'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_reformer import ReformerTokenizer
else:
 snake_case__	:     Optional[Any]      =    None


snake_case__	:     Union[str, Any]      =    logging.get_logger(__name__)


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

snake_case__	:     Union[str, Any]      =    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__	:     List[str]      =    {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

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






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[str]		       =						VOCAB_FILES_NAMES
  lowerCamelCase_						:Dict		       =						PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase_						:str		       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase_						:Dict		       =						['''input_ids''', '''attention_mask''']
  lowerCamelCase_						:Any		       =						ReformerTokenizer


  def __init__(					self ,						snake_case_=None ,						snake_case_=None ,						snake_case_="</s>" ,						snake_case_="<unk>" ,						snake_case_=[] ,						**snake_case_ ,						):




       '''simple docstring'''

       super().__init__(
           snake_case_ ,						tokenizer_file=snake_case_ ,						eos_token=snake_case_ ,						unk_token=snake_case_ ,						additional_special_tokens=snake_case_ ,						**snake_case_ ,						)

       UpperCAmelCase_				:  Dict  = vocab_file
       UpperCAmelCase_				:  Optional[int]  = False if not self.vocab_file else True






  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ = None		):




       '''simple docstring'''

       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'		)

       if not os.path.isdir(snake_case_		):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
       UpperCAmelCase_				:  int  = os.path.join(
           snake_case_ ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(snake_case_		):
            copyfile(self.vocab_file ,						snake_case_		)

       return (out_vocab_file,)













import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
					import torch
class       UpperCamelCase_		(	unittest.TestCase				,	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					load_tool('speech-to-text'	)
										self.tool.setup()




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.tool(torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)






					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					self.tool(audio=torch.ones(3000	)	)
										self.assertEqual(lowerCAmelCase_					,    ' you'	)



