



"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import MobileBertConfig, 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,
					    TFMobileBertForMaskedLM,
					    TFMobileBertForMultipleChoice,
					    TFMobileBertForNextSentencePrediction,
					    TFMobileBertForPreTraining,
					    TFMobileBertForQuestionAnswering,
					    TFMobileBertForSequenceClassification,
					    TFMobileBertForTokenClassification,
					    TFMobileBertModel,
					)



@require_tf
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							(
		    (
		        TFMobileBertModel,
		        TFMobileBertForMaskedLM,
		        TFMobileBertForNextSentencePrediction,
		        TFMobileBertForPreTraining,
		        TFMobileBertForQuestionAnswering,
		        TFMobileBertForSequenceClassification,
		        TFMobileBertForTokenClassification,
		        TFMobileBertForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		_lowerCamelCase      	=							(
		    {
		        '''feature-extraction''': TFMobileBertModel,
		        '''fill-mask''': TFMobileBertForMaskedLM,
		        '''question-answering''': TFMobileBertForQuestionAnswering,
		        '''text-classification''': TFMobileBertForSequenceClassification,
		        '''token-classification''': TFMobileBertForTokenClassification,
		        '''zero-shot''': TFMobileBertForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase=False )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      super()._prepare_for_class(_lowercase				,     _lowercase				,     return_labels=_lowercase )

								if return_labels:
														if model_class in get_values(_lowercase ):
																				snake_case_ :			Optional[int]            =      tf.zeros(self.model_tester.batch_size				,     dtype=tf.intaa )

								return inputs_dict



		class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





				"""simple docstring"""






				def __init__(       self				,     _lowercase				,     _lowercase=1_3				,     _lowercase=7				,     _lowercase=True				,     _lowercase=True				,     _lowercase=True				,     _lowercase=True				,     _lowercase=9_9				,     _lowercase=3_2				,     _lowercase=3_2				,     _lowercase=2				,     _lowercase=4				,     _lowercase=3_7				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=5_1_2				,     _lowercase=1_6				,     _lowercase=2				,     _lowercase=0.02				,     _lowercase=3				,     _lowercase=4				,     _lowercase=None				,     )			->    Tuple:







										'''simple docstring'''

										snake_case_ :			Optional[Any]            =      parent
										snake_case_ :			List[str]            =      batch_size
										snake_case_ :			Union[str, Any]            =      seq_length
										snake_case_ :			List[str]            =      is_training
										snake_case_ :			Dict            =      use_input_mask
										snake_case_ :			int            =      use_token_type_ids
										snake_case_ :			List[Any]            =      use_labels
										snake_case_ :			List[str]            =      vocab_size
										snake_case_ :			List[Any]            =      hidden_size
										snake_case_ :			Optional[Any]            =      num_hidden_layers
										snake_case_ :			List[Any]            =      num_attention_heads
										snake_case_ :			List[Any]            =      intermediate_size
										snake_case_ :			int            =      hidden_act
										snake_case_ :			Any            =      hidden_dropout_prob
										snake_case_ :			List[str]            =      attention_probs_dropout_prob
										snake_case_ :			Union[str, Any]            =      max_position_embeddings
										snake_case_ :			Dict            =      type_vocab_size
										snake_case_ :			str            =      type_sequence_label_size
										snake_case_ :			Union[str, Any]            =      initializer_range
										snake_case_ :			str            =      num_labels
										snake_case_ :			Any            =      num_choices
										snake_case_ :			Optional[int]            =      scope
										snake_case_ :			Tuple            =      embedding_size





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







										'''simple docstring'''

										snake_case_ :			Dict            =      ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size )

										snake_case_ :			str            =      None
										if self.use_input_mask:
																snake_case_ :			Optional[Any]            =      random_attention_mask([self.batch_size, self.seq_length] )

										snake_case_ :			List[Any]            =      None
										if self.use_token_type_ids:
																snake_case_ :			Any            =      ids_tensor([self.batch_size, self.seq_length]				,     self.type_vocab_size )

										snake_case_ :			Optional[int]            =      None
										snake_case_ :			Any            =      None
										snake_case_ :			Dict            =      None
										if self.use_labels:
																snake_case_ :			List[Any]            =      ids_tensor([self.batch_size]				,     self.type_sequence_label_size )
																snake_case_ :			str            =      ids_tensor([self.batch_size, self.seq_length]				,     self.num_labels )
																snake_case_ :			Tuple            =      ids_tensor([self.batch_size]				,     self.num_choices )

										snake_case_ :			Dict            =      MobileBertConfig(
										    vocab_size=self.vocab_size				,     hidden_size=self.hidden_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				,     embedding_size=self.embedding_size				,     )

