




def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				str   , lowerCamelCase:				int   , lowerCamelCase:				int   , lowerCamelCase:				int   , lowerCamelCase:				int   , )    ->	tuple[list[list[int]], list[list[str]]]:


      '''simple docstring'''




      lowerCamelCase__					     = list(SCREAMING_SNAKE_CASE_  )
      lowerCamelCase__					     = list(SCREAMING_SNAKE_CASE_  )
      lowerCamelCase__					     = len(SCREAMING_SNAKE_CASE_  )
      lowerCamelCase__					     = len(SCREAMING_SNAKE_CASE_  )

      lowerCamelCase__					     = [
          [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
      ]
      lowerCamelCase__					     = [
          ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
      ]

      for i in range(1   , len_source_seq + 1  ):
            lowerCamelCase__					     = i * delete_cost
            lowerCamelCase__					     = f"""D{source_seq[i - 1]:c}"""

      for i in range(1   , len_destination_seq + 1  ):
            lowerCamelCase__					     = i * insert_cost
            lowerCamelCase__					     = f"""I{destination_seq[i - 1]:c}"""

      for i in range(1   , len_source_seq + 1  ):
            for j in range(1   , len_destination_seq + 1  ):
                  if source_seq[i - 1] == destination_seq[j - 1]:
                        lowerCamelCase__					     = costs[i - 1][j - 1] + copy_cost
                        lowerCamelCase__					     = f"""C{source_seq[i - 1]:c}"""
                  else:
                        lowerCamelCase__					     = costs[i - 1][j - 1] + replace_cost
                        lowerCamelCase__					     = f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]  )

                  if costs[i - 1][j] + delete_cost < costs[i][j]:
                        lowerCamelCase__					     = costs[i - 1][j] + delete_cost
                        lowerCamelCase__					     = f"""D{source_seq[i - 1]:c}"""

                  if costs[i][j - 1] + insert_cost < costs[i][j]:
                        lowerCamelCase__					     = costs[i][j - 1] + insert_cost
                        lowerCamelCase__					     = f"""I{destination_seq[j - 1]:c}"""

      return costs, ops

def 	lowerCAmelCase_	(     lowerCamelCase:				list[list[str]]   , lowerCamelCase:				int   , lowerCamelCase:				int  )    ->	list[str]:


      '''simple docstring'''




      if i == 0 and j == 0:
            return []
      else:
            if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                  lowerCamelCase__					     = assemble_transformation(SCREAMING_SNAKE_CASE_   , i - 1   , j - 1  )
                  seq.append(ops[i][j]  )
                  return seq
            elif ops[i][j][0] == "D":
                  lowerCamelCase__					     = assemble_transformation(SCREAMING_SNAKE_CASE_   , i - 1   , SCREAMING_SNAKE_CASE_  )
                  seq.append(ops[i][j]  )
                  return seq
            else:
                  lowerCamelCase__					     = assemble_transformation(SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_   , j - 1  )
                  seq.append(ops[i][j]  )
                  return seq


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

      __SCREAMING_SNAKE_CASE			:      List[Any]      			=len(operations)
      __SCREAMING_SNAKE_CASE			:      Any      			=len(operations[0])
      __SCREAMING_SNAKE_CASE			:      Dict      			=assemble_transformation(operations, m - 1, n - 1)

      __SCREAMING_SNAKE_CASE			:      Optional[int]      			=list("""Python""")
      __SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=0
      __SCREAMING_SNAKE_CASE			:      str      			=0

      with open("""min_cost.txt""", """w""") as file:
            for op in sequence:
                  print("""""".join(string))

                  if op[0] == "C":
                        file.write("""%-16s""" % """Copy %c""" % op[1])
                        file.write("""\t\t\t""" + """""".join(string))
                        file.write("""\r\n""")

                        cost -= 1
                  elif op[0] == "R":
                        __SCREAMING_SNAKE_CASE			:      Any      			=op[2]

                        file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
                        file.write("""\t\t""" + """""".join(string))
                        file.write("""\r\n""")

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write("""%-16s""" % """Delete %c""" % op[1])
                        file.write("""\t\t\t""" + """""".join(string))
                        file.write("""\r\n""")

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write("""%-16s""" % """Insert %c""" % op[1])
                        file.write("""\t\t\t""" + """""".join(string))
                        file.write("""\r\n""")

                        cost += 2

                  i += 1

            print("""""".join(string))
            print("""Cost: """, cost)

            file.write("""\r\nMinimum cost: """ + str(cost))


'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(HeunDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for timesteps in [10, 50, 100, 1000]:
                self.check_over_configs(num_train_timesteps=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """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=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for schedule in ["linear", "scaled_linear", "exp"]:
                self.check_over_configs(beta_schedule=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for clip_sample_range in [1.0, 2.0, 3.0]:
                self.check_over_configs(clip_sample_range=lowercase__				,     clip_sample=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Dict     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			str     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Dict     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.mean(torch.abs(lowercase__			)			)

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			Any     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			Any     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			List[str]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			int     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			int     =       torch.mean(torch.abs(lowercase__			)			)

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 4.69_34e-07			) < 1e-2
                assert abs(result_mean.item() - 6.11_12e-10			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       scheduler_class(**lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			Any     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			str     =       self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			str     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.mean(torch.abs(lowercase__			)			)

          if str(lowercase__			).startswith("cpu"			):
                # The following sum varies between 148 and 156 on mps. Why?
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          elif str(lowercase__			).startswith("mps"			):
                # Larger tolerance on mps
                assert abs(result_mean.item() - 0.0002			) < 1e-2
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			int     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler_class(**lowercase__				,     use_karras_sigmas=lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Any     =       sample.to(lowercase__			)

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 0.00015			) < 1e-2
          assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07			) < 1e-2

