




"""simple docstring"""




def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     lowercase :       Any       =			len(__magic_name__   ), len(grid[0]   )
     if (
         min(__magic_name__			,		__magic_name__   ) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)   )

     lowercase :       Optional[int]       =			0
     count += depth_first_search(__magic_name__			,		row + 1			,		__magic_name__			,		__magic_name__   )
     count += depth_first_search(__magic_name__			,		row - 1			,		__magic_name__			,		__magic_name__   )
     count += depth_first_search(__magic_name__			,		__magic_name__			,		col + 1			,		__magic_name__   )
     count += depth_first_search(__magic_name__			,		__magic_name__			,		col - 1			,		__magic_name__   )

     visit.remove((row, col)   )
     return count


if __name__ == "__main__":
 import doctest

 doctest.testmod()





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      Tuple			=      (PNDMScheduler,)
  _UpperCamelCase       :      Optional[int]			=      (('''num_inference_steps''', 5_0),)






  def  __a      (  self		:     Union[str, Any]				,						**_A		:     Optional[Any]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       List[str]       =			{
           '''num_train_timesteps''': 1_000,
           '''beta_start''': 0.0_001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**_A				)
       return config






  def  __a      (  self		:     Optional[Any]				,						_A		:     Any=0				,						**_A		:     int				)		->   Dict:






       """simple docstring"""







       lowercase :       List[Any]       =			dict(self.forward_default_kwargs				)
       lowercase :       Union[str, Any]       =			kwargs.pop('''num_inference_steps'''				,						_A				)
       lowercase :       Optional[Any]       =			self.dummy_sample
       lowercase :       str       =			0.1 * sample
       lowercase :       Union[str, Any]       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            lowercase :       Dict       =			self.get_scheduler_config(**_A				)
            lowercase :       int       =			scheduler_class(**_A				)
            scheduler.set_timesteps(_A				)
            # copy over dummy past residuals
            lowercase :       List[str]       =			dummy_past_residuals[:]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_A				)
                 lowercase :       Union[str, Any]       =			scheduler_class.from_pretrained(_A				)
                 new_scheduler.set_timesteps(_A				)
                 # copy over dummy past residuals
                 lowercase :       int       =			dummy_past_residuals[:]

            lowercase :       List[str]       =			scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       List[Any]       =			new_scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample

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

            lowercase :       Dict       =			scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       List[Any]       =			new_scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample

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






  def  __a      (  self		:     int				)		->   List[str]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[Any]				,						_A		:     Optional[int]=0				,						**_A		:     str				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Optional[int]       =			dict(self.forward_default_kwargs				)
       lowercase :       Optional[int]       =			kwargs.pop('''num_inference_steps'''				,						_A				)
       lowercase :       Union[str, Any]       =			self.dummy_sample
       lowercase :       Any       =			0.1 * sample
       lowercase :       int       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
            lowercase :       List[str]       =			self.get_scheduler_config()
            lowercase :       List[str]       =			scheduler_class(**_A				)
            scheduler.set_timesteps(_A				)

            # copy over dummy past residuals (must be after setting timesteps)
            lowercase :       Union[str, Any]       =			dummy_past_residuals[:]

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

                 # copy over dummy past residual (must be after setting timesteps)
                 lowercase :       Optional[Any]       =			dummy_past_residuals[:]

            lowercase :       int       =			scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       Tuple       =			new_scheduler.step_prk(_A				,						_A				,						_A				,						**_A				).prev_sample

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

            lowercase :       Any       =			scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample
            lowercase :       Union[str, Any]       =			new_scheduler.step_plms(_A				,						_A				,						_A				,						**_A				).prev_sample

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






  def  __a      (  self		:     Union[str, Any]				,						**_A		:     Optional[int]				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			self.scheduler_classes[0]
       lowercase :       Optional[int]       =			self.get_scheduler_config(**_A				)
       lowercase :       int       =			scheduler_class(**_A				)

       lowercase :       Any       =			10
       lowercase :       Optional[int]       =			self.dummy_model()
       lowercase :       Tuple       =			self.dummy_sample_deter
       scheduler.set_timesteps(_A				)

       for i, t in enumerate(scheduler.prk_timesteps				):
            lowercase :       List[str]       =			model(_A				,						_A				)
            lowercase :       Any       =			scheduler.step_prk(_A				,						_A				,						_A				).prev_sample

       for i, t in enumerate(scheduler.plms_timesteps				):
            lowercase :       Dict       =			model(_A				,						_A				)
            lowercase :       List[Any]       =			scheduler.step_plms(_A				,						_A				,						_A				).prev_sample

       return sample






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






       """simple docstring"""







       lowercase :       int       =			dict(self.forward_default_kwargs				)

       lowercase :       Union[str, Any]       =			kwargs.pop('''num_inference_steps'''				,						_A				)

       for scheduler_class in self.scheduler_classes:
            lowercase :       Optional[Any]       =			self.get_scheduler_config()
            lowercase :       List[Any]       =			scheduler_class(**_A				)

            lowercase :       Dict       =			self.dummy_sample
            lowercase :       str       =			0.1 * sample

            if num_inference_steps is not None and hasattr(_A				,						'''set_timesteps'''				):
                 scheduler.set_timesteps(_A				)
            elif num_inference_steps is not None and not hasattr(_A				,						'''set_timesteps'''				):
                 lowercase :       List[str]       =			num_inference_steps

