

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 			a					(		a__			):
						_lowercase        =     (KDPMaDiscreteScheduler,)
						_lowercase        =     1_0


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      {
										    '''num_train_timesteps''': 1100,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										}

										config.update(**_lowerCamelCase		)
										return config


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=_lowerCamelCase		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,   [0.00_02, 0.0_02, 0.02]		):
														self.check_over_configs(beta_start=_lowerCamelCase  ,   beta_end=_lowerCamelCase		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						Any							=      scheduler_class(**_lowerCamelCase		)

										scheduler.set_timesteps(self.num_inference_steps		)

										_UpperCAmelCase    :						Dict							=      self.dummy_model()
										_UpperCAmelCase    :						int							=      self.dummy_sample_deter * scheduler.init_noise_sigma
										_UpperCAmelCase    :						Optional[Any]							=      sample.to(_lowerCamelCase		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						List[Any]							=      scheduler.scale_model_input(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						Tuple							=      model(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						List[str]							=      scheduler.step(_lowerCamelCase  ,   _lowerCamelCase  ,   _lowerCamelCase		)
														_UpperCAmelCase    :						str							=      output.prev_sample

										_UpperCAmelCase    :						int							=      torch.sum(torch.abs(_lowerCamelCase		)		)
										_UpperCAmelCase    :						Any							=      torch.mean(torch.abs(_lowerCamelCase		)		)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 4.6934e-07		) < 1e-2
														assert abs(result_mean.item() - 6.1112e-10		) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 4.693428650170972e-07		) < 1e-2
														assert abs(result_mean.item() - 0.00_02		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if torch_device == "mps":
														return
										_UpperCAmelCase    :						Dict							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						List[Any]							=      self.get_scheduler_config()
										_UpperCAmelCase    :						str							=      scheduler_class(**_lowerCamelCase		)

										scheduler.set_timesteps(self.num_inference_steps		)

										_UpperCAmelCase    :						Any							=      self.dummy_model()
										_UpperCAmelCase    :						Union[str, Any]							=      self.dummy_sample_deter * scheduler.init_noise_sigma
										_UpperCAmelCase    :						Any							=      sample.to(_lowerCamelCase		)

										for i, t in enumerate(scheduler.timesteps		):
														_UpperCAmelCase    :						str							=      scheduler.scale_model_input(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						Dict							=      model(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step(_lowerCamelCase  ,   _lowerCamelCase  ,   _lowerCamelCase		)
														_UpperCAmelCase    :						Any							=      output.prev_sample

										_UpperCAmelCase    :						Optional[Any]							=      torch.sum(torch.abs(_lowerCamelCase		)		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.mean(torch.abs(_lowerCamelCase		)		)

										if torch_device in ["cpu", "mps"]:
														assert abs(result_sum.item() - 20.41_25		) < 1e-2
														assert abs(result_mean.item() - 0.02_66		) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.41_25		) < 1e-2
														assert abs(result_mean.item() - 0.02_66		) < 1e-3





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if torch_device == "mps":
														return
										_UpperCAmelCase    :						Optional[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config()
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**_lowerCamelCase		)

										scheduler.set_timesteps(self.num_inference_steps  ,   device=_lowerCamelCase		)

										_UpperCAmelCase    :						List[str]							=      self.dummy_model()
										_UpperCAmelCase    :						List[Any]							=      self.dummy_sample_deter.to(_lowerCamelCase		) * scheduler.init_noise_sigma

										for t in scheduler.timesteps:
														_UpperCAmelCase    :						Any							=      scheduler.scale_model_input(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						Optional[Any]							=      model(_lowerCamelCase  ,   _lowerCamelCase		)

														_UpperCAmelCase    :						int							=      scheduler.step(_lowerCamelCase  ,   _lowerCamelCase  ,   _lowerCamelCase		)
														_UpperCAmelCase    :						str							=      output.prev_sample

										_UpperCAmelCase    :						Optional[int]							=      torch.sum(torch.abs(_lowerCamelCase		)		)
										_UpperCAmelCase    :						Dict							=      torch.mean(torch.abs(_lowerCamelCase		)		)

										if str(_lowerCamelCase		).startswith("cpu"		):
														# The following sum varies between 148 and 156 on mps. Why?
														assert abs(result_sum.item() - 20.41_25		) < 1e-2
														assert abs(result_mean.item() - 0.02_66		) < 1e-3
										else:
														# CUDA
														assert abs(result_sum.item() - 20.41_25		) < 1e-2
														assert abs(result_mean.item() - 0.02_66		) < 1e-3





def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					str					, lowerCAmelCase:					str	)				->    bool:
				_UpperCAmelCase    :						Optional[Any]							=      len(lowerCAmelCase	) + 1
				_UpperCAmelCase    :						Optional[int]							=      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.
				_UpperCAmelCase    :						List[str]							=      [[0 for i in range(lowerCAmelCase	)] for j in range(lowerCAmelCase	)]

				# since string of zero length match pattern of zero length
				_UpperCAmelCase    :						List[Any]							=      1

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

				# since string of zero length will match with pattern where there
				# is at least one * alternatively
				for j in range(1					, lowerCAmelCase	):
								_UpperCAmelCase    :						Tuple							=      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] == ".":
																_UpperCAmelCase    :						Optional[Any]							=      dp[i - 1][j - 1]

												elif pattern[j - 1] == "*":
																if dp[i][j - 2] == 1:
																				_UpperCAmelCase    :						List[str]							=      1
																elif pattern[j - 2] in (input_string[i - 1], "."):
																				_UpperCAmelCase    :						str							=      dp[i - 1][j]
																else:
																				_UpperCAmelCase    :						int							=      0
												else:
																_UpperCAmelCase    :						List[Any]							=      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 :")

					SCREAMING_SNAKE_CASE_            = 'aab'
					SCREAMING_SNAKE_CASE_            = '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}''')


