



'''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      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					['''vqvae''']







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				)    ->    Any:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    int:
							'''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 ,				)    ->    Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							'''simple docstring'''






							lowercase        =							steps or self.get_default_steps()
							self.scheduler.set_timesteps(_lowerCAmelCase						)
							lowercase        =							step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size						) == int:
													lowercase        =							(self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
													lowercase        =							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 ,				)
							lowercase        =							noise
							lowercase        =							None

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

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

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

													lowercase        =							(
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													lowercase        =							int(mask_start_secs * pixels_per_second						)
													lowercase        =							int(mask_end_secs * pixels_per_second						)
													lowercase        =							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						):
																			lowercase        =							self.unet(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)["""sample"""]
													else:
																			lowercase        =							self.unet(_lowerCAmelCase ,				_lowerCAmelCase						)["""sample"""]

													if isinstance(self.scheduler ,				_lowerCAmelCase						):
																			lowercase        =							self.scheduler.step(
																			    model_output=_lowerCAmelCase ,				timestep=_lowerCAmelCase ,				sample=_lowerCAmelCase ,				eta=_lowerCAmelCase ,				generator=_lowerCAmelCase ,				)["""prev_sample"""]
													else:
																			lowercase        =							self.scheduler.step(
																			    model_output=_lowerCAmelCase ,				timestep=_lowerCAmelCase ,				sample=_lowerCAmelCase ,				generator=_lowerCAmelCase ,				)["""prev_sample"""]

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

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

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

							lowercase        =							[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:
							'''simple docstring'''






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

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

							return sample







	@staticmethod
	def   _a   (    _lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    torch.Tensor:
							'''simple docstring'''






							lowercase        =							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'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Union[str, Any] ,  lowercase_    :      str						):
						lowercase        =							""""""
						for i in table:
												res += inp[i - 1]
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any]						):
						return data[1:] + data[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Tuple ,  lowercase_    :      Dict						):
						lowercase        =							""""""
						for i in range(len(lowercase_						)						):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      str						):
						lowercase        =							int("""0b""" + data[0] + data[-1] ,  2						)
						lowercase        =							int("""0b""" + data[1:3] ,  2						)
						return bin(s[row][col]						)[2:]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      Tuple ,  lowercase_    :      Tuple ,  lowercase_    :      str ,  lowercase_    :      Any						):
						lowercase        =							message[:4]
						lowercase        =							message[4:]
						lowercase        =							apply_table(lowercase_ ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						lowercase        =							apply_sbox(lowercase_ ,  temp[:4]						)  # noqa: E741
						lowercase        =							apply_sbox(lowercase_ ,  temp[4:]						)
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + l  # noqa: E741
						lowercase        =							"""0""" * (2 - len(lowercase_						)) + r
						lowercase        =							apply_table(l + r ,  lowercase_						)
						lowercase        =							xor(lowercase_ ,  lowercase_						)
						return temp + right


if __name__ == "__main__":
						lowercase_      :				Tuple   					=    input('''Enter 10 bit key: ''')
						lowercase_      :				Any   					=    input('''Enter 8 bit message: ''')

						lowercase_      :				Dict   					=    [6, 3, 7, 4, 8, 5, 10, 9]
						lowercase_      :				str   					=    [3, 5, 2, 7, 4, 10, 1, 9, 8, 6]
						lowercase_      :				List[Any]   					=    [2, 4, 3, 1]
						lowercase_      :				List[str]   					=    [2, 6, 3, 1, 4, 8, 5, 7]
						lowercase_      :				Tuple   					=    [4, 1, 3, 5, 7, 2, 8, 6]
						lowercase_      :				Optional[Any]   					=    [4, 1, 2, 3, 2, 3, 4, 1]
						lowercase_      :				List[str]   					=    [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
						lowercase_      :				List[Any]   					=    [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

						# key generation
						lowercase_      :				Union[str, Any]   					=    apply_table(key, paa_table)
						lowercase_      :				Optional[Any]   					=    temp[:5]
						lowercase_      :				int   					=    temp[5:]
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				int   					=    left_shift(right)
						lowercase_      :				Tuple   					=    apply_table(left + right, pa_table)
						lowercase_      :				List[str]   					=    left_shift(left)
						lowercase_      :				Optional[Any]   					=    left_shift(right)
						lowercase_      :				Union[str, Any]   					=    left_shift(left)
						lowercase_      :				Union[str, Any]   					=    left_shift(right)
						lowercase_      :				Optional[int]   					=    apply_table(left + right, pa_table)

						# encryption
						lowercase_      :				int   					=    apply_table(message, IP)
						lowercase_      :				Dict   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Any   					=    temp[4:] + temp[:4]
						lowercase_      :				List[Any]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Tuple   					=    apply_table(temp, IP_inv)
						print('''Cipher text is:''', CT)

						# decryption
						lowercase_      :				List[str]   					=    apply_table(CT, IP)
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    temp[4:] + temp[:4]
						lowercase_      :				Optional[int]   					=    function(expansion, sa, sa, keya, temp)
						lowercase_      :				Optional[Any]   					=    apply_table(temp, IP_inv)
						print('''Plain text after decypting is:''', PT)


