

'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    ):





      """simple docstring"""





      super().__init__()
      self.register_modules(transformer=_UpperCamelCase      ,    vae=_UpperCamelCase      ,    scheduler=_UpperCamelCase  )

      # create a imagenet -> id dictionary for easier use
      _lowercase      :				List[Any]      					=       {}
      if idalabel is not None:
         for key, value in idalabel.items():
            for label in value.split(","  ):
               _lowercase      :				Dict      					=       int(_UpperCamelCase  )
         _lowercase      :				Optional[int]      					=       dict(sorted(self.labels.items()  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      if not isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
         _lowercase      :				List[Any]      					=       list(_UpperCamelCase  )

      for l in label:
         if l not in self.labels:
            raise ValueError(
                f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''  )

      return [self.labels[l] for l in label]






   @torch.no_grad()
   def __call__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase = 4.0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 50      ,    _UpperCamelCase = "pil"      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





      _lowercase      :				List[str]      					=       len(_UpperCamelCase  )
      _lowercase      :				str      					=       self.transformer.config.sample_size
      _lowercase      :				List[str]      					=       self.transformer.config.in_channels

      _lowercase      :				Any      					=       randn_tensor(
          shape=(batch_size, latent_channels, latent_size, latent_size)      ,    generator=_UpperCamelCase      ,    device=self.device      ,    dtype=self.transformer.dtype      ,    )
      _lowercase      :				Tuple      					=       torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

      _lowercase      :				List[Any]      					=       torch.tensor(_UpperCamelCase      ,    device=self.device  ).reshape(-1  )
      _lowercase      :				Any      					=       torch.tensor([1000] * batch_size      ,    device=self.device  )
      _lowercase      :				Optional[Any]      					=       torch.cat([class_labels, class_null]      ,    0  ) if guidance_scale > 1 else class_labels

      # set step values
      self.scheduler.set_timesteps(_UpperCamelCase  )

      for t in self.progress_bar(self.scheduler.timesteps  ):
         if guidance_scale > 1:
            _lowercase      :				Optional[Any]      					=       latent_model_input[: len(_UpperCamelCase  ) // 2]
            _lowercase      :				List[str]      					=       torch.cat([half, half]      ,    dim=0  )
         _lowercase      :				Tuple      					=       self.scheduler.scale_model_input(_UpperCamelCase      ,    _UpperCamelCase  )

         _lowercase      :				str      					=       t
         if not torch.is_tensor(_UpperCamelCase  ):
            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
            # This would be a good case for the `match` statement (Python 3.10+)
            _lowercase      :				Dict      					=       latent_model_input.device.type == "mps"
            if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
               _lowercase      :				List[str]      					=       torch.floataa if is_mps else torch.floataa
            else:
               _lowercase      :				Dict      					=       torch.intaa if is_mps else torch.intaa
            _lowercase      :				Any      					=       torch.tensor([timesteps]      ,    dtype=_UpperCamelCase      ,    device=latent_model_input.device  )
         elif len(timesteps.shape  ) == 0:
            _lowercase      :				Any      					=       timesteps[None].to(latent_model_input.device  )
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         _lowercase      :				int      					=       timesteps.expand(latent_model_input.shape[0]  )
         # predict noise model_output
         _lowercase      :				Optional[Any]      					=       self.transformer(
             _UpperCamelCase      ,    timestep=_UpperCamelCase      ,    class_labels=_UpperCamelCase  ).sample

         # perform guidance
         if guidance_scale > 1:
            _lowercase					,				_lowercase      :				Tuple      					=       noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
            _lowercase					,				_lowercase      :				List[str]      					=       torch.split(_UpperCamelCase      ,    len(_UpperCamelCase  ) // 2      ,    dim=0  )

            _lowercase      :				str      					=       uncond_eps + guidance_scale * (cond_eps - uncond_eps)
            _lowercase      :				Optional[Any]      					=       torch.cat([half_eps, half_eps]      ,    dim=0  )

            _lowercase      :				Optional[Any]      					=       torch.cat([eps, rest]      ,    dim=1  )

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
            _lowercase					,				_lowercase      :				Any      					=       torch.split(_UpperCamelCase      ,    _UpperCamelCase      ,    dim=1  )
         else:
            _lowercase      :				List[Any]      					=       noise_pred

         # compute previous image: x_t -> x_t-1
         _lowercase      :				List[Any]      					=       self.scheduler.step(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ).prev_sample

      if guidance_scale > 1:
         _lowercase					,				_lowercase      :				Optional[int]      					=       latent_model_input.chunk(2      ,    dim=0  )
      else:
         _lowercase      :				str      					=       latent_model_input

      _lowercase      :				Optional[int]      					=       1 / self.vae.config.scaling_factor * latents
      _lowercase      :				List[Any]      					=       self.vae.decode(_UpperCamelCase  ).sample

      _lowercase      :				Union[str, Any]      					=       (samples / 2 + 0.5).clamp(0      ,    1  )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      _lowercase      :				str      					=       samples.cpu().permute(0      ,    2      ,    3      ,    1  ).float().numpy()

      if output_type == "pil":
         _lowercase      :				Any      					=       self.numpy_to_pil(_UpperCamelCase  )

      if not return_dict:
         return (samples,)

      return ImagePipelineOutput(images=_UpperCamelCase  )









'''simple docstring'''
from math import pi, sqrt, tan







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"					)
   return 6 * side_length**2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"					)
   return 4 * pi * radius**2







def  _A		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("surface_area_hemisphere() only accepts non-negative values"					)
   return 3 * pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     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		(     snake_case		,							snake_case		,							snake_case					)      ->     float:
   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"					)
   _lowercase      :				Union[str, Any]      					=       (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		(     snake_case		,							snake_case					)      ->     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		(     snake_case		,							snake_case					)      ->     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(snake_case		,							2					) * torus_radius * tube_radius







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if length < 0 or width < 0:
      raise ValueError("area_rectangle() only accepts non-negative values"					)
   return length * width







def  _A		(     snake_case					)      ->     float:
   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"					)
   return side_length**2







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_triangle() only accepts non-negative values"					)
   return (base * height) / 2







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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"					)
   _lowercase      :				Any      					=       (sidea + sidea + sidea) / 2
   _lowercase      :				List[str]      					=       sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)					)
   return area







def  _A		(     snake_case		,							snake_case					)      ->     float:
   if base < 0 or height < 0:
      raise ValueError("area_parallelogram() only accepts non-negative values"					)
   return base * height







def  _A		(     snake_case		,							snake_case		,							snake_case					)      ->     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		(     snake_case					)      ->     float:
   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"					)
   return pi * radius**2







def  _A		(     snake_case		,							snake_case					)      ->     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		(     snake_case		,							snake_case					)      ->     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		(     snake_case		,							snake_case					)      ->     float:
   if not isinstance(snake_case		,							snake_case					) 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) = }''')






