
from __future__ import annotations

from decimal import Decimal

from numpy import array





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				Decimal

    # Check if the provided matrix has 2 rows and 2 columns
    # since this implementation only works for 2x2 matrices
    if len(__lowerCAmelCase							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
        # Calculate the determinant of the matrix
        snake_case__						=				float(
            d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."							)

        # Creates a copy of the matrix with swapped positions of the elements
        snake_case__						=				[[0.0, 0.0], [0.0, 0.0]]
        snake_case__						=				matrix[1][1], matrix[0][0]
        snake_case__						=				-matrix[1][0], -matrix[0][1]

        # Calculate the inverse of the matrix
        return [
            [(float(d(__lowerCAmelCase							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
        ]
    elif (
        len(__lowerCAmelCase							) == 3
        and len(matrix[0]							) == 3
        and len(matrix[1]							) == 3
        and len(matrix[2]							) == 3
    ):
        # Calculate the determinant of the matrix using Sarrus rule
        snake_case__						=				float(
            (
                (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
                + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
                + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
            )
            - (
                (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
                + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
                + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
            )							)
        if determinant == 0:
            raise ValueError("This matrix has no inverse."							)

        # Creating cofactor matrix
        snake_case__						=				[
            [d(0.0							), d(0.0							), d(0.0							)],
            [d(0.0							), d(0.0							), d(0.0							)],
            [d(0.0							), d(0.0							), d(0.0							)],
        ]
        snake_case__						=				(d(matrix[1][1]							) * d(matrix[2][2]							)) - (
            d(matrix[1][2]							) * d(matrix[2][1]							)
        )
        snake_case__						=				-(
            (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
        )
        snake_case__						=				(d(matrix[1][0]							) * d(matrix[2][1]							)) - (
            d(matrix[1][1]							) * d(matrix[2][0]							)
        )
        snake_case__						=				-(
            (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
        )
        snake_case__						=				(d(matrix[0][0]							) * d(matrix[2][2]							)) - (
            d(matrix[0][2]							) * d(matrix[2][0]							)
        )
        snake_case__						=				-(
            (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
        )
        snake_case__						=				(d(matrix[0][1]							) * d(matrix[1][2]							)) - (
            d(matrix[0][2]							) * d(matrix[1][1]							)
        )
        snake_case__						=				-(
            (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
        )
        snake_case__						=				(d(matrix[0][0]							) * d(matrix[1][1]							)) - (
            d(matrix[0][1]							) * d(matrix[1][0]							)
        )

        # Transpose the cofactor matrix (Adjoint matrix)
        snake_case__						=				array(__lowerCAmelCase							)
        for i in range(3							):
            for j in range(3							):
                snake_case__						=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
        snake_case__						=				array(__lowerCAmelCase							)
        for i in range(3							):
            for j in range(3							):
                inverse_matrix[i][j] /= d(__lowerCAmelCase							)

        # Calculate the inverse of the matrix
        return [[float(d(__lowerCAmelCase							)							) or 0.0 for n in row] for row in inverse_matrix]
    raise ValueError("Please provide a matrix of size 2x2 or 3x3."							)





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

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

          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() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          scheduler.set_timesteps(self.num_inference_steps )

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

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

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

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

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

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

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

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

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



