






from typing import List, Optional, Union

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


lowerCamelCase       :       List[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       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 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Tuple     =					'''informer'''
					_A  :	Dict     =					{
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					    '''num_hidden_layers''': '''encoder_layers''',
					}



					def __init__(     self :		List[str]						,   __a :		Optional[int] = None						,   __a :		Optional[int] = None						,   __a :		str = "student_t"						,   __a :		str = "nll"						,   __a :		int = 1						,   __a :		List[int] = None						,   __a :		Optional[Union[str, bool]] = "mean"						,   __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 = 32						,   __a :		int = 32						,   __a :		int = 2						,   __a :		int = 2						,   __a :		int = 2						,   __a :		int = 2						,   __a :		bool = True						,   __a :		str = "gelu"						,   __a :		float = 0.05						,   __a :		float = 0.1						,   __a :		float = 0.1						,   __a :		float = 0.1						,   __a :		float = 0.1						,   __a :		int = 100						,   __a :		float = 0.02						,   __a :		Optional[int]=True						,   __a :		str = "prob"						,   __a :		int = 5						,   __a :		bool = True						,   **__a :		List[Any]						,   )	->     Optional[Any]:


												"""simple docstring"""

												__lowercase					:							str			=   prediction_length
												__lowercase					:							Any			=   context_length or prediction_length
												__lowercase					:							Union[str, Any]			=   distribution_output
												__lowercase					:							Optional[int]			=   loss
												__lowercase					:							Tuple			=   input_size
												__lowercase					:							Union[str, Any]			=   num_time_features
												__lowercase					:							List[str]			=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
												__lowercase					:							int			=   scaling
												__lowercase					:							str			=   num_dynamic_real_features
												__lowercase					:							Tuple			=   num_static_real_features
												__lowercase					:							str			=   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`"""							)
																			__lowercase					:							str			=   cardinality
												else:
																			__lowercase					:							int			=   [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`"""							)
																			__lowercase					:							List[str]			=   embedding_dimension
												else:
																			__lowercase					:							Any			=   [min(50						,   (cat + 1) // 2							) for cat in self.cardinality]

												__lowercase					:							Optional[Any]			=   num_parallel_samples

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

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

												__lowercase					:							str			=   activation_function
												__lowercase					:							List[Any]			=   init_std

												__lowercase					:							Optional[int]			=   use_cache

												# Informer
												__lowercase					:							List[str]			=   attention_type
												__lowercase					:							Any			=   sampling_factor
												__lowercase					:							Optional[int]			=   distil

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



					@property
					def 					lowerCAmelCase (     self :		List[Any]							)	->     int:


												"""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
												)







import math
def 					snake_case_						(   lowerCAmelCase_				:  int					):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5				,				int(math.sqrt(lowerCAmelCase_					) + 1					)				,				6					):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True
def 					snake_case_						(   lowerCAmelCase_				:  int = 10001					):

							try:
														__lowercase					:							Optional[int]			=   int(lowerCAmelCase_					)
							except (TypeError, ValueError):
														raise TypeError("""Parameter nth must be int or castable to int."""					) from None
							if nth <= 0:
														raise ValueError("""Parameter nth must be greater than or equal to one."""					)
							__lowercase					:							list[int]			=   []
							__lowercase					:							str			=   2
							while len(lowerCAmelCase_					) < nth:
														if is_prime(lowerCAmelCase_					):
																					primes.append(lowerCAmelCase_					)
																					num += 1
														else:
																					num += 1
							return primes[len(lowerCAmelCase_					) - 1]


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