


"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Dict				  =  (KDPMaAncestralDiscreteScheduler,)
							__magic_name__					:     Union[str, Any]				  =  10




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								snake_case__  :						Dict		=      {
								    "num_train_timesteps": 1_100,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								}

								config.update(**lowerCamelCase__)
								return config




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								for timesteps in [10, 50, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								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=lowerCamelCase__	,					beta_end=lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




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




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







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						Tuple		=      self.scheduler_classes[0]
								snake_case__  :						List[Any]		=      self.get_scheduler_config()
								snake_case__  :						Tuple		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps)

								snake_case__  :						Optional[int]		=      torch.manual_seed(0)

								snake_case__  :						Any		=      self.dummy_model()
								snake_case__  :						Tuple		=      self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case__  :						int		=      sample.to(lowerCamelCase__)

								for i, t in enumerate(scheduler.timesteps):
									snake_case__  :						List[str]		=      scheduler.scale_model_input(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						Any		=      model(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						Tuple		=      scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					generator=lowerCamelCase__)
									snake_case__  :						List[str]		=      output.prev_sample

								snake_case__  :						List[Any]		=      torch.sum(torch.abs(lowerCamelCase__))
								snake_case__  :						Tuple		=      torch.mean(torch.abs(lowerCamelCase__))

								assert abs(result_sum.item() - 1_38_49.38_77) < 1E-2
								assert abs(result_mean.item() - 18.03_31) < 5E-3




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







								'''simple docstring'''




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




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







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						int		=      self.scheduler_classes[0]
								snake_case__  :						Optional[int]		=      self.get_scheduler_config(prediction_type="v_prediction")
								snake_case__  :						int		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps)

								snake_case__  :						List[Any]		=      self.dummy_model()
								snake_case__  :						str		=      self.dummy_sample_deter * scheduler.init_noise_sigma
								snake_case__  :						str		=      sample.to(lowerCamelCase__)

								snake_case__  :						Tuple		=      torch.manual_seed(0)

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

									snake_case__  :						int		=      model(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						Union[str, Any]		=      scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					generator=lowerCamelCase__)
									snake_case__  :						List[str]		=      output.prev_sample

								snake_case__  :						Optional[Any]		=      torch.sum(torch.abs(lowerCamelCase__))
								snake_case__  :						List[Any]		=      torch.mean(torch.abs(lowerCamelCase__))

								assert abs(result_sum.item() - 3_28.99_70) < 1E-2
								assert abs(result_mean.item() - 0.42_84) < 1E-3







							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								if torch_device == "mps":
									return
								snake_case__  :						str		=      self.scheduler_classes[0]
								snake_case__  :						Dict		=      self.get_scheduler_config()
								snake_case__  :						Dict		=      scheduler_class(**lowerCamelCase__)

								scheduler.set_timesteps(self.num_inference_steps	,					device=lowerCamelCase__)
								snake_case__  :						Tuple		=      torch.manual_seed(0)

								snake_case__  :						Tuple		=      self.dummy_model()
								snake_case__  :						int		=      self.dummy_sample_deter.to(lowerCamelCase__) * scheduler.init_noise_sigma

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

									snake_case__  :						int		=      model(lowerCamelCase__	,					lowerCamelCase__)

									snake_case__  :						str		=      scheduler.step(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					generator=lowerCamelCase__)
									snake_case__  :						Dict		=      output.prev_sample

								snake_case__  :						Optional[int]		=      torch.sum(torch.abs(lowerCamelCase__))
								snake_case__  :						Optional[Any]		=      torch.mean(torch.abs(lowerCamelCase__))

								assert abs(result_sum.item() - 1_38_49.38_18) < 1E-1
								assert abs(result_mean.item() - 18.03_31) < 1E-3






def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

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

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       str	      =				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]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

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

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

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       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":
                _lowerCAmelCase        =       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))


