


"""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__				):






		'''simple docstring'''

		lowerCamelCase__				=['vqvae']



		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 50 if isinstance(self.scheduler				,       UpperCamelCase_					) else 10_00



		@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				,       ):





			'''simple docstring'''

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

			if audio_file is not None or raw_audio is not None:
				self.mel.load_audio(UpperCamelCase_				,       UpperCamelCase_					)
				__snake_case	:						List[str]						=				self.mel.audio_slice_to_image(UpperCamelCase_					)
				__snake_case	:						List[Any]						=				np.frombuffer(input_image.tobytes()				,       dtype='''uint8'''					).reshape(
				    (input_image.height, input_image.width)					)
				__snake_case	:						List[str]						=				(input_image / 2_55) * 2 - 1
				__snake_case	:						Any						=				torch.tensor(input_image[np.newaxis, :, :]				,       dtype=torch.float					).to(self.device					)

				if self.vqvae is not None:
					__snake_case	:						int						=				self.vqvae.encode(torch.unsqueeze(UpperCamelCase_				,       0					)					).latent_dist.sample(
					    generator=UpperCamelCase_					)[0]
					__snake_case	:						Dict						=				self.vqvae.config.scaling_factor * input_images

				if start_step > 0:
					__snake_case	:						Any						=				self.scheduler.add_noise(UpperCamelCase_				,       UpperCamelCase_				,       self.scheduler.timesteps[start_step - 1]					)

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

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

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

				if mask is not None:
					if mask_start > 0:
						__snake_case	:						Union[str, Any]						=				mask[:, step, :, :mask_start]
					if mask_end > 0:
						__snake_case	:						List[str]						=				mask[:, step, :, -mask_end:]

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

			__snake_case	:						Union[str, Any]						=				(images / 2 + 0.5).clamp(0				,       1					)
			__snake_case	:						List[Any]						=				images.cpu().permute(0				,       2				,       3				,       1					).numpy()
			__snake_case	:						Optional[Any]						=				(images * 2_55).round().astype('''uint8'''					)
			__snake_case	:						Optional[int]						=				list(
			    (Image.fromarray(_[:, :, 0]					) for _ in images)
			    if images.shape[3] == 1
			    else (Image.fromarray(UpperCamelCase_				,       mode='''RGB'''					).convert('''L'''					) for _ in images)					)

			__snake_case	:						Optional[int]						=				[self.mel.image_to_audio(UpperCamelCase_					) for _ in images]
			if not return_dict:
				return images, (self.mel.get_sample_rate(), audios)

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



		@torch.no_grad()
		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = 50					):





			'''simple docstring'''

			assert isinstance(self.scheduler				,       UpperCamelCase_					)
			self.scheduler.set_timesteps(UpperCamelCase_					)
			__snake_case	:						str						=				np.array(
			    [np.frombuffer(image.tobytes()				,       dtype='''uint8'''					).reshape((1, image.height, image.width)					) for image in images]					)
			__snake_case	:						List[str]						=				(sample / 2_55) * 2 - 1
			__snake_case	:						Dict						=				torch.Tensor(UpperCamelCase_					).to(self.device					)

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

			return sample



		@staticmethod
		def 				SCREAMING_SNAKE_CASE  (a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				acos(torch.dot(torch.flatten(UpperCamelCase_					)				,       torch.flatten(UpperCamelCase_					)					) / torch.norm(UpperCamelCase_					) / torch.norm(UpperCamelCase_					)					)
			return sin((1 - alpha) * theta					) * xa / sin(UpperCamelCase_					) + sin(alpha * theta					) * xa / sin(UpperCamelCase_					)









'''simple docstring'''
from PIL import Image


def 			a      (			__a ,					__a   )  ->	Image:



					'''simple docstring'''






					def brightness(__a   ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''   )
					return img.point(__a   )


if __name__ == "__main__":
		# Load image
		with Image.open('''image_data/lena.jpg''') as img:
				# Change brightness to 100
				__snake_case							      =     change_brightness(img, 100)
				brigt_img.save('''image_data/lena_brightness.png''', format='''png''')