

"""simple docstring"""


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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   '''time_series_transformer'''
    _lowerCamelCase					=   {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
        '''num_hidden_layers''': '''encoder_layers''',
    }




    def __init__( self ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = "student_t" ,lowerCamelCase_ = "nll" ,lowerCamelCase_ = 1 ,lowerCamelCase_ = [1, 2, 3, 4, 5, 6, 7] ,lowerCamelCase_ = "mean" ,lowerCamelCase_ = 0 ,lowerCamelCase_ = 0 ,lowerCamelCase_ = 0 ,lowerCamelCase_ = 0 ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = 3_2 ,lowerCamelCase_ = 3_2 ,lowerCamelCase_ = 2 ,lowerCamelCase_ = 2 ,lowerCamelCase_ = 2 ,lowerCamelCase_ = 2 ,lowerCamelCase_ = True ,lowerCamelCase_ = "gelu" ,lowerCamelCase_ = 6_4 ,lowerCamelCase_ = 0.1 ,lowerCamelCase_ = 0.1 ,lowerCamelCase_ = 0.1 ,lowerCamelCase_ = 0.1 ,lowerCamelCase_ = 0.1 ,lowerCamelCase_ = 1_0_0 ,lowerCamelCase_ = 0.02 ,lowerCamelCase_=True ,**lowerCamelCase_ ,)    ->      List[Any]:
          # 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(lowerCamelCase_    ) != 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(lowerCamelCase_    ) != 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(5_0 ,(cat + 1) // 2    ) for cat in self.cardinality]
          A				  =		num_parallel_samples

          # Transformer architecture configuration
          A				  =		input_size * len(lowerCamelCase_    ) + 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=lowerCamelCase_ ,**lowerCamelCase_    )




    @property
    def UpperCamelCase__		( self    )    ->      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"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


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




