






"""simple docstring"""






from typing import List, Optional, Union

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


UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				Optional[int]         =				{
    """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__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"time_series_transformer"
      __UpperCamelCase							=						{
          "hidden_size": "d_model",
          "num_attention_heads": "encoder_attention_heads",
          "num_hidden_layers": "encoder_layers",
      }
      def __init__(		self  :						Dict		,			lowercase_  :						Optional[int] = None		,			lowercase_  :						Optional[int] = None		,			lowercase_  :						str = "student_t"		,			lowercase_  :						str = "nll"		,			lowercase_  :						int = 1		,			lowercase_  :						List[int] = [1, 2, 3, 4, 5, 6, 7]		,			lowercase_  :						Optional[Union[str, bool]] = "mean"		,			lowercase_  :						int = 0		,			lowercase_  :						int = 0		,			lowercase_  :						int = 0		,			lowercase_  :						int = 0		,			lowercase_  :						Optional[List[int]] = None		,			lowercase_  :						Optional[List[int]] = None		,			lowercase_  :						int = 32		,			lowercase_  :						int = 32		,			lowercase_  :						int = 2		,			lowercase_  :						int = 2		,			lowercase_  :						int = 2		,			lowercase_  :						int = 2		,			lowercase_  :						bool = True		,			lowercase_  :						str = "gelu"		,			lowercase_  :						int = 64		,			lowercase_  :						float = 0.1		,			lowercase_  :						float = 0.1		,			lowercase_  :						float = 0.1		,			lowercase_  :						float = 0.1		,			lowercase_  :						float = 0.1		,			lowercase_  :						int = 100		,			lowercase_  :						float = 0.02		,			lowercase_  :						Union[str, Any]=True		,			**lowercase_  :						Optional[Any]		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	prediction_length
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	context_length or prediction_length
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	distribution_output
          SCREAMING_SNAKE_CASE_   :						str							=	loss
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	input_size
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	num_time_features
          SCREAMING_SNAKE_CASE_   :						Tuple							=	lags_sequence
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	scaling
          SCREAMING_SNAKE_CASE_   :						int							=	num_dynamic_real_features
          SCREAMING_SNAKE_CASE_   :						str							=	num_static_real_features
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	num_static_categorical_features
          if cardinality and num_static_categorical_features > 0:
              if len(lowercase_) != num_static_categorical_features:
                  raise ValueError(
                      '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
              SCREAMING_SNAKE_CASE_   :						str							=	cardinality
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	[0]
          if embedding_dimension and num_static_categorical_features > 0:
              if len(lowercase_) != 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_   :						str							=	embedding_dimension
          else:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[min(50		,			(cat + 1) // 2) for cat in self.cardinality]
          SCREAMING_SNAKE_CASE_   :						Tuple							=	num_parallel_samples

          # Transformer architecture configuration
          SCREAMING_SNAKE_CASE_   :						List[str]							=	input_size * len(lowercase_) + self._number_of_features
          SCREAMING_SNAKE_CASE_   :						Tuple							=	d_model
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	encoder_attention_heads
          SCREAMING_SNAKE_CASE_   :						List[str]							=	decoder_attention_heads
          SCREAMING_SNAKE_CASE_   :						Tuple							=	encoder_ffn_dim
          SCREAMING_SNAKE_CASE_   :						List[str]							=	decoder_ffn_dim
          SCREAMING_SNAKE_CASE_   :						List[str]							=	encoder_layers
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	decoder_layers

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	dropout
          SCREAMING_SNAKE_CASE_   :						Any							=	attention_dropout
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	activation_dropout
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	encoder_layerdrop
          SCREAMING_SNAKE_CASE_   :						str							=	decoder_layerdrop

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	activation_function
          SCREAMING_SNAKE_CASE_   :						Dict							=	init_std

          SCREAMING_SNAKE_CASE_   :						List[str]							=	use_cache

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



      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''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"""






# Algorithm for the pigeonhole sorting





def       _A    (__a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	min(__a							)  # min() finds the minimum value
    SCREAMING_SNAKE_CASE_   :						int							=	max(__a							)  # max() finds the maximum value

    SCREAMING_SNAKE_CASE_   :						Dict							=	max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    SCREAMING_SNAKE_CASE_   :						Any							=	[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(__a					,       __a							), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    for count in range(__a							):
        while holes[count] > 0:
            holes[count] -= 1
            SCREAMING_SNAKE_CASE_   :						Dict							=	count + min_val
            i += 1



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(__a							)
    print('''Sorted order is:'''					,       ''' '''.join(__a							)							)


if __name__ == "__main__":
  main()


