






"""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       =				logging.get_logger(__name__)

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

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



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					VOCAB_FILES_NAMES
	_A							:					str 			=					PRETRAINED_VOCAB_FILES_MAP
	_A							:					str 			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A							:					Optional[int] 			=					["""input_ids""", """attention_mask"""]





	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_="<s>"  ,  lowerCAmelCase_="</s>"  ,  lowerCAmelCase_="<unk>"  ,  lowerCAmelCase_="<pad>"  ,  lowerCAmelCase_ = None  ,  **lowerCAmelCase_  ,  ):
		A_		:    List[str]						     = {} if sp_model_kwargs is None else sp_model_kwargs

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

		A_		:    Optional[Any]						     = vocab_file

		A_		:    List[Any]						     = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
		self.sp_model.Load(lowerCAmelCase_				)





	@property
	def  lowerCamelCase(self				):
		return self.sp_model.get_piece_size()





	def  lowerCamelCase(self				):
		A_		:    Any						     = {self.convert_ids_to_tokens(lowerCAmelCase_				): i for i in range(self.vocab_size				)}
		vocab.update(self.added_tokens_encoder				)
		return vocab





	def __getstate__(self				):
		A_		:    Union[str, Any]						     = self.__dict__.copy()
		A_		:    Any						     = None
		return state





	def __setstate__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = d

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

		A_		:    List[str]						     = spm.SentencePieceProcessor(**self.sp_model_kwargs				)
		self.sp_model.Load(self.vocab_file				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return self.sp_model.encode(lowerCAmelCase_  ,  out_type=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		return self.sp_model.piece_to_id(lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Tuple						     = self.sp_model.IdToPiece(lowerCAmelCase_				)
		return token





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Any						     = []
		A_		:    Optional[int]						     = """"""
		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(lowerCAmelCase_				) + token
				A_		:    Union[str, Any]						     = []
			else:
				current_sub_tokens.append(lowerCAmelCase_				)
		out_string += self.sp_model.decode(lowerCAmelCase_				)
		return out_string.strip()





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=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  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = False				):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=lowerCAmelCase_  ,  token_ids_a=lowerCAmelCase_  ,  already_has_special_tokens=lowerCAmelCase_				)

		A_		:    Tuple						     = [1]
		if token_ids_a is None:
			return ([0] * len(lowerCAmelCase_				)) + suffix_ones
		return ([0] * len(lowerCAmelCase_				)) + ([0] * len(lowerCAmelCase_				)) + suffix_ones





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None				):
		if not os.path.isdir(lowerCAmelCase_				):
			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
			return
		A_		:    Optional[Any]						     = os.path.join(
		    lowerCAmelCase_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

		if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase_				) and os.path.isfile(self.vocab_file				):
			copyfile(self.vocab_file  ,  lowerCAmelCase_				)
		elif not os.path.isfile(self.vocab_file				):
			with open(lowerCAmelCase_  ,  """wb"""				) as fi:
				A_		:    List[str]						     = self.sp_model.serialized_model_proto()
				fi.write(lowerCAmelCase_				)

		return (out_vocab_file,)









"""simple docstring"""
_lowerCAmelCase       =				[
    "Audio",
    "Array2D",
    "Array3D",
    "Array4D",
    "Array5D",
    "ClassLabel",
    "Features",
    "Sequence",
    "Value",
    "Image",
    "Translation",
    "TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages

