





import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ):
									if isinstance(_A     ,					_A     ):
														# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
														# modified it, it will not be accepted here again, since `auto` values would have been overridden
														__A	:						List[Any]       =				deepcopy(_A     )
									elif os.path.exists(_A     ):
														with io.open(_A     ,					'r'     ,					encoding='utf-8'     ) as f:
																			__A	:						List[str]       =				json.load(_A     )
									else:
														try:
																			__A	:						Union[str, Any]       =				baseaa.urlsafe_baadecode(_A     ).decode('utf-8'     )
																			__A	:						List[str]       =				json.loads(_A     )
														except (UnicodeDecodeError, AttributeError, ValueError):
																			raise ValueError(
																			    F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""     )

									__A	:						Dict       =				config

									self.set_stage_and_offload()



				def 					UpperCAmelCase_		(			self     ):
									# zero stage - this is done as early as possible, before model is created, to allow
									# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
									# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
									__A	:						Tuple       =				self.get_value('zero_optimization.stage'     ,					-1     )

									# offload
									__A	:						Optional[int]       =				False
									if self.is_zeroa() or self.is_zeroa():
														__A	:						Optional[Any]       =				set(['cpu', 'nvme']     )
														__A	:						Dict       =				set(
														    [
														        self.get_value('zero_optimization.offload_optimizer.device'     ),
														        self.get_value('zero_optimization.offload_param.device'     ),
														    ]     )
														if len(offload_devices & offload_devices_valid     ) > 0:
																			__A	:						str       =				True



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Tuple       =				self.config

									# find the config node of interest if it exists
									__A	:						Dict       =				ds_key_long.split('.'     )
									__A	:						int       =				nodes.pop()
									for node in nodes:
														__A	:						str       =				config.get(_A     )
														if config is None:
																			return None, ds_key

									return config, ds_key



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=None     ):
									__A    ,				__A	:						int       =				self.find_config_node(_A     )
									if config is None:
														return default
									return config.get(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A=False     ):
									__A	:						str       =				self.config

									# find the config node of interest if it exists
									__A	:						Tuple       =				ds_key_long.split('.'     )
									for node in nodes:
														__A	:						Optional[int]       =				config
														__A	:						Any       =				config.get(_A     )
														if config is None:
																			if must_exist:
																								raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""     )
																			else:
																								return

        # if found remove it
									if parent_config is not None:
														parent_config.pop(_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Tuple       =				self.get_value(_A     )
									return False if value is None else bool(_A     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Any       =				self.get_value(_A     )
									return False if value is None else not bool(_A     )



				def 					UpperCAmelCase_		(			self     ):
									return self._stage == 2



				def 					UpperCAmelCase_		(			self     ):
									return self._stage == 3





				def 					UpperCAmelCase_		(			self     ):
									return self._offload





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ):
									__A	:						Union[str, Any]       =				engine





				def 					UpperCAmelCase_		(			self     ,					_A     ,					**_A     ):
									# runs backpropagation and handles mixed precision
									self.engine.backward(_A     ,					**_A     )

									# Deepspeed's `engine.step` performs the following operations:
									# - gradient accumulation check
									# - gradient clipping
									# - optimizer step
									# - zero grad
									# - checking overflow
									# - lr_scheduler step (only if engine.lr_scheduler is not None)
									self.engine.step()
									# and this plugin overrides the above calls with no-ops when Accelerate runs under
									# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
									# training loop that works transparently under many training regimes.





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ):
									super().__init__(_A     ,					device_placement=_A     ,					scaler=_A     )
									__A	:						str       =				hasattr(self.optimizer     ,					'overflow'     )



				def 					UpperCAmelCase_		(			self     ,					_A=None     ):
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



				def 					UpperCAmelCase_		(			self     ):
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.__has_overflow__:
														return self.optimizer.overflow
									return False





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ):
									super().__init__(_A     ,					_A     )





				def 					UpperCAmelCase_		(			self     ):
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=0.0_0_1     ,					_A=0     ,					**_A     ):
									__A	:						Dict       =				params
									__A	:						int       =				lr
									__A	:						Optional[int]       =				weight_decay
									__A	:						Dict       =				kwargs





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=None     ,					_A=0     ,					**_A     ):
									__A	:						str       =				optimizer
									__A	:						str       =				total_num_steps
									__A	:						int       =				warmup_num_steps
									__A	:						Union[str, Any]       =				kwargs











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




