'''simple docstring'''


from __future__ import annotations

from collections.abc import MutableSequence






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if len(snake_case_						) != degree + 1:
            raise ValueError(
                'The number of coefficients should be equal to the degree + 1.'						)

       UpperCAmelCase_							:       list[float]       =				list(snake_case_						)
       UpperCAmelCase_							:       Any       =				degree


  def __add__(			self				,		snake_case_						):




       '''simple docstring'''

       if self.degree > polynomial_a.degree:
            UpperCAmelCase_							:       List[Any]       =				self.coefficients[:]
            for i in range(polynomial_a.degree + 1						):
                 coefficients[i] += polynomial_a.coefficients[i]
            return Polynomial(self.degree				,		snake_case_						)
       else:
            UpperCAmelCase_							:       Dict       =				polynomial_a.coefficients[:]
            for i in range(self.degree + 1						):
                 coefficients[i] += self.coefficients[i]
            return Polynomial(polynomial_a.degree				,		snake_case_						)


  def __sub__(			self				,		snake_case_						):




       '''simple docstring'''

       return self + polynomial_a * Polynomial(0				,		[-1]						)


  def __neg__(			self						):




       '''simple docstring'''

       return Polynomial(self.degree				,		[-c for c in self.coefficients]						)


  def __mul__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[float]       =				[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				,		snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int | float       =				0
       for i in range(self.degree + 1						):
            result += self.coefficients[i] * (substitution**i)
       return result


  def __str__(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				''
       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(snake_case_						)

       return polynomial


  def __repr__(			self						):




       '''simple docstring'''

       return self.__str__()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[float]       =				[0] * self.degree
       for i in range(self.degree						):
            UpperCAmelCase_							:       Tuple       =				self.coefficients[i + 1] * (i + 1)
       return Polynomial(self.degree - 1				,		snake_case_						)


  def _UpperCamelCase					(			self				,		snake_case_ = 0						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[float]       =				[0] * (self.degree + 2)
       UpperCAmelCase_							:       Dict       =				constant
       for i in range(self.degree + 1						):
            UpperCAmelCase_							:       Union[str, Any]       =				self.coefficients[i] / (i + 1)
       return Polynomial(self.degree + 1				,		snake_case_						)


  def __eq__(			self				,		snake_case_						):




       '''simple docstring'''

       if not isinstance(snake_case_				,		snake_case_						):
            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				,		snake_case_						):




       '''simple docstring'''

       return not self.__eq__(snake_case_						)







'''simple docstring'''


from typing import List, Optional

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


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

snake_case__					:      Optional[Any]				      =  {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:List[str]						   =		'''autoformer'''
  lowerCamelCase_		:Optional[Any]						   =		{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }


  def __init__(			self				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "student_t"				,		snake_case_ = "nll"				,		snake_case_ = 1				,		snake_case_ = [1, 2, 3, 4, 5, 6, 7]				,		snake_case_ = True				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = 0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = 6_4				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 2				,		snake_case_ = 3_2				,		snake_case_ = 3_2				,		snake_case_ = "gelu"				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 0.1				,		snake_case_ = 1_0_0				,		snake_case_ = 0.02				,		snake_case_ = True				,		snake_case_=True				,		snake_case_ = 1_0				,		snake_case_ = 2_5				,		snake_case_ = 3				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				prediction_length
       UpperCAmelCase_							:       List[str]       =				context_length if context_length is not None else prediction_length
       UpperCAmelCase_							:       Optional[int]       =				distribution_output
       UpperCAmelCase_							:       Optional[int]       =				loss
       UpperCAmelCase_							:       Union[str, Any]       =				input_size
       UpperCAmelCase_							:       int       =				num_time_features
       UpperCAmelCase_							:       List[str]       =				lags_sequence
       UpperCAmelCase_							:       Any       =				scaling
       UpperCAmelCase_							:       Any       =				num_dynamic_real_features
       UpperCAmelCase_							:       int       =				num_static_real_features
       UpperCAmelCase_							:       Optional[Any]       =				num_static_categorical_features
       if cardinality is not None 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`'						)
            UpperCAmelCase_							:       List[Any]       =				cardinality
       else:
            UpperCAmelCase_							:       Optional[int]       =				[0]
       if embedding_dimension is not None 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`'						)
            UpperCAmelCase_							:       List[str]       =				embedding_dimension
       else:
            UpperCAmelCase_							:       List[Any]       =				[min(5_0				,		(cat + 1) // 2						) for cat in self.cardinality]
       UpperCAmelCase_							:       List[str]       =				num_parallel_samples

       # Transformer architecture configuration
       UpperCAmelCase_							:       Union[str, Any]       =				input_size * len(self.lags_sequence						) + self._number_of_features
       UpperCAmelCase_							:       str       =				d_model
       UpperCAmelCase_							:       str       =				encoder_attention_heads
       UpperCAmelCase_							:       str       =				decoder_attention_heads
       UpperCAmelCase_							:       str       =				encoder_ffn_dim
       UpperCAmelCase_							:       str       =				decoder_ffn_dim
       UpperCAmelCase_							:       str       =				encoder_layers
       UpperCAmelCase_							:       str       =				decoder_layers

       UpperCAmelCase_							:       str       =				dropout
       UpperCAmelCase_							:       Optional[int]       =				attention_dropout
       UpperCAmelCase_							:       Tuple       =				activation_dropout
       UpperCAmelCase_							:       Any       =				encoder_layerdrop
       UpperCAmelCase_							:       Tuple       =				decoder_layerdrop

       UpperCAmelCase_							:       List[str]       =				activation_function
       UpperCAmelCase_							:       Tuple       =				init_std

       UpperCAmelCase_							:       Union[str, Any]       =				use_cache

       # Autoformer
       UpperCAmelCase_							:       Any       =				label_length
       UpperCAmelCase_							:       Union[str, Any]       =				moving_average
       UpperCAmelCase_							:       Tuple       =				autocorrelation_factor

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






  @property
  def _UpperCamelCase					(			self						):




       '''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
       )






