

from typing import List, Optional

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

lowerCAmelCase__		      =       {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				'autoformer'
			__lowerCamelCase       				=				{
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'encoder_attention_heads',
			    'num_hidden_layers': 'encoder_layers',
			}


			def __init__(			self					,					lowercase = None					,					lowercase = None					,					lowercase = "student_t"					,					lowercase = "nll"					,					lowercase = 1					,					lowercase = [1, 2, 3, 4, 5, 6, 7]					,					lowercase = True					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = 0					,					lowercase = None					,					lowercase = None					,					lowercase = 64					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 2					,					lowercase = 32					,					lowercase = 32					,					lowercase = "gelu"					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 0.1					,					lowercase = 100					,					lowercase = 0.02					,					lowercase = True					,					lowercase=True					,					lowercase = 10					,					lowercase = 25					,					lowercase = 3					,					**lowercase					,					)     ->  Optional[Any]:
							'''simple docstring'''






							A__						=	prediction_length
							A__						=	context_length if context_length is not None else prediction_length
							A__						=	distribution_output
							A__						=	loss
							A__						=	input_size
							A__						=	num_time_features
							A__						=	lags_sequence
							A__						=	scaling
							A__						=	num_dynamic_real_features
							A__						=	num_static_real_features
							A__						=	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
											if len(lowercase				) != num_static_categorical_features:
															raise ValueError(
															    "The cardinality should be a list of the same length as `num_static_categorical_features`"				)
											A__						=	cardinality
							else:
											A__						=	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
											if len(lowercase				) != num_static_categorical_features:
															raise ValueError(
															    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"				)
											A__						=	embedding_dimension
							else:
											A__						=	[min(50					,					(cat + 1) // 2				) for cat in self.cardinality]
							A__						=	num_parallel_samples

							# Transformer architecture configuration
							A__						=	input_size * len(self.lags_sequence				) + self._number_of_features
							A__						=	d_model
							A__						=	encoder_attention_heads
							A__						=	decoder_attention_heads
							A__						=	encoder_ffn_dim
							A__						=	decoder_ffn_dim
							A__						=	encoder_layers
							A__						=	decoder_layers

							A__						=	dropout
							A__						=	attention_dropout
							A__						=	activation_dropout
							A__						=	encoder_layerdrop
							A__						=	decoder_layerdrop

							A__						=	activation_function
							A__						=	init_std

							A__						=	use_cache

							# Autoformer
							A__						=	label_length
							A__						=	moving_average
							A__						=	autocorrelation_factor

							super().__init__(is_encoder_decoder=lowercase					,					**lowercase				)


			@property
			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							return (
							    sum(self.embedding_dimension				)
							    + self.num_dynamic_real_features
							    + self.num_time_features
							    + self.num_static_real_features
							    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
							)








def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					List[Any]       	=   ""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups



def 		A_				(     _lowerCAmelCase     )						-> dict[str, str]:
 UpperCamelCase	:					Optional[Any]       	=   [chr(i + 65     ) for i in range(26     )]
 # Remove duplicate characters from key
 UpperCamelCase	:					Tuple       	=   remove_duplicates(key.upper()     )
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )
 # First fill cipher with key characters
 UpperCamelCase	:					int       	=   {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase     )}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_lowerCAmelCase     )							,   26     ):
  UpperCamelCase	:					Optional[Any]       	=   alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   UpperCamelCase	:					List[str]       	=   alphabet[i - offset]
  UpperCamelCase	:					List[Any]       	=   char
 return cipher_alphabet



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
