

"""simple docstring"""




import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 		_A							(      __a       ):
      __a             =						(LMSDiscreteScheduler,)
      __a             =						10





      def        _lowerCamelCase	(	self     ,    **SCREAMING_SNAKE_CASE__		) ->       List[Any]:
         lowerCamelCase__						       =      {
             "num_train_timesteps": 1100,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**SCREAMING_SNAKE_CASE__		)
         return config





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Any:
         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,    [0.00_02, 0.0_02, 0.02]		):
            self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__     ,    beta_end=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       List[Any]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Tuple:
         for t in [0, 500, 800]:
            self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__		)





      def        _lowerCamelCase	(	self		) ->       Optional[Any]:
         lowerCamelCase__						       =      self.scheduler_classes[0]
         lowerCamelCase__						       =      self.get_scheduler_config()
         lowerCamelCase__						       =      scheduler_class(**SCREAMING_SNAKE_CASE__		)

         scheduler.set_timesteps(self.num_inference_steps		)

         lowerCamelCase__						       =      self.dummy_model()
         lowerCamelCase__						       =      self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps		):
            lowerCamelCase__						       =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      scheduler.step(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      output.prev_sample

         lowerCamelCase__						       =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						       =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 10_06.3_88		) < 1e-2
         assert abs(result_mean.item() - 1.31		) < 1e-3





      def        _lowerCamelCase	(	self		) ->       Tuple:
         lowerCamelCase__						       =      self.scheduler_classes[0]
         lowerCamelCase__						       =      self.get_scheduler_config(prediction_type="v_prediction"		)
         lowerCamelCase__						       =      scheduler_class(**SCREAMING_SNAKE_CASE__		)

         scheduler.set_timesteps(self.num_inference_steps		)

         lowerCamelCase__						       =      self.dummy_model()
         lowerCamelCase__						       =      self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(scheduler.timesteps		):
            lowerCamelCase__						       =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      scheduler.step(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      output.prev_sample

         lowerCamelCase__						       =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						       =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 0.00_17		) < 1e-2
         assert abs(result_mean.item() - 2.2676e-06		) < 1e-3





      def        _lowerCamelCase	(	self		) ->       Union[str, Any]:
         lowerCamelCase__						       =      self.scheduler_classes[0]
         lowerCamelCase__						       =      self.get_scheduler_config()
         lowerCamelCase__						       =      scheduler_class(**SCREAMING_SNAKE_CASE__		)

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

         lowerCamelCase__						       =      self.dummy_model()
         lowerCamelCase__						       =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         lowerCamelCase__						       =      sample.to(SCREAMING_SNAKE_CASE__		)

         for i, t in enumerate(scheduler.timesteps		):
            lowerCamelCase__						       =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      scheduler.step(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      output.prev_sample

         lowerCamelCase__						       =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						       =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 10_06.3_88		) < 1e-2
         assert abs(result_mean.item() - 1.31		) < 1e-3





      def        _lowerCamelCase	(	self		) ->       str:
         lowerCamelCase__						       =      self.scheduler_classes[0]
         lowerCamelCase__						       =      self.get_scheduler_config()
         lowerCamelCase__						       =      scheduler_class(**SCREAMING_SNAKE_CASE__     ,    use_karras_sigmas=SCREAMING_SNAKE_CASE__		)

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

         lowerCamelCase__						       =      self.dummy_model()
         lowerCamelCase__						       =      self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__		) * scheduler.init_noise_sigma
         lowerCamelCase__						       =      sample.to(SCREAMING_SNAKE_CASE__		)

         for t in scheduler.timesteps:
            lowerCamelCase__						       =      scheduler.scale_model_input(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      model(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						       =      scheduler.step(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						       =      output.prev_sample

         lowerCamelCase__						       =      torch.sum(torch.abs(SCREAMING_SNAKE_CASE__		)		)
         lowerCamelCase__						       =      torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

         assert abs(result_sum.item() - 38_12.99_27		) < 2e-2
         assert abs(result_mean.item() - 4.96_48		) < 1e-3







"""simple docstring"""




from __future__ import annotations



def 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__ )   ->   None:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      m - 1, 0
   while i >= 0 and j < n:
      if key == mat[i][j]:
         print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
         return
      if key < mat[i][j]:
         i -= 1
      else:
         j += 1
   print(f'Key {key} not found' )



def 						UpperCAmelCase__ (					)   ->   None:




   """simple docstring"""
   lowerCamelCase__						       =      [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
   lowerCamelCase__						       =      int(input("Enter the element to be searched:" ) )
   print(A__ )
   search_in_a_sorted_matrix(A__						,     len(A__ )						,     len(mat[0] )						,     A__ )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




