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


SCREAMING_SNAKE_CASE							:List[Any]										=    '''▁'''

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {'''vocab_file''': '''spiece.model'''}

SCREAMING_SNAKE_CASE							:Tuple										=    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

SCREAMING_SNAKE_CASE							:Optional[Any]										=    {
    '''google/reformer-crime-and-punishment''': 52_4288,
}





class 							UpperCAmelCase				(  a_  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					["input_ids", "attention_mask"]






	def __init__(	self :			List[Any]							,A :			List[str]							,A :			Optional[int]="</s>"							,A :			List[Any]="<unk>"							,A :			Optional[Any]=[]							,A :			Optional[Dict[str, Any]] = None							,**A :			int							,):
					__A             =			{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=lowercase_							,unk_token=lowercase_							,additional_special_tokens=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[str]			):
					return self.sp_model.get_piece_size()






	def     UpperCamelCase_  (	self :			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 :			List[str]			):
					__A             =			self.__dict__.copy()
					__A             =			None
					return state






	def __setstate__(	self :			List[str]							,A :			Any			):
					__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 :			Dict							,A :			str			):
					return self.sp_model.encode(lowercase_							,out_type=lowercase_			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			int			):
					return self.sp_model.piece_to_id(lowercase_			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					if index < self.sp_model.get_piece_size():
									__A             =			self.sp_model.IdToPiece(lowercase_			)
					return token






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					__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 :			Optional[Any]							,A :			str							,A :			Optional[str] = None			):
					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,)


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					if self.audio_column not in features:
									raise ValueError(f'''Column {self.audio_column} is not present in features.'''			)
					if not isinstance(features[self.audio_column]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			int			):
					return {self.audio_column: "audio", self.transcription_column: "transcription"}
