






"""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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Tuple    =				["""vqvae"""]





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     ):
		super().__init__()
		self.register_modules(unet=lowerCAmelCase_     ,     scheduler=lowerCAmelCase_     ,     mel=lowerCAmelCase_     ,     vqvae=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		return 50 if isinstance(self.scheduler     ,     lowerCAmelCase_							) else 1000





	@torch.no_grad()
	def __call__(self     ,     lowerCAmelCase_ = 1     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = 0     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_ = None     ,     lowerCAmelCase_=True     ,     ):
		A_		:						Optional[Any]	    =   steps or self.get_default_steps()
		self.scheduler.set_timesteps(lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   step_generator or generator
		# For backwards compatibility
		if type(self.unet.config.sample_size							) == int:
			A_		:						List[Any]	    =   (self.unet.config.sample_size, self.unet.config.sample_size)
		if noise is None:
			A_		:						Optional[Any]	    =   randn_tensor(
			    (
			        batch_size,
			        self.unet.config.in_channels,
			        self.unet.config.sample_size[0],
			        self.unet.config.sample_size[1],
			    )     ,     generator=lowerCAmelCase_     ,     device=self.device     ,     )
		A_		:						Optional[int]	    =   noise
		A_		:						Union[str, Any]	    =   None

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

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

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

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

		for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
			if isinstance(self.unet     ,     lowerCAmelCase_							):
				A_		:						Optional[Any]	    =   self.unet(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							)["""sample"""]
			else:
				A_		:						Any	    =   self.unet(lowerCAmelCase_     ,     lowerCAmelCase_							)["""sample"""]

			if isinstance(self.scheduler     ,     lowerCAmelCase_							):
				A_		:						str	    =   self.scheduler.step(
				    model_output=lowerCAmelCase_     ,     timestep=lowerCAmelCase_     ,     sample=lowerCAmelCase_     ,     eta=lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     )["""prev_sample"""]
			else:
				A_		:						Union[str, Any]	    =   self.scheduler.step(
				    model_output=lowerCAmelCase_     ,     timestep=lowerCAmelCase_     ,     sample=lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     )["""prev_sample"""]

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

		if self.vqvae is not None:
			# 0.18215 was scaling factor used in training to ensure unit variance
			A_		:						List[Any]	    =   1 / self.vqvae.config.scaling_factor * images
			A_		:						Union[str, Any]	    =   self.vqvae.decode(lowerCAmelCase_							)["""sample"""]

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

		A_		:						Tuple	    =   [self.mel.image_to_audio(lowerCAmelCase_							) for _ in images]
		if not return_dict:
			return images, (self.mel.get_sample_rate(), audios)

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





	@torch.no_grad()
	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_ = 50							):
		assert isinstance(self.scheduler     ,     lowerCAmelCase_							)
		self.scheduler.set_timesteps(lowerCAmelCase_							)
		A_		:						Optional[int]	    =   np.array(
		    [np.frombuffer(image.tobytes()     ,     dtype="""uint8"""							).reshape((1, image.height, image.width)							) for image in images]							)
		A_		:						Any	    =   (sample / 255) * 2 - 1
		A_		:						Optional[Any]	    =   torch.Tensor(lowerCAmelCase_							).to(self.device							)

		for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,     (0,)							)							):
			A_		:						Tuple	    =   t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
			A_		:						Any	    =   self.scheduler.alphas_cumprod[t]
			A_		:						List[str]	    =   (
			    self.scheduler.alphas_cumprod[prev_timestep]
			    if prev_timestep >= 0
			    else self.scheduler.final_alpha_cumprod
			)
			A_		:						Optional[int]	    =   1 - alpha_prod_t
			A_		:						Dict	    =   self.unet(lowerCAmelCase_     ,     lowerCAmelCase_							)["""sample"""]
			A_		:						Optional[int]	    =   (1 - alpha_prod_t_prev) ** 0.5 * model_output
			A_		:						Tuple	    =   (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
			A_		:						List[Any]	    =   sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

		return sample





	@staticmethod
	def    lowerCamelCase(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[str]	    =   acos(torch.dot(torch.flatten(lowerCAmelCase_							)     ,     torch.flatten(lowerCAmelCase_							)							) / torch.norm(lowerCAmelCase_							) / torch.norm(lowerCAmelCase_							)							)
		return sin((1 - alpha) * theta							) * xa / sin(lowerCAmelCase_							) + sin(alpha * theta							) * xa / sin(lowerCAmelCase_							)









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

