




from typing import List, Optional, Union

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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class 			a__    (					UpperCAmelCase__      ):
						_a							:    str					=    "time_series_transformer"
						_a							:    Tuple					=    {
						    "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 = "mean"		,			_A = 0		,			_A = 0		,			_A = 0		,			_A = 0		,			_A = None		,			_A = None		,			_A = 3_2		,			_A = 3_2		,			_A = 2		,			_A = 2		,			_A = 2		,			_A = 2		,			_A = True		,			_A = "gelu"		,			_A = 6_4		,			_A = 0.1		,			_A = 0.1		,			_A = 0.1		,			_A = 0.1		,			_A = 0.1		,			_A = 1_0_0		,			_A = 0.02		,			_A=True		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       prediction_length
											__lowerCAmelCase				   =       context_length or prediction_length
											__lowerCAmelCase				   =       distribution_output
											__lowerCAmelCase				   =       loss
											__lowerCAmelCase				   =       input_size
											__lowerCAmelCase				   =       num_time_features
											__lowerCAmelCase				   =       lags_sequence
											__lowerCAmelCase				   =       scaling
											__lowerCAmelCase				   =       num_dynamic_real_features
											__lowerCAmelCase				   =       num_static_real_features
											__lowerCAmelCase				   =       num_static_categorical_features
											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`"			)
																__lowerCAmelCase				   =       cardinality
											else:
																__lowerCAmelCase				   =       [0]
											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`"			)
																__lowerCAmelCase				   =       embedding_dimension
											else:
																__lowerCAmelCase				   =       [min(5_0		,			(cat + 1) // 2			) for cat in self.cardinality]
											__lowerCAmelCase				   =       num_parallel_samples

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

											__lowerCAmelCase				   =       dropout
											__lowerCAmelCase				   =       attention_dropout
											__lowerCAmelCase				   =       activation_dropout
											__lowerCAmelCase				   =       encoder_layerdrop
											__lowerCAmelCase				   =       decoder_layerdrop

											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       init_std

											__lowerCAmelCase				   =       use_cache

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






						@property
						def 				__SCREAMING_SNAKE_CASE(   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
											)








'''simple docstring'''







from math import isqrt


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			bool:
       return all(number % divisor != 0 for divisor in range(2    ,isqrt(_UpperCAmelCase  ) + 1  )  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10**6  )  ->			int:
       _a    : List[Any]     					=0
       _a    : str     					=1
       _a    : Optional[Any]     					=7
       while prime_candidate < max_prime:
              primes_count += is_prime(_UpperCAmelCase  )

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


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


