






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


A_	:	List[Any]				=				2 << 20  # 2mb
A_	:	Tuple				=				get_logger(__name__)




class        _lowerCAmelCase( a_      ):





       """simple docstring"""







       def     _a					(      self							):
         UpperCamelCase_:      List[str]					   =       self._options.get_all_options().get('runner'							)
         return runner in [None, "DirectRunner", "PortableRunner"]







def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=False						) ->       Optional[Any]:

  '''simple docstring'''





  UpperCamelCase_:      Any					   =       FileSystems
  if fs.exists(UpperCAmelCase__						):
    if force_upload:
      logger.info(F'''Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
    else:
      logger.info(F'''Remote path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
      return
  with fs.create(UpperCAmelCase__						) as remote_file:
    with open(UpperCAmelCase__			,					'rb'						) as local_file:
      UpperCamelCase_:      Any					   =       local_file.read(UpperCAmelCase__						)
      while chunk:
        remote_file.write(UpperCAmelCase__						)
        UpperCamelCase_:      Dict					   =       local_file.read(UpperCAmelCase__						)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=False						) ->       Union[str, Any]:

  '''simple docstring'''





  UpperCamelCase_:      str					   =       FileSystems
  if os.path.exists(UpperCAmelCase__						):
    if force_download:
      logger.info(F'''Local path already exist: {remote_file_path}. Overwriting it as force_upload=True.'''						)
    else:
      logger.info(F'''Local path already exist: {remote_file_path}. Skipping it as force_upload=False.'''						)
      return
  with fs.open(UpperCAmelCase__						) as remote_file:
    with open(UpperCAmelCase__			,					'wb'						) as local_file:
      UpperCamelCase_:      Any					   =       remote_file.read(UpperCAmelCase__						)
      while chunk:
        local_file.write(UpperCAmelCase__						)
        UpperCamelCase_:      Optional[Any]					   =       remote_file.read(UpperCAmelCase__						)







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:
   A_	:	Union[str, Any]				=				None


A_	:	List[Any]				=				logging.get_logger(__name__)


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    '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'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         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(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       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							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)