






"""simple docstring"""





from __future__ import annotations



def     __lowercase					(    a :				list[float]       ,		a :				Optional[int] )					->					Optional[int]:
      print(f'''Vertex\tShortest Distance from vertex {src}''' )
      for i, d in enumerate(a ):
            print(f'''{i}\t\t{d}''' )



def     __lowercase					(    a :				list[dict[str, int]]       ,		a :				list[float]       ,		a :				int )					->					Optional[int]:
      for j in range(a ):
            __snake_case			,				__snake_case			,				__snake_case      :    List[Any]  			=(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
            if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                  return True
      return False



def     __lowercase					(    a :				list[dict[str, int]]       ,		a :				int       ,		a :				int       ,		a :				int )					->					list[float]:
      __snake_case      :    str  			=[float('''inf''' )] * vertex_count
      __snake_case      :    List[str]  			=0.0

      for _ in range(vertex_count - 1 ):
            for j in range(a ):
                  __snake_case			,				__snake_case			,				__snake_case      :    str  			=(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

                  if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                        __snake_case      :    Optional[Any]  			=distance[u] + w

      __snake_case      :    str  			=check_negative_cycle(a       ,		a       ,		a )
      if negative_cycle_exists:
            raise Exception('''Negative cycle found''' )

      return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase_	:				str										= int(input("""Enter number of vertices: """).strip())
    UpperCamelCase_	:				Tuple										= int(input("""Enter number of edges: """).strip())

    UpperCamelCase_	:				list[dict[str, int]]										= [{} for _ in range(E)]

    for i in range(E):
        print("""Edge """, i + 1)
        UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_	:				Optional[int]										= (
            int(x)
            for x in input("""Enter source, destination, weight: """).strip().split(""" """)
        )
        UpperCamelCase_	:				Optional[Any]										= {"""src""": src, """dst""": dest, """weight""": weight}

    UpperCamelCase_	:				Optional[int]										= int(input("""\nEnter shortest path source:""").strip())
    UpperCamelCase_	:				Dict										= bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)










"""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      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   ['''vqvae''']


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





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            return 5_0 if isinstance(self.scheduler							,   a						) else 1_0_0_0


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





            """simple docstring"""







            __snake_case      :    List[Any]  			=steps or self.get_default_steps()
            self.scheduler.set_timesteps(a						)
            __snake_case      :    int  			=step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size						) == int:
                  __snake_case      :    Any  			=(self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                  __snake_case      :    List[str]  			=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							,   )
            __snake_case      :    int  			=noise
            __snake_case      :    List[str]  			=None

            if audio_file is not None or raw_audio is not None:
                  self.mel.load_audio(a							,   a						)
                  __snake_case      :    List[Any]  			=self.mel.audio_slice_to_image(a						)
                  __snake_case      :    Tuple  			=np.frombuffer(input_image.tobytes()							,   dtype='''uint8'''						).reshape(
                      (input_image.height, input_image.width)						)
                  __snake_case      :    str  			=(input_image / 2_5_5) * 2 - 1
                  __snake_case      :    Optional[int]  			=torch.tensor(input_image[np.newaxis, :, :]							,   dtype=torch.float						).to(self.device						)

                  if self.vqvae is not None:
                        __snake_case      :    Tuple  			=self.vqvae.encode(torch.unsqueeze(a							,   0						)						).latent_dist.sample(
                            generator=a						)[0]
                        __snake_case      :    Optional[int]  			=self.vqvae.config.scaling_factor * input_images

                  if start_step > 0:
                        __snake_case      :    str  			=self.scheduler.add_noise(a							,   a							,   self.scheduler.timesteps[start_step - 1]						)

                  __snake_case      :    Optional[Any]  			=(
                      self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                  )
                  __snake_case      :    int  			=int(mask_start_secs * pixels_per_second						)
                  __snake_case      :    Any  			=int(mask_end_secs * pixels_per_second						)
                  __snake_case      :    Union[str, Any]  			=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						):
                        __snake_case      :    List[str]  			=self.unet(a							,   a							,   a						)['''sample''']
                  else:
                        __snake_case      :    Union[str, Any]  			=self.unet(a							,   a						)['''sample''']

                  if isinstance(self.scheduler							,   a						):
                        __snake_case      :    List[str]  			=self.scheduler.step(
                            model_output=a							,   timestep=a							,   sample=a							,   eta=a							,   generator=a							,   )['''prev_sample''']
                  else:
                        __snake_case      :    List[Any]  			=self.scheduler.step(
                            model_output=a							,   timestep=a							,   sample=a							,   generator=a							,   )['''prev_sample''']

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

            if self.vqvae is not None:
                  # 0.18215 was scaling factor used in training to ensure unit variance
                  __snake_case      :    str  			=1 / self.vqvae.config.scaling_factor * images
                  __snake_case      :    Optional[int]  			=self.vqvae.decode(a						)['''sample''']

            __snake_case      :    int  			=(images / 2 + 0.5).clamp(0							,   1						)
            __snake_case      :    Optional[Any]  			=images.cpu().permute(0							,   2							,   3							,   1						).numpy()
            __snake_case      :    Optional[int]  			=(images * 2_5_5).round().astype('''uint8'''						)
            __snake_case      :    Tuple  			=list(
                (Image.fromarray(_[:, :, 0]						) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(a							,   mode='''RGB'''						).convert('''L'''						) for _ in images)						)

            __snake_case      :    Union[str, Any]  			=[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				:	int							,   a				:	List[Image.Image]							,   a				:	int = 5_0						):





            """simple docstring"""







            assert isinstance(self.scheduler							,   a						)
            self.scheduler.set_timesteps(a						)
            __snake_case      :    Dict  			=np.array(
                [np.frombuffer(image.tobytes()							,   dtype='''uint8'''						).reshape((1, image.height, image.width)						) for image in images]						)
            __snake_case      :    Any  			=(sample / 2_5_5) * 2 - 1
            __snake_case      :    Any  			=torch.Tensor(a						).to(self.device						)

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps							,   (0,)						)						):
                  __snake_case      :    Union[str, Any]  			=t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                  __snake_case      :    Dict  			=self.scheduler.alphas_cumprod[t]
                  __snake_case      :    str  			=(
                      self.scheduler.alphas_cumprod[prev_timestep]
                      if prev_timestep >= 0
                      else self.scheduler.final_alpha_cumprod
                  )
                  __snake_case      :    int  			=1 - alpha_prod_t
                  __snake_case      :    Tuple  			=self.unet(a							,   a						)['''sample''']
                  __snake_case      :    str  			=(1 - alpha_prod_t_prev) ** 0.5 * model_output
                  __snake_case      :    Dict  			=(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                  __snake_case      :    Optional[int]  			=sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample







      @staticmethod
      def _UpperCamelCase						(    a				:	torch.Tensor							,   a				:	torch.Tensor							,   a				:	float						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=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						)


