



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__A       =						TypeVar('''T''')


def 	__a					(   lowerCAmelCase_       : Tuple    )				->			str:






						'''simple docstring'''



						return (position - 1) // 2


def 	__a					(   lowerCAmelCase_       : Dict    )				->			Any:






						'''simple docstring'''



						return (2 * position) + 1


def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



						return (2 * position) + 2






class 		lowercase						(			Generic[T]):






	"""simple docstring"""


	def __init__(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       []
							UpperCAmelCase_=       {}
							UpperCAmelCase_=       0




	def __len__(     self     :						Tuple					)      ->			Any:
							return self.elements




	def __repr__(     self     :						int					)      ->			str:
							return str(self.heap					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							# Check if the priority queue is empty
							return self.elements == 0




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						T						,  __UpperCAmelCase     :						int					)      ->			Any:
							# Add an element with given priority to the queue
							self.heap.append((elem, weight)					)
							UpperCAmelCase_=       self.elements
							self.elements += 1
							self._bubble_up(lowercase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							# Remove and return the element with lowest weight (highest priority)
							if self.elements > 1:
													self._swap_nodes(0						,  self.elements - 1					)
							UpperCAmelCase_=       self.heap.pop()
							del self.position_map[elem]
							self.elements -= 1
							if self.elements > 0:
													UpperCAmelCase_=       self.heap[0]
													self._bubble_down(lowercase_					)
							return elem




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						T						,  __UpperCAmelCase     :						int					)      ->			int:
							# Update the weight of the given key
							UpperCAmelCase_=       self.position_map[elem]
							UpperCAmelCase_=       (elem, weight)
							if position > 0:
													UpperCAmelCase_=       get_parent_position(lowercase_					)
													UpperCAmelCase_=       self.heap[parent_position]
													if parent_weight > weight:
																			self._bubble_up(lowercase_					)
													else:
																			self._bubble_down(lowercase_					)
							else:
													self._bubble_down(lowercase_					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						T					)      ->			Optional[int]:
							# Place a node at the proper position (upward movement) [to be used internally
							# only]
							UpperCAmelCase_=       self.position_map[elem]
							if curr_pos == 0:
													return None
							UpperCAmelCase_=       get_parent_position(lowercase_					)
							UpperCAmelCase_=       self.heap[curr_pos]
							UpperCAmelCase_=       self.heap[parent_position]
							if parent_weight > weight:
													self._swap_nodes(lowercase_						,  lowercase_					)
													return self._bubble_up(lowercase_					)
							return None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						T					)      ->			int:
							# Place a node at the proper position (downward movement) [to be used
							# internally only]
							UpperCAmelCase_=       self.position_map[elem]
							UpperCAmelCase_=       self.heap[curr_pos]
							UpperCAmelCase_=       get_child_left_position(lowercase_					)
							UpperCAmelCase_=       get_child_right_position(lowercase_					)
							if child_left_position < self.elements and child_right_position < self.elements:
													UpperCAmelCase_=       self.heap[child_left_position]
													UpperCAmelCase_=       self.heap[child_right_position]
													if child_right_weight < child_left_weight and child_right_weight < weight:
																			self._swap_nodes(lowercase_						,  lowercase_					)
																			return self._bubble_down(lowercase_					)
							if child_left_position < self.elements:
													UpperCAmelCase_=       self.heap[child_left_position]
													if child_left_weight < weight:
																			self._swap_nodes(lowercase_						,  lowercase_					)
																			return self._bubble_down(lowercase_					)
							else:
													return None
							if child_right_position < self.elements:
													UpperCAmelCase_=       self.heap[child_right_position]
													if child_right_weight < weight:
																			self._swap_nodes(lowercase_						,  lowercase_					)
																			return self._bubble_down(lowercase_					)
							return None




