

"""simple docstring"""



from typing import List, Optional, Union

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


SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	int							  =						{
    '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						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"time_series_transformer"
    UpperCAmelCase						 =					{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
        "num_hidden_layers": "encoder_layers",
    }

    def __init__(     self: int    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: Optional[int] = None    ,						UpperCamelCase: str = "student_t"    ,						UpperCamelCase: str = "nll"    ,						UpperCamelCase: int = 1    ,						UpperCamelCase: List[int] = [1, 2, 3, 4, 5, 6, 7]    ,						UpperCamelCase: Optional[Union[str, bool]] = "mean"    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: int = 0    ,						UpperCamelCase: Optional[List[int]] = None    ,						UpperCamelCase: Optional[List[int]] = None    ,						UpperCamelCase: int = 32    ,						UpperCamelCase: int = 32    ,						UpperCamelCase: int = 2    ,						UpperCamelCase: int = 2    ,						UpperCamelCase: int = 2    ,						UpperCamelCase: int = 2    ,						UpperCamelCase: bool = True    ,						UpperCamelCase: str = "gelu"    ,						UpperCamelCase: int = 64    ,						UpperCamelCase: float = 0.1    ,						UpperCamelCase: float = 0.1    ,						UpperCamelCase: float = 0.1    ,						UpperCamelCase: float = 0.1    ,						UpperCamelCase: float = 0.1    ,						UpperCamelCase: int = 1_00    ,						UpperCamelCase: float = 0.02    ,						UpperCamelCase: List[str]=True    ,						**UpperCamelCase: List[str]    ,						):






         """simple docstring"""


         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(UpperCamelCase       ) != 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(UpperCamelCase       ) != 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(UpperCamelCase       ) + 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=UpperCamelCase    ,						**UpperCamelCase       )





    @property
    def 				UpperCamelCase	(     self: Tuple       ):






         """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"""



import math
import sys





def  _snake_case     (    UpperCAmelCase_  :					int			):
     if number != int(UpperCAmelCase_			):
          raise ValueError("""the value of input must be a natural number"""			)
     if number < 0:
          raise ValueError("""the value of input must not be a negative number"""			)
     if number == 0:
          return 1
     A__										=  [-1] * (number + 1)
     A__										=  0
     for i in range(1					,   number + 1			):
          A__										=  sys.maxsize
          A__										=  int(math.sqrt(UpperCAmelCase_			)			)
          for j in range(1					,   root + 1			):
               A__										=  1 + answers[i - (j**2)]
               A__										=  min(UpperCAmelCase_					,   UpperCAmelCase_			)
          A__										=  answer
     return answers[number]


if __name__ == "__main__":
 import doctest

 doctest.testmod()






