import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		=(UniPCMultistepScheduler,)
     _SCREAMING_SNAKE_CASE     :     Dict		=(("""num_inference_steps""", 25),)



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            _A=	{
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'solver_order': 2,
                'solver_type': 'bh2',
            }

            config.update(**lowerCAmelCase__       )
            return config



     def 			a__  (					self				,      lowerCAmelCase__=0				,      **lowerCAmelCase__       ):
            _A=	dict(self.forward_default_kwargs       )
            _A=	kwargs.pop('num_inference_steps'				,      lowerCAmelCase__       )
            _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(**lowerCAmelCase__       )
                   _A=	scheduler_class(**lowerCAmelCase__       )
                   scheduler.set_timesteps(lowerCAmelCase__       )
                   # copy over dummy past residuals
                   _A=	dummy_past_residuals[: scheduler.config.solver_order]

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

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

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



     def 			a__  (					self				,      lowerCAmelCase__=0				,      **lowerCAmelCase__       ):
            _A=	dict(self.forward_default_kwargs       )
            _A=	kwargs.pop('num_inference_steps'				,      lowerCAmelCase__       )
            _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(**lowerCAmelCase__       )
                   scheduler.set_timesteps(lowerCAmelCase__       )

                   # 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(lowerCAmelCase__       )
                          _A=	scheduler_class.from_pretrained(lowerCAmelCase__       )
                          # copy over dummy past residuals
                          new_scheduler.set_timesteps(lowerCAmelCase__       )

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

                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ).prev_sample
                   _A=	new_scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ).prev_sample

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



     def 			a__  (					self				,      lowerCAmelCase__=None				,      **lowerCAmelCase__       ):
            if scheduler is None:
                   _A=	self.scheduler_classes[0]
                   _A=	self.get_scheduler_config(**lowerCAmelCase__       )
                   _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(**lowerCAmelCase__       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	10
            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter
            scheduler.set_timesteps(lowerCAmelCase__       )

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

            return sample



     def 			a__  (					self       ):
            _A=	dict(self.forward_default_kwargs       )

            _A=	kwargs.pop('num_inference_steps'				,      lowerCAmelCase__       )

            for scheduler_class in self.scheduler_classes:
                   _A=	self.get_scheduler_config()
                   _A=	scheduler_class(**lowerCAmelCase__       )

                   _A=	self.dummy_sample
                   _A=	0.1 * sample

                   if num_inference_steps is not None and hasattr(lowerCAmelCase__				,      'set_timesteps'       ):
                          scheduler.set_timesteps(lowerCAmelCase__       )
                   elif num_inference_steps is not None and not hasattr(lowerCAmelCase__				,      '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(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ).prev_sample
                   _A=	scheduler.step(lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      **lowerCAmelCase__       ).prev_sample

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



     def 			a__  (					self       ):
            # 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=lowerCAmelCase__       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_mean.item() - 0.2464       ) < 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=lowerCAmelCase__       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_mean.item() - 0.2464       ) < 1E-3



     def 			a__  (					self       ):
            for timesteps in [25, 50, 100, 999, 1000]:
                   self.check_over_configs(num_train_timesteps=lowerCAmelCase__       )



     def 			a__  (					self       ):
            self.check_over_configs(thresholding=lowerCAmelCase__       )
            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=lowerCAmelCase__				,      prediction_type=lowerCAmelCase__				,      sample_max_value=lowerCAmelCase__				,      solver_order=lowerCAmelCase__				,      solver_type=lowerCAmelCase__				,      )



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



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



     def 			a__  (					self       ):
            self.check_over_configs(lower_order_final=lowerCAmelCase__       )
            self.check_over_configs(lower_order_final=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
                   self.check_over_forward(num_inference_steps=lowerCAmelCase__				,      time_step=0       )



     def 			a__  (					self       ):
            _A=	self.full_loop()
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_mean.item() - 0.2464       ) < 1E-3



     def 			a__  (					self       ):
            _A=	self.full_loop(prediction_type='v_prediction'       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_mean.item() - 0.1014       ) < 1E-3



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(thresholding=lowerCAmelCase__				,      dynamic_thresholding_ratio=0       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	10
            _A=	self.dummy_model()
            _A=	self.dummy_sample_deter.half()
            scheduler.set_timesteps(lowerCAmelCase__       )

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

            assert sample.dtype == torch.floataa





     def 			a__  (					self				,      **lowerCAmelCase__       ):
            for scheduler_class in self.scheduler_classes:
                   _A=	self.get_scheduler_config(**lowerCAmelCase__       )
                   _A=	scheduler_class(**lowerCAmelCase__       )

                   scheduler.set_timesteps(scheduler.config.num_train_timesteps       )
                   assert len(scheduler.timesteps.unique()       ) == scheduler.num_inference_steps
def 							UpperCamelCase				(    )		->						list[list[int]]:




       '''simple docstring'''






       return [list(range(10_00 - i       ,					-10_00 - i       ,					-1   )   ) for i in range(10_00   )]


UpperCAmelCase_								=							generate_large_matrix()
UpperCAmelCase_								=							(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						None:




       '''simple docstring'''






       assert all(row == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for row in grid   )
       assert all(list(lowerCAmelCase_   ) == sorted(lowerCAmelCase_       ,					reverse=lowerCAmelCase_   ) for col in zip(*lowerCAmelCase_   )   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(lowerCAmelCase_   ) - 1

       # Edge cases such as no values or all numbers are negative.
       if not array or array[0] < 0:
              return 0

       while right + 1 > left:
              _A=	(left + right) // 2
              _A=	array[mid]

              # Num must be negative and the index must be greater than or equal to 0.
              if num < 0 and array[mid - 1] >= 0:
                     return mid

              if num >= 0:
                     _A=	mid + 1
              else:
                     _A=	mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
       return len(lowerCAmelCase_   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       _A=	len(grid[0]   )

       for i in range(len(lowerCAmelCase_   )   ):
              _A=	find_negative_index(grid[i][:bound]   )
              total += bound
       return (len(lowerCAmelCase_   ) * len(grid[0]   )) - total

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       return len([number for row in grid for number in row if number < 0]   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						int:




       '''simple docstring'''






       _A=	0
       for row in grid:
              for i, number in enumerate(lowerCAmelCase_   ):
                     if number < 0:
                            total += len(lowerCAmelCase_   ) - i
                            break
       return total

def 							UpperCamelCase				(    )		->						None:




       '''simple docstring'''






       from timeit import timeit

       print('Running benchmarks'   )
       _A=	(
           'from __main__ import count_negatives_binary_search, '
           'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
       )
       for func in (
           "count_negatives_binary_search",  # took 0.7727 seconds
           "count_negatives_brute_force_with_break",  # took 4.6505 seconds
           "count_negatives_brute_force",  # took 12.8160 seconds
       ):
              _A=	timeit(F"{func}(grid=grid)"       ,					setup=lowerCAmelCase_       ,					number=5_00   )
              print(F"{func}() took {time:0.4f} seconds"   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()