




"""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      a__				(	__magic_name__   ):
			lowercase_    =	["vqvae"]


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


						"""simple docstring"""




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


			def       a_     (      self      :    str):


						"""simple docstring"""




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


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


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					steps or self.get_default_steps()
						self.scheduler.set_timesteps(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					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	: Dict		=					randn_tensor(
									    (
									        batch_size,
									        self.unet.config.in_channels,
									        self.unet.config.sample_size[0],
									        self.unet.config.sample_size[1],
									    )      ,  generator=UpperCamelCase_      ,  device=self.device      ,  )
						__UpperCAmelCase	: Optional[Any]		=					noise
						__UpperCAmelCase	: List[Any]		=					None

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

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

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

									__UpperCAmelCase	: Tuple		=					(
									    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
									)
									__UpperCAmelCase	: List[Any]		=					int(mask_start_secs * pixels_per_second)
									__UpperCAmelCase	: Optional[Any]		=					int(mask_end_secs * pixels_per_second)
									__UpperCAmelCase	: str		=					self.scheduler.add_noise(UpperCamelCase_      ,  UpperCamelCase_      ,  torch.tensor(self.scheduler.timesteps[start_step:]))

						for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
									if isinstance(self.unet      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[str]		=					self.unet(UpperCamelCase_      ,  UpperCamelCase_      ,  UpperCamelCase_)["sample"]
									else:
												__UpperCAmelCase	: List[str]		=					self.unet(UpperCamelCase_      ,  UpperCamelCase_)["sample"]

									if isinstance(self.scheduler      ,  UpperCamelCase_):
												__UpperCAmelCase	: Optional[int]		=					self.scheduler.step(
												    model_output=UpperCamelCase_      ,  timestep=UpperCamelCase_      ,  sample=UpperCamelCase_      ,  eta=UpperCamelCase_      ,  generator=UpperCamelCase_      ,  )["prev_sample"]
									else:
												__UpperCAmelCase	: Optional[int]		=					self.scheduler.step(
												    model_output=UpperCamelCase_      ,  timestep=UpperCamelCase_      ,  sample=UpperCamelCase_      ,  generator=UpperCamelCase_      ,  )["prev_sample"]

									if mask is not None:
												if mask_start > 0:
															__UpperCAmelCase	: Optional[int]		=					mask[:, step, :, :mask_start]
												if mask_end > 0:
															__UpperCAmelCase	: Dict		=					mask[:, step, :, -mask_end:]

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

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

						__UpperCAmelCase	: List[Any]		=					[self.mel.image_to_audio(UpperCamelCase_) for _ in images]
						if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

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


			@torch.no_grad()
			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    List[Image.Image]      ,  UpperCamelCase_      :    int = 50):


						"""simple docstring"""




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

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

						return sample


			@staticmethod
			def       a_     (      UpperCamelCase_      :    torch.Tensor      ,  UpperCamelCase_      :    torch.Tensor      ,  UpperCamelCase_      :    float):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					acos(torch.dot(torch.flatten(UpperCamelCase_)      ,  torch.flatten(UpperCamelCase_)) / torch.norm(UpperCamelCase_) / torch.norm(UpperCamelCase_))
						return sin((1 - alpha) * theta) * xa / sin(UpperCamelCase_) + sin(alpha * theta) * xa / sin(UpperCamelCase_)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


