




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      ):

     '''simple docstring'''





     lowerCAmelCase    :	Tuple       		=  ['vqvae']
     def __init__(     self       :      int ,   __snake_case       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Any ,   __snake_case       :      str ,   ):
         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 10_00
     @torch.no_grad()
     def __call__(     self       :      Tuple ,   __snake_case       :      Optional[Any] = 1 ,   __snake_case       :      int = None ,   __snake_case       :      int = None ,   __snake_case       :      Tuple = 0 ,   __snake_case       :      List[str] = 0 ,   __snake_case       :      Union[str, Any] = None ,   __snake_case       :      Dict = None ,   __snake_case       :      Union[str, Any] = 0 ,   __snake_case       :      List[str] = 0 ,   __snake_case       :      List[str] = None ,   __snake_case       :      Union[str, Any] = 0 ,   __snake_case       :      Tuple = None ,   __snake_case       :      str = None ,   __snake_case       :      Any=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 / 2_55) * 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 * 2_55).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       :      Any ,   __snake_case       :      Any ,   __snake_case       :      Union[str, Any] = 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 / 2_55) * 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_       (     __snake_case       :      Tuple ,   __snake_case       :      Optional[int] ,   __snake_case       :      str    ):
         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_    )












def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    while cur > 1:
        # Find the maximum number in arr
        UpperCAmelCase_										=					arr.index(max(arr[0:cur]				)				)
        # Reverse from 0 to mi
        UpperCAmelCase_										=					arr[mi::-1] + arr[mi + 1 : len(__UpperCamelCase				)]
        # Reverse whole list
        UpperCAmelCase_										=					arr[cur - 1 :: -1] + arr[cur : len(__UpperCamelCase				)]
        cur -= 1
    return arr


if __name__ == "__main__":
 _lowerCamelCase      			=						input('Enter numbers separated by a comma:\n').strip()
 _lowerCamelCase      			=						[int(item) for item in user_input.split(',')]
 print(pancake_sort(unsorted))





