


"""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 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     ["""vqvae"""]







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       )     ->							Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCAmelCase__						,       scheduler=lowerCAmelCase__						,       mel=lowerCAmelCase__						,       vqvae=lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								return 50 if isinstance(self.scheduler						,       lowerCAmelCase__				) else 1_000







		@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						,       )     ->							Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:
								SCREAMING_SNAKE_CASE													=					steps or self.get_default_steps()
								self.scheduler.set_timesteps(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					step_generator or generator
								# For backwards compatibility
								if type(self.unet.config.sample_size				) == int:
														SCREAMING_SNAKE_CASE													=					(self.unet.config.sample_size, self.unet.config.sample_size)
								if noise is None:
														SCREAMING_SNAKE_CASE													=					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						,       )
								SCREAMING_SNAKE_CASE													=					noise
								SCREAMING_SNAKE_CASE													=					None

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

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

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

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

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

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

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

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

								SCREAMING_SNAKE_CASE													=					[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 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = 50				)     ->							np.ndarray:
								assert isinstance(self.scheduler						,       lowerCAmelCase__				)
								self.scheduler.set_timesteps(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					np.array(
								    [np.frombuffer(image.tobytes()						,       dtype='uint8'				).reshape((1, image.height, image.width)				) for image in images]				)
								SCREAMING_SNAKE_CASE													=					(sample / 255) * 2 - 1
								SCREAMING_SNAKE_CASE													=					torch.Tensor(lowerCAmelCase__				).to(self.device				)

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

								return sample







		@staticmethod
		def 							__A						(	lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							torch.Tensor:
								SCREAMING_SNAKE_CASE													=					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"""




__UpperCamelCase						=      0  # The first color of the flag.
__UpperCamelCase						=      1  # The second color of the flag.
__UpperCamelCase						=      2  # The third color of the flag.
__UpperCamelCase						=      (red, white, blue)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list  )   ->						list:
						if not sequence:
												return []
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												return list(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					0
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  ) - 1
						SCREAMING_SNAKE_CASE													=					0
						while mid <= high:
												if sequence[mid] == colors[0]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[mid], sequence[low]
																		low += 1
																		mid += 1
												elif sequence[mid] == colors[1]:
																		mid += 1
												elif sequence[mid] == colors[2]:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					sequence[high], sequence[mid]
																		high -= 1
												else:
																		SCREAMING_SNAKE_CASE													=					F'The elements inside the sequence must contains only {colors} values'
																		raise ValueError(SCREAMING_SNAKE_CASE_  )
						return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase						=      input('''Enter numbers separated by commas:\n''').strip()
	__UpperCamelCase						=      [int(item.strip()) for item in user_input.split(''',''')]
	print(f'''{dutch_national_flag_sort(unsorted)}''')


