


'''simple docstring'''

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Any       =				(UnCLIPScheduler,)



      def  UpperCamelCase__						(self       :       int  ,		**UpperCamelCase       :       Any					):

             '''simple docstring'''







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

             config.update(**UpperCamelCase					)
             return config



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Any					):

             '''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=UpperCamelCase  ,		prev_timestep=UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.scheduler_classes[0]
             lowercase__      =					self.get_scheduler_config(variance_type='''fixed_small_log'''					)
             lowercase__      =					scheduler_class(**UpperCamelCase					)

             assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.00_00E-10					)					) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487					) - 0.0_54_96_25					)					) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999					) - 0.9_99_49_87					)					) < 1E-5



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.scheduler_classes[0]
             lowercase__      =					self.get_scheduler_config(variance_type='''learned_range'''					)
             lowercase__      =					scheduler_class(**UpperCamelCase					)

             lowercase__      =					0.5

             assert scheduler._get_variance(1  ,		predicted_variance=UpperCamelCase					) - -10.1_71_27_90 < 1E-5
             assert scheduler._get_variance(487  ,		predicted_variance=UpperCamelCase					) - -5.7_99_80_52 < 1E-5
             assert scheduler._get_variance(999  ,		predicted_variance=UpperCamelCase					) - -0.0_01_00_11 < 1E-5



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.scheduler_classes[0]
             lowercase__      =					self.get_scheduler_config()
             lowercase__      =					scheduler_class(**UpperCamelCase					)

             lowercase__      =					scheduler.timesteps

             lowercase__      =					self.dummy_model()
             lowercase__      =					self.dummy_sample_deter
             lowercase__      =					torch.manual_seed(0					)

             for i, t in enumerate(UpperCamelCase					):
                    # 1. predict noise residual
                    lowercase__      =					model(UpperCamelCase  ,		UpperCamelCase					)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__      =					scheduler.step(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase  ,		generator=UpperCamelCase					).prev_sample

                    lowercase__      =					pred_prev_sample

             lowercase__      =					torch.sum(torch.abs(UpperCamelCase					)					)
             lowercase__      =					torch.mean(torch.abs(UpperCamelCase					)					)

             assert abs(result_sum.item() - 2_52.2_68_24_95					) < 1E-2
             assert abs(result_mean.item() - 0.3_28_47_43					) < 1E-3



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					self.scheduler_classes[0]
             lowercase__      =					self.get_scheduler_config()
             lowercase__      =					scheduler_class(**UpperCamelCase					)

             scheduler.set_timesteps(25					)

             lowercase__      =					scheduler.timesteps

             lowercase__      =					self.dummy_model()
             lowercase__      =					self.dummy_sample_deter
             lowercase__      =					torch.manual_seed(0					)

             for i, t in enumerate(UpperCamelCase					):
                    # 1. predict noise residual
                    lowercase__      =					model(UpperCamelCase  ,		UpperCamelCase					)

                    if i + 1 == timesteps.shape[0]:
                           lowercase__      =					None
                    else:
                           lowercase__      =					timesteps[i + 1]

                    # 2. predict previous mean of sample x_t-1
                    lowercase__      =					scheduler.step(
                        UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase  ,		prev_timestep=UpperCamelCase  ,		generator=UpperCamelCase					).prev_sample

                    lowercase__      =					pred_prev_sample

             lowercase__      =					torch.sum(torch.abs(UpperCamelCase					)					)
             lowercase__      =					torch.mean(torch.abs(UpperCamelCase					)					)

             assert abs(result_sum.item() - 2_58.2_04_49_83					) < 1E-2
             assert abs(result_mean.item() - 0.3_36_20_38					) < 1E-3



      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             pass






      def  UpperCamelCase__						(self       :       List[str]					):

             '''simple docstring'''







             pass








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       lowercase__      =					[int(A			) for i in ip_va_address.split('''.'''			) if i.isdigit()]
       return len(A			) == 4 and all(0 <= int(A			) <= 254 for octet in octets			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[Any]    		=      input().strip()
 lowerCamelCase				:  Union[str, Any]    		=      'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




