
"""simple docstring"""




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

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



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""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
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   deepcopy(SCREAMING_SNAKE_CASE__    )
											elif os.path.exists(SCREAMING_SNAKE_CASE__    ):
																		with io.open(SCREAMING_SNAKE_CASE__   ,       """r"""   ,       encoding="""utf-8"""    ) as f:
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   json.load(SCREAMING_SNAKE_CASE__    )
											else:
																		try:
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE__    ).decode("""utf-8"""    )
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   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}'''    )

											SCREAMING_SNAKE_CASE__						:       int           =   config

											self.set_stage_and_offload()



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.get_value("""zero_optimization.stage"""   ,       -1    )

											# offload
											SCREAMING_SNAKE_CASE__						:       Any           =   False
											if self.is_zeroa() or self.is_zeroa():
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   set(["""cpu""", """nvme"""]    )
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   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:
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   True



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.config

											# find the config node of interest if it exists
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ds_key_long.split("""."""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   nodes.pop()
											for node in nodes:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   config.get(SCREAMING_SNAKE_CASE__    )
																		if config is None:
																									return None, ds_key

											return config, ds_key



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[int]           =   self.find_config_node(SCREAMING_SNAKE_CASE__    )
											if config is None:
																		return default
											return config.get(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.config

											# find the config node of interest if it exists
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ds_key_long.split("""."""    )
											for node in nodes:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   config
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.get_value(SCREAMING_SNAKE_CASE__    )
											return False if value is None else bool(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.get_value(SCREAMING_SNAKE_CASE__    )
											return False if value is None else not bool(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											return self._stage == 2



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											return self._stage == 3



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											return self._offload






class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   engine



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )					->		Any:



											"""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 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



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



											"""simple docstring"""




											super().__init__(SCREAMING_SNAKE_CASE__   ,       device_placement=SCREAMING_SNAKE_CASE__   ,       scaler=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       int           =   hasattr(self.optimizer   ,       """overflow"""    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__=None    )					->		Optional[Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




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



				@property
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




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






class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											super().__init__(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




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






class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0.001   ,       SCREAMING_SNAKE_CASE__=0   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   params
											SCREAMING_SNAKE_CASE__						:       Any           =   lr
											SCREAMING_SNAKE_CASE__						:       Dict           =   weight_decay
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   kwargs






class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=0   ,       **SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   optimizer
											SCREAMING_SNAKE_CASE__						:       str           =   total_num_steps
											SCREAMING_SNAKE_CASE__						:       Tuple           =   warmup_num_steps
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   kwargs






"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch """
							        """helper utility that will spawn up """
							        """multiple distributed processes"""
							    )						)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""   ,type=_snake_case   ,default=1   ,help="""Number of TPU cores to use (1 or 8)."""						)

							# positional
							parser.add_argument(
							    """training_script"""   ,type=_snake_case   ,help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )   ,)

							# rest from the training program
							parser.add_argument("""training_script_args"""   ,nargs=_snake_case						)

							return parser.parse_args()

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   parse_args()

							# Import training_script as a module.
							SCREAMING_SNAKE_CASE__						:       Dict           =   Path(args.training_script						)
							sys.path.append(str(script_fpath.parent.resolve()						)						)
							SCREAMING_SNAKE_CASE__						:       int           =   script_fpath.stem
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   importlib.import_module(_snake_case						)

							# Patch sys.argv
							SCREAMING_SNAKE_CASE__						:       str           =   [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores						)]
							xmp.spawn(mod._mp_fn   ,args=()   ,nprocs=args.num_cores						)


if __name__ == "__main__":
					main()




