





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


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

SCREAMING_SNAKE_CASE__							:      Tuple         =    '''▁'''

SCREAMING_SNAKE_CASE__							:      List[Any]         =    {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

SCREAMING_SNAKE_CASE__							:      Tuple         =    {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

SCREAMING_SNAKE_CASE__							:      List[Any]         =    ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

SCREAMING_SNAKE_CASE__							:      Dict         =    {'''mustc''': MUSTC_LANGS}







class 	__lowerCAmelCase      (       __lowerCAmelCase	):
   _UpperCamelCase			:    Optional[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    List[str]  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    List[str]  		=					MAX_MODEL_INPUT_SIZES
   _UpperCamelCase			:    Dict  		=					["""input_ids""", """attention_mask"""]

   _UpperCamelCase			:    List[int]  		=					[]





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case="<s>"       ,      snake_case="</s>"       ,      snake_case="<pad>"       ,      snake_case="<unk>"       ,      snake_case=False       ,      snake_case=False       ,      snake_case=None       ,      snake_case=None       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      a__						:    Dict   		=	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_SCREAMING_SNAKE_CASE       ,      eos_token=_SCREAMING_SNAKE_CASE       ,      unk_token=_SCREAMING_SNAKE_CASE       ,      pad_token=_SCREAMING_SNAKE_CASE       ,      do_upper_case=_SCREAMING_SNAKE_CASE       ,      do_lower_case=_SCREAMING_SNAKE_CASE       ,      tgt_lang=_SCREAMING_SNAKE_CASE       ,      lang_codes=_SCREAMING_SNAKE_CASE       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **_SCREAMING_SNAKE_CASE       ,      )
      a__						:    str   		=	do_upper_case
      a__						:    List[Any]   		=	do_lower_case

      a__						:    List[str]   		=	load_json(_SCREAMING_SNAKE_CASE   )
      a__						:    Optional[Any]   		=	{v: k for k, v in self.encoder.items()}
      a__						:    Optional[Any]   		=	spm_file
      a__						:    str   		=	load_spm(_SCREAMING_SNAKE_CASE       ,      self.sp_model_kwargs   )

      if lang_codes is not None:
         a__						:    Optional[int]   		=	lang_codes
         a__						:    int   		=	LANGUAGES[lang_codes]
         a__						:    Optional[Any]   		=	[F"""<lang:{lang}>""" for lang in self.langs]
         a__						:    Any   		=	{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""   ) for lang in self.langs}

         a__						:    int   		=	self.lang_tokens
         a__						:    int   		=	tgt_lang if tgt_lang is not None else self.langs[0]

         self.set_tgt_lang_special_tokens(self._tgt_lang   )
      else:
         a__						:    Optional[int]   		=	{}





   @property
   def 						_snake_case			(			self   )					->	int:
      """simple docstring"""





      return len(self.encoder   )





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self._tgt_lang





   @tgt_lang.setter
   def 						_snake_case			(			self       ,      snake_case   )					->	None:
      """simple docstring"""





      a__						:    List[Any]   		=	new_tgt_lang
      self.set_tgt_lang_special_tokens(_SCREAMING_SNAKE_CASE   )





   def 						_snake_case			(			self       ,      snake_case   )					->	None:
      """simple docstring"""





      a__						:    List[Any]   		=	self.lang_code_to_id[tgt_lang]
      a__						:    List[str]   		=	[lang_code_id]





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      return self.encoder.get(_SCREAMING_SNAKE_CASE       ,      self.encoder[self.unk_token]   )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      return self.decoder.get(_SCREAMING_SNAKE_CASE       ,      self.unk_token   )





   def 						_snake_case			(			self       ,      snake_case   )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	[]
      a__						:    Optional[Any]   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            a__						:    Tuple   		=	self.sp_model.decode(_SCREAMING_SNAKE_CASE   )
            out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
            a__						:    List[Any]   		=	[]
         else:
            current_sub_tokens.append(_SCREAMING_SNAKE_CASE   )
      a__						:    List[Any]   		=	self.sp_model.decode(_SCREAMING_SNAKE_CASE   )
      out_string += decoded.upper() if self.do_upper_case else decoded
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case=None   )					->	List[int]:
      """simple docstring"""





      if token_ids_a is None:
         return self.prefix_tokens + token_ids_a + [self.eos_token_id]
      # 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.eos_token_id]





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = False   )					->	List[int]:
      """simple docstring"""





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

      a__						:    int   		=	[1] * len(self.prefix_tokens   )
      a__						:    str   		=	[1]
      if token_ids_a is None:
         return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE   )) + suffix_ones
      return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE   )) + ([0] * len(_SCREAMING_SNAKE_CASE   )) + suffix_ones





   def 						_snake_case			(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Optional[int]   		=	self.encoder.copy()
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Dict:
      """simple docstring"""





      a__						:    Any   		=	self.__dict__.copy()
      a__						:    List[str]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	None:
      """simple docstring"""





      a__						:    Dict   		=	d

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

      a__						:    Union[str, Any]   		=	load_spm(self.spm_file       ,      self.sp_model_kwargs   )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      a__						:    Optional[int]   		=	Path(_SCREAMING_SNAKE_CASE   )
      assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
      a__						:    str   		=	save_dir / (
          (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
      )
      a__						:    int   		=	save_dir / (
          (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
      )

      save_json(self.encoder       ,      _SCREAMING_SNAKE_CASE   )

      if os.path.abspath(self.spm_file   ) != os.path.abspath(_SCREAMING_SNAKE_CASE   ) and os.path.isfile(self.spm_file   ):
         copyfile(self.spm_file       ,      _SCREAMING_SNAKE_CASE   )
      elif not os.path.isfile(self.spm_file   ):
         with open(_SCREAMING_SNAKE_CASE       ,      "wb"   ) as fi:
            a__						:    Optional[int]   		=	self.sp_model.serialized_model_proto()
            fi.write(_SCREAMING_SNAKE_CASE   )

      return (str(_SCREAMING_SNAKE_CASE   ), str(_SCREAMING_SNAKE_CASE   ))







def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Dict   		=	sentencepiece.SentencePieceProcessor(**_UpperCamelCase )
   spm.Load(str(_UpperCamelCase ) )
   return spm






def    _A	(   lowerCamelCase ):




   with open(_UpperCamelCase				,			"r" ) as f:
      return json.load(_UpperCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):




   with open(_UpperCamelCase				,			"w" ) as f:
      json.dump(_UpperCamelCase				,			_UpperCamelCase				,			indent=2 )







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_		:     List[Any]          =		{
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Optional[Any]          =		[
       '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''GitForCausalLM''',
       '''GitModel''',
       '''GitPreTrainedModel''',
       '''GitVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
   from .processing_git import GitProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_git import (
          GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          GitForCausalLM,
          GitModel,
          GitPreTrainedModel,
          GitVisionModel,
      )

else:
   import sys

   UpperCamelCase_		:     List[Any]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


