code: 
  
  
  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 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase							,     _UpperCAmelCase				)    ->  List[str]:
  						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 					_UpperCamelCase (      torch.optim.Optimizer	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Tuple=True		,				SCREAMING_SNAKE_CASE_  :				Tuple=None		):
  													_a     =			optimizer
  													_a     =			scaler
  													_a     =			AcceleratorState()
  													_a     =			GradientState()
  													_a     =			device_placement
  													_a     =			False
  													_a     =			None
  
  													# Handle device placement
  													if device_placement:
  																			_a     =			self.optimizer.state_dict()
  																			if self.accelerator_state.distributed_type == DistributedType.TPU:
  																									xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_		,				self.accelerator_state.device		)
  																			else:
  																									_a     =			move_to_device(SCREAMING_SNAKE_CASE_		,				self.accelerator_state.device		)
  																			self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
  													return self.optimizer.state
  
  
  
  
  
  
  
  							@state.setter
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		,				SCREAMING_SNAKE_CASE_  :				List[str]		):
  													_a     =			state
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													return self.optimizer.param_groups
  
  
  
  
  
  
  
  							@param_groups.setter
  							def 		_UpperCAmelCase	(		self  :				int		,				SCREAMING_SNAKE_CASE_  :				List[Any]		):
  													_a     =			param_groups
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Dict		):
  													return self.optimizer.defaults
  
  
  
  
  
  
  
  							@defaults.setter
  							def 		_UpperCAmelCase	(		self  :				Tuple		,				SCREAMING_SNAKE_CASE_  :				Tuple		):
  													_a     =			defaults
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Optional[Any]		):
  													self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		):
  													if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
  																			xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_		,				self.accelerator_state.device		)
  													self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		):
  													return self.optimizer.state_dict()
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				str=None		):
  													if self.gradient_state.sync_gradients:
  																			_a     =			'set_to_none' in inspect.signature(self.optimizer.zero_grad		).parameters
  																			if accept_arg:
  																									if set_to_none is None:
  																															_a     =			False
  																									self.optimizer.zero_grad(set_to_none=SCREAMING_SNAKE_CASE_		)
  																			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  :				int		,				SCREAMING_SNAKE_CASE_  :				List[Any]=None		):
  													if self.gradient_state.sync_gradients:
  																			if self.accelerator_state.distributed_type == DistributedType.TPU:
  																									_a     =			{'closure': closure} if closure is not None else {}
  																									xm.optimizer_step(self.optimizer		,				optimizer_args=SCREAMING_SNAKE_CASE_		)
  																			elif self.scaler is not None:
  																									_a     =			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
  																															_a     =			self.scaler.get_scale()
  																															_a     =			True
  																									self.scaler.step(self.optimizer		,				SCREAMING_SNAKE_CASE_		)
  																									self.scaler.update()
  																									_a     =			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.
  																															_a     =			scale_after < self._last_scale
  																									_a     =			scale_after
  
  																			else:
  																									self.optimizer.step(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		):
  													for param_group in self.optimizer.param_groups:
  																			_a     =			[parameters_map.get(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) for p in param_group['params']]
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Dict		):
  													warnings.warn(
  													    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
  													    '`optimizer.step_was_skipped` instead.'		,				SCREAMING_SNAKE_CASE_		,				)
  													return self._is_overflow
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Tuple		):
  													return self._is_overflow
  
  
  
  
  
  
  
  							def __getstate__(		self  :				Dict		):
  													return self.__dict__.copy()
  
  
  
  
  
  
  
  							def __setstate__(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Any		):
  													self.__dict__.update(SCREAMING_SNAKE_CASE_		)
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase_								=    {
      'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
      'tokenization_codegen': ['CodeGenTokenizer'],
  }
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    ['CodeGenTokenizerFast']
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		lowercase_								=    [
  		    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
  		    'CodeGenForCausalLM',
  		    'CodeGenModel',
  		    'CodeGenPreTrainedModel',
  		]
  
  if TYPE_CHECKING:
  		from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
  		from .tokenization_codegen import CodeGenTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_codegen_fast import CodeGenTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_codegen import (
  				    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    CodeGenForCausalLM,
  				    CodeGenModel,
  				    CodeGenPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		lowercase_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  