

from __future__ import annotations

import bisect

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int					, lowerCAmelCase:					int = 0					, lowerCAmelCase:					int = -1	)				->    int:
				if hi < 0:
								_UpperCAmelCase    :						str							=      len(lowerCAmelCase	)

				while lo < hi:
								_UpperCAmelCase    :						Optional[int]							=      lo + (hi - lo) // 2
								if sorted_collection[mid] < item:
												_UpperCAmelCase    :						Dict							=      mid + 1
								else:
												_UpperCAmelCase    :						Dict							=      mid

				return lo

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int					, lowerCAmelCase:					int = 0					, lowerCAmelCase:					int = -1	)				->    int:
				if hi < 0:
								_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	)

				while lo < hi:
								_UpperCAmelCase    :						Any							=      lo + (hi - lo) // 2
								if sorted_collection[mid] <= item:
												_UpperCAmelCase    :						Optional[Any]							=      mid + 1
								else:
												_UpperCAmelCase    :						str							=      mid

				return lo

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int					, lowerCAmelCase:					int = 0					, lowerCAmelCase:					int = -1	)				->    None:
				sorted_collection.insert(bisect_left(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)					, lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int					, lowerCAmelCase:					int = 0					, lowerCAmelCase:					int = -1	)				->    None:
				sorted_collection.insert(bisect_right(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase	)					, lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int | None:
				_UpperCAmelCase    :						str							=      0
				_UpperCAmelCase    :						Tuple							=      len(lowerCAmelCase	) - 1

				while left <= right:
								_UpperCAmelCase    :						List[Any]							=      left + (right - left) // 2
								_UpperCAmelCase    :						List[str]							=      sorted_collection[midpoint]
								if current_item == item:
												return midpoint
								elif item < current_item:
												_UpperCAmelCase    :						Optional[Any]							=      midpoint - 1
								else:
												_UpperCAmelCase    :						int							=      midpoint + 1
				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int | None:
				_UpperCAmelCase    :						List[str]							=      bisect.bisect_left(lowerCAmelCase					, lowerCAmelCase	)
				if index != len(lowerCAmelCase	) and sorted_collection[index] == item:
								return index
				return None

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int | None:
				if right < left:
								return None

				_UpperCAmelCase    :						Tuple							=      left + (right - left) // 2

				if sorted_collection[midpoint] == item:
								return midpoint
				elif sorted_collection[midpoint] > item:
								return binary_search_by_recursion(lowerCAmelCase					, lowerCAmelCase					, lowerCAmelCase					, midpoint - 1	)
				else:
								return binary_search_by_recursion(lowerCAmelCase					, lowerCAmelCase					, midpoint + 1					, lowerCAmelCase	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = input('Enter numbers separated by comma:\n').strip()
					SCREAMING_SNAKE_CASE_            = sorted(int(item) for item in user_input.split(','))
					SCREAMING_SNAKE_CASE_            = int(input('Enter a single number to be found in the list:\n'))
					SCREAMING_SNAKE_CASE_            = binary_search(collection, target)
					if result is None:
										print(F'''{target} was not found in {collection}.''')
					else:
										print(F'''{target} was found at position {result} in {collection}.''')





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "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_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      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`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [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`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

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

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

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





						@property
						def     _UpperCAmelCase			(    self		):







										'''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
										)


