'''simple docstring'''
import math

from numpy import inf
from scipy.integrate import quad



def  __lowerCamelCase       (      _UpperCamelCase  :  float					):


      '''simple docstring'''


      if num <= 0:
            raise ValueError('''math domain error'''					)

      return quad(_UpperCamelCase							,    0							,    _UpperCamelCase							,    args=(_UpperCamelCase)					)[0]



def  __lowerCamelCase       (      _UpperCamelCase  :  float							,    _UpperCamelCase  :  float					):


      '''simple docstring'''


      return math.pow(_UpperCamelCase							,    z - 1					) * math.exp(-x					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()






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

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


lowercase__  :      int        =					logging.get_logger(__name__)

lowercase__  :      List[str]        =					{
    "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							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''time_series_transformer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							**UpperCAmelCase__			:       Tuple  ,							)				->Optional[int]:
        # time series specific configuration
        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  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        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
        )





