code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import AlbertConfig, is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_tf, 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 tensorflow as tf
  
    from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
    from transformers.models.albert.modeling_tf_albert import (
        TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFAlbertForMaskedLM,
        TFAlbertForMultipleChoice,
        TFAlbertForPreTraining,
        TFAlbertForQuestionAnswering,
        TFAlbertForSequenceClassification,
        TFAlbertForTokenClassification,
        TFAlbertModel,
    )
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=13    ,      SCREAMING_SNAKE_CASE_=7    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=99    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=4    ,      SCREAMING_SNAKE_CASE_=37    ,      SCREAMING_SNAKE_CASE_="gelu"    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=512    ,      SCREAMING_SNAKE_CASE_=16    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_=3    ,      SCREAMING_SNAKE_CASE_=4    ,      SCREAMING_SNAKE_CASE_=None    ,      ):
         UpperCamelCase				:   Any          =							parent
         UpperCamelCase				:   Optional[int]          =							13
         UpperCamelCase				:   List[str]          =							7
         UpperCamelCase				:   Union[str, Any]          =							True
         UpperCamelCase				:   Optional[Any]          =							True
         UpperCamelCase				:   Any          =							True
         UpperCamelCase				:   Tuple          =							True
         UpperCamelCase				:   Optional[int]          =							99
         UpperCamelCase				:   Union[str, Any]          =							16
         UpperCamelCase				:   Optional[int]          =							32
         UpperCamelCase				:   List[Any]          =							2
         UpperCamelCase				:   List[str]          =							4
         UpperCamelCase				:   Dict          =							37
         UpperCamelCase				:   Dict          =							"""gelu"""
         UpperCamelCase				:   Union[str, Any]          =							0.1
         UpperCamelCase				:   List[Any]          =							0.1
         UpperCamelCase				:   List[str]          =							512
         UpperCamelCase				:   Any          =							16
         UpperCamelCase				:   Optional[Any]          =							2
         UpperCamelCase				:   Tuple          =							0.02
         UpperCamelCase				:   Optional[Any]          =							3
         UpperCamelCase				:   Optional[int]          =							4
         UpperCamelCase				:   Optional[Any]          =							None
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size )
  
         UpperCamelCase				:   Union[str, Any]          =							None
         if self.use_input_mask:
             UpperCamelCase				:   List[Any]          =							random_attention_mask([self.batch_size, self.seq_length] )
  
         UpperCamelCase				:   Optional[int]          =							None
         if self.use_token_type_ids:
             UpperCamelCase				:   str          =							ids_tensor([self.batch_size, self.seq_length]    ,      self.type_vocab_size )
  
         UpperCamelCase				:   List[str]          =							None
         UpperCamelCase				:   Optional[int]          =							None
         UpperCamelCase				:   Optional[int]          =							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				:   List[Any]          =							ids_tensor([self.batch_size]    ,      self.num_choices )
  
         UpperCamelCase				:   Optional[Any]          =							AlbertConfig(
             vocab_size=self.vocab_size    ,      hidden_size=self.hidden_size    ,      embedding_size=self.embedding_size    ,      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    ,      )
  
         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							TFAlbertModel(config=SCREAMING_SNAKE_CASE_ )
         # inputs = {'input_ids': input_ids,
         #           'attention_mask': input_mask,
         #           'token_type_ids': token_type_ids}
         # sequence_output, pooled_output = model(**inputs)
         UpperCamelCase				:   int          =							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         UpperCamelCase				:   Tuple          =							model(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Union[str, Any]          =							[input_ids, input_mask]
         UpperCamelCase				:   str          =							model(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Union[str, Any]          =							model(SCREAMING_SNAKE_CASE_ )
  
         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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Dict          =							self.num_labels
         UpperCamelCase				:   Optional[Any]          =							TFAlbertForPreTraining(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         UpperCamelCase				:   List[str]          =							model(SCREAMING_SNAKE_CASE_ )
         self.parent.assertEqual(result.prediction_logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size) )
         self.parent.assertEqual(result.sop_logits.shape    ,      (self.batch_size, self.num_labels) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							TFAlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Union[str, Any]          =							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         UpperCamelCase				:   List[str]          =							model(SCREAMING_SNAKE_CASE_ )
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Dict          =							self.num_labels
         UpperCamelCase				:   Optional[int]          =							TFAlbertForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         UpperCamelCase				:   Tuple          =							model(SCREAMING_SNAKE_CASE_ )
         self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels) )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							TFAlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Union[str, Any]          =							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Union[str, Any]          =							self.num_choices
         UpperCamelCase				:   Any          =							TFAlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,      1 )    ,      (1, self.num_choices, 1) )
         UpperCamelCase				:   Union[str, Any]          =							tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,      1 )    ,      (1, self.num_choices, 1) )
         UpperCamelCase				:   Dict          =							tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_    ,      1 )    ,      (1, self.num_choices, 1) )
         UpperCamelCase				:   List[str]          =							{
             """input_ids""": multiple_choice_inputs_ids,
             """attention_mask""": multiple_choice_input_mask,
             """token_type_ids""": multiple_choice_token_type_ids,
         }
         UpperCamelCase				:   Optional[Any]          =							model(SCREAMING_SNAKE_CASE_ )
         self.parent.assertListEqual(list(result["""logits"""].shape )    ,      [self.batch_size, self.num_choices] )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							self.num_labels
         UpperCamelCase				:   Optional[int]          =							TFAlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         UpperCamelCase				:   int          =							model(SCREAMING_SNAKE_CASE_ )
         self.parent.assertListEqual(list(result["""logits"""].shape )    ,      [self.batch_size, self.seq_length, self.num_labels] )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Dict          =							self.prepare_config_and_inputs()
         (
             UpperCamelCase							
         )				:   Optional[Any]          =							config_and_inputs
         UpperCamelCase				:   int          =							{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase	,  unittest.TestCase				):
     lowercase       :  Optional[int]      =				(
         (
             TFAlbertModel,
             TFAlbertForPreTraining,
             TFAlbertForMaskedLM,
             TFAlbertForSequenceClassification,
             TFAlbertForQuestionAnswering,
             TFAlbertForTokenClassification,
             TFAlbertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     lowercase       :  Tuple      =				(
         {
             'feature-extraction': TFAlbertModel,
             'fill-mask': TFAlbertForMaskedLM,
             'question-answering': TFAlbertForQuestionAnswering,
             'text-classification': TFAlbertForSequenceClassification,
             'token-classification': TFAlbertForTokenClassification,
             'zero-shot': TFAlbertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     lowercase       :  str      =				False
     lowercase       :  List[Any]      =				False
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False ):
         UpperCamelCase				:   str          =							super()._prepare_for_class(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      return_labels=SCREAMING_SNAKE_CASE_ )
  
