






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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 	SCREAMING_SNAKE_CASE__    ( snake_case_	) ->	Union[str, Any]:


	"""simple docstring"""



	a						,			a												=      image.size
	a						,			a												=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
	a												=      image.resize((w, h),	resample=PIL_INTERPOLATION['''lanczos''']	)
	a												=      np.array(_SCREAMING_SNAKE_CASE	).astype(np.floataa	) / 255.0
	a												=      image[None].transpose(0,	3,	1,	2	)
	a												=      torch.from_numpy(_SCREAMING_SNAKE_CASE	)
	return 2.0 * image - 1.0







class 					lowerCamelCase_  (						__snake_case  ):
							def __init__(    self		:       Optional[int]				,__lowerCamelCase		:       Tuple				,__lowerCamelCase		:       int				,__lowerCamelCase		:       Tuple				,):





								'''simple docstring'''



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




							@torch.no_grad()
							def __call__(    self		:       Optional[int]				,__lowerCamelCase		:       Any = None				,__lowerCamelCase		:       Any = 1				,__lowerCamelCase		:       Any = 1_00				,__lowerCamelCase		:       Tuple = 0.0				,__lowerCamelCase		:       List[Any] = None				,__lowerCamelCase		:       Dict = "pil"				,__lowerCamelCase		:       int = True				,):





								'''simple docstring'''



								if isinstance(snake_case_				,PIL.Image.Image       ):
									a												=      1
								elif isinstance(snake_case_				,torch.Tensor       ):
									a												=      image.shape[0]
								else:
									raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_       )}"""       )

								if isinstance(snake_case_				,PIL.Image.Image       ):
									a												=      preprocess(snake_case_       )

								a						,			a												=      image.shape[-2:]

								# in_channels should be 6: 3 for latents, 3 for low resolution image
								a												=      (batch_size, self.unet.config.in_channels // 2, height, width)
								a												=      next(self.unet.parameters()       ).dtype

								a												=      randn_tensor(snake_case_				,generator=snake_case_				,device=self.device				,dtype=snake_case_       )

								a												=      image.to(device=self.device				,dtype=snake_case_       )

								# set timesteps and move to the correct device
								self.scheduler.set_timesteps(snake_case_				,device=self.device       )
								a												=      self.scheduler.timesteps

								# scale the initial noise by the standard deviation required by the scheduler
								a												=      latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								a												=      '''eta''' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
								a												=      {}
								if accepts_eta:
									a												=      eta

								for t in self.progress_bar(snake_case_       ):
									# concat latents and low resolution image in the channel dimension.
									a												=      torch.cat([latents, image]				,dim=1       )
									a												=      self.scheduler.scale_model_input(snake_case_				,snake_case_       )
									# predict the noise residual
									a												=      self.unet(snake_case_				,snake_case_       ).sample
									# compute the previous noisy sample x_t -> x_t-1
									a												=      self.scheduler.step(snake_case_				,snake_case_				,snake_case_				,**snake_case_       ).prev_sample

								# decode the image latents with the VQVAE
								a												=      self.vqvae.decode(snake_case_       ).sample
								a												=      torch.clamp(snake_case_				,-1.0				,1.0       )
								a												=      image / 2 + 0.5
								a												=      image.cpu().permute(0				,2				,3				,1       ).numpy()

								if output_type == "pil":
									a												=      self.numpy_to_pil(snake_case_       )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=snake_case_       )












def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    print('\nThe shortest path matrix using Floyd Warshall algorithm\n'				)
    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            if dist[i][j] != float('inf'				):
                print(int(dist[i][j]				)							,     end='\t'				)
            else:
                print('INF'							,     end='\t'				)
        print()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							[[float('inf'				) for _ in range(_SCREAMING_SNAKE_CASE				)] for _ in range(_SCREAMING_SNAKE_CASE				)]

    for i in range(_SCREAMING_SNAKE_CASE				):
        for j in range(_SCREAMING_SNAKE_CASE				):
            _A     =							graph[i][j]

            # check vertex k against all other vertices (i, j)
    for k in range(_SCREAMING_SNAKE_CASE				):
        # looping through rows of graph array
        for i in range(_SCREAMING_SNAKE_CASE				):
            # looping through columns of graph array
            for j in range(_SCREAMING_SNAKE_CASE				):
                if (
                    dist[i][k] != float('inf'				)
                    and dist[k][j] != float('inf'				)
                    and dist[i][k] + dist[k][j] < dist[i][j]
                ):
                    _A     =							dist[i][k] + dist[k][j]

    _print_dist(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
    return dist, v


if __name__ == "__main__":
 __A							:			Dict         =			int(input("Enter number of vertices: "))
 __A							:			Union[str, Any]         =			int(input("Enter number of edges: "))

 __A							:			List[str]         =			[[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  __A							:			List[Any]         =			0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  __A							:			Union[str, Any]         =			int(input("Enter source:"))
  __A							:			List[str]         =			int(input("Enter destination:"))
  __A							:			Union[str, Any]         =			float(input("Enter weight:"))
  __A							:			Any         =			weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0





