






import math





def   _lowerCAmelCase				(   UpperCamelCase__:     list							,					UpperCamelCase__:     int = 0							,					UpperCamelCase__:     int = 0						) ->   list:





	"""simple docstring"""







	A								=				end or len(UpperCamelCase__						)
	for i in range(UpperCamelCase__							,					UpperCamelCase__						):
		A								=				i
		A								=				array[i]
		while temp_index != start and temp_index_value < array[temp_index - 1]:
			A								=				array[temp_index - 1]
			temp_index -= 1
		A								=				temp_index_value
	return array





def   _lowerCAmelCase				(   UpperCamelCase__:     list							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int						) ->   None:  # Max Heap





	"""simple docstring"""







	A								=				index
	A								=				2 * index + 1  # Left Node
	A								=				2 * index + 2  # Right Node

	if left_index < heap_size and array[largest] < array[left_index]:
		A								=				left_index

	if right_index < heap_size and array[largest] < array[right_index]:
		A								=				right_index

	if largest != index:
		A					,			A								=				array[largest], array[index]
		heapify(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     list						) ->   list:





	"""simple docstring"""







	A								=				len(UpperCamelCase__						)

	for i in range(n // 2							,					-1							,					-1						):
		heapify(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

	for i in range(n - 1							,					0							,					-1						):
		A					,			A								=				array[0], array[i]
		heapify(UpperCamelCase__							,					0							,					UpperCamelCase__						)

	return array





def   _lowerCAmelCase				(   UpperCamelCase__:     list							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	if (array[first_index] > array[middle_index]) != (
	    array[first_index] > array[last_index]
	):
		return array[first_index]
	elif (array[middle_index] > array[first_index]) != (
	    array[middle_index] > array[last_index]
	):
		return array[middle_index]
	else:
		return array[last_index]





def   _lowerCAmelCase				(   UpperCamelCase__:     list							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int						) ->   int:





	"""simple docstring"""







	A								=				low
	A								=				high
	while True:
		while array[i] < pivot:
			i += 1
		j -= 1
		while pivot < array[j]:
			j -= 1
		if i >= j:
			return i
		A					,			A								=				array[j], array[i]
		i += 1





def   _lowerCAmelCase				(   UpperCamelCase__:     list						) ->   list:





	"""simple docstring"""







	if len(UpperCamelCase__						) == 0:
		return array
	A								=				2 * math.ceil(math.loga(len(UpperCamelCase__						)						)						)
	A								=				16
	return intro_sort(UpperCamelCase__							,					0							,					len(UpperCamelCase__						)							,					UpperCamelCase__							,					UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     list							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int						) ->   list:





	"""simple docstring"""







	while end - start > size_threshold:
		if max_depth == 0:
			return heap_sort(UpperCamelCase__						)
		max_depth -= 1
		A								=				median_of_a(UpperCamelCase__							,					UpperCamelCase__							,					start + ((end - start) // 2) + 1							,					end - 1						)
		A								=				partition(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
		intro_sort(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
		A								=				p
	return insertion_sort(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowercase       :      Union[str, Any]	=	input("Enter numbers separated by a comma : ").strip()
				_lowercase       :      Any	=	[float(item) for item in user_input.split(",")]
				print(sort(unsorted))







from typing import List, Optional, Union

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


_lowercase       :      Optional[Any]	=	logging.get_logger(__name__)

_lowercase       :      List[str]	=	{
    "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       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 'informer'
				lowerCAmelCase 		= {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}

				def __init__(						self				,   a__ = None				,   a__ = None				,   a__ = "student_t"				,   a__ = "nll"				,   a__ = 1				,   a__ = None				,   a__ = "mean"				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = 0				,   a__ = None				,   a__ = None				,   a__ = 64				,   a__ = 32				,   a__ = 32				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = 2				,   a__ = True				,   a__ = "gelu"				,   a__ = 0.05				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 0.1				,   a__ = 100				,   a__ = 0.02				,   a__=True				,   a__ = "prob"				,   a__ = 5				,   a__ = True				,   **a__				,   )	->				Union[str, Any]:
					# time series specific configuration
					A								=				prediction_length
					A								=				context_length or prediction_length
					A								=				distribution_output
					A								=				loss
					A								=				input_size
					A								=				num_time_features
					A								=				lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
					A								=				scaling
					A								=				num_dynamic_real_features
					A								=				num_static_real_features
					A								=				num_static_categorical_features

					# set cardinality
					if cardinality 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`"""   )
						A								=				cardinality
					else:
						A								=				[0]

					# set embedding_dimension
					if embedding_dimension 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`"""   )
						A								=				embedding_dimension
					else:
						A								=				[min(50				,   (cat + 1) // 2   ) for cat in self.cardinality]

					A								=				num_parallel_samples

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

					A								=				dropout
					A								=				attention_dropout
					A								=				activation_dropout
					A								=				encoder_layerdrop
					A								=				decoder_layerdrop

					A								=				activation_function
					A								=				init_std

					A								=				use_cache

					# Informer
					A								=				attention_type
					A								=				sampling_factor
					A								=				distil

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

				@property
				def      _UpperCAmelCase					(						self   )	->				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
					)



