

'''simple docstring'''

import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 				_snake_case			(							_UpperCamelCase    ):
   _A							:  str      =       (KDPMaDiscreteScheduler,)
   _A							:  Tuple      =       1_0
   def      __UpperCamelCase    (			self      :   Tuple  ,**SCREAMING_SNAKE_CASE__      :   List[Any]				):
     SCREAMING_SNAKE_CASE:Any       =       {
         "num_train_timesteps": 1_100,
         "beta_start": 0.0_001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**a_				)
     return config
   def      __UpperCamelCase    (			self      :   int				):



     for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=a_				)
   def      __UpperCamelCase    (			self      :   Optional[Any]				):



     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      __UpperCamelCase    (			self      :   Tuple				):



     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=a_				)
   def      __UpperCamelCase    (			self      :   int				):



     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=a_				)
   def      __UpperCamelCase    (			self      :   Any				):
     SCREAMING_SNAKE_CASE:List[Any]       =       self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:Optional[int]       =       self.get_scheduler_config(prediction_type="v_prediction"				)
     SCREAMING_SNAKE_CASE:Dict       =       scheduler_class(**a_				)

     scheduler.set_timesteps(self.num_inference_steps				)

     SCREAMING_SNAKE_CASE:Tuple       =       self.dummy_model()
     SCREAMING_SNAKE_CASE:Dict       =       self.dummy_sample_deter * scheduler.init_noise_sigma
     SCREAMING_SNAKE_CASE:List[Any]       =       sample.to(a_				)

     for i, t in enumerate(scheduler.timesteps				):
       SCREAMING_SNAKE_CASE:Dict       =       scheduler.scale_model_input(a_  ,a_				)

       SCREAMING_SNAKE_CASE:Optional[Any]       =       model(a_  ,a_				)

       SCREAMING_SNAKE_CASE:List[Any]       =       scheduler.step(a_  ,a_  ,a_				)
       SCREAMING_SNAKE_CASE:Union[str, Any]       =       output.prev_sample

     SCREAMING_SNAKE_CASE:Tuple       =       torch.sum(torch.abs(a_				)				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       torch.mean(torch.abs(a_				)				)

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 4.6_934e-07				) < 1e-2
       assert abs(result_mean.item() - 6.1_112e-10				) < 1e-3
     else:
       # CUDA
       assert abs(result_sum.item() - 4.693_428_650_170_972e-07				) < 1e-2
       assert abs(result_mean.item() - 0.0_002				) < 1e-3
   def      __UpperCamelCase    (			self      :   Any				):



     if torch_device == "mps":
       return
     SCREAMING_SNAKE_CASE:List[Any]       =       self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE:str       =       self.get_scheduler_config()
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       scheduler_class(**a_				)

     scheduler.set_timesteps(self.num_inference_steps				)

     SCREAMING_SNAKE_CASE:Optional[int]       =       self.dummy_model()
     SCREAMING_SNAKE_CASE:List[Any]       =       self.dummy_sample_deter * scheduler.init_noise_sigma
     SCREAMING_SNAKE_CASE:Optional[int]       =       sample.to(a_				)

     for i, t in enumerate(scheduler.timesteps				):
       SCREAMING_SNAKE_CASE:Tuple       =       scheduler.scale_model_input(a_  ,a_				)

       SCREAMING_SNAKE_CASE:List[Any]       =       model(a_  ,a_				)

       SCREAMING_SNAKE_CASE:Dict       =       scheduler.step(a_  ,a_  ,a_				)
       SCREAMING_SNAKE_CASE:Optional[int]       =       output.prev_sample

     SCREAMING_SNAKE_CASE:str       =       torch.sum(torch.abs(a_				)				)
     SCREAMING_SNAKE_CASE:Any       =       torch.mean(torch.abs(a_				)				)

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 20.4_125				) < 1e-2
       assert abs(result_mean.item() - 0.0_266				) < 1e-3
     else:
       # CUDA
       assert abs(result_sum.item() - 20.4_125				) < 1e-2
       assert abs(result_mean.item() - 0.0_266				) < 1e-3







   def      __UpperCamelCase    (			self      :   List[Any]				):



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

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

     SCREAMING_SNAKE_CASE:Optional[int]       =       self.dummy_model()
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
       SCREAMING_SNAKE_CASE:int       =       scheduler.scale_model_input(a_  ,a_				)

       SCREAMING_SNAKE_CASE:Union[str, Any]       =       model(a_  ,a_				)

       SCREAMING_SNAKE_CASE:str       =       scheduler.step(a_  ,a_  ,a_				)
       SCREAMING_SNAKE_CASE:Tuple       =       output.prev_sample

     SCREAMING_SNAKE_CASE:Optional[Any]       =       torch.sum(torch.abs(a_				)				)
     SCREAMING_SNAKE_CASE:Union[str, Any]       =       torch.mean(torch.abs(a_				)				)

     if str(a_				).startswith("cpu"				):
       # The following sum varies between 148 and 156 on mps. Why?
       assert abs(result_sum.item() - 20.4_125				) < 1e-2
       assert abs(result_mean.item() - 0.0_266				) < 1e-3
     else:
       # CUDA
       assert abs(result_sum.item() - 20.4_125				) < 1e-2
       assert abs(result_mean.item() - 0.0_266				) < 1e-3













"""simple docstring"""






import os






def      lowercase__ (					)    ->			Optional[Any]:



			'''simple docstring'''
			with open(os.path.dirname(lowerCAmelCase__			) + "/p022_names.txt"			) as file:
						a__  :	Optional[int]		 =			str(file.readlines()[0]			)
						a__  :	Optional[int]		 =			names.replace("\""							,			""			).split(","			)

			names.sort()

			a__  :	int		 =			0
			a__  :	Dict		 =			0

			for i, name in enumerate(lowerCAmelCase__			):
						for letter in name:
									name_score += ord(lowerCAmelCase__			) - 6_4

						total_score += (i + 1) * name_score
						a__  :	Tuple		 =			0
			return total_score


if __name__ == "__main__":
		print(solution())