"""simple docstring"""







from typing import List, Optional

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


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

_a  :  Optional[int]=				{
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   List[Any]					=	"""autoformer"""
				UpperCAmelCase       :   List[Any]					=	{
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}



				def __init__(self       :   Union[str, Any]			,						_A       :   Optional[int] = None			,						_A       :   Optional[int] = None			,						_A       :   str = "student_t"			,						_A       :   str = "nll"			,						_A       :   int = 1			,						_A       :   List[int] = [1, 2, 3, 4, 5, 6, 7]			,						_A       :   bool = True			,						_A       :   int = 0			,						_A       :   int = 0			,						_A       :   int = 0			,						_A       :   int = 0			,						_A       :   Optional[List[int]] = None			,						_A       :   Optional[List[int]] = None			,						_A       :   int = 64			,						_A       :   int = 2			,						_A       :   int = 2			,						_A       :   int = 2			,						_A       :   int = 2			,						_A       :   int = 32			,						_A       :   int = 32			,						_A       :   str = "gelu"			,						_A       :   float = 0.1			,						_A       :   float = 0.1			,						_A       :   float = 0.1			,						_A       :   float = 0.1			,						_A       :   float = 0.1			,						_A       :   int = 1_00			,						_A       :   float = 0.02			,						_A       :   bool = True			,						_A       :   Union[str, Any]=True			,						_A       :   int = 10			,						_A       :   int = 25			,						_A       :   int = 3			,						**_A       :   Union[str, Any]			,						)					->			List[Any]:
					# time series specific configuration
					__snake_case    :							Optional[int]   	=				prediction_length
					__snake_case    :							Union[str, Any]   	=				context_length if context_length is not None else prediction_length
					__snake_case    :							Tuple   	=				distribution_output
					__snake_case    :							int   	=				loss
					__snake_case    :							Optional[int]   	=				input_size
					__snake_case    :							List[str]   	=				num_time_features
					__snake_case    :							Optional[Any]   	=				lags_sequence
					__snake_case    :							Optional[Any]   	=				scaling
					__snake_case    :							Union[str, Any]   	=				num_dynamic_real_features
					__snake_case    :							Tuple   	=				num_static_real_features
					__snake_case    :							Any   	=				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`')
						__snake_case    :							List[str]   	=				cardinality
					else:
						__snake_case    :							Dict   	=				[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`')
						__snake_case    :							int   	=				embedding_dimension
					else:
						__snake_case    :							Optional[int]   	=				[min(50			,						(cat + 1) // 2) for cat in self.cardinality]
					__snake_case    :							Any   	=				num_parallel_samples

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

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

					__snake_case    :							Optional[int]   	=				activation_function
					__snake_case    :							Optional[int]   	=				init_std

					__snake_case    :							str   	=				use_cache

					# Autoformer
					__snake_case    :							Dict   	=				label_length
					__snake_case    :							Any   	=				moving_average
					__snake_case    :							Tuple   	=				autocorrelation_factor

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






				@property
				def       _lowercase  (self       :   List[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       (					UpperCAmelCase_					:       int						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				[True] * limit
	__snake_case    :							Tuple   	=				False
	__snake_case    :							Union[str, Any]   	=				False
	__snake_case    :							List[str]   	=				True

	for i in range(3    ,							int(limit**0.5 + 1						)    ,							2						):
		__snake_case    :							List[str]   	=				i * 2
		while index < limit:
			__snake_case    :							List[str]   	=				False
			__snake_case    :							Union[str, Any]   	=				index + i

	__snake_case    :							str   	=				[2]

	for i in range(3    ,							UpperCAmelCase_    ,							2						):
		if is_prime[i]:
			primes.append(UpperCAmelCase_						)

	return primes
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int = 1_00_00_00						)							->    int:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				prime_sieve(UpperCAmelCase_						)
	__snake_case    :							int   	=				0
	__snake_case    :							str   	=				0

	for i in range(len(UpperCAmelCase_						)						):
		for j in range(i + length    ,							len(UpperCAmelCase_						)						):
			__snake_case    :							Union[str, Any]   	=				sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				__snake_case    :							List[Any]   	=				j - i
				__snake_case    :							Optional[Any]   	=				sol

	return largest


if __name__ == "__main__":
	print(f'''{solution() = }''')





