






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     len(lowerCamelCase_							) + 1
				lowercase__  					=     len(lowerCamelCase_							) + 1

				# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
				# length i of input_string matches with prefix string of length j of
				# given pattern.
				# "dp" stands for dynamic programming.
				lowercase__  					=     [[0 for i in range(lowerCamelCase_							)] for j in range(lowerCamelCase_							)]

				# since string of zero length match pattern of zero length
				lowercase__  					=     1

				# since pattern of zero length will never match with string of non-zero length
				for i in range(1						,  lowerCamelCase_							):
								lowercase__  					=     0

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1						,  lowerCamelCase_							):
								lowercase__  					=     dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

				# now using bottom-up approach to find for all remaining lengths
				for i in range(1						,  lowerCamelCase_							):
								for j in range(1						,  lowerCamelCase_							):
												if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
																lowercase__  					=     dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				lowercase__  					=     1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				lowercase__  					=     dp[i - 1][j]
																else:
																				lowercase__  					=     0
												else:
																lowercase__  					=     0

				return bool(dp[-1][-1]							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							# inputing the strings
							# input_string = input("input a string :")
							# pattern = input("input a pattern :")

							A__      :     Optional[Any]       =					'aab'
							A__      :     int       =					'c*a*b'

							# using function to check whether given string matches the given pattern
							if match_pattern(input_string, pattern):
														print(F"{input_string} matches the given pattern {pattern}")
							else:
														print(F"{input_string} does not match with the given pattern {pattern}")












import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     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 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     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)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     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 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

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

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

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

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

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

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




