


from typing import List, Optional, Union

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


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

snake_case							: Any      =   {
    '''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  snake_case_     (lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[int]					   =    '''time_series_transformer'''
					UpperCAmelCase__      :					Optional[int]					   =    {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					    '''num_hidden_layers''': '''encoder_layers''',
					}
					def __init__(   self     :Any    ,__snake_case     :Optional[int] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :str = "student_t"    ,__snake_case     :str = "nll"    ,__snake_case     :int = 1    ,__snake_case     :List[int] = [1, 2, 3, 4, 5, 6, 7]    ,__snake_case     :Optional[Union[str, bool]] = "mean"    ,__snake_case     :int = 0    ,__snake_case     :int = 0    ,__snake_case     :int = 0    ,__snake_case     :int = 0    ,__snake_case     :Optional[List[int]] = None    ,__snake_case     :Optional[List[int]] = None    ,__snake_case     :int = 32    ,__snake_case     :int = 32    ,__snake_case     :int = 2    ,__snake_case     :int = 2    ,__snake_case     :int = 2    ,__snake_case     :int = 2    ,__snake_case     :bool = True    ,__snake_case     :str = "gelu"    ,__snake_case     :int = 64    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :float = 0.1    ,__snake_case     :int = 1_00    ,__snake_case     :float = 0.02    ,__snake_case     :Optional[int]=True    ,**__snake_case     :Optional[Any]    ,)      ->					str:
												# 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
												a__    =  scaling
												a__    =  num_dynamic_real_features
												a__    =  num_static_real_features
												a__    =  num_static_categorical_features
												if cardinality and num_static_categorical_features > 0:
																			if len(__snake_case   ) != 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]
												if embedding_dimension and num_static_categorical_features > 0:
																			if len(__snake_case   ) != 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(__snake_case   ) + 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

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



					@property
					def 				lowerCamelCase__(   self     :Dict   )      ->					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
												)








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




