
"""simple docstring"""

from typing import List, Optional, Union

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


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

UpperCamelCase			:       List[Any]									=				{
    "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	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"time_series_transformer"
     lowercase		     =		{
         "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 = 32			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = 2			,  __UpperCAmelCase = True			,  __UpperCAmelCase = "gelu"			,  __UpperCAmelCase = 64			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 0.1			,  __UpperCAmelCase = 100			,  __UpperCAmelCase = 0.0_2			,  __UpperCAmelCase=True			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= prediction_length
      __UpperCamelCase       						= context_length or prediction_length
      __UpperCamelCase       						= distribution_output
      __UpperCamelCase       						= loss
      __UpperCamelCase       						= input_size
      __UpperCamelCase       						= num_time_features
      __UpperCamelCase       						= lags_sequence
      __UpperCamelCase       						= scaling
      __UpperCamelCase       						= num_dynamic_real_features
      __UpperCamelCase       						= num_static_real_features
      __UpperCamelCase       						= 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       						= cardinality
      else:
       __UpperCamelCase       						= [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       						= embedding_dimension
      else:
       __UpperCamelCase       						= [min(50			,  (cat + 1) // 2   ) for cat in self.cardinality]
      __UpperCamelCase       						= num_parallel_samples

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

      __UpperCamelCase       						= dropout
      __UpperCamelCase       						= attention_dropout
      __UpperCamelCase       						= activation_dropout
      __UpperCamelCase       						= encoder_layerdrop
      __UpperCamelCase       						= decoder_layerdrop

      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= init_std

      __UpperCamelCase       						= use_cache

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







     @property
     def 		UpperCAmelCase      (			self   ):

      '''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
      )





"""simple docstring"""

def 							A		(       snake_case   :int	)   ->			bool:
 return str(snake_case	) == str(snake_case	)[::-1]






def 							A		(       snake_case   :int	)   ->			int:
 return int(snake_case	) + int(str(snake_case	)[::-1]	)






def 							A		(       snake_case   :int = 1_0_0_0_0	)   ->			int:
 __UpperCamelCase       						= []
 for num in range(1       ,      snake_case	):
  __UpperCamelCase       						= 0
  __UpperCamelCase       						= num
  while iterations < 5_0:
   __UpperCamelCase       						= sum_reverse(snake_case	)
   iterations += 1
   if is_palindrome(snake_case	):
    break
  else:
   lychrel_nums.append(snake_case	)
 return len(snake_case	)


if __name__ == "__main__":
      print(f'''{solution() = }''')



