



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					,      __a     ):






           '''simple docstring'''



           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
                 lowerCamelCase             =		deepcopy(__a     )
           elif os.path.exists(__a     ):
                 with io.open(__a					,      "r"					,      encoding="utf-8"     ) as f:
                       lowerCamelCase             =		json.load(__a     )
           else:
                 try:
                       lowerCamelCase             =		baseaa.urlsafe_baadecode(__a     ).decode("utf-8"     )
                       lowerCamelCase             =		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}"""     )

           lowerCamelCase             =		config

           self.set_stage_and_offload()







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_value("zero_optimization.stage"					,      -1     )

           # offload
           lowerCamelCase             =		False
           if self.is_zeroa() or self.is_zeroa():
                 lowerCamelCase             =		set(["cpu", "nvme"]     )
                 lowerCamelCase             =		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:
                       lowerCamelCase             =		True







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.config

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

           return config, ds_key







     def     _a   (self					,      __a					,      __a=None     ):






           '''simple docstring'''



           lowerCamelCase      ,				lowerCamelCase             =		self.find_config_node(__a     )
           if config is None:
                 return default
           return config.get(__a					,      __a     )







     def     _a   (self					,      __a					,      __a=False     ):






           '''simple docstring'''



           lowerCamelCase             =		self.config

           # find the config node of interest if it exists
           lowerCamelCase             =		ds_key_long.split("."     )
           for node in nodes:
                 lowerCamelCase             =		config
                 lowerCamelCase             =		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     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_value(__a     )
           return False if value is None else bool(__a     )







     def     _a   (self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_value(__a     )
           return False if value is None else not bool(__a     )







     def     _a   (self     ):






           '''simple docstring'''



           return self._stage == 2







     def     _a   (self     ):






           '''simple docstring'''



           return self._stage == 3







     def     _a   (self     ):






           '''simple docstring'''



           return self._offload





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           lowerCamelCase             =		engine







     def     _a   (self					,      __a					,      **__a     ):






           '''simple docstring'''



           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  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a     ):






           '''simple docstring'''



           super().__init__(__a					,      device_placement=__a					,      scaler=__a     )
           lowerCamelCase             =		hasattr(self.optimizer					,      "overflow"     )







     def     _a   (self					,      __a=None     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



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





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a     ):






           '''simple docstring'''



           super().__init__(__a					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



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





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=0.001					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		params
           lowerCamelCase             =		lr
           lowerCamelCase             =		weight_decay
           lowerCamelCase             =		kwargs





class  lowerCamelCase__	:
     """simple docstring"""



     def __init__(self					,      __a					,      __a=None					,      __a=0					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		optimizer
           lowerCamelCase             =		total_num_steps
           lowerCamelCase             =		warmup_num_steps
           lowerCamelCase             =		kwargs




from __future__ import annotations

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		2
      lowerCamelCase             =		[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(UpperCAmelCase__			)
      if n > 1:
            factors.append(UpperCAmelCase__			)
      return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()