


'''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 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				['vqvae']


     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          return 50 if isinstance(self.scheduler			,     lowerCamelCase		) else 1000


     @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			,     ):


          '''simple docstring'''





          __A	:       Dict      =							steps or self.get_default_steps()
          self.scheduler.set_timesteps(lowerCamelCase		)
          __A	:       Dict      =							step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
               __A	:       Optional[int]      =							(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
               __A	:       int      =							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			,     )
          __A	:       Tuple      =							noise
          __A	:       List[str]      =							None

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

               if self.vqvae is not None:
                    __A	:       Union[str, Any]      =							self.vqvae.encode(torch.unsqueeze(lowerCamelCase			,     0		)		).latent_dist.sample(
                        generator=lowerCamelCase		)[0]
                    __A	:       Optional[int]      =							self.vqvae.config.scaling_factor * input_images

               if start_step > 0:
                    __A	:       Any      =							self.scheduler.add_noise(lowerCamelCase			,     lowerCamelCase			,     self.scheduler.timesteps[start_step - 1]		)

               __A	:       Tuple      =							(
                   self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
               )
               __A	:       Union[str, Any]      =							int(mask_start_secs * pixels_per_second		)
               __A	:       Tuple      =							int(mask_end_secs * pixels_per_second		)
               __A	:       Any      =							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		):
                    __A	:       List[Any]      =							self.unet(lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		)["sample"]
               else:
                    __A	:       Tuple      =							self.unet(lowerCamelCase			,     lowerCamelCase		)["sample"]

               if isinstance(self.scheduler			,     lowerCamelCase		):
                    __A	:       Dict      =							self.scheduler.step(
                        model_output=lowerCamelCase			,     timestep=lowerCamelCase			,     sample=lowerCamelCase			,     eta=lowerCamelCase			,     generator=lowerCamelCase			,     )["prev_sample"]
               else:
                    __A	:       Any      =							self.scheduler.step(
                        model_output=lowerCamelCase			,     timestep=lowerCamelCase			,     sample=lowerCamelCase			,     generator=lowerCamelCase			,     )["prev_sample"]

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

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

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

          __A	:       Dict      =							[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			,     lowerCamelCase			,     lowerCamelCase = 50		):


          '''simple docstring'''





          assert isinstance(self.scheduler			,     lowerCamelCase		)
          self.scheduler.set_timesteps(lowerCamelCase		)
          __A	:       List[Any]      =							np.array(
              [np.frombuffer(image.tobytes()			,     dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          __A	:       List[str]      =							(sample / 255) * 2 - 1
          __A	:       List[Any]      =							torch.Tensor(lowerCamelCase		).to(self.device		)

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

          return sample


     @staticmethod
     def lowerCAmelCase__  (   lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							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'''


# Function to print upper half of diamond (pyramid)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(0       ,					SCREAMING_SNAKE_CASE							):
          for _ in range(0       ,					n - i - 1							):  # printing spaces
               print(" "       ,					end=""							)
          for _ in range(0       ,					i + 1							):  # printing stars
               print("* "       ,					end=""							)
          print()


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     for i in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):
          for _ in range(SCREAMING_SNAKE_CASE       ,					0       ,					-1							):  # printing stars
               print("* "       ,					end=""							)
          print()
          for _ in range(n - i + 1       ,					0       ,					-1							):  # printing spaces
               print(" "       ,					end=""							)


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if n <= 0:
          print("       ...       ....        nothing printing :("							)
          return
     floyd(SCREAMING_SNAKE_CASE							)  # upper half
     reverse_floyd(SCREAMING_SNAKE_CASE							)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      _UpperCamelCase 							=  1
      while K:
            _UpperCamelCase 							=  int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            _UpperCamelCase 							=  int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")

