





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler



class 				_UpperCamelCase       (						unittest.TestCase  ):
       # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
       _UpperCamelCase					:       str       = (ScoreSdeVeScheduler,)
       _UpperCamelCase					:       Optional[Any]       = ()

       @property
       def lowercase			(		self:			Dict		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	8
              UpperCamelCase_ 							=	8

              UpperCamelCase_ 							=	torch.rand((batch_size, num_channels, height, width)		)

              return sample

       @property
       def lowercase			(		self:			List[str]		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	4
              UpperCamelCase_ 							=	3
              UpperCamelCase_ 							=	8
              UpperCamelCase_ 							=	8

              UpperCamelCase_ 							=	batch_size * num_channels * height * width
              UpperCamelCase_ 							=	torch.arange(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sample.reshape(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	sample / num_elems
              UpperCamelCase_ 							=	sample.permute(3       , 0       , 1       , 2		)

              return sample

       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


              def model(_SCREAMING_SNAKE_CASE:			List[str]       , _SCREAMING_SNAKE_CASE:			int       , *_SCREAMING_SNAKE_CASE:			int		):
                     return sample * t / (t + 1)

              return model

       def lowercase			(		self:			int       , **_SCREAMING_SNAKE_CASE:			Dict		)							->			Optional[Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	{
                  "num_train_timesteps": 2000,
                  "snr": 0.15,
                  "sigma_min": 0.01,
                  "sigma_max": 1348,
                  "sampling_eps": 1e-5,
              }

              config.update(**_SCREAMING_SNAKE_CASE		)
              return config

       def lowercase			(		self:			Any       , _SCREAMING_SNAKE_CASE:			str=0       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     UpperCamelCase_ 							=	self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

                     UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_correct(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Tuple=0       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)
              kwargs.update(_SCREAMING_SNAKE_CASE		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     UpperCamelCase_ 							=	self.get_scheduler_config()
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_pred(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

                     UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	new_scheduler.step_correct(
                         _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"

       def lowercase			(		self:			List[Any]		)							->			Dict:




              """simple docstring"""


              for timesteps in [10, 100, 1000]:
                     self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			List[str]		)							->			List[str]:




              """simple docstring"""


              for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]       , [1, 100, 1000]		):
                     self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       , sigma_max=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			str		)							->			str:




              """simple docstring"""


              for t in [0.1, 0.5, 0.75]:
                     self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              UpperCamelCase_ 							=	self.scheduler_classes[0]
              UpperCamelCase_ 							=	self.get_scheduler_config()
              UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	3

              UpperCamelCase_ 							=	self.dummy_model()
              UpperCamelCase_ 							=	self.dummy_sample_deter

              scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	torch.manual_seed(0		)

              for i, t in enumerate(scheduler.timesteps		):
                     UpperCamelCase_ 							=	scheduler.sigmas[i]

                     for _ in range(scheduler.config.correct_steps		):
                            with torch.no_grad():
                                   UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_ 							=	scheduler.step_correct(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     with torch.no_grad():
                            UpperCamelCase_ 							=	model(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , generator=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_							,			UpperCamelCase_ 							=	output.prev_sample, output.prev_sample_mean

              UpperCamelCase_ 							=	torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_ 							=	torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

              assert np.isclose(result_sum.item()       , 1_43_72_75_85_28.0		)
              assert np.isclose(result_mean.item()       , 18_71_45_30.0		)



       def lowercase			(		self:			Any		)							->			Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_ 							=	dict(self.forward_default_kwargs		)

              UpperCamelCase_ 							=	kwargs.pop("num_inference_steps"       , _SCREAMING_SNAKE_CASE		)

              for scheduler_class in self.scheduler_classes:
                     UpperCamelCase_ 							=	self.get_scheduler_config()
                     UpperCamelCase_ 							=	scheduler_class(**_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_ 							=	self.dummy_sample
                     UpperCamelCase_ 							=	0.1 * sample

                     if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       , "set_timesteps"		):
                            scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
                     elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       , "set_timesteps"		):
                            UpperCamelCase_ 							=	num_inference_steps

                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , 0       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample
                     UpperCamelCase_ 							=	scheduler.step_pred(_SCREAMING_SNAKE_CASE       , 1       , _SCREAMING_SNAKE_CASE       , generator=torch.manual_seed(0		)       , **_SCREAMING_SNAKE_CASE		).prev_sample

                     self.assertEqual(output_a.shape       , sample.shape		)
                     self.assertEqual(output_a.shape       , output_a.shape		)








'''simple docstring'''

from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
UpperCAmelCase       :		List[Any]		=				{
    '1': ['2', '3', '4', '5'],
    '2': ['1', '3', '4', '5'],
    '3': ['1', '2', '4', '5', '10'],
    '4': ['1', '2', '3', '5', '6'],
    '5': ['1', '2', '3', '4', '7'],
    '6': ['7', '8', '9', '10', '4'],
    '7': ['6', '8', '9', '10', '5'],
    '8': ['6', '7', '9', '10'],
    '9': ['6', '7', '8', '10'],
    '10': ['6', '7', '8', '9', '3'],
}




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						Dict         =  {node: {node} for node in graph}

      _snake_case		:						str         =  {node: graph[node][:] for node in graph}

      while len(lowerCAmelCase_						) > 2:
            # Choose a random edge.
            _snake_case		:						Optional[Any]         =  random.choice(list(graph_copy.keys()						)						)
            _snake_case		:						Union[str, Any]         =  random.choice(graph_copy[u]						)

            # Contract edge (u, v) to new node uv
            _snake_case		:						List[str]         =  u + v
            _snake_case		:						List[str]         =  list(set(graph_copy[u] + graph_copy[v]						)						)
            uv_neighbors.remove(lowerCAmelCase_						)
            uv_neighbors.remove(lowerCAmelCase_						)
            _snake_case		:						Tuple         =  uv_neighbors
            for neighbor in uv_neighbors:
                  graph_copy[neighbor].append(lowerCAmelCase_						)

            _snake_case		:						Tuple         =  set(contracted_nodes[u].union(contracted_nodes[v]						)						)

            # Remove nodes u and v.
            del graph_copy[u]
            del graph_copy[v]
            for neighbor in uv_neighbors:
                  if u in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)
                  if v in graph_copy[neighbor]:
                        graph_copy[neighbor].remove(lowerCAmelCase_						)

    # Find cutset.
      _snake_case		:						Any         =  [contracted_nodes[node] for node in graph_copy]
      return {
          (node, neighbor)
          for node in groups[0]
          for neighbor in graph[node]
          if neighbor in groups[1]
      }


if __name__ == "__main__":
   print(partition_graph(TEST_GRAPH))


