'''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


__SCREAMING_SNAKE_CASE       :     int     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  {"""vocab_file""": """spm_char.model"""}

__SCREAMING_SNAKE_CASE       :     List[str]     =  {
    """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""",
    }
}

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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  List[str]						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  Dict						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  List[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Tuple						,					A						:				Dict						,					A						:				Optional[int]="<s>"						,					A						:				Any="</s>"						,					A						:				int="<unk>"						,					A						:				int="<pad>"						,					A						:				Optional[Dict[str, Any]] = None						,					**A						:				List[Any]						,					):
             _UpperCAmelCase     :  Union[str, Any]							      =    {} 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						,					)

             _UpperCAmelCase     :  int							      =    vocab_file

             _UpperCAmelCase     :  Any							      =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
             self.sp_model.Load(A       )




      @property
      def      _A	(       self						:				Optional[int]       ):
             return self.sp_model.get_piece_size()




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Any							      =    {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]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.__dict__.copy()
             _UpperCAmelCase     :  Any							      =    None
             return state




      def __setstate__(       self						:				int						,					A						:				List[Any]       ):
             _UpperCAmelCase     :  List[str]							      =    d

             # for backward compatibility
             if not hasattr(self						,					"sp_model_kwargs"       ):
                    _UpperCAmelCase     :  Union[str, Any]							      =    {}

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




      def      _A	(       self						:				List[Any]						,					A						:				str       ):
             return self.sp_model.encode(A						,					out_type=A       )




      def      _A	(       self						:				Any						,					A						:				Tuple       ):
             return self.sp_model.piece_to_id(A       )




      def      _A	(       self						:				int						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  int							      =    self.sp_model.IdToPiece(A       )
             return token




      def      _A	(       self						:				Dict						,					A						:				List[str]       ):
             _UpperCAmelCase     :  Optional[int]							      =    []
             _UpperCAmelCase     :  Dict							      =    ""
             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
                           _UpperCAmelCase     :  Any							      =    []
                    else:
                           current_sub_tokens.append(A       )
             out_string += self.sp_model.decode(A       )
             return out_string.strip()




      def      _A	(       self						:				Dict						,					A						:				Any						,					A						:				int=None       ):
             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						:				Tuple						,					A						:				List[int]						,					A						:				Optional[List[int]] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=A						,					token_ids_a=A						,					already_has_special_tokens=A       )

             _UpperCAmelCase     :  int							      =    [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						:				Any						,					A						:				str						,					A						:				Optional[str] = None       ):
             if not os.path.isdir(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[str]							      =    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:
                           _UpperCAmelCase     :  List[Any]							      =    self.sp_model.serialized_model_proto()
                           fi.write(A       )

             return (out_vocab_file,)


'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     Dict     =  {
    """vocab_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
        ),
        """distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
        """distilbert-base-uncased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
        """distilbert-base-cased-distilled-squad""": (
            """https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-german-cased""": (
            """https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
        ),
        """distilbert-base-multilingual-cased""": (
            """https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
        ),
    },
}

__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  {
    """distilbert-base-uncased""": 512,
    """distilbert-base-uncased-distilled-squad""": 512,
    """distilbert-base-cased""": 512,
    """distilbert-base-cased-distilled-squad""": 512,
    """distilbert-base-german-cased""": 512,
    """distilbert-base-multilingual-cased""": 512,
}


__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """distilbert-base-uncased""": {"""do_lower_case""": True},
    """distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
    """distilbert-base-cased""": {"""do_lower_case""": False},
    """distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
    """distilbert-base-german-cased""": {"""do_lower_case""": False},
    """distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  str						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  Optional[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Any						=						PRETRAINED_INIT_CONFIGURATION
      __UpperCamelCase:  str						=						["input_ids", "attention_mask"]
      __UpperCamelCase:  List[str]						=						DistilBertTokenizer




      def __init__(       self						:				str						,					A						:				int=None						,					A						:				Tuple=None						,					A						:				Tuple=True						,					A						:				Dict="[UNK]"						,					A						:				List[Any]="[SEP]"						,					A						:				Optional[Any]="[PAD]"						,					A						:				Dict="[CLS]"						,					A						:				Tuple="[MASK]"						,					A						:				str=True						,					A						:				Dict=None						,					**A						:				List[Any]						,					):
             super().__init__(
                 A						,					tokenizer_file=A						,					do_lower_case=A						,					unk_token=A						,					sep_token=A						,					pad_token=A						,					cls_token=A						,					mask_token=A						,					tokenize_chinese_chars=A						,					strip_accents=A						,					**A						,					)

             _UpperCAmelCase     :  str							      =    json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
             if (
                 normalizer_state.get("lowercase"						,					A       ) != do_lower_case
                 or normalizer_state.get("strip_accents"						,					A       ) != strip_accents
                 or normalizer_state.get("handle_chinese_chars"						,					A       ) != tokenize_chinese_chars
             ):
                    _UpperCAmelCase     :  Dict							      =    getattr(A						,					normalizer_state.pop("type"       )       )
                    _UpperCAmelCase     :  int							      =    do_lower_case
                    _UpperCAmelCase     :  Optional[int]							      =    strip_accents
                    _UpperCAmelCase     :  str							      =    tokenize_chinese_chars
                    _UpperCAmelCase     :  List[Any]							      =    normalizer_class(**A       )

             _UpperCAmelCase     :  Dict							      =    do_lower_case




      def      _A	(       self						:				List[Any]						,					A						:				Tuple						,					A						:				Any=None       ):
             _UpperCAmelCase     :  Optional[int]							      =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

             if token_ids_a:
                    output += token_ids_a + [self.sep_token_id]

             return output




      def      _A	(       self						:				int						,					A						:				List[int]						,					A						:				Optional[List[int]] = None       ):
             _UpperCAmelCase     :  Any							      =    [self.sep_token_id]
             _UpperCAmelCase     :  str							      =    [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       ) * [0] + len(token_ids_a + sep       ) * [1]





      def      _A	(       self						:				Dict						,					A						:				str						,					A						:				Optional[str] = None       ):
             _UpperCAmelCase     :  Any							      =    self._tokenizer.model.save(A						,					name=A       )
             return tuple(A       )

