






from typing import List, Optional, Union

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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    str       						=  '''time_series_transformer'''
		_snake_case    :    Optional[int]       						=  {
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		    '''num_hidden_layers''': '''encoder_layers''',
		}



		def __init__(							self				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = "student_t"				,		_UpperCamelCase = "nll"				,		_UpperCamelCase = 1				,		_UpperCamelCase = [1, 2, 3, 4, 5, 6, 7]				,		_UpperCamelCase = "mean"				,		_UpperCamelCase = 0				,		_UpperCamelCase = 0				,		_UpperCamelCase = 0				,		_UpperCamelCase = 0				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 3_2				,		_UpperCamelCase = 2				,		_UpperCamelCase = 2				,		_UpperCamelCase = 2				,		_UpperCamelCase = 2				,		_UpperCamelCase = True				,		_UpperCamelCase = "gelu"				,		_UpperCamelCase = 6_4				,		_UpperCamelCase = 0.1				,		_UpperCamelCase = 0.1				,		_UpperCamelCase = 0.1				,		_UpperCamelCase = 0.1				,		_UpperCamelCase = 0.1				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 0.02				,		_UpperCamelCase=True				,		**_UpperCamelCase				,		)	->							Optional[int]:
							# time series specific configuration
							UpperCAmelCase_    :      Any            =						prediction_length
							UpperCAmelCase_    :      int            =						context_length or prediction_length
							UpperCAmelCase_    :      Tuple            =						distribution_output
							UpperCAmelCase_    :      Optional[int]            =						loss
							UpperCAmelCase_    :      Optional[Any]            =						input_size
							UpperCAmelCase_    :      List[Any]            =						num_time_features
							UpperCAmelCase_    :      str            =						lags_sequence
							UpperCAmelCase_    :      Union[str, Any]            =						scaling
							UpperCAmelCase_    :      List[Any]            =						num_dynamic_real_features
							UpperCAmelCase_    :      Tuple            =						num_static_real_features
							UpperCAmelCase_    :      Tuple            =						num_static_categorical_features
							if cardinality and num_static_categorical_features > 0:
												if len(_UpperCamelCase   ) != num_static_categorical_features:
																	raise ValueError(
																	    'The cardinality should be a list of the same length as `num_static_categorical_features`'   )
												UpperCAmelCase_    :      str            =						cardinality
							else:
												UpperCAmelCase_    :      List[str]            =						[0]
							if embedding_dimension and num_static_categorical_features > 0:
												if len(_UpperCamelCase   ) != num_static_categorical_features:
																	raise ValueError(
																	    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'   )
												UpperCAmelCase_    :      Any            =						embedding_dimension
							else:
												UpperCAmelCase_    :      str            =						[min(5_0				,		(cat + 1) // 2   ) for cat in self.cardinality]
							UpperCAmelCase_    :      int            =						num_parallel_samples

							# Transformer architecture configuration
							UpperCAmelCase_    :      str            =						input_size * len(_UpperCamelCase   ) + self._number_of_features
							UpperCAmelCase_    :      Optional[Any]            =						d_model
							UpperCAmelCase_    :      List[Any]            =						encoder_attention_heads
							UpperCAmelCase_    :      List[Any]            =						decoder_attention_heads
							UpperCAmelCase_    :      int            =						encoder_ffn_dim
							UpperCAmelCase_    :      Any            =						decoder_ffn_dim
							UpperCAmelCase_    :      Dict            =						encoder_layers
							UpperCAmelCase_    :      Tuple            =						decoder_layers

							UpperCAmelCase_    :      Union[str, Any]            =						dropout
							UpperCAmelCase_    :      Optional[Any]            =						attention_dropout
							UpperCAmelCase_    :      Optional[Any]            =						activation_dropout
							UpperCAmelCase_    :      List[str]            =						encoder_layerdrop
							UpperCAmelCase_    :      List[str]            =						decoder_layerdrop

							UpperCAmelCase_    :      int            =						activation_function
							UpperCAmelCase_    :      List[Any]            =						init_std

							UpperCAmelCase_    :      Optional[Any]            =						use_cache

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



		@property
		def 					__UpperCAmelCase			(							self   )	->							int:
							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
							)







'''simple docstring'''





import math





def 	lowerCAmelCase_				(					)    ->      None:


     '''simple docstring'''




     UpperCAmelCase_           =   input("Enter message: "			)
     UpperCAmelCase_           =   int(input(f"""Enter key [2-{len(snake_case_			) - 1}]: """			)			)
     UpperCAmelCase_           =   input("Encryption/Decryption [e/d]: "			)

     if mode.lower().startswith("e"			):
          UpperCAmelCase_           =   encrypt_message(snake_case_					,    snake_case_			)
     elif mode.lower().startswith("d"			):
          UpperCAmelCase_           =   decrypt_message(snake_case_					,    snake_case_			)

     # Append pipe symbol (vertical bar) to identify spaces at the end.
     print(f"""Output:\n{text + "|"}"""			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   [""] * key
     for col in range(snake_case_			):
          UpperCAmelCase_           =   col
          while pointer < len(snake_case_			):
               cipher_text[col] += message[pointer]
               pointer += key
     return "".join(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : int					,    snake_case_       : str			)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   math.ceil(len(snake_case_			) / key			)
     UpperCAmelCase_           =   key
     UpperCAmelCase_           =   (num_cols * num_rows) - len(snake_case_			)
     UpperCAmelCase_           =   [""] * num_cols
     UpperCAmelCase_           =   0
     UpperCAmelCase_           =   0

     for symbol in message:
          plain_text[col] += symbol
          col += 1

          if (
              (col == num_cols)
              or (col == num_cols - 1)
              and (row >= num_rows - num_shaded_boxes)
          ):
               UpperCAmelCase_           =   0
               row += 1

     return "".join(snake_case_			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()




