
"""simple docstring"""




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class     lowercase(_lowercase       ):
     __snake_case:	List[Any]						=     (UniPCMultistepScheduler,)
     __snake_case:	int						=     (('num_inference_steps', 25),)



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			{
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
             'solver_type': 'bh2',
         }

         config.update(**__SCREAMING_SNAKE_CASE   )
         return config



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0     ,				**__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			dict(self.forward_default_kwargs   )
         a__		=			kwargs.pop('num_inference_steps'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.dummy_sample
         a__		=			0.1 * sample
         a__		=			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
             a__		=			self.get_scheduler_config(**__SCREAMING_SNAKE_CASE   )
             a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )
             # copy over dummy past residuals
             a__		=			dummy_past_residuals[: scheduler.config.solver_order]

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

             a__	,     a__		=			sample, sample
             for t in range(__SCREAMING_SNAKE_CASE     ,				time_step + scheduler.config.solver_order + 1   ):
                 a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample
                 a__		=			new_scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample

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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=0     ,				**__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			dict(self.forward_default_kwargs   )
         a__		=			kwargs.pop('num_inference_steps'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.dummy_sample
         a__		=			0.1 * sample
         a__		=			[residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
             a__		=			self.get_scheduler_config()
             a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )
             scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )

             # copy over dummy past residuals (must be after setting timesteps)
             a__		=			dummy_past_residuals[: scheduler.config.solver_order]

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(__SCREAMING_SNAKE_CASE   )
                 a__		=			scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE   )
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )

                 # copy over dummy past residual (must be after setting timesteps)
                 a__		=			dummy_past_residuals[: new_scheduler.config.solver_order]

             a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample
             a__		=			new_scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample

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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=None     ,				**__SCREAMING_SNAKE_CASE   )		->     List[str]:







         """simple docstring"""



         if scheduler is None:
             a__		=			self.scheduler_classes[0]
             a__		=			self.get_scheduler_config(**__SCREAMING_SNAKE_CASE   )
             a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config(**__SCREAMING_SNAKE_CASE   )
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			1_0
         a__		=			self.dummy_model()
         a__		=			self.dummy_sample_deter
         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )

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

         return sample



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         a__		=			dict(self.forward_default_kwargs   )

         a__		=			kwargs.pop('num_inference_steps'     ,				__SCREAMING_SNAKE_CASE   )

         for scheduler_class in self.scheduler_classes:
             a__		=			self.get_scheduler_config()
             a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

             a__		=			self.dummy_sample
             a__		=			0.1 * sample

             if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE     ,				'set_timesteps'   ):
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )
             elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE     ,				'set_timesteps'   ):
                 a__		=			num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             a__		=			[residual + 0.2, residual + 0.15, residual + 0.10]
             a__		=			dummy_past_residuals[: scheduler.config.solver_order]

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

             a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample
             a__		=			scheduler.step(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   ).prev_sample

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



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         a__		=			UniPCMultistepScheduler(**self.get_scheduler_config()   )
         a__		=			self.full_loop(scheduler=__SCREAMING_SNAKE_CASE   )
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_mean.item() - 0.24_64   ) < 1e-3

         a__		=			DPMSolverSinglestepScheduler.from_config(scheduler.config   )
         a__		=			DEISMultistepScheduler.from_config(scheduler.config   )
         a__		=			DPMSolverMultistepScheduler.from_config(scheduler.config   )
         a__		=			UniPCMultistepScheduler.from_config(scheduler.config   )

         a__		=			self.full_loop(scheduler=__SCREAMING_SNAKE_CASE   )
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_mean.item() - 0.24_64   ) < 1e-3



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE   )
         for order in [1, 2, 3]:
             for solver_type in ["bh1", "bh2"]:
                 for threshold in [0.5, 1.0, 2.0]:
                     for prediction_type in ["epsilon", "sample"]:
                         self.check_over_configs(
                             thresholding=__SCREAMING_SNAKE_CASE     ,				prediction_type=__SCREAMING_SNAKE_CASE     ,				sample_max_value=__SCREAMING_SNAKE_CASE     ,				solver_order=__SCREAMING_SNAKE_CASE     ,				solver_type=__SCREAMING_SNAKE_CASE     ,				)



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







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         for solver_type in ["bh1", "bh2"]:
             for order in [1, 2, 3]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=__SCREAMING_SNAKE_CASE     ,				solver_type=__SCREAMING_SNAKE_CASE     ,				prediction_type=__SCREAMING_SNAKE_CASE     ,				)
                     a__		=			self.full_loop(
                         solver_order=__SCREAMING_SNAKE_CASE     ,				solver_type=__SCREAMING_SNAKE_CASE     ,				prediction_type=__SCREAMING_SNAKE_CASE     ,				)
                     assert not torch.isnan(__SCREAMING_SNAKE_CASE   ).any(), "Samples have nan numbers"



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







         """simple docstring"""



         self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE   )
         self.check_over_configs(lower_order_final=__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE     ,				time_step=0   )



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







         """simple docstring"""



         a__		=			self.full_loop()
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_mean.item() - 0.24_64   ) < 1e-3



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







         """simple docstring"""



         a__		=			self.full_loop(prediction_type='v_prediction'   )
         a__		=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE   )   )

         assert abs(result_mean.item() - 0.10_14   ) < 1e-3



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







         """simple docstring"""



         a__		=			self.scheduler_classes[0]
         a__		=			self.get_scheduler_config(thresholding=__SCREAMING_SNAKE_CASE     ,				dynamic_thresholding_ratio=0   )
         a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

         a__		=			1_0
         a__		=			self.dummy_model()
         a__		=			self.dummy_sample_deter.half()
         scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   )

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

         assert sample.dtype == torch.floataa




     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         for scheduler_class in self.scheduler_classes:
             a__		=			self.get_scheduler_config(**__SCREAMING_SNAKE_CASE   )
             a__		=			scheduler_class(**__SCREAMING_SNAKE_CASE   )

             scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
             assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps




"""simple docstring"""




from itertools import product



def     __magic_name__		(					UpperCamelCase     :		int							,		UpperCamelCase     :		int     )     ->						list[int]:
    a__		=			sides_number
    a__		=			max_face_number * dice_number
    a__		=			[0] * (max_total + 1)

    a__		=			1
    a__		=			range(UpperCamelCase							,		max_face_number + 1     )
    for dice_numbers in product(UpperCamelCase							,		repeat=UpperCamelCase     ):
        a__		=			sum(UpperCamelCase     )
        totals_frequencies[total] += 1

    return totals_frequencies



def     __magic_name__		(					)     ->						float:
    a__		=			total_frequency_distribution(
        sides_number=4							,		dice_number=9     )
    a__		=			total_frequency_distribution(
        sides_number=6							,		dice_number=6     )

    a__		=			0
    a__		=			9
    a__		=			4 * 9
    a__		=			6
    for peter_total in range(UpperCamelCase							,		max_peter_total + 1     ):
        peter_wins_count += peter_totals_frequencies[peter_total] * sum(
            colin_totals_frequencies[min_colin_total:peter_total]     )

    a__		=			(4**9) * (6**6)
    a__		=			peter_wins_count / total_games_number

    a__		=			round(UpperCamelCase							,		ndigits=7     )

    return rounded_peter_win_probability


if __name__ == "__main__":
    print(F'''{solution() = }''')


