

"""simple docstring"""


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (EulerDiscreteScheduler,)
     _SCREAMING_SNAKE_CASE						:Tuple       = 10

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

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


           """simple docstring"""


           for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]	,    [0.0_002, 0.002, 0.02]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

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


           """simple docstring"""


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

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


           """simple docstring"""


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

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


           """simple docstring"""


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

           scheduler.set_timesteps(self.num_inference_steps  )

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

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

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      str      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 10.0_807  ) < 1E-2
           assert abs(result_mean.item() - 0.0_131  ) < 1E-3

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


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

           scheduler.set_timesteps(self.num_inference_steps  )

           SCREAMING_SNAKE_CASE__   :      int      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.dummy_sample_deter * scheduler.init_noise_sigma
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	sample.to(_a  )

           for i, t in enumerate(scheduler.timesteps  ):
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 0.0_002  ) < 1E-2
           assert abs(result_mean.item() - 2.2_6_7_6E-0_6  ) < 1E-3

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	scheduler_class(**_a  )

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

           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	sample.to(_a  )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__   :      str      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 10.0_807  ) < 1E-2
           assert abs(result_mean.item() - 0.0_131  ) < 1E-3





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler_class(**_a	,    use_karras_sigmas=_a  )

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

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	sample.to(_a  )

           for t in scheduler.timesteps:
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	scheduler.scale_model_input(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  )
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.prev_sample

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 124.52_299_499_511_719  ) < 1E-2
           assert abs(result_mean.item() - 0.16_213_932_633_399_963  ) < 1E-3







"""simple docstring"""


def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      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.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

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

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	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					:Any    = "aab"
  a					:Optional[Any]    = "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}')




