


'''simple docstring'''







import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (UnCLIPScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       {
                'num_train_timesteps': 1000,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for clip_sample_range in [1, 5, 10, 20]:
                   self.check_over_configs(clip_sample_range=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(prediction_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for time_step in [0, 500, 999]:
                   for prev_timestep in [None, 5, 100, 250, 500, 750]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=snake_case__      ,	prev_timestep=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[Any]   			=       self.get_scheduler_config(variance_type='fixed_small_log'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.054_9625   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.999_4987   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config(variance_type='learned_range'   )
            _lowerCAmelCase    :							int   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							int   			=       0.5

            assert scheduler._get_variance(1      ,	predicted_variance=snake_case__   ) - -10.171_2790 < 1E-5
            assert scheduler._get_variance(487      ,	predicted_variance=snake_case__   ) - -5.799_8052 < 1E-5
            assert scheduler._get_variance(999      ,	predicted_variance=snake_case__   ) - -0.001_0011 < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							str   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       scheduler.timesteps

            _lowerCAmelCase    :							List[str]   			=       self.dummy_model()
            _lowerCAmelCase    :							int   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Dict   			=       torch.manual_seed(0   )

            for i, t in enumerate(snake_case__   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							List[str]   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   _lowerCAmelCase    :							int   			=       pred_prev_sample

            _lowerCAmelCase    :							List[str]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 252.268_2495   ) < 1E-2
            assert abs(result_mean.item() - 0.328_4743   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[str]   			=       scheduler_class(**snake_case__   )

            scheduler.set_timesteps(25   )

            _lowerCAmelCase    :							List[str]   			=       scheduler.timesteps

            _lowerCAmelCase    :							List[str]   			=       self.dummy_model()
            _lowerCAmelCase    :							List[Any]   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Dict   			=       torch.manual_seed(0   )

            for i, t in enumerate(snake_case__   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							Optional[Any]   			=       model(snake_case__      ,	snake_case__   )

                   if i + 1 == timesteps.shape[0]:
                          _lowerCAmelCase    :							List[Any]   			=       None
                   else:
                          _lowerCAmelCase    :							str   			=       timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Optional[int]   			=       scheduler.step(
                       snake_case__      ,	snake_case__      ,	snake_case__      ,	prev_timestep=snake_case__      ,	generator=snake_case__   ).prev_sample

                   _lowerCAmelCase    :							Any   			=       pred_prev_sample

            _lowerCAmelCase    :							Union[str, Any]   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							str   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.204_4983   ) < 1E-2
            assert abs(result_mean.item() - 0.336_2038   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            pass





'''simple docstring'''







from collections import Counter
from timeit import timeit






def 			lowercase				(_A = ""   ,						):







       """simple docstring"""







       return sum(c % 2 for c in Counter(input_str.replace(' '   ,						''					).lower()					).values()					) < 2






def 			lowercase				(_A = ""					):







       """simple docstring"""







       if len(_A					) == 0:
              return True
       _lowerCAmelCase    :							Union[str, Any]   			=       input_str.replace(' '   ,						''					).lower()
       # character_freq_dict: Stores the frequency of every character in the input string
       _lowerCAmelCase    :							dict[str, int]   			=       {}

       for character in lower_case_input_str:
              _lowerCAmelCase    :							Union[str, Any]   			=       character_freq_dict.get(_A   ,						0					) + 1
       _lowerCAmelCase    :							List[Any]   			=       0

       for character_count in character_freq_dict.values():
              if character_count % 2:
                     odd_char += 1
       if odd_char > 1:
              return False
       return True






def 			lowercase				(_A = ""					):







       """simple docstring"""







       print('\nFor string = '   ,						_A   ,						':'					)
       print(
           '> can_string_be_rearranged_as_palindrome_counter()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome_counter(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)
       print(
           '> can_string_be_rearranged_as_palindrome()'   ,						'\tans ='   ,						can_string_be_rearranged_as_palindrome(_A					)   ,						'\ttime ='   ,						timeit(
               'z.can_string_be_rearranged_as_palindrome(z.check_str)'   ,						setup='import __main__ as z'   ,						)   ,						'seconds'   ,						)


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						input(
           """Enter string to determine if it can be rearranged as a palindrome or not: """
       ).strip()
       benchmark(check_str)
       lowerCAmelCase    :     Optional[Any]					=						can_string_be_rearranged_as_palindrome_counter(check_str)
       print(F'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')

