
"""simple docstring"""



from __future__ import annotations

from collections.abc import MutableSequence

class       lowerCAmelCase__				:







     def __init__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			List[str]			):
       if len(__UpperCAmelCase			) != degree + 1:
         raise ValueError(
             '''The number of coefficients should be equal to the degree + 1.'''			)

       _snake_case					     =       list(__UpperCAmelCase			)
       _snake_case					     =       degree







     def __add__(							self			:			Optional[int]    ,      _lowerCamelCase			:			Any			):

       if self.degree > polynomial_a.degree:
         _snake_case					     =       self.coefficients[:]
         for i in range(polynomial_a.degree + 1			):
           coefficients[i] += polynomial_a.coefficients[i]
         return Polynomial(self.degree    ,      __UpperCAmelCase			)
       else:
         _snake_case					     =       polynomial_a.coefficients[:]
         for i in range(self.degree + 1			):
           coefficients[i] += self.coefficients[i]
         return Polynomial(polynomial_a.degree    ,      __UpperCAmelCase			)







     def __sub__(							self			:			int    ,      _lowerCamelCase			:			Dict			):
       return self + polynomial_a * Polynomial(0    ,      [-1]			)







     def __neg__(							self			:			Any			):
       return Polynomial(self.degree    ,      [-c for c in self.coefficients]			)







     def __mul__(							self			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       [0] * (self.degree + polynomial_a.degree + 1)
       for i in range(self.degree + 1			):
         for j in range(polynomial_a.degree + 1			):
           coefficients[i + j] += (
               self.coefficients[i] * polynomial_a.coefficients[j]
           )

       return Polynomial(self.degree + polynomial_a.degree    ,      __UpperCAmelCase			)







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Optional[int]			):
       _snake_case					     =       0
       for i in range(self.degree + 1			):
         result += self.coefficients[i] * (substitution**i)
       return result







     def __str__(							self			:			List[str]			):
       _snake_case					     =       ''''''
       for i in range(self.degree    ,      -1    ,      -1			):
         if self.coefficients[i] == 0:
           continue
         elif self.coefficients[i] > 0:
           if polynomial:
             polynomial += " + "
         else:
           polynomial += " - "

         if i == 0:
           polynomial += str(abs(self.coefficients[i]			)			)
         elif i == 1:
           polynomial += str(abs(self.coefficients[i]			)			) + "x"
         else:
           polynomial += str(abs(self.coefficients[i]			)			) + "x^" + str(__UpperCAmelCase			)

       return polynomial







     def __repr__(							self			:			List[Any]			):
       return self.__str__()







     def      lowercase	(							self			:			int			):
       _snake_case					     =       [0] * self.degree
       for i in range(self.degree			):
         _snake_case					     =       self.coefficients[i + 1] * (i + 1)
       return Polynomial(self.degree - 1    ,      __UpperCAmelCase			)







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Union[str, Any] = 0			):
       _snake_case					     =       [0] * (self.degree + 2)
       _snake_case					     =       constant
       for i in range(self.degree + 1			):
         _snake_case					     =       self.coefficients[i] / (i + 1)
       return Polynomial(self.degree + 1    ,      __UpperCAmelCase			)







     def __eq__(							self			:			Optional[int]    ,      _lowerCamelCase			:			int			):
       if not isinstance(__UpperCAmelCase    ,      __UpperCAmelCase			):
         return False

       if self.degree != polynomial_a.degree:
         return False

       for i in range(self.degree + 1			):
         if self.coefficients[i] != polynomial_a.coefficients[i]:
           return False

       return True







     def __ne__(							self			:			Tuple    ,      _lowerCamelCase			:			Any			):
       return not self.__eq__(__UpperCAmelCase			)




"""simple docstring"""




from typing import List, Optional, Union

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


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {
    "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__(	__A       ):
						lowerCAmelCase__     :  List[Any]  							=						'time_series_transformer'
						lowerCAmelCase__     :  Dict  							=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = "student_t"						,__UpperCAmelCase = "nll"						,__UpperCAmelCase = 1						,__UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]						,__UpperCAmelCase = "mean"						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 32						,__UpperCAmelCase = 32						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,__UpperCAmelCase = True						,__UpperCAmelCase = "gelu"						,__UpperCAmelCase = 64						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 0.1						,__UpperCAmelCase = 1_00						,__UpperCAmelCase = 0.0_2						,__UpperCAmelCase=True						,**__UpperCAmelCase						,)    -> List[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(__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  snake_case__		(				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
													)