            # copy over dummy past residuals (must be done after set_timesteps)
            lowercase :       Any       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
            lowercase :       Optional[Any]       =			dummy_past_residuals[:]

            lowercase :       Optional[int]       =			scheduler.step_prk(_A				,						0				,						_A				,						**_A				).prev_sample
            lowercase :       str       =			scheduler.step_prk(_A				,						1				,						_A				,						**_A				).prev_sample

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

            lowercase :       Dict       =			scheduler.step_plms(_A				,						0				,						_A				,						**_A				).prev_sample
            lowercase :       Tuple       =			scheduler.step_plms(_A				,						1				,						_A				,						**_A				).prev_sample

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






  def  __a      (  self		:     Any				)		->   Optional[Any]:






       """simple docstring"""







       for timesteps in [100, 1_000]:
            self.check_over_configs(num_train_timesteps=_A				)






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







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

       lowercase :       str       =			self.scheduler_classes[0]
       lowercase :       int       =			self.get_scheduler_config(steps_offset=1				)
       lowercase :       Union[str, Any]       =			scheduler_class(**_A				)
       scheduler.set_timesteps(10				)
       assert torch.equal(
           scheduler.timesteps				,						torch.LongTensor(
               [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)				,						)






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






       """simple docstring"""







       for beta_start, beta_end in zip([0.0_001, 0.001]				,						[0.002, 0.02]				):
            self.check_over_configs(beta_start=_A				,						beta_end=_A				)






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






       """simple docstring"""







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






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






       """simple docstring"""







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






  def  __a      (  self		:     Tuple				)		->   str:






       """simple docstring"""







       for t in [1, 5, 10]:
            self.check_over_forward(time_step=_A				)






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






       """simple docstring"""







       for t, num_inference_steps in zip([1, 5, 10]				,						[10, 50, 100]				):
            self.check_over_forward(num_inference_steps=_A				)






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






       """simple docstring"""







       lowercase :       str       =			27

       for scheduler_class in self.scheduler_classes:
            lowercase :       List[str]       =			self.dummy_sample
            lowercase :       Union[str, Any]       =			0.1 * sample

            lowercase :       int       =			self.get_scheduler_config()
            lowercase :       Union[str, Any]       =			scheduler_class(**_A				)

            scheduler.set_timesteps(_A				)

            # before power of 3 fix, would error on first step, so we only need to do two
            for i, t in enumerate(scheduler.prk_timesteps[:2]				):
                 lowercase :       Union[str, Any]       =			scheduler.step_prk(_A				,						_A				,						_A				).prev_sample






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






       """simple docstring"""







       with self.assertRaises(_A				):
            lowercase :       Dict       =			self.scheduler_classes[0]
            lowercase :       Union[str, Any]       =			self.get_scheduler_config()
            lowercase :       Optional[int]       =			scheduler_class(**_A				)

            scheduler.step_plms(self.dummy_sample				,						1				,						self.dummy_sample				).prev_sample






  def  __a      (  self		:     str				)		->   Dict:






       """simple docstring"""







       lowercase :       List[str]       =			self.full_loop()
       lowercase :       List[Any]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Any       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 198.1_318				) < 1E-2
       assert abs(result_mean.item() - 0.2_580				) < 1E-3






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






       """simple docstring"""







       lowercase :       List[str]       =			self.full_loop(prediction_type='''v_prediction'''				)
       lowercase :       Optional[int]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Tuple       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 67.3_986				) < 1E-2
       assert abs(result_mean.item() - 0.0_878				) < 1E-3






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






       """simple docstring"""







       lowercase :       Any       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       List[str]       =			torch.sum(torch.abs(_A				)				)
       lowercase :       Optional[int]       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 230.0_399				) < 1E-2
       assert abs(result_mean.item() - 0.2_995				) < 1E-3






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






       """simple docstring"""







       lowercase :       Union[str, Any]       =			self.full_loop(set_alpha_to_one=_A				,						beta_start=0.01				)
       lowercase :       int       =			torch.sum(torch.abs(_A				)				)
       lowercase :       int       =			torch.mean(torch.abs(_A				)				)

       assert abs(result_sum.item() - 186.9_482				) < 1E-2
       assert abs(result_mean.item() - 0.2_434				) < 1E-3