         if return_labels:
             if model_class in get_values(SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   Union[str, Any]          =							tf.zeros(self.model_tester.batch_size    ,      dtype=tf.intaa )
  
         return inputs_dict
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[Any]          =							TFAlbertModelTester(self )
         UpperCamelCase				:   List[Any]          =							ConfigTester(self    ,      config_class=SCREAMING_SNAKE_CASE_    ,      hidden_size=37 )
  
  
  
  
  
  
  
     def       a_							(							self ):
         self.config_tester.run_common_tests()
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[Any]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_model(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[Any]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_pretraining(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   List[str]          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   str          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   int          =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_question_answering(*SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @slow
     def       a_							(							self ):
         for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase				:   Tuple          =							TFAlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
  @require_tf
  class 							lowerCamelCase		( unittest.TestCase				):
  
  
  
  
  
  
  
     @slow
     def       a_							(							self ):
         UpperCamelCase				:   Any          =							TFAlbertForPreTraining.from_pretrained("""albert-base-v2""" )
         UpperCamelCase				:   Optional[int]          =							tf.constant([[0, 1, 2, 3, 4, 5]] )
         UpperCamelCase				:   Union[str, Any]          =							model(SCREAMING_SNAKE_CASE_ )[0]
  
         UpperCamelCase				:   List[Any]          =							[1, 6, 3_0000]
         self.assertEqual(output.shape    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[int]          =							tf.constant(
             [
                 [
                     [4.595668, 0.74462754, -1.818147],
                     [4.5954347, 0.7454184, -1.8188258],
                     [4.5954905, 0.7448235, -1.8182316],
                 ]
             ] )
         tf.debugging.assert_near(output[:, :3, :3]    ,      SCREAMING_SNAKE_CASE_    ,      atol=1e-4 )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __A :		Tuple      							=    None
  
  
  __A :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[Any]      							=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __A :		Optional[Any]      							=    {
      '''vocab_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __A :		List[Any]      							=    {
      '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
  }
  
  # fmt: off
  __A :		Dict      							=    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  List[Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  Tuple      =				['input_ids', 'attention_mask']
     lowercase       :  Optional[int]      =				MBartaaTokenizer
  
     lowercase       :  List[int]      =				[]
     lowercase       :  List[int]      =				[]
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="<s>"    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="<mask>"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase				:   Optional[int]          =							AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) else mask_token
  
         UpperCamelCase				:   Dict          =							kwargs.get("""additional_special_tokens"""    ,      [] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]
  
         super().__init__(
             SCREAMING_SNAKE_CASE_    ,      src_lang=SCREAMING_SNAKE_CASE_    ,      tgt_lang=SCREAMING_SNAKE_CASE_    ,      tokenizer_file=SCREAMING_SNAKE_CASE_    ,      eos_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   int          =							vocab_file
         UpperCamelCase				:   Optional[Any]          =							False if not self.vocab_file else True
  
         UpperCamelCase				:   List[Any]          =							{
             lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }
  
         UpperCamelCase				:   List[str]          =							src_lang if src_lang is not None else """en_XX"""
         UpperCamelCase				:   Union[str, Any]          =							tgt_lang
         UpperCamelCase				:   Union[str, Any]          =							self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._src_lang
  
  
  
  
  
  
  
     @src_lang.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "en_XX"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "ro_RO"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   str          =							src_lang
         UpperCamelCase				:   Optional[Any]          =							tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_src_lang_special_tokens(self.src_lang )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Optional[int]          =							[self.eos_token_id]
  
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Any          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Tuple          =							[self.eos_token_id]
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Dict          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCamelCase				:   Optional[int]          =							src_lang
         UpperCamelCase				:   Optional[int]          =							self(SCREAMING_SNAKE_CASE_    ,      add_special_tokens=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							tgt_lang_id
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )
  
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Optional[int]          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  