

'''simple docstring'''
from typing import List, Optional

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


lowerCamelCase :      Optional[int]					 =     logging.get_logger(__name__)

lowerCamelCase :      Tuple					 =     {
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}







class 							A__    (       A__  ):
  A__		 =				'autoformer'
  A__		 =				{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =prediction_length
       _SCREAMING_SNAKE_CASE							    =context_length if context_length is not None else prediction_length
       _SCREAMING_SNAKE_CASE							    =distribution_output
       _SCREAMING_SNAKE_CASE							    =loss
       _SCREAMING_SNAKE_CASE							    =input_size
       _SCREAMING_SNAKE_CASE							    =num_time_features
       _SCREAMING_SNAKE_CASE							    =lags_sequence
       _SCREAMING_SNAKE_CASE							    =scaling
       _SCREAMING_SNAKE_CASE							    =num_dynamic_real_features
       _SCREAMING_SNAKE_CASE							    =num_static_real_features
       _SCREAMING_SNAKE_CASE							    =num_static_categorical_features
       if cardinality is not None 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`' )
            _SCREAMING_SNAKE_CASE							    =cardinality
       else:
            _SCREAMING_SNAKE_CASE							    =[0]
       if embedding_dimension is not None 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`' )
            _SCREAMING_SNAKE_CASE							    =embedding_dimension
       else:
            _SCREAMING_SNAKE_CASE							    =[min(50							,					(cat + 1) // 2 ) for cat in self.cardinality]
       _SCREAMING_SNAKE_CASE							    =num_parallel_samples

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

       _SCREAMING_SNAKE_CASE							    =dropout
       _SCREAMING_SNAKE_CASE							    =attention_dropout
       _SCREAMING_SNAKE_CASE							    =activation_dropout
       _SCREAMING_SNAKE_CASE							    =encoder_layerdrop
       _SCREAMING_SNAKE_CASE							    =decoder_layerdrop

       _SCREAMING_SNAKE_CASE							    =activation_function
       _SCREAMING_SNAKE_CASE							    =init_std

       _SCREAMING_SNAKE_CASE							    =use_cache

       # Autoformer
       _SCREAMING_SNAKE_CASE							    =label_length
       _SCREAMING_SNAKE_CASE							    =moving_average
       _SCREAMING_SNAKE_CASE							    =autocorrelation_factor

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





  @property
  def 						A  (		self      :	Any )	->    int:





       '''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"""
from math import pow, sqrt







def 						__a   (	*_SCREAMING_SNAKE_CASE   )	->bool:
  a__:     Union[str, Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 0 and all(value > 0.0 for value in values   )
  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError('Input Error: Molar mass values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,     2   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,     2   ) / molar_mass		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )


