEsmModel,
               "fill-mask": TFEsmForMaskedLM,
               "text-classification": TFEsmForSequenceClassification,
               "token-classification": TFEsmForTokenClassification,
               "zero-shot": TFEsmForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_      : Any      =  False
       SCREAMING_SNAKE_CASE_      : str      =  False







       def    __UpperCAmelCase (    self	:	Any  )						->					Union[str, Any]:
         _lowercase							= TFEsmModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=_SCREAMING_SNAKE_CASE						,hidden_size=37  )







       def    __UpperCAmelCase (    self	:	int  )						->					Dict:
         self.config_tester.run_common_tests()







       def    __UpperCAmelCase (    self	:	Dict  )						->					Union[str, Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )







       def    __UpperCAmelCase (    self	:	str  )						->					Dict:
         _lowercase							= self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*_SCREAMING_SNAKE_CASE  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					str:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					List[str]:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE  )







       @slow
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Any:
         for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase							= TFEsmModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )







       @unittest.skip('Protein models do not support embedding resizing.'  )
       def    __UpperCAmelCase (    self	:	Dict  )						->					str:
         pass







       @unittest.skip('Protein models do not support embedding resizing.'  )
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					List[str]:
         pass







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					int:
         _lowercase						,		_lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= model_class(_SCREAMING_SNAKE_CASE  )
           assert isinstance(model.get_input_embeddings()						,tf.keras.layers.Layer  )
           if model_class is TFEsmForMaskedLM:
             # Output embedding test differs from the main test because they're a matrix, not a layer
             _lowercase							= model.get_bias()
             assert isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE  )
             for k, v in name.items():
               assert isinstance(_SCREAMING_SNAKE_CASE						,tf.Variable  )
           else:
             _lowercase							= model.get_output_embeddings()
             assert x is None
             _lowercase							= model.get_bias()
             assert name is None





@require_tf
class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       @slow
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Tuple:
         _lowercase							= TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D'  )

         _lowercase							= tf.constant([[0, 1, 2, 3, 4, 5]]  )
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )[0]
         _lowercase							= [1, 6, 33]
         self.assertEqual(list(output.numpy().shape  )						,_SCREAMING_SNAKE_CASE  )
         # compare the actual values for a slice.
         _lowercase							= tf.constant(
             [
                 [
                     [8.921518, -10.58_9814, -6.4671307],
                     [-6.3967156, -13.91_1377, -1.1211915],
                     [-7.781247, -13.95_1557, -3.740592],
                 ]
             ]  )
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,expected_slice.numpy()						,atol=1e-2  )  )







       @slow
       def    __UpperCAmelCase (    self	:	Any  )						->					int:
         _lowercase							= TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'  )

         _lowercase							= tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
         _lowercase							= model(_SCREAMING_SNAKE_CASE  )[0]
         # compare the actual values for a slice.
         _lowercase							= tf.constant(
             [
                 [
                     [0.14443092, 0.54125327, 0.3247739],
                     [0.30340484, 0.00526676, 0.31077722],
                     [0.32278043, -0.24987096, 0.3414628],
                 ]
             ]  )
         self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()						,expected_slice.numpy()						,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


snake_case 				=       get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")





@require_sentencepiece
@require_tokenizers
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizer
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  PegasusTokenizerFast
       SCREAMING_SNAKE_CASE_      : Any      =  True
       SCREAMING_SNAKE_CASE_      : Optional[int]      =  True







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Any:
         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase							= PegasusTokenizer(__A  )
         tokenizer.save_pretrained(self.tmpdirname  )







       @cached_property
       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[Any]:
         return PegasusTokenizer.from_pretrained('google/pegasus-large'  )







       def    __UpperCAmelCase (    self	:	Any						,**__A	:	int  )						->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	int  )						->					List[str]:
         return ("This is a test", "This is a test")







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[Any]:
         _lowercase							= '</s>'
         _lowercase							= 1

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







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Optional[Any]:
         _lowercase							= 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(__A  )						,1103  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         self.assertEqual(self.get_tokenizer().vocab_size						,1103  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					List[str]:
         _lowercase							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= self.tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= (
             '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							= rust_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         _lowercase							= py_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         _lowercase							= '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
         _lowercase							= [2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase							= tokenizer([raw_input_str]						,return_tensors=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         _lowercase							= self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6103
         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 == 1024
         _lowercase							= 'To ensure a smooth flow of bank resolutions.'
         _lowercase							= [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
         _lowercase							= tokenizer([raw_input_str]						,return_tensors=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







       @require_torch
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[str]:
         _lowercase							= ['This is going to be way too long.' * 150, 'short example']
         _lowercase							= ['not super long but more than 5 tokens', 'tiny']
         _lowercase							= self._large_tokenizer(__A						,padding=__A						,truncation=__A						,return_tensors='pt'  )
         _lowercase							= self._large_tokenizer(
             text_target=__A						,max_length=5						,padding=__A						,truncation=__A						,return_tensors='pt'  )

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(__A  ) == 2  # input_ids, attention_mask.







       @slow
       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         # fmt: off
         _lowercase							= {'input_ids': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 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, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 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=__A						,model_name='google/bigbird-pegasus-large-arxiv'						,revision='ba85d0851d708441f91440d509690f1ab6353415'						,)





@require_sentencepiece
@require_tokenizers
class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizer
       SCREAMING_SNAKE_CASE_      : int      =  PegasusTokenizerFast
       SCREAMING_SNAKE_CASE_      : str      =  True
       SCREAMING_SNAKE_CASE_      : List[Any]      =  True







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         _lowercase							= PegasusTokenizer(__A						,offset=0						,mask_token_sent=__A						,mask_token='[MASK]'  )
         tokenizer.save_pretrained(self.tmpdirname  )







       @cached_property
       def    __UpperCAmelCase (    self	:	str  )						->					Optional[Any]:
         return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,**__A	:	Union[str, Any]  )						->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	int  )						->					Tuple:
         return ("This is a test", "This is a test")







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Dict:
         _lowercase							= self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= self.tokenizer_class.from_pretrained(self.tmpdirname  )
         _lowercase							= (
             'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
             ' <pad> <pad> <pad>'
         )
         _lowercase							= rust_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         _lowercase							= py_tokenizer([raw_input_str]						,return_tensors=__A						,add_special_tokens=__A  ).input_ids[0]
         self.assertListEqual(__A						,__A  )







       @require_torch
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Dict:
         _lowercase							= ['This is going to be way too long.' * 1000, 'short example']
         _lowercase							= ['not super long but more than 5 tokens', 'tiny']
         _lowercase							= self._large_tokenizer(__A						,padding=__A						,truncation=__A						,return_tensors='pt'  )
         _lowercase							= self._large_tokenizer(
             text_target=__A						,max_length=5						,padding=__A						,truncation=__A						,return_tensors='pt'  )

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(__A  ) == 2  # input_ids, attention_mask.







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         _lowercase							= (
             'This is an example string that is used to test the original TF implementation against the HF'
             ' implementation'
         )

         _lowercase							= self._large_tokenizer(__A  ).input_ids

         self.assertListEqual(
             __A						,[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]						,)