





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")







class    __snake_case	:





							'''simple docstring'''







							def __init__(  self			:   List[Any]							,    A			:   List[str]							,    A			:   Any							,    A			:   bool = True							,    A			:   bool = False		):
									__snake_case:						Dict			  =						scheduler
									__snake_case:						Tuple			  =						optimizers if isinstance(A							,    (list, tuple)		) else [optimizers]
									__snake_case:						str			  =						split_batches
									__snake_case:						str			  =						step_with_optimizer
									__snake_case:						Dict			  =						GradientState()




							def 							UpperCAmelCase__			(  self			:   List[Any]							,    *A			:   Tuple							,    **A			:   int		):
									if not self.step_with_optimizer:
											# No link between scheduler and optimizer -> just step
											self.scheduler.step(*A							,    **A		)
											return

									# Otherwise, first make sure the optimizer was stepped.
									if not self.gradient_state.sync_gradients:
											if self.gradient_state.adjust_scheduler:
													self.scheduler._step_count += 1
											return

									for opt in self.optimizers:
											if opt.step_was_skipped:
													return
									if self.split_batches:
											# Split batches -> the training dataloader batch size is not changed so one step per training step
											self.scheduler.step(*A							,    **A		)
									else:
											# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
											# num_processes steps per training step
											__snake_case:						int			  =						AcceleratorState().num_processes
											for _ in range(A		):
													# Special case when using OneCycle and `drop_last` was not used
													if hasattr(self.scheduler							,    """total_steps"""		):
															if self.scheduler._step_count <= self.scheduler.total_steps:
																	self.scheduler.step(*A							,    **A		)
													else:
															self.scheduler.step(*A							,    **A		)




							def 							UpperCAmelCase__			(  self			:   Tuple		):
									return self.scheduler.get_last_lr()




							def 							UpperCAmelCase__			(  self			:   Optional[Any]		):
									return self.scheduler.state_dict()




							def 							UpperCAmelCase__			(  self			:   Dict							,    A			:   Union[str, Any]		):
									self.scheduler.load_state_dict(A		)




							def 							UpperCAmelCase__			(  self			:   List[Any]		):
									return self.scheduler.get_lr()






							def 							UpperCAmelCase__			(  self			:   List[str]							,    *A			:   Any							,    **A			:   Tuple		):
									return self.scheduler.print_lr(*A							,    **A		)













from __future__ import annotations

from decimal import Decimal

from numpy import array







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   list[list[float]]:
  __snake_case:						Any			  =						Decimal

  # Check if the provided matrix has 2 rows and 2 columns
  # since this implementation only works for 2x2 matrices
  if len(SCREAMING_SNAKE_CASE__) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
    # Calculate the determinant of the matrix
    __snake_case:						Tuple			  =						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:						Optional[int]			  =						[[0.0, 0.0], [0.0, 0.0]]
    __snake_case  ,       __snake_case:						Optional[Any]			  =						matrix[1][1], matrix[0][0]
    __snake_case  ,       __snake_case:						Union[str, Any]			  =						-matrix[1][0], -matrix[0][1]

    # Calculate the inverse of the matrix
    return [
        [(float(d(SCREAMING_SNAKE_CASE__)) / determinant) or 0.0 for n in row] for row in swapped_matrix
    ]
  elif (
      len(SCREAMING_SNAKE_CASE__) == 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:						Any			  =						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:						Tuple			  =						[
        [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:						Dict			  =						(d(matrix[1][1]) * d(matrix[2][2])) - (
        d(matrix[1][2]) * d(matrix[2][1])
    )
    __snake_case:						Tuple			  =						-(
        (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[1][0]) * d(matrix[2][1])) - (
        d(matrix[1][1]) * d(matrix[2][0])
    )
    __snake_case:						Union[str, Any]			  =						-(
        (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
    )
    __snake_case:						str			  =						(d(matrix[0][0]) * d(matrix[2][2])) - (
        d(matrix[0][2]) * d(matrix[2][0])
    )
    __snake_case:						List[Any]			  =						-(
        (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
    )
    __snake_case:						Optional[Any]			  =						(d(matrix[0][1]) * d(matrix[1][2])) - (
        d(matrix[0][2]) * d(matrix[1][1])
    )
    __snake_case:						List[str]			  =						-(
        (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
    )
    __snake_case:						Optional[int]			  =						(d(matrix[0][0]) * d(matrix[1][1])) - (
        d(matrix[0][1]) * d(matrix[1][0])
    )

    # Transpose the cofactor matrix (Adjoint matrix)
    __snake_case:						List[Any]			  =						array(SCREAMING_SNAKE_CASE__)
    for i in range(3):
      for j in range(3):
        __snake_case:						Tuple			  =						cofactor_matrix[j][i]

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

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






