"""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__(	__snake_case       ):
						lowerCAmelCase__     :  Dict  							=						["""vqvae"""]





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,)    -> int:
													super().__init__()
													self.register_modules(unet=__UpperCAmelCase						,scheduler=__UpperCAmelCase						,mel=__UpperCAmelCase						,vqvae=__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Tuple:
													return 50 if isinstance(self.scheduler						,__UpperCAmelCase   ) else 10_00





						@torch.no_grad()
						def __call__(				self						,__UpperCAmelCase = 1						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = 0						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = 0						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase=True						,)    -> str:
													A__		   =							steps or self.get_default_steps()
													self.scheduler.set_timesteps(__UpperCAmelCase   )
													A__		   =							step_generator or generator
													# For backwards compatibility
													if type(self.unet.config.sample_size   ) == int:
																				A__		   =							(self.unet.config.sample_size, self.unet.config.sample_size)
													if noise is None:
																				A__		   =							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						,)
													A__		   =							noise
													A__		   =							None

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

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

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

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

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

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

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

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

													A__		   =							[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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = 50   )    -> List[str]:
													assert isinstance(self.scheduler						,__UpperCAmelCase   )
													self.scheduler.set_timesteps(__UpperCAmelCase   )
													A__		   =							np.array(
													    [np.frombuffer(image.tobytes()						,dtype='uint8'   ).reshape((1, image.height, image.width)   ) for image in images]   )
													A__		   =							(sample / 2_55) * 2 - 1
													A__		   =							torch.Tensor(__UpperCAmelCase   ).to(self.device   )

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

													return sample





						@staticmethod
						def  snake_case__		(				__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Dict:
													A__		   =							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   )








def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if not all(char in '01' for char in bin_string			):
          raise ValueError('Non-binary value was passed to the function'			)
     if not bin_string:
          raise ValueError('Empty string was passed to the function'			)
     lowercase__    :   Union[str, Any]      =       ''
     while len(SCREAMING_SNAKE_CASE_			) % 3 != 0:
          lowercase__    :   List[str]      =       '0' + bin_string
     lowercase__    :   Any      =       [
         bin_string[index : index + 3]
         for index in range(len(SCREAMING_SNAKE_CASE_			)			)
         if index % 3 == 0
     ]
     for bin_group in bin_string_in_3_list:
          lowercase__    :   str      =       0
          for index, val in enumerate(SCREAMING_SNAKE_CASE_			):
               oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE_			)			)
          oct_string += str(SCREAMING_SNAKE_CASE_			)
     return oct_string


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

       testmod()