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						int					)      ->			List[str]:
							# Swap the nodes at the given positions
							UpperCAmelCase_=       self.heap[nodea_pos][0]
							UpperCAmelCase_=       self.heap[nodea_pos][0]
							UpperCAmelCase_=       (
							    self.heap[nodea_pos],
							    self.heap[nodea_pos],
							)
							UpperCAmelCase_=       nodea_pos
							UpperCAmelCase_=       nodea_pos






class 		lowercase						(			Generic[T]):






	"""simple docstring"""


	def __init__(     self     :						Union[str, Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       {}
							UpperCAmelCase_=       0




	def __repr__(     self     :						Optional[Any]					)      ->			int:
							return str(self.connections					)




	def __len__(     self     :						Tuple					)      ->			List[str]:
							return self.nodes




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]						,  __UpperCAmelCase     :						T					)      ->			Tuple:
							# Add a node in the graph if it is not in the graph
							if node not in self.connections:
													UpperCAmelCase_=       {}
													self.nodes += 1




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						T						,  __UpperCAmelCase     :						T						,  __UpperCAmelCase     :						int					)      ->			str:
							# Add an edge between 2 nodes in the graph
							self.add_node(lowercase_					)
							self.add_node(lowercase_					)
							UpperCAmelCase_=       weight
							UpperCAmelCase_=       weight


def 	__a					(   lowerCAmelCase_       : int      ,)				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       {node: maxsize for node in graph.connections}
						UpperCAmelCase_=       {node: None for node in graph.connections}

						UpperCAmelCase_=       MinPriorityQueue()
						for node, weight in dist.items():
												priority_queue.push(A_      ,A_    )

						if priority_queue.is_empty():
												return dist, parent

						# initialization
						UpperCAmelCase_=       priority_queue.extract_min()
						UpperCAmelCase_=       0
						for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																		UpperCAmelCase_=       dist[node] + graph.connections[node][neighbour]
																		priority_queue.update_key(A_      ,dist[neighbour]    )
																		UpperCAmelCase_=       node

    # running prim's algorithm
						while not priority_queue.is_empty():
												UpperCAmelCase_=       priority_queue.extract_min()
												for neighbour in graph.connections[node]:
																		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																								UpperCAmelCase_=       dist[node] + graph.connections[node][neighbour]
																								priority_queue.update_key(A_      ,dist[neighbour]    )
																								UpperCAmelCase_=       node
						return dist, parent









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 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						AutoencoderKL						,  __UpperCAmelCase     :						UNetaDConditionModel						,  __UpperCAmelCase     :						Mel						,  __UpperCAmelCase     :						Union[DDIMScheduler, DDPMScheduler]						,  )      ->			str:
							super().__init__()
							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase						,  mel=__UpperCAmelCase						,  vqvae=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




	@torch.no_grad()
	def __call__(     self     :						List[Any]						,  __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						,  )      ->			Union[
	    Union[AudioPipelineOutput, ImagePipelineOutput],
	    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
	]:
							UpperCAmelCase_=       steps or self.get_default_steps()
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							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=__UpperCAmelCase						,  device=self.device						,  )
							UpperCAmelCase_=       noise
							UpperCAmelCase_=       None

							if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__UpperCAmelCase						,  __UpperCAmelCase					)
													UpperCAmelCase_=       self.mel.audio_slice_to_image(__UpperCAmelCase					)
													UpperCAmelCase_=       np.frombuffer(input_image.tobytes()						,  dtype="""uint8"""					).reshape(
													    (input_image.height, input_image.width)					)
													UpperCAmelCase_=       (input_image / 255) * 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(__UpperCAmelCase						,  0					)					).latent_dist.sample(
																			    generator=__UpperCAmelCase					)[0]
																			UpperCAmelCase_=       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																			UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  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(__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_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]
													else:
																			UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					)["""sample"""]

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

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

							UpperCAmelCase_=       [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   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).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(__UpperCAmelCase						,  __UpperCAmelCase					)["""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   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       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					)




