



"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import (
          TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaubertConfig,
          TFFlaubertForMultipleChoice,
          TFFlaubertForQuestionAnsweringSimple,
          TFFlaubertForSequenceClassification,
          TFFlaubertForTokenClassification,
          TFFlaubertModel,
          TFFlaubertWithLMHeadModel,
      )



class       SCREAMING_SNAKE_CASE__  :

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	parent
    UpperCAmelCase	:					Optional[Any]	=	13
    UpperCAmelCase	:					Union[str, Any]	=	7
    UpperCAmelCase	:					Tuple	=	True
    UpperCAmelCase	:					List[str]	=	True
    UpperCAmelCase	:					str	=	True
    UpperCAmelCase	:					Tuple	=	True
    UpperCAmelCase	:					Dict	=	True
    UpperCAmelCase	:					Union[str, Any]	=	False
    UpperCAmelCase	:					Tuple	=	False
    UpperCAmelCase	:					int	=	False
    UpperCAmelCase	:					Any	=	2
    UpperCAmelCase	:					Tuple	=	99
    UpperCAmelCase	:					Any	=	0
    UpperCAmelCase	:					Dict	=	32
    UpperCAmelCase	:					Dict	=	2
    UpperCAmelCase	:					List[str]	=	4
    UpperCAmelCase	:					str	=	0.1
    UpperCAmelCase	:					List[Any]	=	0.1
    UpperCAmelCase	:					str	=	512
    UpperCAmelCase	:					str	=	16
    UpperCAmelCase	:					Optional[Any]	=	2
    UpperCAmelCase	:					Union[str, Any]	=	0.02
    UpperCAmelCase	:					Optional[int]	=	3
    UpperCAmelCase	:					Tuple	=	4
    UpperCAmelCase	:					Union[str, Any]	=	"""last"""
    UpperCAmelCase	:					Optional[Any]	=	True
    UpperCAmelCase	:					Union[str, Any]	=	None
    UpperCAmelCase	:					int	=	0

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size						)
    UpperCAmelCase	:					Dict	=	random_attention_mask([self.batch_size, self.seq_length]      ,     dtype=tf.floataa						)

    UpperCAmelCase	:					List[str]	=	None
    if self.use_input_lengths:
     UpperCAmelCase	:					Union[str, Any]	=	(
         ids_tensor([self.batch_size]      ,     vocab_size=2						) + self.seq_length - 2
     )  # small variation of seq_length

    UpperCAmelCase	:					Any	=	None
    if self.use_token_type_ids:
     UpperCAmelCase	:					Dict	=	ids_tensor([self.batch_size, self.seq_length]      ,     self.n_langs						)

    UpperCAmelCase	:					Union[str, Any]	=	None
    UpperCAmelCase	:					Dict	=	None
    UpperCAmelCase	:					List[str]	=	None
    if self.use_labels:
     UpperCAmelCase	:					str	=	ids_tensor([self.batch_size]      ,     self.type_sequence_label_size						)
     UpperCAmelCase	:					Any	=	ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels						)
     UpperCAmelCase	:					int	=	ids_tensor([self.batch_size]      ,     2      ,     dtype=tf.floataa						)
     UpperCAmelCase	:					int	=	ids_tensor([self.batch_size]      ,     self.num_choices						)

    UpperCAmelCase	:					str	=	FlaubertConfig(
        vocab_size=self.vocab_size      ,     n_special=self.n_special      ,     emb_dim=self.hidden_size      ,     n_layers=self.num_hidden_layers      ,     n_heads=self.num_attention_heads      ,     dropout=self.hidden_dropout_prob      ,     attention_dropout=self.attention_probs_dropout_prob      ,     gelu_activation=self.gelu_activation      ,     sinusoidal_embeddings=self.sinusoidal_embeddings      ,     asm=self.asm      ,     causal=self.causal      ,     n_langs=self.n_langs      ,     max_position_embeddings=self.max_position_embeddings      ,     initializer_range=self.initializer_range      ,     summary_type=self.summary_type      ,     use_proj=self.use_proj      ,     bos_token_id=self.bos_token_id      ,     )

    return (
        config,
        input_ids,
        token_type_ids,
        input_lengths,
        sequence_labels,
        token_labels,
        is_impossible_labels,
        choice_labels,
        input_mask,
    )

