
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			['''vqvae''']



     def __init__(				self							:							Dict     ,						_A							:							AutoencoderKL     ,						_A							:							UNetaDConditionModel     ,						_A							:							Mel     ,						_A							:							Union[DDIMScheduler, DDPMScheduler]     ,						):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       return 50 if isinstance(self.scheduler     ,						_A ) else 1000



     @torch.no_grad()
     def __call__(				self							:							Dict     ,						_A							:							int = 1     ,						_A							:							str = None     ,						_A							:							np.ndarray = None     ,						_A							:							int = 0     ,						_A							:							int = 0     ,						_A							:							int = None     ,						_A							:							torch.Generator = None     ,						_A							:							float = 0     ,						_A							:							float = 0     ,						_A							:							torch.Generator = None     ,						_A							:							float = 0     ,						_A							:							torch.Tensor = None     ,						_A							:							torch.Tensor = None     ,						_A							:							Union[str, Any]=True     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size ) == int:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
         __SCREAMING_SNAKE_CASE      :	int					=		randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )     ,						generator=_A     ,						device=self.device     ,						)
       __SCREAMING_SNAKE_CASE      :	Any					=		noise
       __SCREAMING_SNAKE_CASE      :	List[str]					=		None

       if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(_A     ,						_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.mel.audio_slice_to_image(_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		np.frombuffer(input_image.tobytes()     ,						dtype='''uint8''' ).reshape(
             (input_image.height, input_image.width) )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		(input_image / 255) * 2 - 1
         __SCREAMING_SNAKE_CASE      :	str					=		torch.tensor(input_image[np.newaxis, :, :]     ,						dtype=torch.float ).to(self.device )

         if self.vqvae is not None:
           __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.vqvae.encode(torch.unsqueeze(_A     ,						0 ) ).latent_dist.sample(
               generator=_A )[0]
           __SCREAMING_SNAKE_CASE      :	Any					=		self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.scheduler.add_noise(_A     ,						_A     ,						self.scheduler.timesteps[start_step - 1] )

         __SCREAMING_SNAKE_CASE      :	int					=		(
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		int(mask_start_secs * pixels_per_second )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		int(mask_end_secs * pixels_per_second )
         __SCREAMING_SNAKE_CASE      :	str					=		self.scheduler.add_noise(_A     ,						_A     ,						torch.tensor(self.scheduler.timesteps[start_step:] ) )

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
         if isinstance(self.unet     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	List[str]					=		self.unet(_A     ,						_A     ,						_A )['''sample''']
         else:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.unet(_A     ,						_A )['''sample''']

         if isinstance(self.scheduler     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	int					=		self.scheduler.step(
               model_output=_A     ,						timestep=_A     ,						sample=_A     ,						eta=_A     ,						generator=_A     ,						)['''prev_sample''']
         else:
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.scheduler.step(
               model_output=_A     ,						timestep=_A     ,						sample=_A     ,						generator=_A     ,						)['''prev_sample''']

         if mask is not None:
           if mask_start > 0:
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask[:, step, :, :mask_start]
           if mask_end > 0:
             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		1 / self.vqvae.config.scaling_factor * images
         __SCREAMING_SNAKE_CASE      :	Dict					=		self.vqvae.decode(_A )['''sample''']

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(images / 2 + 0.5).clamp(0     ,						1 )
       __SCREAMING_SNAKE_CASE      :	int					=		images.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(images * 255).round().astype('''uint8''' )
       __SCREAMING_SNAKE_CASE      :	Dict					=		list(
           (Image.fromarray(_[:, :, 0] ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(_A     ,						mode='''RGB''' ).convert('''L''' ) for _ in images) )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[self.mel.image_to_audio(_A ) for _ in images]
       if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(_A )[:, np.newaxis, :] )     ,						**ImagePipelineOutput(_A ) )



     @torch.no_grad()
     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Image.Image]     ,						_A							:							int = 50 ):







       """simple docstring"""




       assert isinstance(self.scheduler     ,						_A )
       self.scheduler.set_timesteps(_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		np.array(
           [np.frombuffer(image.tobytes()     ,						dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(sample / 255) * 2 - 1
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.Tensor(_A ).to(self.device )

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,						(0,) ) ):
         __SCREAMING_SNAKE_CASE      :	int					=		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.scheduler.alphas_cumprod[t]
         __SCREAMING_SNAKE_CASE      :	Any					=		(
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		1 - alpha_prod_t
         __SCREAMING_SNAKE_CASE      :	Dict					=		self.unet(_A     ,						_A )['''sample''']
         __SCREAMING_SNAKE_CASE      :	str					=		(1 - alpha_prod_t_prev) ** 0.5 * model_output
         __SCREAMING_SNAKE_CASE      :	Dict					=		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __SCREAMING_SNAKE_CASE      :	Tuple					=		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample




     @staticmethod
     def  UpperCAmelCase__							(				_A							:							torch.Tensor     ,						_A							:							torch.Tensor     ,						_A							:							float ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		acos(torch.dot(torch.flatten(_A )     ,						torch.flatten(_A ) ) / torch.norm(_A ) / torch.norm(_A ) )
       return sin((1 - alpha) * theta ) * xa / sin(_A ) + sin(alpha * theta ) * xa / sin(_A )







'''simple docstring'''

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(boundary[1] - boundary[0]) / steps
   __SCREAMING_SNAKE_CASE   				=							boundary[0]
   __SCREAMING_SNAKE_CASE   				=							boundary[1]
   __SCREAMING_SNAKE_CASE   				=							make_points(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							0.0
   y += (h / 2.0) * f(__UpperCAmelCase   )
   for i in x_i:
      # print(i)
      y += h * f(__UpperCAmelCase   )
   y += (h / 2.0) * f(__UpperCAmelCase   )
   return y

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							a + h
   while x < (b - h):
      yield x
      __SCREAMING_SNAKE_CASE   				=							x + h

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:  # enter your function here

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							(x - 0) * (x - 0)
   return y

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							0.0  # Lower bound of integration
   __SCREAMING_SNAKE_CASE   				=							1.0  # Upper bound of integration
   __SCREAMING_SNAKE_CASE   				=							1_0.0  # define number of steps or resolution
   __SCREAMING_SNAKE_CASE   				=							[a, b]  # define boundary of integration
   __SCREAMING_SNAKE_CASE   				=							method_a(__UpperCAmelCase				,						__UpperCAmelCase   )
   print(f"""y = {y}"""   )


if __name__ == "__main__":
    main()





