
'''simple docstring'''


from math import pi, sqrt, tan



def 			__snake_case  (     _UpperCAmelCase       :							List[str]):
   if side_length < 0:
      raise ValueError('''surface_area_cube() only accepts non-negative values''')
   return 6 * side_length**2



def 			__snake_case  (     _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							Tuple,   _UpperCAmelCase       :							Tuple):
   if length < 0 or breadth < 0 or height < 0:
      raise ValueError('''surface_area_cuboid() only accepts non-negative values''')
   return 2 * ((length * breadth) + (breadth * height) + (length * height))



def 			__snake_case  (     _UpperCAmelCase       :							int):
   if radius < 0:
      raise ValueError('''surface_area_sphere() only accepts non-negative values''')
   return 4 * pi * radius**2



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int]):
   if radius < 0:
      raise ValueError('''surface_area_hemisphere() only accepts non-negative values''')
   return 3 * pi * radius**2



def 			__snake_case  (     _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							Optional[Any]):
   if radius < 0 or height < 0:
      raise ValueError('''surface_area_cone() only accepts non-negative values''')
   return pi * radius * (radius + (height**2 + radius**2) ** 0.5)



def 			__snake_case  (     _UpperCAmelCase       :							Dict,   _UpperCAmelCase       :							List[str],   _UpperCAmelCase       :							Tuple):
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          '''surface_area_conical_frustum() only accepts non-negative values''')
   UpperCamelCase	  =	(height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							List[Any]):
   if radius < 0 or height < 0:
      raise ValueError('''surface_area_cylinder() only accepts non-negative values''')
   return 2 * pi * radius * (height + radius)



def 			__snake_case  (     _UpperCAmelCase       :							List[Any],   _UpperCAmelCase       :							str):
   if torus_radius < 0 or tube_radius < 0:
      raise ValueError('''surface_area_torus() only accepts non-negative values''')
   if torus_radius < tube_radius:
      raise ValueError(
          '''surface_area_torus() does not support spindle or self intersecting tori''')
   return 4 * pow(_UpperCAmelCase,   2) * torus_radius * tube_radius



def 			__snake_case  (     _UpperCAmelCase       :							Dict,   _UpperCAmelCase       :							Optional[int]):
   if length < 0 or width < 0:
      raise ValueError('''area_rectangle() only accepts non-negative values''')
   return length * width



def 			__snake_case  (     _UpperCAmelCase       :							Tuple):
   if side_length < 0:
      raise ValueError('''area_square() only accepts non-negative values''')
   return side_length**2



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							Union[str, Any]):
   if base < 0 or height < 0:
      raise ValueError('''area_triangle() only accepts non-negative values''')
   return (base * height) / 2



def 			__snake_case  (     _UpperCAmelCase       :							Optional[int],   _UpperCAmelCase       :							Any,   _UpperCAmelCase       :							int):
   if sidea < 0 or sidea < 0 or sidea < 0:
      raise ValueError('''area_triangle_three_sides() only accepts non-negative values''')
   elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
      raise ValueError('''Given three sides do not form a triangle''')
   UpperCamelCase	  =	(sidea + sidea + sidea) / 2
   UpperCamelCase	  =	sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea))
   return area



def 			__snake_case  (     _UpperCAmelCase       :							Tuple,   _UpperCAmelCase       :							Union[str, Any]):
   if base < 0 or height < 0:
      raise ValueError('''area_parallelogram() only accepts non-negative values''')
   return base * height



def 			__snake_case  (     _UpperCAmelCase       :							str,   _UpperCAmelCase       :							Optional[Any],   _UpperCAmelCase       :							Optional[int]):
   if basea < 0 or basea < 0 or height < 0:
      raise ValueError('''area_trapezium() only accepts non-negative values''')
   return 1 / 2 * (basea + basea) * height



def 			__snake_case  (     _UpperCAmelCase       :							Optional[Any]):
   if radius < 0:
      raise ValueError('''area_circle() only accepts non-negative values''')
   return pi * radius**2



def 			__snake_case  (     _UpperCAmelCase       :							Union[str, Any],   _UpperCAmelCase       :							List[str]):
   if radius_x < 0 or radius_y < 0:
      raise ValueError('''area_ellipse() only accepts non-negative values''')
   return pi * radius_x * radius_y



def 			__snake_case  (     _UpperCAmelCase       :							Union[str, Any],   _UpperCAmelCase       :							Dict):
   if diagonal_a < 0 or diagonal_a < 0:
      raise ValueError('''area_rhombus() only accepts non-negative values''')
   return 1 / 2 * diagonal_a * diagonal_a



def 			__snake_case  (     _UpperCAmelCase       :							Dict,   _UpperCAmelCase       :							Union[str, Any]):
   if not isinstance(_UpperCAmelCase,   _UpperCAmelCase) or sides < 3:
      raise ValueError(
          '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''')
   elif length < 0:
      raise ValueError(
          '''area_reg_polygon() only accepts non-negative values as \
length of a side''')
   return (sides * length**2) / (4 * tan(pi / sides))
   return (sides * length**2) / (4 * tan(pi / sides))


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

   print('[DEMO] Areas of various geometric shapes: \n')
   print(F'''Rectangle: {area_rectangle(10, 20) = }''')
   print(F'''Square: {area_square(10) = }''')
   print(F'''Triangle: {area_triangle(10, 10) = }''')
   print(F'''Triangle: {area_triangle_three_sides(5, 12, 13) = }''')
   print(F'''Parallelogram: {area_parallelogram(10, 20) = }''')
   print(F'''Rhombus: {area_rhombus(10, 20) = }''')
   print(F'''Trapezium: {area_trapezium(10, 20, 30) = }''')
   print(F'''Circle: {area_circle(20) = }''')
   print(F'''Ellipse: {area_ellipse(10, 20) = }''')
   print('\nSurface Areas of various geometric shapes: \n')
   print(F'''Cube: {surface_area_cube(20) = }''')
   print(F'''Cuboid: {surface_area_cuboid(10, 20, 30) = }''')
   print(F'''Sphere: {surface_area_sphere(20) = }''')
   print(F'''Hemisphere: {surface_area_hemisphere(20) = }''')
   print(F'''Cone: {surface_area_cone(10, 20) = }''')
   print(F'''Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }''')
   print(F'''Cylinder: {surface_area_cylinder(10, 20) = }''')
   print(F'''Torus: {surface_area_torus(20, 10) = }''')
   print(F'''Equilateral Triangle: {area_reg_polygon(3, 10) = }''')
   print(F'''Square: {area_reg_polygon(4, 10) = }''')
   print(F'''Reqular Pentagon: {area_reg_polygon(5, 10) = }''')













"""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 _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

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

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

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

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







						@property
						def 			A      (    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
										)

