



'''simple docstring'''




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  __lowerCamelCase    (   _lowercase      ,							_lowercase						) ->     Tuple:

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


class      UpperCamelCase_					(				torch.optim.Optimizer     ):





	def __init__(    self						,						A						,						A=True						,						A=None       )     ->     Dict:
								UpperCAmelCase	:						str					=						optimizer
								UpperCAmelCase	:						List[Any]					=						scaler
								UpperCAmelCase	:						Dict					=						AcceleratorState()
								UpperCAmelCase	:						int					=						GradientState()
								UpperCAmelCase	:						Any					=						device_placement
								UpperCAmelCase	:						Union[str, Any]					=						False
								UpperCAmelCase	:						Optional[Any]					=						None

								# Handle device placement
								if device_placement:
															UpperCAmelCase	:						Tuple					=						self.optimizer.state_dict()
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																						xm.send_cpu_data_to_device(lowerCAmelCase__						,						self.accelerator_state.device       )
															else:
																						UpperCAmelCase	:						Optional[int]					=						move_to_device(lowerCAmelCase__						,						self.accelerator_state.device       )
															self.optimizer.load_state_dict(lowerCAmelCase__       )





	@property
	def 					_lowercase(    self       )     ->     List[Any]:
								return self.optimizer.state





	@state.setter
	def 					_lowercase(    self						,						A       )     ->     Optional[int]:
								UpperCAmelCase	:						str					=						state





	@property
	def 					_lowercase(    self       )     ->     str:
								return self.optimizer.param_groups





	@param_groups.setter
	def 					_lowercase(    self						,						A       )     ->     Union[str, Any]:
								UpperCAmelCase	:						Tuple					=						param_groups





	@property
	def 					_lowercase(    self       )     ->     List[Any]:
								return self.optimizer.defaults





	@defaults.setter
	def 					_lowercase(    self						,						A       )     ->     Dict:
								UpperCAmelCase	:						Any					=						defaults





	def 					_lowercase(    self						,						A       )     ->     Tuple:
								self.optimizer.add_param_group(lowerCAmelCase__       )





	def 					_lowercase(    self						,						A       )     ->     List[Any]:
								if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
															xm.send_cpu_data_to_device(lowerCAmelCase__						,						self.accelerator_state.device       )
								self.optimizer.load_state_dict(lowerCAmelCase__       )





	def 					_lowercase(    self       )     ->     Optional[Any]:
								return self.optimizer.state_dict()





	def 					_lowercase(    self						,						A=None       )     ->     str:
								if self.gradient_state.sync_gradients:
															UpperCAmelCase	:						List[Any]					=						"""set_to_none""" in inspect.signature(self.optimizer.zero_grad       ).parameters
															if accept_arg:
																						if set_to_none is None:
																													UpperCAmelCase	:						Dict					=						False
																						self.optimizer.zero_grad(set_to_none=lowerCAmelCase__       )
															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 					_lowercase(    self						,						A=None       )     ->     Any:
								if self.gradient_state.sync_gradients:
															if self.accelerator_state.distributed_type == DistributedType.TPU:
																						UpperCAmelCase	:						List[str]					=						{"""closure""": closure} if closure is not None else {}
																						xm.optimizer_step(self.optimizer						,						optimizer_args=lowerCAmelCase__       )
															elif self.scaler is not None:
																						UpperCAmelCase	:						Optional[Any]					=						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	:						int					=						self.scaler.get_scale()
																													UpperCAmelCase	:						Any					=						True
																						self.scaler.step(self.optimizer						,						lowerCAmelCase__       )
																						self.scaler.update()
																						UpperCAmelCase	:						List[Any]					=						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	:						Dict					=						scale_after < self._last_scale
																						UpperCAmelCase	:						Dict					=						scale_after

															else:
																						self.optimizer.step(lowerCAmelCase__       )





	def 					_lowercase(    self						,						A       )     ->     List[str]:
								for param_group in self.optimizer.param_groups:
															UpperCAmelCase	:						str					=						[parameters_map.get(lowerCAmelCase__						,						lowerCAmelCase__       ) for p in param_group["""params"""]]





	@property
	def 					_lowercase(    self       )     ->     Optional[int]:
								warnings.warn(
								    """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
								    """`optimizer.step_was_skipped` instead."""						,						lowerCAmelCase__						,						)
								return self._is_overflow





	@property
	def 					_lowercase(    self       )     ->     str:
								return self._is_overflow





	def __getstate__(    self       )     ->     Optional[Any]:
								return self.__dict__.copy()






	def __setstate__(    self						,						A       )     ->     Union[str, Any]:
								self.__dict__.update(lowerCAmelCase__       )









from collections import namedtuple

__UpperCAmelCase      =				namedtuple("from_to", "from_ to")

__UpperCAmelCase      =				{
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__UpperCAmelCase      =				{
    "mm": from_to(0.001, 1_0_0_0),
    "cm": from_to(0.01, 1_0_0),
    "m": from_to(1, 1),
    "km": from_to(1_0_0_0, 0.001),
    "in": from_to(0.0_254, 39.3_701),
    "ft": from_to(0.3_048, 3.28_084),
    "yd": from_to(0.9_144, 1.09_361),
    "mi": from_to(1_609.34, 0.000_621_371),
}





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		from_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		to_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 if new_from not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'from_type\' value: {from_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 if new_to not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'to_type\' value: {to_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()




