






'''simple docstring'''



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =(EulerDiscreteScheduler,)
      __a	       =10




      def 			__UpperCamelCase				(	self			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     {
             'num_train_timesteps': 1100,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**lowerCamelCase   )
         return config




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			, [0.0002, 0.002, 0.02]   ):
            self.check_over_configs(beta_start=lowerCamelCase			, beta_end=lowerCamelCase   )




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



         '''simple docstring'''
         for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowerCamelCase   )




      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     self.scheduler_classes[0]
         __a					  =     self.get_scheduler_config()
         __a					  =     scheduler_class(**lowerCamelCase   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __a					  =     torch.manual_seed(0   )

         __a					  =     self.dummy_model()
         __a					  =     self.dummy_sample_deter * scheduler.init_noise_sigma
         __a					  =     sample.to(lowerCamelCase   )

         for i, t in enumerate(scheduler.timesteps   ):
            __a					  =     scheduler.scale_model_input(lowerCamelCase			, lowerCamelCase   )

            __a					  =     model(lowerCamelCase			, lowerCamelCase   )

            __a					  =     scheduler.step(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, generator=lowerCamelCase   )
            __a					  =     output.prev_sample

         __a					  =     torch.sum(torch.abs(lowerCamelCase   )   )
         __a					  =     torch.mean(torch.abs(lowerCamelCase   )   )

         assert abs(result_sum.item() - 10.0807   ) < 1e-2
         assert abs(result_mean.item() - 0.0131   ) < 1e-3




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



         '''simple docstring'''
         __a					  =     self.scheduler_classes[0]
         __a					  =     self.get_scheduler_config(prediction_type='v_prediction'   )
         __a					  =     scheduler_class(**lowerCamelCase   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __a					  =     torch.manual_seed(0   )

         __a					  =     self.dummy_model()
         __a					  =     self.dummy_sample_deter * scheduler.init_noise_sigma
         __a					  =     sample.to(lowerCamelCase   )

         for i, t in enumerate(scheduler.timesteps   ):
            __a					  =     scheduler.scale_model_input(lowerCamelCase			, lowerCamelCase   )

            __a					  =     model(lowerCamelCase			, lowerCamelCase   )

            __a					  =     scheduler.step(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, generator=lowerCamelCase   )
            __a					  =     output.prev_sample

         __a					  =     torch.sum(torch.abs(lowerCamelCase   )   )
         __a					  =     torch.mean(torch.abs(lowerCamelCase   )   )

         assert abs(result_sum.item() - 0.0002   ) < 1e-2
         assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3




      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         __a					  =     self.scheduler_classes[0]
         __a					  =     self.get_scheduler_config()
         __a					  =     scheduler_class(**lowerCamelCase   )

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

         __a					  =     torch.manual_seed(0   )

         __a					  =     self.dummy_model()
         __a					  =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __a					  =     sample.to(lowerCamelCase   )

         for t in scheduler.timesteps:
            __a					  =     scheduler.scale_model_input(lowerCamelCase			, lowerCamelCase   )

            __a					  =     model(lowerCamelCase			, lowerCamelCase   )

            __a					  =     scheduler.step(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, generator=lowerCamelCase   )
            __a					  =     output.prev_sample

         __a					  =     torch.sum(torch.abs(lowerCamelCase   )   )
         __a					  =     torch.mean(torch.abs(lowerCamelCase   )   )

         assert abs(result_sum.item() - 10.0807   ) < 1e-2
         assert abs(result_mean.item() - 0.0131   ) < 1e-3






      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         __a					  =     self.scheduler_classes[0]
         __a					  =     self.get_scheduler_config()
         __a					  =     scheduler_class(**lowerCamelCase			, use_karras_sigmas=lowerCamelCase   )

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

         __a					  =     torch.manual_seed(0   )

         __a					  =     self.dummy_model()
         __a					  =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __a					  =     sample.to(lowerCamelCase   )

         for t in scheduler.timesteps:
            __a					  =     scheduler.scale_model_input(lowerCamelCase			, lowerCamelCase   )

            __a					  =     model(lowerCamelCase			, lowerCamelCase   )

            __a					  =     scheduler.step(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, generator=lowerCamelCase   )
            __a					  =     output.prev_sample

         __a					  =     torch.sum(torch.abs(lowerCamelCase   )   )
         __a					  =     torch.mean(torch.abs(lowerCamelCase   )   )

         assert abs(result_sum.item() - 124.52299499511719   ) < 1e-2
         assert abs(result_mean.item() - 0.16213932633399963   ) < 1e-3







'''simple docstring'''



import os



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = "matrix.txt"     )	->      int:

   """simple docstring"""
   with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__     ),       SCREAMING_SNAKE_CASE__     )     ) as in_file:
      __a					  =     in_file.read()

   __a					  =     [[int(SCREAMING_SNAKE_CASE__     ) for cell in row.split(','     )] for row in data.strip().splitlines()]
   __a					  =     [[0 for cell in row] for row in grid]
   __a					  =     len(grid[0]     )

   __a					  =     [[0 for i in range(SCREAMING_SNAKE_CASE__     )] for j in range(SCREAMING_SNAKE_CASE__     )]
   __a					  =     grid[0][0]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[0][i] + dp[0][i - 1]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[i][0] + dp[i - 1][0]

   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      for j in range(1,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     grid[i][j] + min(dp[i - 1][j],       dp[i][j - 1]     )

   return dp[-1][-1]


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