

'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

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

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


if is_tf_available():
  import tensorflow as tf

  from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : str=None							, SCREAMING_SNAKE_CASE   : int=None    ):
  if attention_mask is None:
    UpperCAmelCase    				=						tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE							, config.pad_token_id    )							, tf.inta    )
  return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			int     	= OPTConfig
  __lowerCAmelCase       :			int     	= {}
  __lowerCAmelCase       :			List[str]     	= "gelu"
  def __init__(  self ,			a_ ,			a_=1_3 ,			a_=7 ,			a_=True ,			a_=False ,			a_=9_9 ,			a_=1_6 ,			a_=2 ,			a_=4 ,			a_=4 ,			a_="gelu" ,			a_=0.1 ,			a_=0.1 ,			a_=2_0 ,			a_=2 ,			a_=1 ,			a_=0 ,			a_=1_6 ,			a_=1_6 ,			)				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						parent
    UpperCAmelCase    				=						batch_size
    UpperCAmelCase    				=						seq_length
    UpperCAmelCase    				=						is_training
    UpperCAmelCase    				=						use_labels
    UpperCAmelCase    				=						vocab_size
    UpperCAmelCase    				=						hidden_size
    UpperCAmelCase    				=						num_hidden_layers
    UpperCAmelCase    				=						num_attention_heads
    UpperCAmelCase    				=						intermediate_size
    UpperCAmelCase    				=						hidden_act
    UpperCAmelCase    				=						hidden_dropout_prob
    UpperCAmelCase    				=						attention_probs_dropout_prob
    UpperCAmelCase    				=						max_position_embeddings
    UpperCAmelCase    				=						eos_token_id
    UpperCAmelCase    				=						pad_token_id
    UpperCAmelCase    				=						bos_token_id
    UpperCAmelCase    				=						embed_dim
    UpperCAmelCase    				=						word_embed_proj_dim
    UpperCAmelCase    				=						False
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						ids_tensor([self.batch_size, self.seq_length - 1] ,			self.vocab_size  )
    UpperCAmelCase    				=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,			1  )
    UpperCAmelCase    				=						tf.concat([input_ids, eos_tensor] ,			axis=1  )

    UpperCAmelCase    				=						self.config_cls(
        vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			ffn_dim=self.intermediate_size ,			dropout=self.hidden_dropout_prob ,			attention_dropout=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			eos_token_id=self.eos_token_id ,			bos_token_id=self.bos_token_id ,			pad_token_id=self.pad_token_id ,			embed_dim=self.embed_dim ,			word_embed_proj_dim=self.word_embed_proj_dim ,			is_encoder_decoder=a_ ,			**self.config_updates ,			)
    UpperCAmelCase    				=						prepare_opt_inputs_dict(a_ ,			a_  )
    return config, inputs_dict





  def     snake_case_      (  self ,			a_ ,			a_  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						TFOPTModel(config=a_  )
    UpperCAmelCase    				=						inputs_dict['input_ids']

    UpperCAmelCase    				=						input_ids[:1, :]
    UpperCAmelCase    				=						inputs_dict['attention_mask'][:1, :]
    UpperCAmelCase    				=						1

    # first forward pass
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			use_cache=a_  )

    UpperCAmelCase				,   UpperCAmelCase    				=						outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    UpperCAmelCase    				=						ids_tensor((self.batch_size, 3) ,			config.vocab_size  )
    UpperCAmelCase    				=						tf.cast(ids_tensor((self.batch_size, 3) ,			2  ) ,			tf.inta  )

    # append to next input_ids and
    UpperCAmelCase    				=						tf.concat([input_ids, next_tokens] ,			axis=-1  )
    UpperCAmelCase    				=						tf.concat([attention_mask, next_attn_mask] ,			axis=-1  )

    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_  )[0]
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			past_key_values=a_  )[0]

    self.parent.assertEqual(next_tokens.shape[1] ,			output_from_past.shape[1]  )

    # select random slice
    UpperCAmelCase    				=						int(ids_tensor((1,) ,			output_from_past.shape[-1]  )  )
    UpperCAmelCase    				=						output_from_no_past[:, -3:, random_slice_idx]
    UpperCAmelCase    				=						output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(a_ ,			a_ ,			rtol=1E-3  )




