





'''simple docstring'''




from typing import List, Optional, Union

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


_A:					Any							=				logging.get_logger(__name__)

_A:					str							=				{
    """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 UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[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 = 32     ,			__A = 32     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = 2     ,			__A = True     ,			__A = "gelu"     ,			__A = 64     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 0.1     ,			__A = 100     ,			__A = 0.0_2     ,			__A=True     ,			**__A     ,			):
     # time series specific configuration
     __UpperCAmelCase					=   prediction_length
     __UpperCAmelCase					=   context_length or prediction_length
     __UpperCAmelCase					=   distribution_output
     __UpperCAmelCase					=   loss
     __UpperCAmelCase					=   input_size
     __UpperCAmelCase					=   num_time_features
     __UpperCAmelCase					=   lags_sequence
     __UpperCAmelCase					=   scaling
     __UpperCAmelCase					=   num_dynamic_real_features
     __UpperCAmelCase					=   num_static_real_features
     __UpperCAmelCase					=   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`'					)
      __UpperCAmelCase					=   cardinality
     else:
      __UpperCAmelCase					=   [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`'					)
      __UpperCAmelCase					=   embedding_dimension
     else:
      __UpperCAmelCase					=   [min(50     ,			(cat + 1) // 2					) for cat in self.cardinality]
     __UpperCAmelCase					=   num_parallel_samples

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

     __UpperCAmelCase					=   dropout
     __UpperCAmelCase					=   attention_dropout
     __UpperCAmelCase					=   activation_dropout
     __UpperCAmelCase					=   encoder_layerdrop
     __UpperCAmelCase					=   decoder_layerdrop

     __UpperCAmelCase					=   activation_function
     __UpperCAmelCase					=   init_std

     __UpperCAmelCase					=   use_cache

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




    @property
    def 					__lowerCamelCase						(  self					):
     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 collections.abc import Sequence







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase    )    )







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     float:
 __UpperCAmelCase					=   0.0
 for coeff in reversed(_lowerCAmelCase    ):
  __UpperCAmelCase					=   result * x + coeff
 return result


if __name__ == "__main__":
    _A:					str							=				(0.0, 0.0, 5.0, 9.3, 7.0)
    _A:					Dict							=				10.0
    print(evaluate_poly(poly, x))
    print(horner(poly, x))





