code: 
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _UpperCAmelCase   :      Tuple 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      Union[str, 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 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   Tuple          =    'reformer'
  		A__  :   List[str]          =    ['past_buckets_states']
  		A__  :   Union[str, Any]          =    {}
  		def __init__(    self  ,					_snake_case=64  ,					_snake_case=["local", "lsh", "local", "lsh", "local", "lsh"]  ,					_snake_case=1.0  ,					_snake_case=True  ,					_snake_case=[64, 64]  ,					_snake_case=[64, 192]  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=512  ,					_snake_case=None  ,					_snake_case="relu"  ,					_snake_case=0.05  ,					_snake_case=256  ,					_snake_case=0.02  ,					_snake_case=False  ,					_snake_case=1E-12  ,					_snake_case=1  ,					_snake_case=0  ,					_snake_case=0.05  ,					_snake_case=64  ,					_snake_case=64  ,					_snake_case=0.0  ,					_snake_case=1  ,					_snake_case=0  ,					_snake_case=4096  ,					_snake_case=12  ,					_snake_case=None  ,					_snake_case=1  ,					_snake_case=0  ,					_snake_case=320  ,					_snake_case=False  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[str]:
  				_UpperCamelCase       : Optional[Any]    =      hash_seed
  				_UpperCamelCase       : Optional[int]    =      vocab_size
  				_UpperCamelCase       : Tuple    =      attention_head_size
  				_UpperCamelCase       : Union[str, Any]    =      hidden_size
  				_UpperCamelCase       : Dict    =      num_attention_heads
  				_UpperCamelCase       : Optional[int]    =      num_hashes
  				_UpperCamelCase       : Optional[int]    =      len(_snake_case     )
  				_UpperCamelCase       : Dict    =      tuple(_snake_case     ) if isinstance(_snake_case  ,					_snake_case     ) else num_buckets
  				_UpperCamelCase       : Tuple    =      lsh_attn_chunk_length
  				_UpperCamelCase       : Optional[Any]    =      local_attn_chunk_length
  				_UpperCamelCase       : Optional[int]    =      lsh_num_chunks_after
  				_UpperCamelCase       : List[Any]    =      lsh_num_chunks_before
  				_UpperCamelCase       : int    =      local_num_chunks_after
  				_UpperCamelCase       : List[str]    =      local_num_chunks_before
  				_UpperCamelCase       : Optional[int]    =      hidden_act
  				_UpperCamelCase       : Optional[Any]    =      feed_forward_size
  				_UpperCamelCase       : List[Any]    =      hidden_dropout_prob
  				_UpperCamelCase       : Dict    =      lsh_attention_probs_dropout_prob
  				_UpperCamelCase       : Tuple    =      local_attention_probs_dropout_prob
  				_UpperCamelCase       : Union[str, Any]    =      max_position_embeddings
  				_UpperCamelCase       : Optional[int]    =      initializer_range
  				_UpperCamelCase       : Optional[Any]    =      layer_norm_eps
  				_UpperCamelCase       : Any    =      axial_pos_embds
  				_UpperCamelCase       : List[str]    =      tuple(_snake_case     )
  				_UpperCamelCase       : List[str]    =      tuple(_snake_case     )
  				_UpperCamelCase       : str    =      axial_norm_std
  				_UpperCamelCase       : str    =      chunk_size_lm_head
  				_UpperCamelCase       : Optional[int]    =      attn_layers
  				_UpperCamelCase       : Optional[Any]    =      use_cache
  				_UpperCamelCase       : Dict    =      classifier_dropout
  				super().__init__(
  				    pad_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_decoder=_snake_case  ,					tie_word_embeddings=_snake_case  ,					**_snake_case  ,					)
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_mobilenet_va import MobileNetVaImageProcessor
  
  
  _UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					*_snake_case  ,					**_snake_case     )				->    None:
  				warnings.warn(
  				    '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
  				    ''' Please use MobileNetV2ImageProcessor instead.'''  ,					_snake_case  ,					)
  				super().__init__(*_snake_case  ,					**_snake_case     )
  
  
  
  
  
  