

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 					__UpperCamelCase					(       _snake_case      ):
	_lowercase	: Union[str, Any]												=	(DDIMScheduler,)
	_lowercase	: List[str]												=	(("""eta""", 0.0), ("""num_inference_steps""", 5_0))







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			**__UpperCamelCase:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     {
						    "num_train_timesteps": 10_00,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "clip_sample": True,
						}

						config.update(**snake_case_   )
						return config







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			**__UpperCamelCase:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config(**snake_case_   )
						__magic_name__				=     scheduler_class(**snake_case_   )

						__magic_name__				=     10, 0.0

						__magic_name__				=     self.dummy_model()
						__magic_name__				=     self.dummy_sample_deter

						scheduler.set_timesteps(snake_case_   )

						for t in scheduler.timesteps:
											__magic_name__				=     model(snake_case_	,			snake_case_   )
											__magic_name__				=     scheduler.step(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_   ).prev_sample

						return sample







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						for timesteps in [1_00, 5_00, 10_00]:
											self.check_over_configs(num_train_timesteps=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=snake_case_   )

						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config(steps_offset=1   )
						__magic_name__				=     scheduler_class(**snake_case_   )
						scheduler.set_timesteps(5   )
						assert torch.equal(scheduler.timesteps	,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]   )   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]	,			[0.002, 0.02, 0.2, 2]   ):
											self.check_over_configs(beta_start=snake_case_	,			beta_end=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						self.check_over_configs(thresholding=snake_case_   )
						for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=snake_case_	,			prediction_type=snake_case_	,			sample_max_value=snake_case_	,			)







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						for t in [1, 10, 49]:
											self.check_over_forward(time_step=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						for t, num_inference_steps in zip([1, 10, 50]	,			[10, 50, 5_00]   ):
											self.check_over_forward(time_step=snake_case_	,			num_inference_steps=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						for t, eta in zip([1, 10, 49]	,			[0.0, 0.5, 1.0]   ):
											self.check_over_forward(time_step=snake_case_	,			eta=snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.scheduler_classes[0]
						__magic_name__				=     self.get_scheduler_config()
						__magic_name__				=     scheduler_class(**snake_case_   )

						assert torch.sum(torch.abs(scheduler._get_variance(0	,			0   ) - 0.0   )   ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_20	,			4_00   ) - 0.14771   )   ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_80	,			9_60   ) - 0.32460   )   ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0	,			0   ) - 0.0   )   ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(4_87	,			4_86   ) - 0.00979   )   ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(9_99	,			9_98   ) - 0.02   )   ) < 1E-5







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop()

						__magic_name__				=     torch.sum(torch.abs(snake_case_   )   )
						__magic_name__				=     torch.mean(torch.abs(snake_case_   )   )

						assert abs(result_sum.item() - 172.0067   ) < 1E-2
						assert abs(result_mean.item() - 0.223967   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop(prediction_type='v_prediction'   )

						__magic_name__				=     torch.sum(torch.abs(snake_case_   )   )
						__magic_name__				=     torch.mean(torch.abs(snake_case_   )   )

						assert abs(result_sum.item() - 52.5302   ) < 1E-2
						assert abs(result_mean.item() - 0.0684   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop(set_alpha_to_one=snake_case_	,			beta_start=0.01   )
						__magic_name__				=     torch.sum(torch.abs(snake_case_   )   )
						__magic_name__				=     torch.mean(torch.abs(snake_case_   )   )

						assert abs(result_sum.item() - 149.8295   ) < 1E-2
						assert abs(result_mean.item() - 0.1951   ) < 1E-3







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     self.full_loop(set_alpha_to_one=snake_case_	,			beta_start=0.01   )
						__magic_name__				=     torch.sum(torch.abs(snake_case_   )   )
						__magic_name__				=     torch.mean(torch.abs(snake_case_   )   )

						assert abs(result_sum.item() - 149.0784   ) < 1E-2
						assert abs(result_mean.item() - 0.1941   ) < 1E-3






"""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
_a   :      List[str]       =				{
    """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__ ( a :					dict[str, list[str]]		):

  """simple docstring"""







  _snake_case  :    List[Any]            = {node: {node} for node in graph}

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

  while len(a		) > 2:
    # Choose a random edge.
    _snake_case  :    str            = 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  :    Union[str, Any]            = u + v
    _snake_case  :    Union[str, Any]            = list(set(graph_copy[u] + graph_copy[v]		)		)
    uv_neighbors.remove(a		)
    uv_neighbors.remove(a		)
    _snake_case  :    Union[str, Any]            = uv_neighbors
    for neighbor in uv_neighbors:
      graph_copy[neighbor].append(a		)

    _snake_case  :    Dict            = 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(a		)
      if v in graph_copy[neighbor]:
        graph_copy[neighbor].remove(a		)

    # Find cutset.
  _snake_case  :    List[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))

