






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		,		_UpperCAmelCase )			-> Union[str, Any]:
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        # 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(_UpperCAmelCase )
     elif os.path.exists(_UpperCAmelCase ):
        with io.open(_UpperCAmelCase		,		'r'		,		encoding='utf-8' ) as f:
           UpperCamelCase_									=     json.load(_UpperCAmelCase )
     else:
        try:
           UpperCamelCase_									=     baseaa.urlsafe_baadecode(_UpperCAmelCase ).decode('utf-8' )
           UpperCamelCase_									=     json.loads(_UpperCAmelCase )
        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 )			-> Any:
     # 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.
     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		,		_UpperCAmelCase )			-> List[str]:
     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(_UpperCAmelCase )
        if config is None:
           return None, ds_key

     return config, ds_key




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> str:
     UpperCamelCase_		,      UpperCamelCase_									=     self.find_config_node(_UpperCAmelCase )
     if config is None:
        return default
     return config.get(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> Dict:
     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(_UpperCAmelCase )
        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(_UpperCAmelCase )




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




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     self.get_value(_UpperCAmelCase )
     return False if value is None else not bool(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> str:
     return self._stage == 2




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     return self._stage == 3




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     return self._offload




class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> Dict:
     UpperCamelCase_									=     engine




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> str:
     # runs backpropagation and handles mixed precision
     self.engine.backward(_UpperCAmelCase		,		**_UpperCAmelCase )

     # 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__    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> Any:
     super().__init__(_UpperCAmelCase		,		device_placement=_UpperCAmelCase		,		scaler=_UpperCAmelCase )
     UpperCamelCase_									=     hasattr(self.optimizer		,		'overflow' )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase=None )			-> List[Any]:
     pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




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




  @property
  def    _UpperCAmelCase     (     self )			-> Tuple:
     if self.__has_overflow__:
        return self.optimizer.overflow
     return False




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Dict:
     super().__init__(_UpperCAmelCase		,		_UpperCAmelCase )




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




class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=0.0_0_1		,		_UpperCAmelCase=0		,		**_UpperCAmelCase )			-> Optional[Any]:
     UpperCamelCase_									=     params
     UpperCamelCase_									=     lr
     UpperCamelCase_									=     weight_decay
     UpperCamelCase_									=     kwargs




class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None		,		_UpperCAmelCase=0		,		**_UpperCAmelCase )			-> int:
     UpperCamelCase_									=     optimizer
     UpperCamelCase_									=     total_num_steps
     UpperCamelCase_									=     warmup_num_steps
     UpperCamelCase_									=     kwargs













import os
from math import logaa






def     _snake_case    (__lowercase = "base_exp.txt"):
   UpperCamelCase_									=     0
   UpperCamelCase_									=     0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__lowercase)    ,      __lowercase))):
      UpperCamelCase_		,      UpperCamelCase_									=     list(map(__lowercase    ,      line.split(',')))
      if x * logaa(__lowercase) > largest:
         UpperCamelCase_									=     x * logaa(__lowercase)
         UpperCamelCase_									=     i + 1
   return result


if __name__ == "__main__":
 print(solution())