@require_tf
class 							lowercase_						(		a			,							a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[Any]     	= (TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  __lowerCAmelCase       :			Tuple     	= (TFOPTForCausalLM,) if is_tf_available() else ()
  __lowerCAmelCase       :			Dict     	= (
      {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
  )
  __lowerCAmelCase       :			Tuple     	= False
  __lowerCAmelCase       :			Any     	= False
  __lowerCAmelCase       :			str     	= False
  __lowerCAmelCase       :			List[Any]     	= 10
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						TFOPTModelTester(self  )
    UpperCAmelCase    				=						ConfigTester(self ,			config_class=a_  )
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    self.config_tester.run_common_tests()
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*a_  )





  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase				,   UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(a_ ,			a_  ):
      if hasattr(a_ ,			'weight'  ):
        return embedding_layer.weight
      else:
        # Here we build the word embeddings weights if not exists.
        # And then we retry to get the attribute once built.
        model.build()
        if hasattr(a_ ,			'weight'  ):
          return embedding_layer.weight
        else:
          return None

    for model_class in self.all_model_classes:
      for size in [config.vocab_size - 1_0, config.vocab_size + 1_0]:
        # build the embeddings
        UpperCAmelCase    				=						model_class(config=a_  )
        UpperCAmelCase    				=						_get_word_embedding_weight(a_ ,			model.get_input_embeddings()  )
        UpperCAmelCase    				=						_get_word_embedding_weight(a_ ,			model.get_output_embeddings()  )

        # reshape the embeddings
        model.resize_token_embeddings(a_  )
        UpperCAmelCase    				=						_get_word_embedding_weight(a_ ,			model.get_input_embeddings()  )
        UpperCAmelCase    				=						_get_word_embedding_weight(a_ ,			model.get_output_embeddings()  )

        # check that the resized embeddings size matches the desired size.
        UpperCAmelCase    				=						size if size is not None else config.vocab_size

        self.assertEqual(new_input_embeddings.shape[0] ,			a_  )

        # check that weights remain the same after resizing
        UpperCAmelCase    				=						True
        for pa, pa in zip(old_input_embeddings.value() ,			new_input_embeddings.value()  ):
          if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
            UpperCAmelCase    				=						False
        self.assertTrue(a_  )

        if old_output_embeddings is not None and new_output_embeddings is not None:
          self.assertEqual(new_output_embeddings.shape[0] ,			a_  )

          UpperCAmelCase    				=						True
          for pa, pa in zip(old_output_embeddings.value() ,			new_output_embeddings.value()  ):
            if tf.math.reduce_sum(tf.math.abs(pa - pa  )  ) > 0:
              UpperCAmelCase    				=						False
          self.assertTrue(a_  )




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]    ):
  return tf.constant(SCREAMING_SNAKE_CASE							, dtype=tf.intaa    )



@require_tf
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= 99
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						tf.ones((4, 1) ,			dtype=tf.intaa  ) * 2
    UpperCAmelCase    				=						tf.concat([ids_tensor((4, 6) ,			self.vocab_size - 3  ) + 3, eos_column_vector] ,			axis=1  )
    UpperCAmelCase    				=						input_ids.shape[0]
    UpperCAmelCase    				=						OPTConfig(
        vocab_size=self.vocab_size ,			hidden_size=2_4 ,			num_hidden_layers=2 ,			num_attention_heads=2 ,			ffn_dim=3_2 ,			max_position_embeddings=4_8 ,			eos_token_id=2 ,			pad_token_id=1 ,			bos_token_id=0 ,			)
    return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  @slow
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						TFOPTModel.from_pretrained('facebook/opt-350m'  )
    UpperCAmelCase    				=						_long_tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]  )
    UpperCAmelCase    				=						tf.not_equal(a_ ,			model.config.pad_token_id  )
    with tf.GradientTape():
      UpperCAmelCase    				=						model(input_ids=a_ ,			attention_mask=a_  ).last_hidden_state
    UpperCAmelCase    				=						(1, 1_1, 5_1_2)
    self.assertEqual(output.shape ,			a_  )
    UpperCAmelCase    				=						tf.constant(
        [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]  )
    self.assertTrue(np.allclose(output[:, :3, :3] ,			a_ ,			atol=4E-3  )  )

