import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			(UniPCMultistepScheduler,)
  _SCREAMING_SNAKE_CASE    				=			(("""num_inference_steps""", 25),)

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  {
       "num_train_timesteps": 10_00,
       "beta_start": 0.00_01,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "solver_order": 2,
       "solver_type": "bh2",
   }

   config.update(**__SCREAMING_SNAKE_CASE       )
   return config

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							**__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  dict(self.forward_default_kwargs       )
   __a       =  kwargs.pop("num_inference_steps"     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  self.dummy_sample
   __a       =  0.1 * sample
   __a       =  [residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
    __a       =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
    __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )
    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
    # copy over dummy past residuals
    __a       =  dummy_past_residuals[: scheduler.config.solver_order]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(__SCREAMING_SNAKE_CASE       )
     __a       =  scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )
     new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )
     # copy over dummy past residuals
     __a       =  dummy_past_residuals[: new_scheduler.config.solver_order]

    __a       ,     __a       =  sample, sample
    for t in range(__SCREAMING_SNAKE_CASE     ,							time_step + scheduler.config.solver_order + 1       ):
     __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample
     __a       =  new_scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample

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

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Dict=0     ,							**__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  dict(self.forward_default_kwargs       )
   __a       =  kwargs.pop("num_inference_steps"     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  self.dummy_sample
   __a       =  0.1 * sample
   __a       =  [residual + 0.2, residual + 0.15, residual + 0.10]

   for scheduler_class in self.scheduler_classes:
    __a       =  self.get_scheduler_config()
    __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )
    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

    # copy over dummy past residuals (must be after setting timesteps)
    __a       =  dummy_past_residuals[: scheduler.config.solver_order]

    with tempfile.TemporaryDirectory() as tmpdirname:
     scheduler.save_config(__SCREAMING_SNAKE_CASE       )
     __a       =  scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE       )
     # copy over dummy past residuals
     new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

     # copy over dummy past residual (must be after setting timesteps)
     __a       =  dummy_past_residuals[: new_scheduler.config.solver_order]

    __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample
    __a       =  new_scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample

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

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=None     ,							**__SCREAMING_SNAKE_CASE :      List[Any]       ):
   if scheduler is None:
    __a       =  self.scheduler_classes[0]
    __a       =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
    __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

   __a       =  self.scheduler_classes[0]
   __a       =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
   __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

   __a       =  10
   __a       =  self.dummy_model()
   __a       =  self.dummy_sample_deter
   scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

   for i, t in enumerate(scheduler.timesteps       ):
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ).prev_sample

   return sample

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  dict(self.forward_default_kwargs       )

   __a       =  kwargs.pop("num_inference_steps"     ,							__SCREAMING_SNAKE_CASE       )

   for scheduler_class in self.scheduler_classes:
    __a       =  self.get_scheduler_config()
    __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

    __a       =  self.dummy_sample
    __a       =  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"       ):
     __a       =  num_inference_steps

    # copy over dummy past residuals (must be done after set_timesteps)
    __a       =  [residual + 0.2, residual + 0.15, residual + 0.10]
    __a       =  dummy_past_residuals[: scheduler.config.solver_order]

    __a       =  scheduler.timesteps[5]
    __a       =  scheduler.timesteps[6]

    __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample
    __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       ).prev_sample

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

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   # make sure that iterating over schedulers with same config names gives same results
   # for defaults
   __a       =  UniPCMultistepScheduler(**self.get_scheduler_config()       )
   __a       =  self.full_loop(scheduler=__SCREAMING_SNAKE_CASE       )
   __a       =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

   assert abs(result_mean.item() - 0.24_64       ) < 1E-3

   __a       =  DPMSolverSinglestepScheduler.from_config(scheduler.config       )
   __a       =  DEISMultistepScheduler.from_config(scheduler.config       )
   __a       =  DPMSolverMultistepScheduler.from_config(scheduler.config       )
   __a       =  UniPCMultistepScheduler.from_config(scheduler.config       )