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





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[str]:







										'''simple docstring'''

										snake_case_ :			int            =      TFMobileBertModel(config=_lowercase )
										snake_case_ :			List[str]            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Optional[Any]            =      model(_lowercase )

										snake_case_ :			Dict            =      [input_ids, input_mask]
										snake_case_ :			Any            =      model(_lowercase )

										snake_case_ :			Any            =      model(_lowercase )

										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  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[Any]:







										'''simple docstring'''

										snake_case_ :			str            =      TFMobileBertForMaskedLM(config=_lowercase )
										snake_case_ :			int            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Optional[Any]            =      model(_lowercase )
										self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







										'''simple docstring'''

										snake_case_ :			str            =      TFMobileBertForNextSentencePrediction(config=_lowercase )
										snake_case_ :			Optional[int]            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			List[str]            =      model(_lowercase )
										self.parent.assertEqual(result.logits.shape				,     (self.batch_size, 2) )





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    List[str]:







										'''simple docstring'''

										snake_case_ :			Optional[int]            =      TFMobileBertForPreTraining(config=_lowercase )
										snake_case_ :			Optional[Any]            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Dict            =      model(_lowercase )
										self.parent.assertEqual(
										    result.prediction_logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size) )
										self.parent.assertEqual(result.seq_relationship_logits.shape				,     (self.batch_size, 2) )





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







										'''simple docstring'''

										snake_case_ :			Optional[int]            =      self.num_labels
										snake_case_ :			Optional[Any]            =      TFMobileBertForSequenceClassification(config=_lowercase )
										snake_case_ :			Optional[Any]            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Any            =      model(_lowercase )
										self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels) )





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    Tuple:







										'''simple docstring'''

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





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    str:







										'''simple docstring'''

										snake_case_ :			Any            =      self.num_labels
										snake_case_ :			Dict            =      TFMobileBertForTokenClassification(config=_lowercase )
										snake_case_ :			int            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Tuple            =      model(_lowercase )
										self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels) )





				def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase )			->    int:







										'''simple docstring'''

										snake_case_ :			Optional[Any]            =      TFMobileBertForQuestionAnswering(config=_lowercase )
										snake_case_ :			str            =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
										snake_case_ :			Union[str, Any]            =      model(_lowercase )
										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  UpperCAmelCase__				(       self )			->    Union[str, Any]:







										'''simple docstring'''

										snake_case_ :			Any            =      self.prepare_config_and_inputs()
										(
										    (
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		(
										    snake_case_
										)      ,		
										) :			List[str]            =      config_and_inputs
										snake_case_ :			Optional[Any]            =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict





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







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      TFMobileBertModelTest.TFMobileBertModelTester(self )
								snake_case_ :			List[Any]            =      ConfigTester(self				,     config_class=_lowercase				,     hidden_size=3_7 )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								self.config_tester.run_common_tests()





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







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Any            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_lowercase )





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







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			str            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*_lowercase )





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







								'''simple docstring'''

								snake_case_ :			Dict            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								snake_case_ :			int            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*_lowercase )





		@slow
		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								for model_name in ["google/mobilebert-uncased"]:
														snake_case_ :			int            =      TFMobileBertModel.from_pretrained(_lowercase )
														self.assertIsNotNone(_lowercase )




@require_tf
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@slow
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""" )
								snake_case_ :			Optional[Any]            =      tf.constant([[0, 1, 2, 3, 4, 5]] )
								snake_case_ :			int            =      model(_lowercase )[0]

								snake_case_ :			str            =      [1, 6, 3_0_5_2_2]
								self.assertEqual(output.shape				,     _lowercase )

								snake_case_ :			int            =      tf.constant(
								    [
								        [
								            [-4.591_9547, -9.24_8295, -9.64_5256],
								            [-6.730_6175, -6.44_0284, -6.605_2837],
								            [-7.274_3506, -6.784_7915, -6.02_4673],
								        ]
								    ] )
								tf.debugging.assert_near(output[:, :3, :3]				,     _lowercase				,     atol=1E-4 )







'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  str				     =  '▁'

lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_      :  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'
        ),
    }
}

lowerCAmelCase_      :  List[str]				     =  {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
lowerCAmelCase_      :  Optional[Any]				     =  ['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      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[Any]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Union[str, Any]   							=					['''input_ids''', '''attention_mask''']

