





"""simple docstring"""


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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= "▁"

lowerCamelCase__						= {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

lowerCamelCase__						= {
    "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"
        )
    },
}

lowerCamelCase__						= {
    "facebook/s2t-small-librispeech-asr": 1024,
}

lowerCamelCase__						= ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

lowerCamelCase__						= {"mustc": MUSTC_LANGS}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:Tuple						  =       PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:Dict						  =       MAX_MODEL_INPUT_SIZES
  SCREAMING_SNAKE_CASE__				:Tuple						  =       ["input_ids", "attention_mask"]

  SCREAMING_SNAKE_CASE__				:List[int]						  =       []

  def __init__(		self :							List[str]       , __a :							Optional[Any]       , __a :							str       , __a :							Union[str, Any]="<s>"       , __a :							List[str]="</s>"       , __a :							Optional[Any]="<pad>"       , __a :							str="<unk>"       , __a :							List[str]=False       , __a :							Optional[Any]=False       , __a :							Union[str, Any]=None       , __a :							Optional[Any]=None       , __a :							Optional[Dict[str, Any]] = None       , **__a :							str       , )		->					None:
        _UpperCamelCase   :						List[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       , do_upper_case=__a       , do_lower_case=__a       , tgt_lang=__a       , lang_codes=__a       , sp_model_kwargs=self.sp_model_kwargs       , **__a       , )
        _UpperCamelCase   :						str    =							do_upper_case
        _UpperCamelCase   :						List[str]    =							do_lower_case

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

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

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

              self.set_tgt_lang_special_tokens(self._tgt_lang						)
        else:
              _UpperCamelCase   :						int    =							{}

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					int:
        return len(self.encoder						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					str:
        return self._tgt_lang

  @tgt_lang.setter
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							List[Any]						)		->					None:
        _UpperCamelCase   :						Tuple    =							new_tgt_lang
        self.set_tgt_lang_special_tokens(__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							str						)		->					None:
        _UpperCamelCase   :						int    =							self.lang_code_to_id[tgt_lang]
        _UpperCamelCase   :						Any    =							[lang_code_id]

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							str						)		->					List[str]:
        return self.sp_model.encode(__a       , out_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							Dict						)		->					Optional[Any]:
        return self.encoder.get(__a       , self.encoder[self.unk_token]						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							int						)		->					str:
        return self.decoder.get(__a       , self.unk_token						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							List[str]						)		->					str:
        _UpperCamelCase   :						Optional[Any]    =							[]
        _UpperCamelCase   :						Tuple    =							""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    _UpperCamelCase   :						Dict    =							self.sp_model.decode(__a						)
                    out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                    _UpperCamelCase   :						Any    =							[]
              else:
                    current_sub_tokens.append(__a						)
        _UpperCamelCase   :						List[str]    =							self.sp_model.decode(__a						)
        out_string += decoded.upper() if self.do_upper_case else decoded
        return out_string.strip()

  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Tuple       , __a :							List[Any]=None						)		->					List[int]:
        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  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							List[int]       , __a :							Optional[List[int]] = None       , __a :							bool = False						)		->					List[int]:

        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   :						Tuple    =							[1] * len(self.prefix_tokens						)
        _UpperCamelCase   :						Any    =							[1]
        if token_ids_a is None:
              return prefix_ones + ([0] * len(__a						)) + suffix_ones
        return prefix_ones + ([0] * len(__a						)) + ([0] * len(__a						)) + suffix_ones

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Dict:
        _UpperCamelCase   :						Any    =							self.encoder.copy()
        vocab.update(self.added_tokens_encoder						)
        return vocab

  def __getstate__(		self :							List[str]						)		->					Dict:
        _UpperCamelCase   :						Any    =							self.__dict__.copy()
        _UpperCamelCase   :						List[Any]    =							None
        return state

  def __setstate__(		self :							List[Any]       , __a :							Dict						)		->					None:
        _UpperCamelCase   :						List[Any]    =							d

        # for backward compatibility
        if not hasattr(self       , "sp_model_kwargs"						):
              _UpperCamelCase   :						Optional[int]    =							{}

        _UpperCamelCase   :						str    =							load_spm(self.spm_file       , self.sp_model_kwargs						)


  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							str       , __a :							Optional[str] = None						)		->					Tuple[str]:
        _UpperCamelCase   :						List[str]    =							Path(__a						)
        assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
        _UpperCamelCase   :						Optional[int]    =							save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        _UpperCamelCase   :						List[str]    =							save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder       , __a						)

        if os.path.abspath(self.spm_file						) != os.path.abspath(__a						) and os.path.isfile(self.spm_file						):
              copyfile(self.spm_file       , __a						)
        elif not os.path.isfile(self.spm_file						):
              with open(__a       , "wb"						) as fi:
                    _UpperCamelCase   :						List[Any]    =							self.sp_model.serialized_model_proto()
                    fi.write(__a						)

        return (str(__a						), str(__a						))





def        lowercase__     (       lowercase_		,lowercase_      )       ->		sentencepiece.SentencePieceProcessor:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							sentencepiece.SentencePieceProcessor(**lowercase_      )
      spm.Load(str(lowercase_      )      )
      return spm




def        lowercase__     (       lowercase_      )       ->		Union[Dict, List]:




      """simple docstring"""

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




def        lowercase__     (       lowercase_		,lowercase_      )       ->		None:




      """simple docstring"""

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







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
