






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__	):
	_lowerCAmelCase	:	int				=			['''vqvae''']







	def __init__(		self :       Any							,       SCREAMING_SNAKE_CASE :       AutoencoderKL							,       SCREAMING_SNAKE_CASE :       UNetaDConditionModel							,       SCREAMING_SNAKE_CASE :       Mel							,       SCREAMING_SNAKE_CASE :       Union[DDIMScheduler, DDPMScheduler]							,       ):


						'''simple docstring'''






						super().__init__()
						self.register_modules(unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE							,       mel=SCREAMING_SNAKE_CASE							,       vqvae=SCREAMING_SNAKE_CASE						)







	def  __lowercase (		self :       List[Any]						):


						'''simple docstring'''






						return 50 if isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						) else 10_00







	@torch.no_grad()
	def __call__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       int = 1							,       SCREAMING_SNAKE_CASE :       str = None							,       SCREAMING_SNAKE_CASE :       np.ndarray = None							,       SCREAMING_SNAKE_CASE :       int = 0							,       SCREAMING_SNAKE_CASE :       int = 0							,       SCREAMING_SNAKE_CASE :       int = None							,       SCREAMING_SNAKE_CASE :       torch.Generator = None							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       torch.Generator = None							,       SCREAMING_SNAKE_CASE :       float = 0							,       SCREAMING_SNAKE_CASE :       torch.Tensor = None							,       SCREAMING_SNAKE_CASE :       torch.Tensor = None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=True							,       ):


						'''simple docstring'''






						UpperCamelCase__      :       int	    =  steps or self.get_default_steps()
						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
						UpperCamelCase__      :       Any	    =  step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size						) == int:
											UpperCamelCase__      :       List[str]	    =  (self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
											UpperCamelCase__      :       Optional[Any]	    =  randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )							,       generator=SCREAMING_SNAKE_CASE							,       device=self.device							,       )
						UpperCamelCase__      :       Dict	    =  noise
						UpperCamelCase__      :       Any	    =  None

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

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

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

											UpperCamelCase__      :       List[Any]	    =  (
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											UpperCamelCase__      :       Optional[int]	    =  int(mask_start_secs * pixels_per_second						)
											UpperCamelCase__      :       List[Any]	    =  int(mask_end_secs * pixels_per_second						)
											UpperCamelCase__      :       int	    =  self.scheduler.add_noise(SCREAMING_SNAKE_CASE							,       SCREAMING_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							,       SCREAMING_SNAKE_CASE						):
																UpperCamelCase__      :       str	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)["sample"]
											else:
																UpperCamelCase__      :       Optional[int]	    =  self.unet(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)["sample"]

											if isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						):
																UpperCamelCase__      :       Optional[Any]	    =  self.scheduler.step(
																    model_output=SCREAMING_SNAKE_CASE							,       timestep=SCREAMING_SNAKE_CASE							,       sample=SCREAMING_SNAKE_CASE							,       eta=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       )["prev_sample"]
											else:
																UpperCamelCase__      :       List[Any]	    =  self.scheduler.step(
																    model_output=SCREAMING_SNAKE_CASE							,       timestep=SCREAMING_SNAKE_CASE							,       sample=SCREAMING_SNAKE_CASE							,       generator=SCREAMING_SNAKE_CASE							,       )["prev_sample"]

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

						if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											UpperCamelCase__      :       int	    =  1 / self.vqvae.config.scaling_factor * images
											UpperCamelCase__      :       Union[str, Any]	    =  self.vqvae.decode(SCREAMING_SNAKE_CASE						)["sample"]

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

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

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







	@torch.no_grad()
	def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       List[Image.Image]							,       SCREAMING_SNAKE_CASE :       int = 50						):


						'''simple docstring'''






						assert isinstance(self.scheduler							,       SCREAMING_SNAKE_CASE						)
						self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						)
						UpperCamelCase__      :       List[str]	    =  np.array(
						    [np.frombuffer(image.tobytes()							,       dtype="uint8"						).reshape((1, image.height, image.width)						) for image in images]						)
						UpperCamelCase__      :       Optional[Any]	    =  (sample / 2_55) * 2 - 1
						UpperCamelCase__      :       List[Any]	    =  torch.Tensor(SCREAMING_SNAKE_CASE						).to(self.device						)

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

						return sample







	@staticmethod
	def  __lowercase (		SCREAMING_SNAKE_CASE :       torch.Tensor							,       SCREAMING_SNAKE_CASE :       torch.Tensor							,       SCREAMING_SNAKE_CASE :       float						):


						'''simple docstring'''






						UpperCamelCase__      :       List[str]	    =  acos(torch.dot(torch.flatten(SCREAMING_SNAKE_CASE						)							,       torch.flatten(SCREAMING_SNAKE_CASE						)						) / torch.norm(SCREAMING_SNAKE_CASE						) / torch.norm(SCREAMING_SNAKE_CASE						)						)
						return sin((1 - alpha) * theta						) * xa / sin(SCREAMING_SNAKE_CASE						) + sin(alpha * theta						) * xa / sin(SCREAMING_SNAKE_CASE						)







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     list:
     UpperCamelCase__      :       Tuple	    =  False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCamelCase__      :       Tuple	    =  True
          for i in range(0 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Dict	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[str]	    =  False

          for i in range(1 ,   len(__lowerCAmelCase      ) - 1 ,   2      ):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCamelCase__      :       List[Any]	    =  False
     return input_list


if __name__ == "__main__":
 print('''Enter list to be sorted''')
 lowerCamelCase     :							Any    =[int(x) for x in input().split()]
 # inputing elements of the list in one line
 lowerCamelCase     :							Optional[int]    =odd_even_sort(input_list)
 print('''The sorted list is''')
 print(sorted_list)