"""simple docstring"""





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_	(    _a):
      lowerCamelCase__		:    Optional[int]   =  ["vqvae"]


      def __init__(   self		,			a		,			a		,			a		,			a		,			)       ->					Optional[int]:
             super().__init__()
             self.register_modules(unet=a		,			scheduler=a		,			mel=a		,			vqvae=a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             return 5_0 if isinstance(self.scheduler		,			a							) else 1_0_0_0


      @torch.no_grad()
      def __call__(   self		,			a = 1		,			a = None		,			a = None		,			a = 0		,			a = 0		,			a = None		,			a = None		,			a = 0		,			a = 0		,			a = None		,			a = 0		,			a = None		,			a = None		,			a=True		,			)       ->					Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:
             lowercase__			:		Optional[Any]       =	steps or self.get_default_steps()
             self.scheduler.set_timesteps(a							)
             lowercase__			:		Dict       =	step_generator or generator
             # For backwards compatibility
             if type(self.unet.config.sample_size							) == int:
                    lowercase__			:		str       =	(self.unet.config.sample_size, self.unet.config.sample_size)
             if noise is None:
                    lowercase__			:		Union[str, Any]       =	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		,			)
             lowercase__			:		Dict       =	noise
             lowercase__			:		str       =	None

             if audio_file is not None or raw_audio is not None:
                    self.mel.load_audio(a		,			a							)
                    lowercase__			:		str       =	self.mel.audio_slice_to_image(a							)
                    lowercase__			:		Tuple       =	np.frombuffer(input_image.tobytes()		,			dtype='uint8'							).reshape(
                        (input_image.height, input_image.width)							)
                    lowercase__			:		List[str]       =	(input_image / 2_5_5) * 2 - 1
                    lowercase__			:		Optional[Any]       =	torch.tensor(input_image[np.newaxis, :, :]		,			dtype=torch.float							).to(self.device							)

                    if self.vqvae is not None:
                           lowercase__			:		List[str]       =	self.vqvae.encode(torch.unsqueeze(a		,			0							)							).latent_dist.sample(
                               generator=a							)[0]
                           lowercase__			:		Tuple       =	self.vqvae.config.scaling_factor * input_images

                    if start_step > 0:
                           lowercase__			:		Union[str, Any]       =	self.scheduler.add_noise(a		,			a		,			self.scheduler.timesteps[start_step - 1]							)

                    lowercase__			:		Any       =	(
                        self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                    )
                    lowercase__			:		Optional[int]       =	int(mask_start_secs * pixels_per_second							)
                    lowercase__			:		Optional[int]       =	int(mask_end_secs * pixels_per_second							)
                    lowercase__			:		Optional[Any]       =	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							):
                           lowercase__			:		Any       =	self.unet(a		,			a		,			a							)['sample']
                    else:
                           lowercase__			:		Optional[int]       =	self.unet(a		,			a							)['sample']

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

                    if mask is not None:
                           if mask_start > 0:
                                  lowercase__			:		int       =	mask[:, step, :, :mask_start]
                           if mask_end > 0:
                                  lowercase__			:		Optional[int]       =	mask[:, step, :, -mask_end:]

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

             lowercase__			:		Any       =	(images / 2 + 0.5).clamp(0		,			1							)
             lowercase__			:		List[str]       =	images.cpu().permute(0		,			2		,			3		,			1							).numpy()
             lowercase__			:		Optional[int]       =	(images * 2_5_5).round().astype('uint8'							)
             lowercase__			:		List[str]       =	list(
                 (Image.fromarray(_[:, :, 0]							) for _ in images)
                 if images.shape[3] == 1
                 else (Image.fromarray(a		,			mode='RGB'							).convert('L'							) for _ in images)							)

             lowercase__			:		int       =	[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		,			a		,			a = 5_0							)       ->					np.ndarray:
             assert isinstance(self.scheduler		,			a							)
             self.scheduler.set_timesteps(a							)
             lowercase__			:		Optional[Any]       =	np.array(
                 [np.frombuffer(image.tobytes()		,			dtype='uint8'							).reshape((1, image.height, image.width)							) for image in images]							)
             lowercase__			:		Any       =	(sample / 2_5_5) * 2 - 1
             lowercase__			:		Optional[int]       =	torch.Tensor(a							).to(self.device							)

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

             return sample


      @staticmethod
      def 		_UpperCAmelCase       (   a		,			a		,			a							)       ->					torch.Tensor:
             lowercase__			:		List[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 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		assert (
		    isinstance(_lowerCamelCase       ,      _lowerCamelCase					) and number_of_steps > 0
		), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
		if number_of_steps == 1:
				return 1
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		1, 1
		for _ in range(number_of_steps - 1					):
				__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Tuple           =		current + previous, current
		return current


if __name__ == "__main__":
						import doctest

						doctest.testmod()