    UpperCAmelCase    				=						tf.function(a_ ,			jit_compile=a_  )
    UpperCAmelCase    				=						xla_generate(a_ ,			a_  )[0]
    self.assertTrue(np.allclose(output[:, :3, :3] ,			a_ ,			atol=4E-2  )  )




@require_tf
@slow
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    super().setUp()
    UpperCAmelCase    				=						'facebook/opt-350m'





  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						TFOPTForCausalLM.from_pretrained(self.path_model  )
    UpperCAmelCase    				=						GPTaTokenizer.from_pretrained(self.path_model  )

    UpperCAmelCase    				=						[
        'Today is a beautiful day and I want to',
        'In the city of',
        'Paris is the capital of France and',
        'Computers and mobile phones have taken',
    ]
    # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
    UpperCAmelCase    				=						tokenizer(a_ ,			return_tensors='tf' ,			padding=a_ ,			add_special_tokens=a_  )
    UpperCAmelCase    				=						tf.math.reduce_mean(model(inputs.input_ids ,			attention_mask=inputs.attention_mask  )[0] ,			axis=-1  )
    UpperCAmelCase    				=						tf.constant(
        [
            [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
            [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
            [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
            [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
        ]  )
    self.assertTrue(np.allclose(a_ ,			a_ ,			atol=1E-4  )  )

    UpperCAmelCase    				=						tf.function(a_ ,			jit_compile=a_  )
    UpperCAmelCase    				=						tf.math.reduce_mean(xla_generate(inputs.input_ids ,			attention_mask=inputs.attention_mask  )[0] ,			axis=-1  )
    self.assertTrue(np.allclose(a_ ,			a_ ,			atol=1E-4  )  )




@require_tf
@slow
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  @property
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    return [
        "Today is a beautiful day and I want",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/opt-125m'

    UpperCAmelCase    				=						[
        'Today is a beautiful day and I want to',
        'In the city of New York, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						GPTaTokenizer.from_pretrained(a_  )
    UpperCAmelCase    				=						TFOPTForCausalLM.from_pretrained(a_  )

    for prompt in self.prompts:
      UpperCAmelCase    				=						tokenizer(a_ ,			return_tensors='tf'  ).input_ids

      UpperCAmelCase    				=						model.generate(a_ ,			max_length=1_0  )

      UpperCAmelCase    				=						tokenizer.batch_decode(a_ ,			skip_special_tokens=a_  )
      predicted_outputs += generated_string

    self.assertListEqual(a_ ,			a_  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/opt-350m'

    UpperCAmelCase    				=						GPTaTokenizer.from_pretrained(a_  )
    UpperCAmelCase    				=						TFOPTForCausalLM.from_pretrained(a_  )

    UpperCAmelCase    				=						'left'

    # use different length sentences to test batching
    UpperCAmelCase    				=						[
        'Hello, my dog is a little',
        'Today, I',
    ]

    UpperCAmelCase    				=						tokenizer(a_ ,			return_tensors='tf' ,			padding=a_  )
    UpperCAmelCase    				=						inputs['input_ids']

    UpperCAmelCase    				=						model.generate(input_ids=a_ ,			attention_mask=inputs['attention_mask']  )

    UpperCAmelCase    				=						tokenizer(sentences[0] ,			return_tensors='tf'  ).input_ids
    UpperCAmelCase    				=						model.generate(input_ids=a_  )

    UpperCAmelCase    				=						inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs['attention_mask'][-1] ,			tf.intaa  )  )
    UpperCAmelCase    				=						tokenizer(sentences[1] ,			return_tensors='tf'  ).input_ids
    UpperCAmelCase    				=						model.generate(input_ids=a_ ,			max_length=model.config.max_length - num_paddings  )

    UpperCAmelCase    				=						tokenizer.batch_decode(a_ ,			skip_special_tokens=a_  )
    UpperCAmelCase    				=						tokenizer.decode(output_non_padded[0] ,			skip_special_tokens=a_  )
    UpperCAmelCase    				=						tokenizer.decode(output_padded[0] ,			skip_special_tokens=a_  )