      __magic_name__  :       List[int]   							=					[]
      __magic_name__  :       List[int]   							=					[]






      def __init__( self   :  int						,						lowercase__   :  Tuple						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  str=None						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  Tuple="</s>"						,						lowercase__   :  Any="<s>"						,						lowercase__   :  Tuple="<unk>"						,						lowercase__   :  List[Any]="<pad>"						,						lowercase__   :  Dict="<mask>"						,						lowercase__   :  Optional[Dict[str, Any]] = None						,						**lowercase__   :  Tuple						,						):


           '''simple docstring'''

           a_     :							Any     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else mask_token

           a_     :							Union[str, Any]     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							int     						=  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__(
               src_lang=lowercase__						,						tgt_lang=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Union[str, Any]     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  vocab_file

           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
           # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
           # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

           # Mimic fairseq token-to-id alignment for the first 4 token
           a_     :							Tuple     						=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           a_     :							List[str]     						=  1

           a_     :							List[Any]     						=  len(self.sp_model       )
           a_     :							List[str]     						=  {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__       )
           }
           a_     :							str     						=  {v: k for k, v in self.lang_code_to_id.items()}
           a_     :							Any     						=  len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
           a_     :							int     						=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

           a_     :							List[Any]     						=  src_lang if src_lang is not None else """en_XX"""
           a_     :							Any     						=  self.lang_code_to_id[self._src_lang]
           a_     :							Union[str, Any]     						=  tgt_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      @property
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token






      @property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return self._src_lang






      @src_lang.setter
      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Tuple     						=  new_src_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      def __getstate__( self   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  self.__dict__.copy()
           a_     :							Dict     						=  None
           return state






      def __setstate__( self   :  Optional[Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Tuple     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
           a_     :							List[str]     						=  self.sp_model.PieceToId(lowercase__       )

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  []
           a_     :							List[str]     						=  """"""
           a_     :							Dict     						=  False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     if not prev_is_special:
                          out_string += " "
                     out_string += self.sp_model.decode(lowercase__       ) + token
                     a_     :							str     						=  True
                     a_     :							Optional[Any]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							Union[str, Any]     						=  False
           out_string += self.sp_model.decode(lowercase__       )
           return out_string.strip()






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							Dict     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ) and os.path.isfile(self.vocab_file       ):
                copyfile(self.vocab_file						,						lowercase__       )
           elif not os.path.isfile(self.vocab_file       ):
                with open(lowercase__						,						"""wb"""       ) as fi:
                     a_     :							Dict     						=  self.sp_model.serialized_model_proto()
                     fi.write(lowercase__       )

           return (out_vocab_file,)






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None						,						lowercase__   :  bool = False       ):


           '''simple docstring'''

           if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=lowercase__						,						token_ids_a=lowercase__						,						already_has_special_tokens=lowercase__       )

           a_     :							Union[str, Any]     						=  [1] * len(self.prefix_tokens       )
           a_     :							List[Any]     						=  [1] * len(self.suffix_tokens       )
           if token_ids_a is None:
                return prefix_ones + ([0] * len(lowercase__       )) + suffix_ones
           return prefix_ones + ([0] * len(lowercase__       )) + ([0] * len(lowercase__       )) + suffix_ones






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           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 							lowercase_						( self   :  Any						,						lowercase__   :  Optional[int]						,						lowercase__   :  str						,						lowercase__   :  Optional[str]						,						lowercase__   :  Optional[str]						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
           a_     :							Union[str, Any]     						=  src_lang
           a_     :							Dict     						=  self(lowercase__						,						add_special_tokens=lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )
           a_     :							Tuple     						=  self.convert_tokens_to_ids(lowercase__       )
           a_     :							Optional[Any]     						=  tgt_lang_id
           return inputs






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  str = "en_XX"						,						lowercase__   :  Optional[List[str]] = None						,						lowercase__   :  str = "ro_RO"						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							str     						=  src_lang
           a_     :							Dict     						=  tgt_lang
           return super().prepare_seqaseq_batch(lowercase__						,						lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  self.lang_code_to_id[src_lang]
           a_     :							Dict     						=  [self.cur_lang_code_id]
           a_     :							Any     						=  [self.eos_token_id]






      def 							lowercase_						( self   :  Any						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.lang_code_to_id[tgt_lang]
           a_     :							str     						=  [self.cur_lang_code_id]
           a_     :							Union[str, Any]     						=  [self.eos_token_id]
