"""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        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["vqvae"]







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







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return 50 if isinstance(self.scheduler					,lowerCamelCase__    ) else 1_000







					@torch.no_grad()
					def __call__(       self     :			List[str]					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			str = None					,lowerCamelCase__     :			np.ndarray = None					,lowerCamelCase__     :			int = 0					,lowerCamelCase__     :			int = 0					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			torch.Generator = None					,lowerCamelCase__     :			float = 0					,lowerCamelCase__     :			float = 0					,lowerCamelCase__     :			torch.Generator = None					,lowerCamelCase__     :			float = 0					,lowerCamelCase__     :			torch.Tensor = None					,lowerCamelCase__     :			torch.Tensor = None					,lowerCamelCase__     :			List[str]=True					,):
									UpperCAmelCase__    =							steps or self.get_default_steps()
									self.scheduler.set_timesteps(lowerCamelCase__    )
									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=lowerCamelCase__					,device=self.device					,)
									UpperCAmelCase__    =							noise
									UpperCAmelCase__    =							None

									if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(lowerCamelCase__					,lowerCamelCase__    )
													UpperCAmelCase__    =							self.mel.audio_slice_to_image(lowerCamelCase__    )
													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(lowerCamelCase__					,0    )    ).latent_dist.sample(
																	    generator=lowerCamelCase__    )[0]
																	UpperCAmelCase__    =							self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																	UpperCAmelCase__    =							self.scheduler.add_noise(lowerCamelCase__					,lowerCamelCase__					,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(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__    ):
																	UpperCAmelCase__    =							self.unet(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )['sample']
													else:
																	UpperCAmelCase__    =							self.unet(lowerCamelCase__					,lowerCamelCase__    )['sample']

													if isinstance(self.scheduler					,lowerCamelCase__    ):
																	UpperCAmelCase__    =							self.scheduler.step(
																	    model_output=lowerCamelCase__					,timestep=lowerCamelCase__					,sample=lowerCamelCase__					,eta=lowerCamelCase__					,generator=lowerCamelCase__					,)['prev_sample']
													else:
																	UpperCAmelCase__    =							self.scheduler.step(
																	    model_output=lowerCamelCase__					,timestep=lowerCamelCase__					,sample=lowerCamelCase__					,generator=lowerCamelCase__					,)['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(lowerCamelCase__    )['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(lowerCamelCase__					,mode='RGB'    ).convert('L'    ) for _ in images)    )

									UpperCAmelCase__    =							[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     :			List[Any]					,lowerCamelCase__     :			List[Image.Image]					,lowerCamelCase__     :			int = 50    ):
									assert isinstance(self.scheduler					,lowerCamelCase__    )
									self.scheduler.set_timesteps(lowerCamelCase__    )
									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(lowerCamelCase__    ).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(lowerCamelCase__					,lowerCamelCase__    )['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 	__lowerCAmelCase		(       lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			float    ):
									UpperCAmelCase__    =							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"""






import socket



def 						a_		(      ):
				UpperCAmelCase__    =							socket.socket(socket.AF_INET      ,    socket.SOCK_STREAM )
				UpperCAmelCase__    =							socket.gethostname()
				UpperCAmelCase__    =							1_2_3_1_2

				sock.connect((host, port) )
				sock.send(b'Hello server!' )

				with open('Received_file'      ,    'wb' ) as out_file:
								print('File opened' )
								print('Receiving data...' )
								while True:
												UpperCAmelCase__    =							sock.recv(1_0_2_4 )
												if not data:
																break
												out_file.write(lowerCamelCase )

				print('Successfully received the file' )
				sock.close()
				print('Connection closed' )


if __name__ == "__main__":
						main()



