
from __future__ import annotations

from scipy.special import comb  # type: ignore
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : list[tuple[float, float]]  )	->    Tuple:
             a_      :	Optional[Any]							=						list_of_points
             # Degree determines the flexibility of the curve.
             # Degree = 1 will produce a straight line.
             a_      :	Any							=						len(SCREAMING_SNAKE_CASE__  ) - 1
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : float  )	->    list[float]:
             assert 0 <= t <= 1, "Time t must be between 0 and 1."
             a_      :	list[float]							=						[]
             for i in range(len(self.list_of_points  )  ):
                    # basis function for each i
                    output_values.append(
                        comb(self.degree  ,							SCREAMING_SNAKE_CASE__  ) * ((1 - t) ** (self.degree - i)) * (t**i)  )
             # the basis must sum up to 1 for it to produce a valid Bezier curve.
             assert round(sum(SCREAMING_SNAKE_CASE__  )  ,							5  ) == 1
             return output_values
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : float  )	->    tuple[float, float]:
             assert 0 <= t <= 1, "Time t must be between 0 and 1."

             a_      :	List[str]							=						self.basis_function(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						0.0
             a_      :	Union[str, Any]							=						0.0
             for i in range(len(self.list_of_points  )  ):
                    # For all points, sum up the product of i-th basis function and i-th point.
                    x += basis_function[i] * self.list_of_points[i][0]
                    y += basis_function[i] * self.list_of_points[i][1]
             return (x, y)

      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : float = 0.01  )	->    Dict:
             from matplotlib import pyplot as plt  # type: ignore

             a_      :	list[float]							=						[]  # x coordinates of points to plot
             a_      :	list[float]							=						[]  # y coordinates of points to plot

             a_      :	Any							=						0.0
             while t <= 1:
                    a_      :	int							=						self.bezier_curve_function(SCREAMING_SNAKE_CASE__  )
                    to_plot_x.append(value[0]  )
                    to_plot_y.append(value[1]  )
                    t += step_size

             a_      :	Optional[int]							=						[i[0] for i in self.list_of_points]
             a_      :	int							=						[i[1] for i in self.list_of_points]

             plt.plot(
                 SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							color='blue'  ,							label='Curve of Degree ' + str(self.degree  )  ,							)
             plt.scatter(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							color='red'  ,							label='Control Points'  )
             plt.legend()
             plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
   BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
   BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3






from typing import List, Optional

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


UpperCAmelCase_   :				Dict						      = logging.get_logger(__name__)

UpperCAmelCase_   :				int						      = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Union[str, Any]       =		'''autoformer'''
      snake_case__       :  Any       =		{
          '''hidden_size''': '''d_model''',
          '''num_attention_heads''': '''encoder_attention_heads''',
          '''num_hidden_layers''': '''encoder_layers''',
      }
      def __init__( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : str = "student_t"  ,							SCREAMING_SNAKE_CASE__       : str = "nll"  ,							SCREAMING_SNAKE_CASE__       : int = 1  ,							SCREAMING_SNAKE_CASE__       : List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : int = 0  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[List[int]] = None  ,							SCREAMING_SNAKE_CASE__       : int = 6_4  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : int = 3_2  ,							SCREAMING_SNAKE_CASE__       : str = "gelu"  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : float = 0.1  ,							SCREAMING_SNAKE_CASE__       : int = 1_0_0  ,							SCREAMING_SNAKE_CASE__       : float = 0.02  ,							SCREAMING_SNAKE_CASE__       : bool = True  ,							SCREAMING_SNAKE_CASE__       : List[str]=True  ,							SCREAMING_SNAKE_CASE__       : int = 1_0  ,							SCREAMING_SNAKE_CASE__       : int = 2_5  ,							SCREAMING_SNAKE_CASE__       : int = 3  ,							**SCREAMING_SNAKE_CASE__       : int  ,							)	->    Union[str, Any]:
             # time series specific configuration
             a_      :	Optional[int]							=						prediction_length
             a_      :	Tuple							=						context_length if context_length is not None else prediction_length
             a_      :	Tuple							=						distribution_output
             a_      :	int							=						loss
             a_      :	Tuple							=						input_size
             a_      :	int							=						num_time_features
             a_      :	Dict							=						lags_sequence
             a_      :	List[str]							=						scaling
             a_      :	Tuple							=						num_dynamic_real_features
             a_      :	Dict							=						num_static_real_features
             a_      :	Dict							=						num_static_categorical_features
             if cardinality is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The cardinality should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Any							=						cardinality
             else:
                    a_      :	Tuple							=						[0]
             if embedding_dimension is not None and num_static_categorical_features > 0:
                    if len(SCREAMING_SNAKE_CASE__  ) != num_static_categorical_features:
                           raise ValueError(
                               'The embedding dimension should be a list of the same length as `num_static_categorical_features`'  )
                    a_      :	Tuple							=						embedding_dimension
             else:
                    a_      :	Optional[Any]							=						[min(5_0  ,							(cat + 1) // 2  ) for cat in self.cardinality]
             a_      :	Optional[Any]							=						num_parallel_samples

             # Transformer architecture configuration
             a_      :	Dict							=						input_size * len(self.lags_sequence  ) + self._number_of_features
             a_      :	Tuple							=						d_model
             a_      :	int							=						encoder_attention_heads
             a_      :	Optional[int]							=						decoder_attention_heads
             a_      :	str							=						encoder_ffn_dim
             a_      :	List[str]							=						decoder_ffn_dim
             a_      :	Any							=						encoder_layers
             a_      :	Any							=						decoder_layers

             a_      :	List[Any]							=						dropout
             a_      :	Tuple							=						attention_dropout
             a_      :	Union[str, Any]							=						activation_dropout
             a_      :	List[Any]							=						encoder_layerdrop
             a_      :	List[str]							=						decoder_layerdrop

             a_      :	Optional[Any]							=						activation_function
             a_      :	Union[str, Any]							=						init_std

             a_      :	List[str]							=						use_cache

             # Autoformer
             a_      :	Tuple							=						label_length
             a_      :	List[Any]							=						moving_average
             a_      :	Optional[Any]							=						autocorrelation_factor

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

      @property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    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
             )