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	TFFlaubertModel(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
    UpperCAmelCase	:					Optional[Any]	=	model(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	[input_ids, input_mask]
    UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	TFFlaubertWithLMHeadModel(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
    UpperCAmelCase	:					str	=	model(_SCREAMING_SNAKE_CASE						)

    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	TFFlaubertForQuestionAnsweringSimple(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	{"""input_ids""": input_ids, """lengths""": input_lengths}

    UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)

    self.parent.assertEqual(result.start_logits.shape      ,     (self.batch_size, self.seq_length)						)
    self.parent.assertEqual(result.end_logits.shape      ,     (self.batch_size, self.seq_length)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	TFFlaubertForSequenceClassification(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	{"""input_ids""": input_ids, """lengths""": input_lengths}

    UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)

    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.type_sequence_label_size)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.num_labels
    UpperCAmelCase	:					List[str]	=	TFFlaubertForTokenClassification(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
    UpperCAmelCase	:					Union[str, Any]	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.num_labels)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.num_choices
    UpperCAmelCase	:					Union[str, Any]	=	TFFlaubertForMultipleChoice(config=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,     1						)      ,     (1, self.num_choices, 1)						)
    UpperCAmelCase	:					Optional[Any]	=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,     1						)      ,     (1, self.num_choices, 1)						)
    UpperCAmelCase	:					List[str]	=	tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE      ,     1						)      ,     (1, self.num_choices, 1)						)
    UpperCAmelCase	:					Optional[Any]	=	{
        """input_ids""": multiple_choice_inputs_ids,
        """attention_mask""": multiple_choice_input_mask,
        """token_type_ids""": multiple_choice_token_type_ids,
    }
    UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE						)
    self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_choices)						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	self.prepare_config_and_inputs()
    (
        (
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		(
        UpperCAmelCase
    )	,		
    )	:					Union[str, Any]	=	config_and_inputs
    UpperCAmelCase	:					str	=	{
        """input_ids""": input_ids,
        """token_type_ids""": token_type_ids,
        """langs""": token_type_ids,
        """lengths""": input_lengths,
    }
    return config, inputs_dict



@require_tf
class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Union[str, Any]						=						(
       (
           TFFlaubertModel,
           TFFlaubertWithLMHeadModel,
           TFFlaubertForSequenceClassification,
           TFFlaubertForQuestionAnsweringSimple,
           TFFlaubertForTokenClassification,
           TFFlaubertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase  :   Union[str, Any]						=						(
       (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Check other models whether language generation is also applicable
   __lowerCAmelCase  :   str						=						(
       {
           'feature-extraction': TFFlaubertModel,
           'fill-mask': TFFlaubertWithLMHeadModel,
           'question-answering': TFFlaubertForQuestionAnsweringSimple,
           'text-classification': TFFlaubertForSequenceClassification,
           'token-classification': TFFlaubertForTokenClassification,
           'zero-shot': TFFlaubertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase  :   int						=						False
   __lowerCAmelCase  :   List[Any]						=						False

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    if (
        pipeline_test_casse_name == "QAPipelineTests"
        and tokenizer_name is not None
        and not tokenizer_name.endswith("""Fast"""						)
    ):
     # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
     # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
     # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
     return True

    return False

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	TFFlaubertModelTester(self						)
    UpperCAmelCase	:					Any	=	ConfigTester(self      ,     config_class=_SCREAMING_SNAKE_CASE      ,     emb_dim=37						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    self.config_tester.run_common_tests()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_model(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_lm_head(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_qa(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_sequence_classif(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_flaubert_for_token_classification(*_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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




   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase	:					Union[str, Any]	=	TFFlaubertModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
     self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



@require_tf
@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__  (					unittest.TestCase ):

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""						)

    UpperCAmelCase	:					List[Any]	=	tf.convert_to_tensor(
        [[0, 158, 735, 2592, 1424, 6727, 82, 1]]      ,     dtype=tf.intaa      ,     )  # "J'aime flaubert !"

