'''simple docstring'''

def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase	):





 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path	)
def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase	):





 if curr_ind == len(__snake_case	):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0						,	len(__snake_case	)	):
  if valid_connection(__snake_case						,	__snake_case						,	__snake_case						,	__snake_case	):
   # Insert current vertex  into path as next transition
   lowercase__							:       Any										=   next_ver
   # Validate created path
   if util_hamilton_cycle(__snake_case						,	__snake_case						,	curr_ind + 1	):
    return True
   # Backtrack
   lowercase__							:       List[str]										=   -1
 return False





def      __UpperCamelCase			(  UpperCAmelCase						,	UpperCAmelCase = 0	):
 lowercase__							:       List[Any]										=   [-1] * (len(__snake_case	) + 1)
 # initialize start and end of path with starting index
 lowercase__							:       List[Any]										=   start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(__snake_case						,	__snake_case						,	1	) else []



"""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(       lowerCamelCase      ):
		lowercase__         = ['vqvae']



		def __init__(   self							,						__a							,						__a							,						__a							,						__a							,						)			->			List[str]:






							'''simple docstring'''
							super().__init__()
							self.register_modules(unet=__a							,						scheduler=__a							,						mel=__a							,						vqvae=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 50 if isinstance(self.scheduler							,						__a) else 10_00



		@torch.no_grad()
		def __call__(   self							,						__a = 1							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = None							,						__a = 0							,						__a = 0							,						__a = None							,						__a = 0							,						__a = None							,						__a = None							,						__a=True							,						)			->			Union[
		    Union[AudioPipelineOutput, ImagePipelineOutput],
		    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
		]:






							'''simple docstring'''
							_UpperCamelCase									=    steps or self.get_default_steps()
							self.scheduler.set_timesteps(__a)
							_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=__a							,						device=self.device							,						)
							_UpperCamelCase									=    noise
							_UpperCamelCase									=    None

							if audio_file is not None or raw_audio is not None:
												self.mel.load_audio(__a							,						__a)
												_UpperCamelCase									=    self.mel.audio_slice_to_image(__a)
												_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(__a							,						0)).latent_dist.sample(
																	    generator=__a)[0]
																	_UpperCamelCase									=    self.vqvae.config.scaling_factor * input_images

												if start_step > 0:
																	_UpperCamelCase									=    self.scheduler.add_noise(__a							,						__a							,						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(__a							,						__a							,						torch.tensor(self.scheduler.timesteps[start_step:]))

							for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
												if isinstance(self.unet							,						__a):
																	_UpperCamelCase									=    self.unet(__a							,						__a							,						__a)['''sample''']
												else:
																	_UpperCamelCase									=    self.unet(__a							,						__a)['''sample''']

												if isinstance(self.scheduler							,						__a):
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						eta=__a							,						generator=__a							,						)['''prev_sample''']
												else:
																	_UpperCamelCase									=    self.scheduler.step(
																	    model_output=__a							,						timestep=__a							,						sample=__a							,						generator=__a							,						)['''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(__a)['''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(__a							,						mode='''RGB''').convert('''L''') for _ in images))

							_UpperCamelCase									=    [self.mel.image_to_audio(__a) for _ in images]
							if not return_dict:
												return images, (self.mel.get_sample_rate(), audios)

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



		@torch.no_grad()
		def 		UpperCAmelCase				(   self							,						__a							,						__a = 50)			->			np.ndarray:






							'''simple docstring'''
							assert isinstance(self.scheduler							,						__a)
							self.scheduler.set_timesteps(__a)
							_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(__a).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(__a							,						__a)['''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 		UpperCAmelCase				(   __a							,						__a							,						__a)			->			torch.Tensor:






							'''simple docstring'''
							_UpperCamelCase									=    acos(torch.dot(torch.flatten(__a)							,						torch.flatten(__a)) / torch.norm(__a) / torch.norm(__a))
							return sin((1 - alpha) * theta) * xa / sin(__a) + sin(alpha * theta) * xa / sin(__a)






