

from typing import List, Optional, Union

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


lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  int		     =							"informer"
			SCREAMING_SNAKE_CASE	:  int		     =							{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			    "num_hidden_layers": "encoder_layers",
			}







			def __init__(	self					:							Dict				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							Optional[int] = None				,  _UpperCamelCase					:							str = "student_t"				,  _UpperCamelCase					:							str = "nll"				,  _UpperCamelCase					:							int = 1				,  _UpperCamelCase					:							List[int] = None				,  _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 = 6_4				,  _UpperCamelCase					:							int = 3_2				,  _UpperCamelCase					:							int = 3_2				,  _UpperCamelCase					:							int = 2				,  _UpperCamelCase					:							int = 2				,  _UpperCamelCase					:							int = 2				,  _UpperCamelCase					:							int = 2				,  _UpperCamelCase					:							bool = True				,  _UpperCamelCase					:							str = "gelu"				,  _UpperCamelCase					:							float = 0.05				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							float = 0.1				,  _UpperCamelCase					:							int = 1_0_0				,  _UpperCamelCase					:							float = 0.02				,  _UpperCamelCase					:							Dict=True				,  _UpperCamelCase					:							str = "prob"				,  _UpperCamelCase					:							int = 5				,  _UpperCamelCase					:							bool = True				,  **_UpperCamelCase					:							Optional[Any]				,  ) ->Optional[int]:
									# time series specific configuration
									snake_case_  =      prediction_length
									snake_case_  =      context_length or prediction_length
									snake_case_  =      distribution_output
									snake_case_  =      loss
									snake_case_  =      input_size
									snake_case_  =      num_time_features
									snake_case_  =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
									snake_case_  =      scaling
									snake_case_  =      num_dynamic_real_features
									snake_case_  =      num_static_real_features
									snake_case_  =      num_static_categorical_features

									# set cardinality
									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`'''				)
															snake_case_  =      cardinality
									else:
															snake_case_  =      [0]

									# set embedding_dimension
									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`'''				)
															snake_case_  =      embedding_dimension
									else:
															snake_case_  =      [min(5_0				,  (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

									# Informer
									snake_case_  =      attention_type
									snake_case_  =      sampling_factor
									snake_case_  =      distil

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







			@property
			def 							snake_case__(	self					:							Optional[Any]				) ->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
									)



'''simple docstring'''






from __future__ import annotations







def 							UpperCamelCase_     (       A__   :  list				,       A__   :  int				,       A__   :  int				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







def 							UpperCamelCase_     (       A__   :  list   ):
  '''simple docstring'''





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



