





from typing import List, Optional, Union

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


_lowerCAmelCase 				=     logging.get_logger(__name__)

_lowerCAmelCase 				=     {
    """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    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''time_series_transformer'''
      a						       =							{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }
      def __init__(		self      ,       a__ = None      ,       a__ = None      ,       a__ = "student_t"      ,       a__ = "nll"      ,       a__ = 1      ,       a__ = [1, 2, 3, 4, 5, 6, 7]      ,       a__ = "mean"      ,       a__ = 0      ,       a__ = 0      ,       a__ = 0      ,       a__ = 0      ,       a__ = None      ,       a__ = None      ,       a__ = 32      ,       a__ = 32      ,       a__ = 2      ,       a__ = 2      ,       a__ = 2      ,       a__ = 2      ,       a__ = True      ,       a__ = "gelu"      ,       a__ = 64      ,       a__ = 0.1      ,       a__ = 0.1      ,       a__ = 0.1      ,       a__ = 0.1      ,       a__ = 0.1      ,       a__ = 100      ,       a__ = 0.02      ,       a__=True      ,       **a__      ,       ):
         # time series specific configuration
         A_     :				Tuple			      =	prediction_length
         A_     :				Optional[int]			      =	context_length or prediction_length
         A_     :				Optional[Any]			      =	distribution_output
         A_     :				Union[str, Any]			      =	loss
         A_     :				List[Any]			      =	input_size
         A_     :				List[str]			      =	num_time_features
         A_     :				List[Any]			      =	lags_sequence
         A_     :				List[str]			      =	scaling
         A_     :				int			      =	num_dynamic_real_features
         A_     :				Optional[int]			      =	num_static_real_features
         A_     :				Dict			      =	num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
            if len(a__       ) != num_static_categorical_features:
               raise ValueError(
                   """The cardinality should be a list of the same length as `num_static_categorical_features`"""       )
            A_     :				Tuple			      =	cardinality
         else:
            A_     :				int			      =	[0]
         if embedding_dimension and num_static_categorical_features > 0:
            if len(a__       ) != num_static_categorical_features:
               raise ValueError(
                   """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""       )
            A_     :				Union[str, Any]			      =	embedding_dimension
         else:
            A_     :				List[str]			      =	[min(50      ,       (cat + 1) // 2       ) for cat in self.cardinality]
         A_     :				Dict			      =	num_parallel_samples

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

         A_     :				Optional[int]			      =	dropout
         A_     :				List[Any]			      =	attention_dropout
         A_     :				Union[str, Any]			      =	activation_dropout
         A_     :				Dict			      =	encoder_layerdrop
         A_     :				Optional[Any]			      =	decoder_layerdrop

         A_     :				List[Any]			      =	activation_function
         A_     :				Union[str, Any]			      =	init_std

         A_     :				List[Any]			      =	use_cache

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






      @property
      def 		_lowerCamelCase      (		self       ):
         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
         )













_lowerCAmelCase 				=     """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Any			      =	input("""Enter message: """     )
   A_     :				Dict			      =	input("""Enter key [alphanumeric]: """     )
   A_     :				Dict			      =	input("""Encrypt/Decrypt [e/d]: """     )

   if mode.lower().startswith("""e"""     ):
      A_     :				Union[str, Any]			      =	"""encrypt"""
      A_     :				Optional[int]			      =	encrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )
   elif mode.lower().startswith("""d"""     ):
      A_     :				str			      =	"""decrypt"""
      A_     :				Dict			      =	decrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )

   print(f"""\n{mode.title()}ed message:"""     )
   print(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""encrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""decrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	0
   A_     :				int			      =	key.upper()

   for symbol in message:
      A_     :				Optional[int]			      =	LETTERS.find(symbol.upper()     )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index]     )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index]     )

         num %= len(_lowerCAmelCase     )

         if symbol.isupper():
            translated.append(LETTERS[num]     )
         elif symbol.islower():
            translated.append(LETTERS[num].lower()     )

         key_index += 1
         if key_index == len(_lowerCAmelCase     ):
            A_     :				Dict			      =	0
      else:
         translated.append(_lowerCAmelCase     )
   return "".join(_lowerCAmelCase     )


if __name__ == "__main__":
      main()






