






'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__A							:							Optional[Any]  							=		logging.get_logger(__name__)  # pylint: disable=invalid-name
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




     super().__init__()
     self.register_modules(unet=__lowerCamelCase				,     scheduler=__lowerCamelCase     )




    @torch.no_grad()
    def __call__(					self     :     int				,     __lowerCamelCase     :     int = 1				,     __lowerCamelCase     :     int = 100				,     __lowerCamelCase     :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,     __lowerCamelCase     :     Optional[float] = None				,     __lowerCamelCase     :     bool = True				,     )							->							Union[AudioPipelineOutput, Tuple]:

     '''simple docstring'''




     if audio_length_in_s is None:
      lowerCamelCase__  =     self.unet.config.sample_size / self.unet.config.sample_rate

     lowerCamelCase__  =     audio_length_in_s * self.unet.config.sample_rate

     lowerCamelCase__  =     2 ** len(self.unet.up_blocks     )
     if sample_size < 3 * down_scale_factor:
      raise ValueError(
          f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
          f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

     lowerCamelCase__  =     int(__lowerCamelCase     )
     if sample_size % down_scale_factor != 0:
      lowerCamelCase__  =     (
          (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
      ) * down_scale_factor
      logger.info(
          f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
          f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
          " process."     )
     lowerCamelCase__  =     int(__lowerCamelCase     )

     lowerCamelCase__  =     next(iter(self.unet.parameters()     )     ).dtype
     lowerCamelCase__  =     (batch_size, self.unet.config.in_channels, sample_size)
     if isinstance(__lowerCamelCase				,     __lowerCamelCase     ) and len(__lowerCamelCase     ) != batch_size:
      raise ValueError(
          f'''You have passed a list of generators of length {len(__lowerCamelCase     )}, but requested an effective batch'''
          f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

     lowerCamelCase__  =     randn_tensor(__lowerCamelCase				,     generator=__lowerCamelCase				,     device=self.device				,     dtype=__lowerCamelCase     )

     # set step values
     self.scheduler.set_timesteps(__lowerCamelCase				,     device=audio.device     )
     lowerCamelCase__  =     self.scheduler.timesteps.to(__lowerCamelCase     )

     for t in self.progress_bar(self.scheduler.timesteps     ):
      # 1. predict noise model_output
      lowerCamelCase__  =     self.unet(__lowerCamelCase				,     __lowerCamelCase     ).sample

      # 2. compute previous image: x_t -> t_t-1
      lowerCamelCase__  =     self.scheduler.step(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     ).prev_sample

     lowerCamelCase__  =     audio.clamp(-1				,     1     ).float().cpu().numpy()

     lowerCamelCase__  =     audio[:, :, :original_sample_size]

     if not return_dict:
      return (audio,)

     return AudioPipelineOutput(audios=__lowerCamelCase     )









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

