




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					: Optional[int] ,       snake_case					: int				):
          if isinstance(snake_case ,       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(snake_case				)
          elif os.path.exists(snake_case				):
              with io.open(snake_case ,       '''r''' ,       encoding='''utf-8'''				) as f:
                  __UpperCamelCase  		=						json.load(snake_case				)
          else:
              try:
                  __UpperCamelCase  		=						baseaa.urlsafe_baadecode(snake_case				).decode('''utf-8'''				)
                  __UpperCamelCase  		=						json.loads(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       snake_case	(     self					: str				):
          # 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       snake_case	(     self					: Optional[Any] ,       snake_case					: Tuple				):
          __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(snake_case				)
              if config is None:
                  return None, ds_key

          return config, ds_key







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Union[str, Any] ,       snake_case					: Any=None				):
          __UpperCamelCase		,      __UpperCamelCase  		=						self.find_config_node(snake_case				)
          if config is None:
              return default
          return config.get(snake_case ,       snake_case				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: Dict ,       snake_case					: List[Any]=False				):
          __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(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(snake_case				)







      def       snake_case	(     self					: Tuple ,       snake_case					: Union[str, Any]				):
          __UpperCamelCase  		=						self.get_value(snake_case				)
          return False if value is None else bool(snake_case				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Dict				):
          __UpperCamelCase  		=						self.get_value(snake_case				)
          return False if value is None else not bool(snake_case				)







      def       snake_case	(     self					: Optional[int]				):
          return self._stage == 2







      def       snake_case	(     self					: Union[str, Any]				):
          return self._stage == 3







      def       snake_case	(     self					: List[Any]				):
          return self._offload







class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: int ,       snake_case					: Any				):
          __UpperCamelCase  		=						engine







      def       snake_case	(     self					: Any ,       snake_case					: str ,       **snake_case					: Optional[Any]				):
          # runs backpropagation and handles mixed precision
          self.engine.backward(snake_case ,       **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						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Dict ,       snake_case					: int				):
          super().__init__(snake_case ,       device_placement=snake_case ,       scaler=snake_case				)
          __UpperCamelCase  		=						hasattr(self.optimizer ,       '''overflow'''				)







      def       snake_case	(     self					: Dict ,       snake_case					: Optional[int]=None				):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







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







      @property
      def       snake_case	(     self					: Union[str, Any]				):
          if self.__has_overflow__:
              return self.optimizer.overflow
          return False







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: List[str] ,       snake_case					: Tuple ,       snake_case					: Tuple				):
          super().__init__(snake_case ,       snake_case				)







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







class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: str ,       snake_case					: Optional[int] ,       snake_case					: int=0.0_01 ,       snake_case					: int=0 ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						params
          __UpperCamelCase  		=						lr
          __UpperCamelCase  		=						weight_decay
          __UpperCamelCase  		=						kwargs







class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Union[str, Any] ,       snake_case					: Optional[int] ,       snake_case					: List[Any]=None ,       snake_case					: Optional[Any]=0 ,       **snake_case					: Dict				):
          __UpperCamelCase  		=						optimizer
          __UpperCamelCase  		=						total_num_steps
          __UpperCamelCase  		=						warmup_num_steps
          __UpperCamelCase  		=						kwargs












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






