





'''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


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

__lowerCAmelCase				:      List[Any]						      ={"vocab_file": "spm_char.model"}

__lowerCAmelCase				:      Dict						      ={
    "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",
    }
}

__lowerCAmelCase				:      int						      ={
    "microsoft/speecht5_asr": 1024,
    "microsoft/speecht5_tts": 1024,
    "microsoft/speecht5_vc": 1024,
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							VOCAB_FILES_NAMES
 __lowercase  						=							PRETRAINED_VOCAB_FILES_MAP
 __lowercase  						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase  						=							["""input_ids""", """attention_mask"""]
 def __init__(							self :Any		,			lowercase_ :Tuple		,			lowercase_ :Dict="<s>"		,			lowercase_ :Any="</s>"		,			lowercase_ :Dict="<unk>"		,			lowercase_ :str="<pad>"		,			lowercase_ :Optional[Dict[str, Any]] = None		,			**lowercase_ :Tuple		,			)->				None:
     A__							=     {} if sp_model_kwargs is None else sp_model_kwargs

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

     A__							=     vocab_file

     A__							=     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
     self.sp_model.Load(lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Dict:
     return self.sp_model.get_piece_size()
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[Any]:
     A__							=     {self.convert_ids_to_tokens(lowercase_				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab
 def __getstate__(							self :Optional[int]				)->				List[str]:
     A__							=     self.__dict__.copy()
     A__							=     None
     return state
 def __setstate__(							self :List[str]		,			lowercase_ :Union[str, Any]				)->				int:
     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 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :str				)->				List[str]:
     return self.sp_model.encode(lowercase_		,			out_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :List[Any]				)->				Any:
     return self.sp_model.piece_to_id(lowercase_				)
 def 	UpperCAmelCase_				(							self :Tuple		,			lowercase_ :int				)->				List[Any]:
     A__							=     self.sp_model.IdToPiece(lowercase_				)
     return token
 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			lowercase_ :Any				)->				List[str]:
     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(lowercase_				) + token
             A__							=     []
         else:
             current_sub_tokens.append(lowercase_				)
     out_string += self.sp_model.decode(lowercase_				)
     return out_string.strip()
 def 	UpperCAmelCase_				(							self :Dict		,			lowercase_ :Dict		,			lowercase_ :Dict=None				)->				List[int]:
     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 	UpperCAmelCase_				(							self :List[Any]		,			lowercase_ :List[int]		,			lowercase_ :Optional[List[int]] = None		,			lowercase_ :bool = False				)->				List[int]:
     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__							=     [1]
     if token_ids_a is None:
         return ([0] * len(lowercase_				)) + suffix_ones
     return ([0] * len(lowercase_				)) + ([0] * len(lowercase_				)) + suffix_ones



 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :str		,			lowercase_ :Optional[str] = None				)->				Tuple[str]:
     if not os.path.isdir(lowercase_				):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
         return
     A__							=     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__							=     self.sp_model.serialized_model_proto()
             fi.write(lowercase_				)

     return (out_vocab_file,)










'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase				:      Union[str, Any]						      ={"configuration_mra": ["MRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MraConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "MRA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MraForMaskedLM",
      "MraForMultipleChoice",
      "MraForQuestionAnswering",
      "MraForSequenceClassification",
      "MraForTokenClassification",
      "MraLayer",
      "MraModel",
      "MraPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mra import (
        MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MraForMaskedLM,
        MraForMultipleChoice,
        MraForQuestionAnswering,
        MraForSequenceClassification,
        MraForTokenClassification,
        MraLayer,
        MraModel,
        MraPreTrainedModel,
    )
else:
  import sys

  __lowerCAmelCase				:      Dict						      =_LazyModule(__name__, globals()["__file__"], _import_structure)