    UpperCAmelCase	:					List[Any]	=	model(_SCREAMING_SNAKE_CASE						)[0]
    UpperCAmelCase	:					Tuple	=	tf.TensorShape((1, 8, 512)						)
    self.assertEqual(output.shape      ,     _SCREAMING_SNAKE_CASE						)
    # compare the actual values for a slice.
    UpperCAmelCase	:					int	=	tf.convert_to_tensor(
        [
            [
                [-1.876_8773, -1.56_6555, 0.2707_2418],
                [-1.692_0038, -0.587_3505, 1.932_9599],
                [-2.956_3985, -1.699_3835, 1.797_2052],
            ]
        ]      ,     dtype=tf.floataa      ,     )

    self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,     expected_slice.numpy()      ,     atol=1E-4						)						)





"""simple docstring"""
import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A:   str								=		logging.get_logger(__name__)

A:   List[Any]								=		{"vocab_file": "vocab.txt"}

A:   List[str]								=		{
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

A:   Dict								=		{
    "facebook/esm2_t6_8M_UR50D": 1_0_2_4,
    "facebook/esm2_t12_35M_UR50D": 1_0_2_4,
}

def       _snake_case						(						UpperCamelCase						:		int						):
 with open(UpperCamelCase			,		"""r"""						) as f:
  UpperCAmelCase	:					int	=	f.read().splitlines()
  return [l.strip() for l in lines]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<cls>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE="<eos>"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	load_vocab_file(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	dict(enumerate(self.all_tokens						)						)
    UpperCAmelCase	:					List[str]	=	{tok: ind for ind, tok in enumerate(self.all_tokens						)}
    UpperCAmelCase	:					Any	=	unk_token
    UpperCAmelCase	:					str	=	cls_token
    UpperCAmelCase	:					int	=	pad_token
    UpperCAmelCase	:					Tuple	=	mask_token
    UpperCAmelCase	:					str	=	eos_token
    UpperCAmelCase	:					List[str]	=	self.all_tokens
    self._create_trie(self.unique_no_split_tokens						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return text.split()

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Union[str, Any]:





    '''simple docstring'''




    return len(self._id_to_token						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return {token: i for i, token in enumerate(self.all_tokens						)}

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




    return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,     self._token_to_id.get(self.unk_token						)						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




    return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,     self.unk_token						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.cls_token_id]
    UpperCAmelCase	:					Tuple	=	[self.eos_token_id]  # No sep token in ESM vocabulary
    if token_ids_a is None:
     if self.eos_token_id is None:
      return cls + token_ids_a
     else:
      return cls + token_ids_a + sep
    elif self.eos_token_id is None:
     raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""						)
    return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     if token_ids_a is not None:
      raise ValueError(
          """You should not supply a second sequence if the provided sequence of """
          """ids is already formatted with special tokens for the model."""						)

     return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
    UpperCAmelCase	:					str	=	[1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    if token_ids_a is not None:
     mask += [0] * len(_SCREAMING_SNAKE_CASE						) + [1]
    return mask

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""						)
    with open(_SCREAMING_SNAKE_CASE      ,     """w"""						) as f:
     f.write("""\n""".join(self.all_tokens						)						)
    return (vocab_file,)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False						)  ->      int:





    '''simple docstring'''




    return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,     special_tokens=_SCREAMING_SNAKE_CASE						)
