






import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						if isinstance(_UpperCAmelCase					,						(list, tuple)      ):
												return honor_type(_UpperCAmelCase					,						(move_to_device(_UpperCAmelCase					,						_UpperCAmelCase      ) for t in state)      )
						elif isinstance(_UpperCAmelCase					,						_UpperCAmelCase      ):
												return type(_UpperCAmelCase      )({k: move_to_device(_UpperCAmelCase					,						_UpperCAmelCase      ) for k, v in state.items()}      )
						elif isinstance(_UpperCAmelCase					,						torch.Tensor      ):
												return state.to(_UpperCAmelCase      )
						return state





class __lowerCamelCase					( torch.optim.Optimizer):





	"""simple docstring"""






	def __init__(				self			,       UpperCAmelCase			,       UpperCAmelCase=True			,       UpperCAmelCase=None       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     optimizer
							_UpperCAmelCase							   =     scaler
							_UpperCAmelCase							   =     AcceleratorState()
							_UpperCAmelCase							   =     GradientState()
							_UpperCAmelCase							   =     device_placement
							_UpperCAmelCase							   =     False
							_UpperCAmelCase							   =     None

							# Handle device placement
							if device_placement:
													_UpperCAmelCase							   =     self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(UpperCAmelCase_			,       self.accelerator_state.device       )
													else:
																			_UpperCAmelCase							   =     move_to_device(UpperCAmelCase_			,       self.accelerator_state.device       )
													self.optimizer.load_state_dict(UpperCAmelCase_       )

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.optimizer.state

	@state.setter
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     state

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.optimizer.param_groups

	@param_groups.setter
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     param_groups

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.optimizer.defaults

	@defaults.setter
	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     defaults

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							self.optimizer.add_param_group(UpperCAmelCase_       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
													xm.send_cpu_data_to_device(UpperCAmelCase_			,       self.accelerator_state.device       )
							self.optimizer.load_state_dict(UpperCAmelCase_       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.optimizer.state_dict()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase=None       ):
							"""simple docstring"""







							if self.gradient_state.sync_gradients:
													_UpperCAmelCase							   =     "set_to_none" in inspect.signature(self.optimizer.zero_grad       ).parameters
													if accept_arg:
																			if set_to_none is None:
																									_UpperCAmelCase							   =     False
																			self.optimizer.zero_grad(set_to_none=UpperCAmelCase_       )
													else:
																			if set_to_none is not None:
																									raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'       )
																			self.optimizer.zero_grad()

	def 			UpperCamelCase       (				self			,       UpperCAmelCase=None       ):
							"""simple docstring"""







							if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			_UpperCAmelCase							   =     {"closure": closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer			,       optimizer_args=UpperCAmelCase_       )
													elif self.scaler is not None:
																			_UpperCAmelCase							   =     False
																			if self._last_scale is None:
																									# `get_scale` is an async operation requiring full synchronization
																									# on CPU and GPUs before finishing. As a result, we store away
																									# the prior one to reduce the call overhead
																									_UpperCAmelCase							   =     self.scaler.get_scale()
																									_UpperCAmelCase							   =     True
																			self.scaler.step(self.optimizer			,       UpperCAmelCase_       )
																			self.scaler.update()
																			_UpperCAmelCase							   =     self.scaler.get_scale()
																			if not new_scale:
																									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																									_UpperCAmelCase							   =     scale_after < self._last_scale
																			_UpperCAmelCase							   =     scale_after

													else:
																			self.optimizer.step(UpperCAmelCase_       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							for param_group in self.optimizer.param_groups:
													_UpperCAmelCase							   =     [parameters_map.get(UpperCAmelCase_			,       UpperCAmelCase_       ) for p in param_group["params"]]

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							warnings.warn(
							    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
							    '`optimizer.step_was_skipped` instead.'			,       UpperCAmelCase_			,       )
							return self._is_overflow

	@property
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self._is_overflow

	def __getstate__(				self       ):
							"""simple docstring"""







							return self.__dict__.copy()

	def __setstate__(				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							self.__dict__.update(UpperCAmelCase_       )







'''simple docstring'''

def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       ):
		__UpperCAmelCase	:							Optional[int]     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     list(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     len(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     len(_UpperCAmelCase  )

		__UpperCAmelCase	:							int     =     [
		    [0 for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]
		__UpperCAmelCase	:							Any     =     [
		    ["0" for _ in range(len_destination_seq + 1  )] for _ in range(len_source_seq + 1  )
		]

		for i in range(1,       len_source_seq + 1  ):
				__UpperCAmelCase	:							Optional[Any]     =     i * delete_cost
				__UpperCAmelCase	:							Optional[int]     =     F"D{source_seq[i - 1]:c}"

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

						if costs[i - 1][j] + delete_cost < costs[i][j]:
								__UpperCAmelCase	:							Union[str, Any]     =     costs[i - 1][j] + delete_cost
								__UpperCAmelCase	:							int     =     F"D{source_seq[i - 1]:c}"

						if costs[i][j - 1] + insert_cost < costs[i][j]:
								__UpperCAmelCase	:							List[str]     =     costs[i][j - 1] + insert_cost
								__UpperCAmelCase	:							Dict     =     F"I{destination_seq[j - 1]:c}"

		return costs, ops


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase  ):
		if i == 0 and j == 0:
				return []
		else:
				if ops[i][j][0] == "C" or ops[i][j][0] == "R":
						__UpperCAmelCase	:							int     =     assemble_transformation(_UpperCAmelCase,       i - 1,       j - 1  )
						seq.append(ops[i][j]  )
						return seq
				elif ops[i][j][0] == "D":
						__UpperCAmelCase	:							List[Any]     =     assemble_transformation(_UpperCAmelCase,       i - 1,       _UpperCAmelCase  )
						seq.append(ops[i][j]  )
						return seq
				else:
						__UpperCAmelCase	:							Optional[Any]     =     assemble_transformation(_UpperCAmelCase,       _UpperCAmelCase,       j - 1  )
						seq.append(ops[i][j]  )
						return seq


if __name__ == "__main__":
			lowerCAmelCase__	,			lowerCAmelCase__      :   Optional[int]      	=			compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

			lowerCAmelCase__      :   Optional[Any]      	=			len(operations)
			lowerCAmelCase__      :   int      	=			len(operations[0])
			lowerCAmelCase__      :   Dict      	=			assemble_transformation(operations, m - 1, n - 1)

			lowerCAmelCase__      :   str      	=			list("Python")
			lowerCAmelCase__      :   Any      	=			0
			lowerCAmelCase__      :   Dict      	=			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__      :   Optional[int]      	=			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))




