



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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       ["vqvae"]



	def __init__(						self							:    Dict				,	snake_case_							:    AutoencoderKL				,	snake_case_							:    UNetaDConditionModel				,	snake_case_							:    Mel				,	snake_case_							:    Union[DDIMScheduler, DDPMScheduler]				,	)->							int:
						super().__init__()
						self.register_modules(unet=snake_case_				,	scheduler=snake_case_				,	mel=snake_case_				,	vqvae=snake_case_)



	def 		UpperCamelCase							(						self							:    List[str])->							int:
						return 50 if isinstance(self.scheduler				,	snake_case_) else 10_00



	@torch.no_grad()
	def __call__(						self							:    Dict				,	snake_case_							:    int = 1				,	snake_case_							:    str = None				,	snake_case_							:    np.ndarray = None				,	snake_case_							:    int = 0				,	snake_case_							:    int = 0				,	snake_case_							:    int = None				,	snake_case_							:    torch.Generator = None				,	snake_case_							:    float = 0				,	snake_case_							:    float = 0				,	snake_case_							:    torch.Generator = None				,	snake_case_							:    float = 0				,	snake_case_							:    torch.Tensor = None				,	snake_case_							:    torch.Tensor = None				,	snake_case_							:    str=True				,	)->							Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
						__lowerCAmelCase	   =steps or self.get_default_steps()
						self.scheduler.set_timesteps(snake_case_)
						__lowerCAmelCase	   =step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size) == int:
											__lowerCAmelCase	   =(self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
											__lowerCAmelCase	   =randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )				,	generator=snake_case_				,	device=self.device				,	)
						__lowerCAmelCase	   =noise
						__lowerCAmelCase	   =None

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

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

											if start_step > 0:
																__lowerCAmelCase	   =self.scheduler.add_noise(snake_case_				,	snake_case_				,	self.scheduler.timesteps[start_step - 1])

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

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

											if isinstance(self.scheduler				,	snake_case_):
																__lowerCAmelCase	   =self.scheduler.step(
																    model_output=snake_case_				,	timestep=snake_case_				,	sample=snake_case_				,	eta=snake_case_				,	generator=snake_case_				,	)["""prev_sample"""]
											else:
																__lowerCAmelCase	   =self.scheduler.step(
																    model_output=snake_case_				,	timestep=snake_case_				,	sample=snake_case_				,	generator=snake_case_				,	)["""prev_sample"""]

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

						if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											__lowerCAmelCase	   =1 / self.vqvae.config.scaling_factor * images
											__lowerCAmelCase	   =self.vqvae.decode(snake_case_)["""sample"""]

						__lowerCAmelCase	   =(images / 2 + 0.5).clamp(0				,	1)
						__lowerCAmelCase	   =images.cpu().permute(0				,	2				,	3				,	1).numpy()
						__lowerCAmelCase	   =(images * 2_55).round().astype("""uint8""")
						__lowerCAmelCase	   =list(
						    (Image.fromarray(_[:, :, 0]) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(snake_case_				,	mode="""RGB""").convert("""L""") for _ in images))

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

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



	@torch.no_grad()
	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    List[Image.Image]				,	snake_case_							:    int = 50)->							np.ndarray:
						assert isinstance(self.scheduler				,	snake_case_)
						self.scheduler.set_timesteps(snake_case_)
						__lowerCAmelCase	   =np.array(
						    [np.frombuffer(image.tobytes()				,	dtype="""uint8""").reshape((1, image.height, image.width)) for image in images])
						__lowerCAmelCase	   =(sample / 2_55) * 2 - 1
						__lowerCAmelCase	   =torch.Tensor(snake_case_).to(self.device)

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps				,	(0,))):
											__lowerCAmelCase	   =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											__lowerCAmelCase	   =self.scheduler.alphas_cumprod[t]
											__lowerCAmelCase	   =(
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											__lowerCAmelCase	   =1 - alpha_prod_t
											__lowerCAmelCase	   =self.unet(snake_case_				,	snake_case_)["""sample"""]
											__lowerCAmelCase	   =(1 - alpha_prod_t_prev) ** 0.5 * model_output
											__lowerCAmelCase	   =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											__lowerCAmelCase	   =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample



	@staticmethod
	def 		UpperCamelCase							(						snake_case_							:    torch.Tensor				,	snake_case_							:    torch.Tensor				,	snake_case_							:    float)->							torch.Tensor:
						__lowerCAmelCase	   =acos(torch.dot(torch.flatten(snake_case_)				,	torch.flatten(snake_case_)) / torch.norm(snake_case_) / torch.norm(snake_case_))
						return sin((1 - alpha) * theta) * xa / sin(snake_case_) + sin(alpha * theta) * xa / sin(snake_case_)









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




