





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase = None					,  )			->						List[Any]:







          '''simple docstring'''

          super().__init__()
          self.register_modules(transformer=lowercase					,  vae=lowercase					,  scheduler=lowercase)

          # create a imagenet -> id dictionary for easier use
          a__						:	int =				{}
          if idalabel is not None:
                 for key, value in idalabel.items():
                        for label in value.split(','):
                               a__						:	List[str] =				int(lowercase)
                 a__						:	Union[str, Any] =				dict(sorted(self.labels.items()))

   def 							__lowercase		(					self					,  lowercase)			->						List[int]:







          '''simple docstring'''

          if not isinstance(lowercase					,  lowercase):
                 a__						:	Optional[int] =				list(lowercase)

          for l in label:
                 if l not in self.labels:
                        raise ValueError(
                            F'{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.')

          return [self.labels[l] for l in label]

   @torch.no_grad()
   def __call__(					self					,  lowercase					,  lowercase = 4.0					,  lowercase = None					,  lowercase = 50					,  lowercase = "pil"					,  lowercase = True					,  )			->						Union[ImagePipelineOutput, Tuple]:







          '''simple docstring'''

          a__						:	Optional[int] =				len(lowercase)
          a__						:	Any =				self.transformer.config.sample_size
          a__						:	Tuple =				self.transformer.config.in_channels

          a__						:	Dict =				randn_tensor(
              shape=(batch_size, latent_channels, latent_size, latent_size)					,  generator=lowercase					,  device=self.device					,  dtype=self.transformer.dtype					,  )
          a__						:	int =				torch.cat([latents] * 2) if guidance_scale > 1 else latents

          a__						:	Union[str, Any] =				torch.tensor(lowercase					,  device=self.device).reshape(-1)
          a__						:	Any =				torch.tensor([1000] * batch_size					,  device=self.device)
          a__						:	List[str] =				torch.cat([class_labels, class_null]					,  0) if guidance_scale > 1 else class_labels

          # set step values
          self.scheduler.set_timesteps(lowercase)

          for t in self.progress_bar(self.scheduler.timesteps):
                 if guidance_scale > 1:
                        a__						:	Union[str, Any] =				latent_model_input[: len(lowercase) // 2]
                        a__						:	str =				torch.cat([half, half]					,  dim=0)
                 a__						:	Any =				self.scheduler.scale_model_input(lowercase					,  lowercase)

                 a__						:	Tuple =				t
                 if not torch.is_tensor(lowercase):
                        # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                        # This would be a good case for the `match` statement (Python 3.10+)
                        a__						:	List[Any] =				latent_model_input.device.type == 'mps'
                        if isinstance(lowercase					,  lowercase):
                               a__						:	int =				torch.floataa if is_mps else torch.floataa
                        else:
                               a__						:	Optional[Any] =				torch.intaa if is_mps else torch.intaa
                        a__						:	str =				torch.tensor([timesteps]					,  dtype=lowercase					,  device=latent_model_input.device)
                 elif len(timesteps.shape) == 0:
                        a__						:	Optional[int] =				timesteps[None].to(latent_model_input.device)
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 a__						:	List[str] =				timesteps.expand(latent_model_input.shape[0])
                 # predict noise model_output
                 a__						:	Optional[int] =				self.transformer(
                     lowercase					,  timestep=lowercase					,  class_labels=lowercase).sample

                 # perform guidance
                 if guidance_scale > 1:
                        a__ ,							a__						:	Optional[Any] =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                        a__ ,							a__						:	List[str] =				torch.split(lowercase					,  len(lowercase) // 2					,  dim=0)

                        a__						:	Tuple =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                        a__						:	Union[str, Any] =				torch.cat([half_eps, half_eps]					,  dim=0)

                        a__						:	Optional[Any] =				torch.cat([eps, rest]					,  dim=1)

                 # learned sigma
                 if self.transformer.config.out_channels // 2 == latent_channels:
                        a__ ,							a__						:	Optional[Any] =				torch.split(lowercase					,  lowercase					,  dim=1)
                 else:
                        a__						:	Optional[Any] =				noise_pred

                 # compute previous image: x_t -> x_t-1
                 a__						:	Dict =				self.scheduler.step(lowercase					,  lowercase					,  lowercase).prev_sample

          if guidance_scale > 1:
                 a__ ,							a__						:	Optional[Any] =				latent_model_input.chunk(2					,  dim=0)
          else:
                 a__						:	Optional[int] =				latent_model_input

          a__						:	Dict =				1 / self.vae.config.scaling_factor * latents
          a__						:	Union[str, Any] =				self.vae.decode(lowercase).sample

          a__						:	Optional[Any] =				(samples / 2 + 0.5).clamp(0					,  1)

          # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
          a__						:	str =				samples.cpu().permute(0					,  2					,  3					,  1).float().numpy()

          if output_type == "pil":
                 a__						:	Any =				self.numpy_to_pil(lowercase)

          if not return_dict:
                 return (samples,)

          return ImagePipelineOutput(images=lowercase)







'''simple docstring'''


import math

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]=0 ):  # a graph with Node 0,1,...,N-1


       """simple docstring"""







       UpperCamelCase					=  n
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # adjacency matrix for weight
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # dp[i][j] stores minimum distance from i to j






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  w






    def  A       (  self      :					str ):


       """simple docstring"""







       for k in range(0  ,						self.n ):
          for i in range(0  ,						self.n ):
             for j in range(0  ,						self.n ):
                UpperCamelCase					=  min(self.dp[i][j]  ,						self.dp[i][k] + self.dp[k][j] )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.dp[u][v]


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	Graph(5)
   graph.add_edge(0, 2, 9)
   graph.add_edge(0, 4, 10)
   graph.add_edge(1, 3, 5)
   graph.add_edge(2, 3, 7)
   graph.add_edge(3, 0, 10)
   graph.add_edge(3, 1, 2)
   graph.add_edge(3, 2, 1)
   graph.add_edge(3, 4, 6)
   graph.add_edge(4, 1, 3)
   graph.add_edge(4, 2, 4)
   graph.add_edge(4, 3, 9)
   graph.floyd_warshall()
   graph.show_min(1, 4)
   graph.show_min(0, 3)

