






from typing import List, Optional, Union

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


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{
    """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 			SCREAMING_SNAKE_CASE      (      _lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						'''time_series_transformer'''
     UpperCamelCase_				:		Any								=						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
         '''num_hidden_layers''': '''encoder_layers''',
     }


     def __init__(					self							:						Any       ,			UpperCAmelCase_							:						Union[str, Any] = None       ,			UpperCAmelCase_							:						Tuple = None       ,			UpperCAmelCase_							:						Optional[int] = "student_t"       ,			UpperCAmelCase_							:						Optional[Any] = "nll"       ,			UpperCAmelCase_							:						List[str] = 1       ,			UpperCAmelCase_							:						int = [1, 2, 3, 4, 5, 6, 7]       ,			UpperCAmelCase_							:						List[Any] = "mean"       ,			UpperCAmelCase_							:						List[Any] = 0       ,			UpperCAmelCase_							:						Optional[int] = 0       ,			UpperCAmelCase_							:						Tuple = 0       ,			UpperCAmelCase_							:						Tuple = 0       ,			UpperCAmelCase_							:						int = None       ,			UpperCAmelCase_							:						Optional[int] = None       ,			UpperCAmelCase_							:						str = 32       ,			UpperCAmelCase_							:						Optional[Any] = 32       ,			UpperCAmelCase_							:						str = 2       ,			UpperCAmelCase_							:						Any = 2       ,			UpperCAmelCase_							:						str = 2       ,			UpperCAmelCase_							:						str = 2       ,			UpperCAmelCase_							:						Optional[Any] = True       ,			UpperCAmelCase_							:						Union[str, Any] = "gelu"       ,			UpperCAmelCase_							:						List[str] = 64       ,			UpperCAmelCase_							:						List[str] = 0.1       ,			UpperCAmelCase_							:						Dict = 0.1       ,			UpperCAmelCase_							:						Any = 0.1       ,			UpperCAmelCase_							:						str = 0.1       ,			UpperCAmelCase_							:						Optional[Any] = 0.1       ,			UpperCAmelCase_							:						Tuple = 100       ,			UpperCAmelCase_							:						int = 0.02       ,			UpperCAmelCase_							:						int=True       ,			**UpperCAmelCase_							:						Dict       ,			):
       SCREAMING_SNAKE_CASE							:   Any						    =     prediction_length
       SCREAMING_SNAKE_CASE							:   Tuple						    =     context_length or prediction_length
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     distribution_output
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     loss
       SCREAMING_SNAKE_CASE							:   List[str]						    =     input_size
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     num_time_features
       SCREAMING_SNAKE_CASE							:   Any						    =     lags_sequence
       SCREAMING_SNAKE_CASE							:   Tuple						    =     scaling
       SCREAMING_SNAKE_CASE							:   Dict						    =     num_dynamic_real_features
       SCREAMING_SNAKE_CASE							:   int						    =     num_static_real_features
       SCREAMING_SNAKE_CASE							:   Dict						    =     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`"   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     cardinality
       else:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [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`"   )
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     embedding_dimension
       else:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [min(50       ,			(cat + 1) // 2   ) for cat in self.cardinality]
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     num_parallel_samples

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

       SCREAMING_SNAKE_CASE							:   str						    =     dropout
       SCREAMING_SNAKE_CASE							:   Any						    =     attention_dropout
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     activation_dropout
       SCREAMING_SNAKE_CASE							:   int						    =     encoder_layerdrop
       SCREAMING_SNAKE_CASE							:   Any						    =     decoder_layerdrop

       SCREAMING_SNAKE_CASE							:   Tuple						    =     activation_function
       SCREAMING_SNAKE_CASE							:   List[str]						    =     init_std

       SCREAMING_SNAKE_CASE							:   int						    =     use_cache

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






     @property
     def 					_A							(					self							:						str   ):
       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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if not all(x.isalpha() for x in string     ):
      raise ValueError('String must only contain alphabetic characters.'     )

   __a					  =     sorted(string.lower()     )
   return len(SCREAMING_SNAKE_CASE__     ) == len(set(SCREAMING_SNAKE_CASE__     )     )


if __name__ == "__main__":
 __UpperCamelCase   :							int         =   input("""Enter a string """).strip()

 __UpperCamelCase   :							Tuple         =   is_isogram(input_str)
 print(f"""{input_str} is {"an" if isogram else "not an"} isogram.""")