

'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def    a__       (  a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      image.size
     __SCREAMING_SNAKE_CASE     =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     __SCREAMING_SNAKE_CASE     =      image.resize((w, h)      ,    resample=PIL_INTERPOLATION["""lanczos"""]						)
     __SCREAMING_SNAKE_CASE     =      np.array(__snake_case						).astype(np.floataa						) / 255.0
     __SCREAMING_SNAKE_CASE     =      image[None].transpose(0      ,    3      ,    1      ,    2						)
     __SCREAMING_SNAKE_CASE     =      torch.from_numpy(__snake_case						)
     return 2.0 * image - 1.0







class 						lowerCAmelCase__   (      a__					):



 """simple docstring"""



 def __init__(		self					:       int					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							)  -> List[str]:


      """simple docstring"""




      super().__init__()
      self.register_modules(vqvae=_UpperCAmelCase					,							unet=_UpperCAmelCase					,							scheduler=_UpperCAmelCase			)


 @torch.no_grad()
 def __call__(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any] = None					,							__SCREAMING_SNAKE_CASE					:       Tuple = 1					,							__SCREAMING_SNAKE_CASE					:       Dict = 100					,							__SCREAMING_SNAKE_CASE					:       Optional[Any] = 0.0					,							__SCREAMING_SNAKE_CASE					:       str = None					,							__SCREAMING_SNAKE_CASE					:       List[str] = "pil"					,							__SCREAMING_SNAKE_CASE					:       Any = True					,							)  -> Any:


      """simple docstring"""




      if isinstance(_UpperCAmelCase					,							PIL.Image.Image			):
           __SCREAMING_SNAKE_CASE     =      1
      elif isinstance(_UpperCAmelCase					,							torch.Tensor			):
           __SCREAMING_SNAKE_CASE     =      image.shape[0]
      else:
           raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_UpperCAmelCase			)}'			)

      if isinstance(_UpperCAmelCase					,							PIL.Image.Image			):
           __SCREAMING_SNAKE_CASE     =      preprocess(_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      __SCREAMING_SNAKE_CASE     =      (batch_size, self.unet.config.in_channels // 2, height, width)
      __SCREAMING_SNAKE_CASE     =      next(self.unet.parameters()			).dtype

      __SCREAMING_SNAKE_CASE     =      randn_tensor(_UpperCAmelCase					,							generator=_UpperCAmelCase					,							device=self.device					,							dtype=_UpperCAmelCase			)

      __SCREAMING_SNAKE_CASE     =      image.to(device=self.device					,							dtype=_UpperCAmelCase			)

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(_UpperCAmelCase					,							device=self.device			)
      __SCREAMING_SNAKE_CASE     =      self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      __SCREAMING_SNAKE_CASE     =      latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      __SCREAMING_SNAKE_CASE     =      'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
      __SCREAMING_SNAKE_CASE     =      {}
      if accepts_eta:
           __SCREAMING_SNAKE_CASE     =      eta

      for t in self.progress_bar(_UpperCAmelCase			):
           # concat latents and low resolution image in the channel dimension.
           __SCREAMING_SNAKE_CASE     =      torch.cat([latents, image]					,							dim=1			)
           __SCREAMING_SNAKE_CASE     =      self.scheduler.scale_model_input(_UpperCAmelCase					,							_UpperCAmelCase			)
           # predict the noise residual
           __SCREAMING_SNAKE_CASE     =      self.unet(_UpperCAmelCase					,							_UpperCAmelCase			).sample
           # compute the previous noisy sample x_t -> x_t-1
           __SCREAMING_SNAKE_CASE     =      self.scheduler.step(_UpperCAmelCase					,							_UpperCAmelCase					,							_UpperCAmelCase					,							**_UpperCAmelCase			).prev_sample

      # decode the image latents with the VQVAE
      __SCREAMING_SNAKE_CASE     =      self.vqvae.decode(_UpperCAmelCase			).sample
      __SCREAMING_SNAKE_CASE     =      torch.clamp(_UpperCAmelCase					,							-1.0					,							1.0			)
      __SCREAMING_SNAKE_CASE     =      image / 2 + 0.5
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			).numpy()

      if output_type == "pil":
           __SCREAMING_SNAKE_CASE     =      self.numpy_to_pil(_UpperCAmelCase			)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=_UpperCAmelCase			)













'''simple docstring'''




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def        _lowerCAmelCase			(  __snake_case  : float ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     list[float]:
     if radian_mode:
          return [magnitude * cos(__snake_case			), magnitude * sin(__snake_case			)]
     return [magnitude * cos(radians(__snake_case			)			), magnitude * sin(radians(__snake_case			)			)]






def        _lowerCAmelCase			(  __snake_case  : NDArray[floataa] ,							__snake_case  : NDArray[floataa] ,							__snake_case  : float = 10**-1			)     ->     bool:
     __A						: NDArray[floataa]      =       cross(__snake_case ,							__snake_case			)
     __A						: float      =       sum(__snake_case			)
     return abs(__snake_case			) < eps


if __name__ == "__main__":
  # Test to check if it works
  lowercase__    :      List[Any]						=    array(
      [
          polar_force(718.4, 1_80 - 30),
          polar_force(879.54, 45),
          polar_force(1_00, -90),
      ]
  )

  lowercase__    :      NDArray[floataa]						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  lowercase__    :      Dict						=    array(
      [
          polar_force(30 * 9.81, 15),
          polar_force(2_15, 1_80 - 45),
          polar_force(2_64, 90 - 30),
      ]
  )

  lowercase__    :      Any						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  lowercase__    :      Optional[int]						=    array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

  lowercase__    :      Optional[int]						=    array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()