




'''simple docstring'''



from typing import List, Optional

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


UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)

UpperCAmelCase_							:	Any	     =		{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}





class      lowerCAmelCase (			_snake_case):
       __lowercase	:   Optional[int]										=      '''autoformer'''
       __lowercase	:   List[Any]										=      {
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
           '''num_hidden_layers''': '''encoder_layers''',
       }


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



            '''simple docstring'''




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

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

            __snake_case            =	dropout
            __snake_case            =	attention_dropout
            __snake_case            =	activation_dropout
            __snake_case            =	encoder_layerdrop
            __snake_case            =	decoder_layerdrop

            __snake_case            =	activation_function
            __snake_case            =	init_std

            __snake_case            =	use_cache

            # Autoformer
            __snake_case            =	label_length
            __snake_case            =	moving_average
            __snake_case            =	autocorrelation_factor

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




       @property
       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''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__     (					UpperCamelCase_						:      int | float | str						):
   try:
      UpperCAmelCase__	:Union[str, Any]      =      float(UpperCamelCase_						)
   except ValueError:
      raise ValueError('''Please enter a valid number'''						)
   UpperCAmelCase__	:List[str]      =      decimal - int(UpperCamelCase_						)
   if fractional_part == 0:
      return int(UpperCamelCase_						), 1
   else:
      UpperCAmelCase__	:List[Any]      =      len(str(UpperCamelCase_						).split('''.'''						)[1]						)
      UpperCAmelCase__	:Tuple      =      int(decimal * (10**number_of_frac_digits)						)
      UpperCAmelCase__	:int      =      10**number_of_frac_digits
      UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      denominator, numerator
      while True:
         UpperCAmelCase__	:Optional[Any]      =      dividend % divisor
         if remainder == 0:
            break
         UpperCAmelCase__   ,						UpperCAmelCase__	:List[str]      =      divisor, remainder
      UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      numerator / divisor, denominator / divisor
      return int(UpperCamelCase_						), int(UpperCamelCase_						)


if __name__ == "__main__":
    print(F'''{decimal_to_fraction(2) = }''')
    print(F'''{decimal_to_fraction(89.0) = }''')
    print(F'''{decimal_to_fraction("67") = }''')
    print(F'''{decimal_to_fraction("45.0") = }''')
    print(F'''{decimal_to_fraction(1.5) = }''')
    print(F'''{decimal_to_fraction("6.25") = }''')
    print(F'''{decimal_to_fraction("78td") = }''')



