


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

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

class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																# 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
																_UpperCAmelCase											=     deepcopy(_SCREAMING_SNAKE_CASE		)
									elif os.path.exists(_SCREAMING_SNAKE_CASE		):
																with io.open(_SCREAMING_SNAKE_CASE     ,       'r'     ,       encoding='utf-8'		) as f:
																							_UpperCAmelCase											=     json.load(_SCREAMING_SNAKE_CASE		)
									else:
																try:
																							_UpperCAmelCase											=     baseaa.urlsafe_baadecode(_SCREAMING_SNAKE_CASE		).decode('utf-8'		)
																							_UpperCAmelCase											=     json.loads(_SCREAMING_SNAKE_CASE		)
																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}'''		)

									_UpperCAmelCase											=     config

									self.set_stage_and_offload()
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_value('zero_optimization.stage'     ,       -1		)

									# offload
									_UpperCAmelCase											=     False
									if self.is_zeroa() or self.is_zeroa():
																_UpperCAmelCase											=     set(['cpu', 'nvme']		)
																_UpperCAmelCase											=     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:
																							_UpperCAmelCase											=     True
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     self.config

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

									return config, ds_key
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=None		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.find_config_node(_SCREAMING_SNAKE_CASE		)
									if config is None:
																return default
									return config.get(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=False		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.config

									# find the config node of interest if it exists
									_UpperCAmelCase											=     ds_key_long.split('.'		)
									for node in nodes:
																_UpperCAmelCase											=     config
																_UpperCAmelCase											=     config.get(_SCREAMING_SNAKE_CASE		)
																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(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_value(_SCREAMING_SNAKE_CASE		)
									return False if value is None else bool(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.get_value(_SCREAMING_SNAKE_CASE		)
									return False if value is None else not bool(_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									return self._stage == 2
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									return self._stage == 3






		def       UpperCAmelCase__							(     self		)					->			Tuple:




									"""simple docstring"""





									return self._offload



class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     engine






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									self.engine.backward(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

									# 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 (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE		)					->			Optional[int]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE     ,       device_placement=_SCREAMING_SNAKE_CASE     ,       scaler=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     hasattr(self.optimizer     ,       'overflow'		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE=None		)					->			Union[str, Any]:




									"""simple docstring"""





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




									"""simple docstring"""





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






		@property
		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





									if self.__has_overflow__:
																return self.optimizer.overflow
									return False



class 						__a (							UpperCAmelCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Union[str, Any]:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			Union[str, Any]:




									"""simple docstring"""





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



class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=0.001     ,       _SCREAMING_SNAKE_CASE=0     ,       **_SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     params
									_UpperCAmelCase											=     lr
									_UpperCAmelCase											=     weight_decay
									_UpperCAmelCase											=     kwargs



class 						__a :
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=0     ,       **_SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									_UpperCAmelCase											=     optimizer
									_UpperCAmelCase											=     total_num_steps
									_UpperCAmelCase											=     warmup_num_steps
									_UpperCAmelCase											=     kwargs




from .imports import is_rich_available


if is_rich_available():
		from rich.traceback import install

		install(show_locals=False)

else:
		raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
