



'''simple docstring'''





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{"vocab_file": "spm_char.model"}

lowercase_	 =						{
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

lowercase_	 =						{
    "microsoft/speecht5_asr": 1_024,
    "microsoft/speecht5_tts": 1_024,
    "microsoft/speecht5_vc": 1_024,
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Dict    = VOCAB_FILES_NAMES
 __lowerCamelCase					: List[str]    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: List[str]    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: List[Any]    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A="<s>"				,     A="</s>"				,     A="<unk>"				,     A="<pad>"				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      _a											=						{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=A				,     eos_token=A				,     unk_token=A				,     pad_token=A				,     sp_model_kwargs=self.sp_model_kwargs				,     **A				,     )

      _a											=						vocab_file

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(A )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.sp_model.get_piece_size()



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _a											=						{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
      vocab.update(self.added_tokens_encoder )
      return vocab



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


      """simple docstring"""



      _a											=						self.__dict__.copy()
      _a											=						None
      return state



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


      """simple docstring"""



      _a											=						d

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

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(self.vocab_file )



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



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



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


      """simple docstring"""



      return self.sp_model.piece_to_id(A )



 def       a__						(self				,     A )					->  int:


      """simple docstring"""



      _a											=						self.sp_model.IdToPiece(A )
      return token



 def       a__						(self				,     A )					->  Any:


      """simple docstring"""



      _a											=						[]
      _a											=						''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A ) + token
                _a											=						[]
           else:
                current_sub_tokens.append(A )
      out_string += self.sp_model.decode(A )
      return out_string.strip()



 def       a__						(self				,     A				,     A=None )					->  List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]



 def       a__						(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 )

      _a											=						[1]
      if token_ids_a is None:
           return ([0] * len(A )) + suffix_ones
      return ([0] * len(A )) + ([0] * len(A )) + suffix_ones



 def       a__						(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
      _a											=						os.path.join(
          A				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''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:
                _a											=						self.sp_model.serialized_model_proto()
                fi.write(A )

      return (out_vocab_file,)








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



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


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



