




'''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 			_a  (	__a ):
       __a				:     str   				=					["""vqvae"""]
       def __init__(	self						:	str   ,				lowercase						:	AutoencoderKL   ,				lowercase						:	UNetaDConditionModel   ,				lowercase						:	Mel   ,				lowercase						:	Union[DDIMScheduler, DDPMScheduler]   ,				):



           '''simple docstring'''

           super().__init__()
           self.register_modules(unet=lowercase   ,				scheduler=lowercase   ,				mel=lowercase   ,				vqvae=lowercase				)
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return 50 if isinstance(self.scheduler   ,				lowercase				) else 1_000
       @torch.no_grad()
       def __call__(	self						:	Optional[Any]   ,				lowercase						:	int = 1   ,				lowercase						:	str = None   ,				lowercase						:	np.ndarray = None   ,				lowercase						:	int = 0   ,				lowercase						:	int = 0   ,				lowercase						:	int = None   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Generator = None   ,				lowercase						:	float = 0   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	torch.Tensor = None   ,				lowercase						:	Tuple=True   ,				):



           '''simple docstring'''

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

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

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

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

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

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

               if isinstance(self.scheduler   ,				lowercase				):
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				eta=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']
               else:
                   UpperCAmelCase			       =		self.scheduler.step(
                       model_output=lowercase   ,				timestep=lowercase   ,				sample=lowercase   ,				generator=lowercase   ,				)['''prev_sample''']

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

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

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

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

           return BaseOutput(**AudioPipelineOutput(np.array(lowercase				)[:, np.newaxis, :]				)   ,				**ImagePipelineOutput(lowercase				)				)
       @torch.no_grad()
       def     A      (	self						:	Dict   ,				lowercase						:	List[Image.Image]   ,				lowercase						:	int = 50				):



           '''simple docstring'''

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

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

           return sample







       @staticmethod
       def     A      (	lowercase						:	torch.Tensor   ,				lowercase						:	torch.Tensor   ,				lowercase						:	float				):



           '''simple docstring'''

           UpperCAmelCase			       =		acos(torch.dot(torch.flatten(lowercase				)   ,				torch.flatten(lowercase				)				) / torch.norm(lowercase				) / torch.norm(lowercase				)				)
           return sin((1 - alpha) * theta				) * xa / sin(lowercase				) + sin(alpha * theta				) * xa / sin(lowercase				)









'''simple docstring'''




import re



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]"""						,      str_    )]



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     split_input(str_    )
			return "".join(
			    ["""""".join([char.capitalize() for char in sub_str]    ) for sub_str in string_split]    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						List[Any]	=     split_input(lowerCamelCase__    )
						if upper:
									A_	:						Tuple	=     """""".join(
									    [
									        separator.join([char.upper() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						else:
									A_	:						Optional[int]	=     """""".join(
									    [
									        separator.join([char.lower() for char in sub_str]    )
									        for sub_str in string_split
									    ]    )
						return res_str
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			return to_simple_case(lowerCamelCase__    )



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			try:
						A_	:						Tuple	=     to_simple_case(lowerCamelCase__    )
						return res_str[0].lower() + res_str[1:]
			except IndexError:
						return "not valid string"



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """_"""    )



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			return to_complex_case(lowerCamelCase__						,      lowerCamelCase__						,      """-"""    )


if __name__ == "__main__":
						__import__('''doctest''').testmod()