





'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

snake_case_  :							Optional[Any]            =	{
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}


class        __a   (lowerCamelCase  ):
							__a       : Tuple          =							"reformer"
							__a       : str          =							["past_buckets_states"]
							__a       : str          =							{}

							def __init__(				self				:  Union[str, Any]							,  __magic_name__				:  List[str]=64							,  __magic_name__				:  Any=["local", "lsh", "local", "lsh", "local", "lsh"]							,  __magic_name__				:  Optional[int]=1.0							,  __magic_name__				:  Dict=True							,  __magic_name__				:  List[str]=[64, 64]							,  __magic_name__				:  Optional[int]=[64, 1_92]							,  __magic_name__				:  Union[str, Any]=0							,  __magic_name__				:  Dict=2							,  __magic_name__				:  List[str]=5_12							,  __magic_name__				:  List[Any]=None							,  __magic_name__				:  Optional[int]="relu"							,  __magic_name__				:  Dict=0.0_5							,  __magic_name__				:  int=2_56							,  __magic_name__				:  List[Any]=0.0_2							,  __magic_name__				:  Optional[Any]=False							,  __magic_name__				:  Any=1E-12							,  __magic_name__				:  Any=1							,  __magic_name__				:  int=0							,  __magic_name__				:  int=0.0_5							,  __magic_name__				:  List[Any]=64							,  __magic_name__				:  List[str]=64							,  __magic_name__				:  Optional[int]=0.0							,  __magic_name__				:  str=1							,  __magic_name__				:  Union[str, Any]=0							,  __magic_name__				:  Union[str, Any]=40_96							,  __magic_name__				:  int=12							,  __magic_name__				:  Optional[int]=None							,  __magic_name__				:  Optional[Any]=1							,  __magic_name__				:  int=0							,  __magic_name__				:  Tuple=3_20							,  __magic_name__				:  int=False							,  __magic_name__				:  int=True							,  __magic_name__				:  Optional[int]=None							,  **__magic_name__				:  List[str]							,  )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	hash_seed
													UpperCAmelCase_      :   int      =	vocab_size
													UpperCAmelCase_      :   Optional[int]      =	attention_head_size
													UpperCAmelCase_      :   Any      =	hidden_size
													UpperCAmelCase_      :   Union[str, Any]      =	num_attention_heads
													UpperCAmelCase_      :   Dict      =	num_hashes
													UpperCAmelCase_      :   Union[str, Any]      =	len(__magic_name__ )
													UpperCAmelCase_      :   Dict      =	tuple(__magic_name__ ) if isinstance(__magic_name__							,  __magic_name__ ) else num_buckets
													UpperCAmelCase_      :   Union[str, Any]      =	lsh_attn_chunk_length
													UpperCAmelCase_      :   Tuple      =	local_attn_chunk_length
													UpperCAmelCase_      :   int      =	lsh_num_chunks_after
													UpperCAmelCase_      :   List[Any]      =	lsh_num_chunks_before
													UpperCAmelCase_      :   Union[str, Any]      =	local_num_chunks_after
													UpperCAmelCase_      :   str      =	local_num_chunks_before
													UpperCAmelCase_      :   List[Any]      =	hidden_act
													UpperCAmelCase_      :   Optional[Any]      =	feed_forward_size
													UpperCAmelCase_      :   Union[str, Any]      =	hidden_dropout_prob
													UpperCAmelCase_      :   int      =	lsh_attention_probs_dropout_prob
													UpperCAmelCase_      :   List[Any]      =	local_attention_probs_dropout_prob
													UpperCAmelCase_      :   Union[str, Any]      =	max_position_embeddings
													UpperCAmelCase_      :   Tuple      =	initializer_range
													UpperCAmelCase_      :   Optional[int]      =	layer_norm_eps
													UpperCAmelCase_      :   Optional[Any]      =	axial_pos_embds
													UpperCAmelCase_      :   Union[str, Any]      =	tuple(__magic_name__ )
													UpperCAmelCase_      :   str      =	tuple(__magic_name__ )
													UpperCAmelCase_      :   str      =	axial_norm_std
													UpperCAmelCase_      :   int      =	chunk_size_lm_head
													UpperCAmelCase_      :   List[str]      =	attn_layers
													UpperCAmelCase_      :   List[Any]      =	use_cache
													UpperCAmelCase_      :   Optional[Any]      =	classifier_dropout
													super().__init__(
													    pad_token_id=__magic_name__							,  eos_token_id=__magic_name__							,  is_decoder=__magic_name__							,  tie_word_embeddings=__magic_name__							,  **__magic_name__							,  )








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:
       UpperCamelCase__	:   Optional[int]								=     None


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


UpperCamelCase__	:   Any								=     "▁"

UpperCamelCase__	:   Any								=     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__	:   List[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"
        )
    },
}

UpperCamelCase__	:   Optional[Any]								=     {
    "google/reformer-crime-and-punishment": 524_288,
}



class    __snake_case	(     lowerCAmelCase__							):
  __lowerCAmelCase  :						int    			=	VOCAB_FILES_NAMES
  __lowerCAmelCase  :						int    			=	PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase  :						Tuple    			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase  :						List[str]    			=	['input_ids', 'attention_mask']
  __lowerCAmelCase  :						Dict    			=	ReformerTokenizer

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

      SCREAMING_SNAKE_CASE_						=    vocab_file
      SCREAMING_SNAKE_CASE_						=    False if not self.vocab_file else True


  def     lowerCAmelCase__  (       self    ,		_A    ,		_A = 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(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_						=    os.path.join(
          _A    ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

      return (out_vocab_file,)