   __a       =  self.full_loop(scheduler=__SCREAMING_SNAKE_CASE       )
   __a       =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

   assert abs(result_mean.item() - 0.24_64       ) < 1E-3

  def 		_UpperCAmelCase   (		self :      Any       ):
   for timesteps in [25, 50, 1_00, 9_99, 10_00]:
    self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE       )
   for order in [1, 2, 3]:
    for solver_type in ["bh1", "bh2"]:
     for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(
           thresholding=__SCREAMING_SNAKE_CASE     ,							prediction_type=__SCREAMING_SNAKE_CASE     ,							sample_max_value=__SCREAMING_SNAKE_CASE     ,							solver_order=__SCREAMING_SNAKE_CASE     ,							solver_type=__SCREAMING_SNAKE_CASE     ,							)

  def 		_UpperCAmelCase   (		self :      Any       ):
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int       ):
   for solver_type in ["bh1", "bh2"]:
    for order in [1, 2, 3]:
     for prediction_type in ["epsilon", "sample"]:
      self.check_over_configs(
          solver_order=__SCREAMING_SNAKE_CASE     ,							solver_type=__SCREAMING_SNAKE_CASE     ,							prediction_type=__SCREAMING_SNAKE_CASE     ,							)
      __a       =  self.full_loop(
          solver_order=__SCREAMING_SNAKE_CASE     ,							solver_type=__SCREAMING_SNAKE_CASE     ,							prediction_type=__SCREAMING_SNAKE_CASE     ,							)
      assert not torch.isnan(__SCREAMING_SNAKE_CASE       ).any(), "Samples have nan numbers"

  def 		_UpperCAmelCase   (		self :      Dict       ):
   self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE       )
   self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
    self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE     ,							time_step=0       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.full_loop()
   __a       =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

   assert abs(result_mean.item() - 0.24_64       ) < 1E-3

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  self.full_loop(prediction_type="v_prediction"       )
   __a       =  torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

   assert abs(result_mean.item() - 0.10_14       ) < 1E-3

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.scheduler_classes[0]
   __a       =  self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE     ,							dynamic_thresholding_ratio=0       )
   __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

   __a       =  10
   __a       =  self.dummy_model()
   __a       =  self.dummy_sample_deter.half()
   scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

   for i, t in enumerate(scheduler.timesteps       ):
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  scheduler.step(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ).prev_sample

   assert sample.dtype == torch.floataa

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							**__SCREAMING_SNAKE_CASE :      List[str]       ):
   for scheduler_class in self.scheduler_classes:
    __a       =  self.get_scheduler_config(**__SCREAMING_SNAKE_CASE       )
    __a       =  scheduler_class(**__SCREAMING_SNAKE_CASE       )

    scheduler.set_timesteps(scheduler.config.num_train_timesteps       )
    assert len(scheduler.timesteps.unique()       ) == scheduler.num_inference_steps



from __future__ import annotations

SCREAMING_SNAKE_CASE :	Optional[int]							     =		[]



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  if board[row][i] == 1:
   return False
 for i in range(len(_A					)					):
  if board[i][column] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				-1							,				-1					)					):
  if board[i][j] == 1:
   return False
 for i, j in zip(range(_A							,				-1							,				-1					)							,				range(_A							,				len(_A					)					)					):
  if board[i][j] == 1:
   return False
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if row >= len(_A					):
  solution.append(_A					)
  printboard(_A					)
  print()
  return True
 for i in range(len(_A					)					):
  if is_safe(_A							,				_A							,				_A					):
   __a       =  1
   solve(_A							,				row + 1					)
   __a       =  0
 return False



def   __A		(			_A					):



 """simple docstring"""


 for i in range(len(_A					)					):
  for j in range(len(_A					)					):
   if board[i][j] == 1:
    print("Q"							,				end=" "					)
   else:
    print("."							,				end=" "					)
  print()


# n=int(input("The no. of queens"))
SCREAMING_SNAKE_CASE :	List[str]							     =		8
SCREAMING_SNAKE_CASE :	str							     =		[[0 for i in range(n)] for j in range(n)]
solve(board, 0)






print("""The total no. of solutions are :""", len(solution))


