





'''simple docstring'''

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""
       _snake_case     :					Union[str, Any]						    =   (DEISMultistepScheduler,)
       _snake_case     :					Optional[Any]						    =   (('num_inference_steps', 2_5),)





       def snake_case__						(							self       :		List[Any]	,			**lowerCAmelCase__       :		int					)   ->					Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       =	{
                '''num_train_timesteps''': 1000,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
                '''solver_order''': 2,
            }

            config.update(**lowerCAmelCase__					)
            return config





       def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		str=0	,			**lowerCAmelCase__       :		Dict					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	dict(self.forward_default_kwargs					)
            _UpperCamelCase       =	kwargs.pop('''num_inference_steps'''	,			lowerCAmelCase__					)
            _UpperCamelCase       =	self.dummy_sample
            _UpperCamelCase       =	0.1 * sample
            _UpperCamelCase       =	[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

                 _UpperCamelCase   ,    _UpperCamelCase       =	sample, sample
                 for t in range(lowerCAmelCase__	,			time_step + scheduler.config.solver_order + 1					):
                      _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			**lowerCAmelCase__					).prev_sample
                      _UpperCamelCase       =	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 snake_case__						(							self       :		Dict					)   ->					Dict:

            '''simple docstring'''




            pass





       def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		List[Any]=0	,			**lowerCAmelCase__       :		Optional[int]					)   ->					int:

            '''simple docstring'''




            _UpperCamelCase       =	dict(self.forward_default_kwargs					)
            _UpperCamelCase       =	kwargs.pop('''num_inference_steps'''	,			lowerCAmelCase__					)
            _UpperCamelCase       =	self.dummy_sample
            _UpperCamelCase       =	0.1 * sample
            _UpperCamelCase       =	[residual + 0.2, residual + 0.15, residual + 0.10]

            for scheduler_class in self.scheduler_classes:
                 _UpperCamelCase       =	self.get_scheduler_config()
                 _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)
                 scheduler.set_timesteps(lowerCAmelCase__					)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(lowerCAmelCase__					)
                      _UpperCamelCase       =	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)
                      _UpperCamelCase       =	dummy_past_residuals[: new_scheduler.config.solver_order]

                 _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			**lowerCAmelCase__					).prev_sample
                 _UpperCamelCase       =	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 snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		Optional[Any]=None	,			**lowerCAmelCase__       :		Optional[int]					)   ->					List[str]:

            '''simple docstring'''




            if scheduler is None:
                 _UpperCamelCase       =	self.scheduler_classes[0]
                 _UpperCamelCase       =	self.get_scheduler_config(**lowerCAmelCase__					)
                 _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

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

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

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

            return sample





       def snake_case__						(							self       :		Optional[int]					)   ->					Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       =	dict(self.forward_default_kwargs					)

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

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

                 _UpperCamelCase       =	self.dummy_sample
                 _UpperCamelCase       =	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'''					):
                      _UpperCamelCase       =	num_inference_steps

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

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

                 _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			**lowerCAmelCase__					).prev_sample
                 _UpperCamelCase       =	scheduler.step(lowerCAmelCase__	,			lowerCAmelCase__	,			lowerCAmelCase__	,			**lowerCAmelCase__					).prev_sample

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





       def snake_case__						(							self       :		Dict					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	DEISMultistepScheduler(**self.get_scheduler_config()					)
            _UpperCamelCase       =	self.full_loop(scheduler=lowerCAmelCase__					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_mean.item() - 0.23916					) < 1e-3

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

            _UpperCamelCase       =	self.full_loop(scheduler=lowerCAmelCase__					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_mean.item() - 0.23916					) < 1e-3





       def snake_case__						(							self       :		Optional[Any]					)   ->					str:

            '''simple docstring'''




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





       def snake_case__						(							self       :		Any					)   ->					List[str]:

            '''simple docstring'''




            self.check_over_configs(thresholding=lowerCAmelCase__					)
            for order in [1, 2, 3]:
                 for solver_type in ["logrho"]:
                      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__	,			algorithm_type='''deis'''	,			solver_order=lowerCAmelCase__	,			solver_type=lowerCAmelCase__	,			)





       def snake_case__						(							self       :		Optional[int]					)   ->					Any:

            '''simple docstring'''




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





       def snake_case__						(							self       :		List[Any]					)   ->					Any:

            '''simple docstring'''




            for algorithm_type in ["deis"]:
                 for solver_type in ["logrho"]:
                      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__	,			algorithm_type=lowerCAmelCase__	,			)
                                _UpperCamelCase       =	self.full_loop(
                                    solver_order=lowerCAmelCase__	,			solver_type=lowerCAmelCase__	,			prediction_type=lowerCAmelCase__	,			algorithm_type=lowerCAmelCase__	,			)
                                assert not torch.isnan(lowerCAmelCase__					).any(), "Samples have nan numbers"





       def snake_case__						(							self       :		List[str]					)   ->					Tuple:

            '''simple docstring'''




            self.check_over_configs(lower_order_final=lowerCAmelCase__					)
            self.check_over_configs(lower_order_final=lowerCAmelCase__					)





       def snake_case__						(							self       :		List[str]					)   ->					List[Any]:

            '''simple docstring'''




            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 snake_case__						(							self       :		str					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop()
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_mean.item() - 0.23916					) < 1e-3





       def snake_case__						(							self       :		str					)   ->					Any:

            '''simple docstring'''




            _UpperCamelCase       =	self.full_loop(prediction_type='''v_prediction'''					)
            _UpperCamelCase       =	torch.mean(torch.abs(lowerCAmelCase__					)					)

            assert abs(result_mean.item() - 0.091					) < 1e-3







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

            '''simple docstring'''




            _UpperCamelCase       =	self.scheduler_classes[0]
            _UpperCamelCase       =	self.get_scheduler_config(thresholding=lowerCAmelCase__	,			dynamic_thresholding_ratio=0					)
            _UpperCamelCase       =	scheduler_class(**lowerCAmelCase__					)

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

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

            assert sample.dtype == torch.floataa










'''simple docstring'''

def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square(lowercase :					int, lowercase :					int				) -> int:
          # BASE CASE
          if row >= rows or col >= cols:
               return 0

          _UpperCamelCase       =	update_area_of_max_square(lowercase, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, col + 1				)
          _UpperCamelCase       =	update_area_of_max_square(row + 1, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     update_area_of_max_square(0, 0				)
     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     def update_area_of_max_square_using_dp_array(
         lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				) -> int:
          if row >= rows or col >= cols:
               return 0
          if dp_array[row][col] != -1:
               return dp_array[row][col]

          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(lowercase, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, col + 1, lowercase				)
          _UpperCamelCase       =	update_area_of_max_square_using_dp_array(row + 1, lowercase, lowercase				)

          if mat[row][col]:
               _UpperCamelCase       =	1 + min([right, diagonal, down]				)
               _UpperCamelCase       =	max(largest_square_area[0], lowercase				)
               _UpperCamelCase       =	sub_problem_sol
               return sub_problem_sol
          else:
               return 0

     _UpperCamelCase       =	[0]
     _UpperCamelCase       =	[[-1] * cols for _ in range(lowercase				)]
     update_area_of_max_square_using_dp_array(0, 0, lowercase				)

     return largest_square_area[0]







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[[0] * (cols + 1) for _ in range(rows + 1				)]
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	dp_array[row][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col + 1]
               _UpperCamelCase       =	dp_array[row + 1][col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(dp_array[row][col], lowercase				)
               else:
                    _UpperCamelCase       =	0

     return largest_square_area







def  a__    (    lowercase :					int, lowercase :					int, lowercase :					list[list[int]]				)		->		int:





     """simple docstring"""

     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	[0] * (cols + 1)
     _UpperCamelCase       =	0
     for row in range(rows - 1, -1, -1				):
          for col in range(cols - 1, -1, -1				):
               _UpperCamelCase       =	current_row[col + 1]
               _UpperCamelCase       =	next_row[col + 1]
               _UpperCamelCase       =	next_row[col]

               if mat[row][col] == 1:
                    _UpperCamelCase       =	1 + min(lowercase, lowercase, lowercase				)
                    _UpperCamelCase       =	max(current_row[col], lowercase				)
               else:
                    _UpperCamelCase       =	0
          _UpperCamelCase       =	current_row

     return largest_square_area


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))