    UpperCAmelCase    				=						[
        'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
        'Today, I was in the middle of a conversation with a friend about the',
    ]
    self.assertListEqual(a_ ,			a_  )
    self.assertListEqual(a_ ,			[non_padded_sentence, padded_sentence]  )





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


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/opt-350m'

    UpperCAmelCase    				=						[
        'Today is a beautiful day and I want to',
        'In the city of San Francisco, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    UpperCAmelCase    				=						[]
    UpperCAmelCase    				=						GPTaTokenizer.from_pretrained(a_  )
    UpperCAmelCase    				=						TFOPTForCausalLM.from_pretrained(a_  )

    for prompt in self.prompts:
      UpperCAmelCase    				=						tokenizer(a_ ,			return_tensors='tf'  ).input_ids

      UpperCAmelCase    				=						model.generate(a_ ,			max_length=1_0  )

      UpperCAmelCase    				=						tokenizer.batch_decode(a_ ,			skip_special_tokens=a_  )
      predicted_outputs += generated_string

    self.assertListEqual(a_ ,			a_  )





'''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, PreTrainedTokenizer
from ...utils import logging


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[int]							=    '▁'

_a					:			str							=    {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a					:			Optional[Any]							=    {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a					:			Union[str, Any]							=    {'vinai/bartpho-syllable': 1_024}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			str     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Any     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			int     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_ ,			a_="<s>" ,			a_="</s>" ,			a_="</s>" ,			a_="<s>" ,			a_="<unk>" ,			a_="<pad>" ,			a_="<mask>" ,			a_ = None ,			**a_ ,			)				->					None:


    """simple docstring"""
    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase    				=						AddedToken(a_ ,			lstrip=a_ ,			rstrip=a_  ) if isinstance(a_ ,			a_  ) else mask_token

    UpperCAmelCase    				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=a_ ,			eos_token=a_ ,			unk_token=a_ ,			sep_token=a_ ,			cls_token=a_ ,			pad_token=a_ ,			mask_token=a_ ,			sp_model_kwargs=self.sp_model_kwargs ,			**a_ ,			)

    UpperCAmelCase    				=						vocab_file
    UpperCAmelCase    				=						monolingual_vocab_file
    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(str(a_  )  )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase    				=						{}
    UpperCAmelCase    				=						0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(a_  ) not in self.fairseq_tokens_to_ids:
        UpperCAmelCase    				=						cnt
        cnt += 1
    with open(a_ ,			'r' ,			encoding='utf-8'  ) as f:
      for line in f.readlines():
        UpperCAmelCase    				=						line.strip().split()[0]
        UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )
    if str(a_  ) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )

    UpperCAmelCase    				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__(  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
    return state
  def __setstate__(  self ,			a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						d

    # for backward compatibility
    if not hasattr(self ,			'sp_model_kwargs'  ):
      UpperCAmelCase    				=						{}

    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = False  )				->					List[int]:


    """simple docstring"""

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

    if token_ids_a is None:
      return [1] + ([0] * len(a_  )) + [1]
    return [1] + ([0] * len(a_  )) + [1, 1] + ([0] * len(a_  )) + [1]
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return len(self.fairseq_ids_to_tokens  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{self.convert_ids_to_tokens(a_  ): i for i in range(self.vocab_size  )}
    vocab.update(self.added_tokens_encoder  )
    return vocab
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self.sp_model.encode(a_ ,			out_type=a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.fairseq_ids_to_tokens[index]
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						''.join(a_  ).replace(a_ ,			' '  ).strip()
    return out_string





  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Tuple[str]:


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

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

    if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
        a_  ) and os.path.isfile(self.monolingual_vocab_file  ):
      copyfile(self.monolingual_vocab_file ,			a_  )
    elif not os.path.isfile(self.monolingual_vocab_file  ):
      with open(a_ ,			'w' ,			encoding='utf-8'  ) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(a_  )} \n'''  )

    return out_vocab_file, out_monolingual_vocab_file


