





from math import pi, sqrt, tan

def   a_   (			lowerCAmelCase_				:      float     ):
   if side_length < 0:
      raise ValueError('surface_area_cube() only accepts non-negative values'     )
   return 6 * side_length**2

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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   a_   (			lowerCAmelCase_				:      float     ):
   if radius < 0:
      raise ValueError('surface_area_sphere() only accepts non-negative values'     )
   return 4 * pi * radius**2

def   a_   (			lowerCAmelCase_				:      float     ):
   if radius < 0:
      raise ValueError('surface_area_hemisphere() only accepts non-negative values'     )
   return 3 * pi * radius**2

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          'surface_area_conical_frustum() only accepts non-negative values'     )
   __lowerCAmelCase							     =     (height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if radius < 0 or height < 0:
      raise ValueError('surface_area_cylinder() only accepts non-negative values'     )
   return 2 * pi * radius * (height + radius)

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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(lowerCAmelCase_,     2     ) * torus_radius * tube_radius

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if length < 0 or width < 0:
      raise ValueError('area_rectangle() only accepts non-negative values'     )
   return length * width

def   a_   (			lowerCAmelCase_				:      float     ):
   if side_length < 0:
      raise ValueError('area_square() only accepts non-negative values'     )
   return side_length**2

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if base < 0 or height < 0:
      raise ValueError('area_triangle() only accepts non-negative values'     )
   return (base * height) / 2

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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'     )
   __lowerCAmelCase							     =     (sidea + sidea + sidea) / 2
   __lowerCAmelCase							     =     sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)     )
   return area

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if base < 0 or height < 0:
      raise ValueError('area_parallelogram() only accepts non-negative values'     )
   return base * height

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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   a_   (			lowerCAmelCase_				:      float     ):
   if radius < 0:
      raise ValueError('area_circle() only accepts non-negative values'     )
   return pi * radius**2

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   if radius_x < 0 or radius_y < 0:
      raise ValueError('area_ellipse() only accepts non-negative values'     )
   return pi * radius_x * radius_y

def   a_   (			lowerCAmelCase_				:      float,     lowerCAmelCase_				:      float     ):
   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   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      float     ):
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ) 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) = }""")








from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		int = 6_5_5_3_6 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 2 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		str = "fourier" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,  lowerCAmelCase_			:		Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,  lowerCAmelCase_			:		Tuple[str] = "UNetMidBlock1D" ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		Tuple[int] = (3_2, 3_2, 6_4) ,  lowerCAmelCase_			:		str = None ,  lowerCAmelCase_			:		int = 8 ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		bool = False ,  )							->						Optional[int]:
     super().__init__()
     __lowerCAmelCase							     =     sample_size

     # time
     if time_embedding_type == "fourier":
        __lowerCAmelCase							     =     GaussianFourierProjection(
            embedding_size=8 ,  set_W_to_weight=lowerCAmelCase_ ,  log=lowerCAmelCase_ ,  flip_sin_to_cos=lowerCAmelCase_					)
        __lowerCAmelCase							     =     2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        __lowerCAmelCase							     =     Timesteps(
            block_out_channels[0] ,  flip_sin_to_cos=lowerCAmelCase_ ,  downscale_freq_shift=lowerCAmelCase_					)
        __lowerCAmelCase							     =     block_out_channels[0]

     if use_timestep_embedding:
        __lowerCAmelCase							     =     block_out_channels[0] * 4
        __lowerCAmelCase							     =     TimestepEmbedding(
            in_channels=lowerCAmelCase_ ,  time_embed_dim=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  out_dim=block_out_channels[0] ,  )

     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None
     __lowerCAmelCase							     =     nn.ModuleList([]					)
     __lowerCAmelCase							     =     None

     # down
     __lowerCAmelCase							     =     in_channels
     for i, down_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     block_out_channels[i]

        if i == 0:
           input_channel += extra_in_channels

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_down_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_downsample=not is_final_block or downsample_each_block ,  )
        self.down_blocks.append(lowerCAmelCase_					)

     # mid
     __lowerCAmelCase							     =     get_mid_block(
         lowerCAmelCase_ ,  in_channels=block_out_channels[-1] ,  mid_channels=block_out_channels[-1] ,  out_channels=block_out_channels[-1] ,  embed_dim=block_out_channels[0] ,  num_layers=lowerCAmelCase_ ,  add_downsample=lowerCAmelCase_ ,  )

     # up
     __lowerCAmelCase							     =     list(reversed(lowerCAmelCase_					)					)
     __lowerCAmelCase							     =     reversed_block_out_channels[0]
     if out_block_type is None:
        __lowerCAmelCase							     =     out_channels
     else:
        __lowerCAmelCase							     =     block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase_					):
        __lowerCAmelCase							     =     output_channel
        __lowerCAmelCase							     =     (
            reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase_					) - 1 else final_upsample_channels
        )

        __lowerCAmelCase							     =     i == len(lowerCAmelCase_					) - 1

        __lowerCAmelCase							     =     get_up_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_upsample=not is_final_block ,  )
        self.up_blocks.append(lowerCAmelCase_					)
        __lowerCAmelCase							     =     output_channel

     # out
     __lowerCAmelCase							     =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,  3_2					)
     __lowerCAmelCase							     =     get_out_block(
         out_block_type=lowerCAmelCase_ ,  num_groups_out=lowerCAmelCase_ ,  embed_dim=block_out_channels[0] ,  out_channels=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  fc_dim=block_out_channels[-1] // 4 ,  )






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		torch.FloatTensor ,  lowerCAmelCase_			:		Union[torch.Tensor, float, int] ,  lowerCAmelCase_			:		bool = True ,  )							->						Union[UNetaDOutput, Tuple]:
     __lowerCAmelCase							     =     timestep
     if not torch.is_tensor(lowerCAmelCase_					):
        __lowerCAmelCase							     =     torch.tensor([timesteps] ,  dtype=torch.long ,  device=sample.device					)
     elif torch.is_tensor(lowerCAmelCase_					) and len(timesteps.shape					) == 0:
        __lowerCAmelCase							     =     timesteps[None].to(sample.device					)

     __lowerCAmelCase							     =     self.time_proj(lowerCAmelCase_					)
     if self.config.use_timestep_embedding:
        __lowerCAmelCase							     =     self.time_mlp(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     timestep_embed[..., None]
        __lowerCAmelCase							     =     timestep_embed.repeat([1, 1, sample.shape[2]]					).to(sample.dtype					)
        __lowerCAmelCase							     =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])					)

     # 2. down
     __lowerCAmelCase							     =     ()
     for downsample_block in self.down_blocks:
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     downsample_block(hidden_states=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)
        down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
        __lowerCAmelCase							     =     self.mid_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks					):
        __lowerCAmelCase							     =     down_block_res_samples[-1:]
        __lowerCAmelCase							     =     down_block_res_samples[:-1]
        __lowerCAmelCase							     =     upsample_block(lowerCAmelCase_ ,  res_hidden_states_tuple=lowerCAmelCase_ ,  temb=lowerCAmelCase_					)

     # 5. post-process
     if self.out_block:
        __lowerCAmelCase							     =     self.out_block(lowerCAmelCase_ ,  lowerCAmelCase_					)

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase_					)

