
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    ,  __magic_name__      ):
       if isinstance(__magic_name__    ,  __magic_name__      ):
        # 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		:	Any							=      deepcopy(__magic_name__      )
       elif os.path.exists(__magic_name__      ):
        with io.open(__magic_name__    ,  """r"""    ,  encoding="""utf-8"""      ) as f:
         lowerCamelCase		:	Tuple							=      json.load(__magic_name__      )
       else:
        try:
         lowerCamelCase		:	Tuple							=      baseaa.urlsafe_baadecode(__magic_name__      ).decode("""utf-8"""      )
         lowerCamelCase		:	Optional[int]							=      json.loads(__magic_name__      )
        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		:	Union[str, Any]							=      config

       self.set_stage_and_offload()
      def 						UpperCamelCase__							(      self      ):
       # 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.
       lowerCamelCase		:	str							=      self.get_value("""zero_optimization.stage"""    ,  -1      )

       # offload
       lowerCamelCase		:	Union[str, Any]							=      False
       if self.is_zeroa() or self.is_zeroa():
        lowerCamelCase		:	Tuple							=      set(["""cpu""", """nvme"""]      )
        lowerCamelCase		:	List[Any]							=      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		:	Tuple							=      True
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[Any]							=      self.config

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

       return config, ds_key
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=None      ):
       lowerCamelCase			,    lowerCamelCase		:	Optional[Any]							=      self.find_config_node(__magic_name__      )
       if config is None:
        return default
       return config.get(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=False      ):
       lowerCamelCase		:	int							=      self.config

       # find the config node of interest if it exists
       lowerCamelCase		:	Dict							=      ds_key_long.split("""."""      )
       for node in nodes:
        lowerCamelCase		:	Optional[Any]							=      config
        lowerCamelCase		:	Dict							=      config.get(__magic_name__      )
        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(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      self.get_value(__magic_name__      )
       return False if value is None else bool(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	Tuple							=      self.get_value(__magic_name__      )
       return False if value is None else not bool(__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       return self._stage == 2
      def 						UpperCamelCase__							(      self      ):
       return self._stage == 3






      def 						UpperCamelCase__							(      self      ):
       return self._offload





class 							A__		:
      def __init__(      self    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      engine






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  **__magic_name__      ):
       # runs backpropagation and handles mixed precision
       self.engine.backward(__magic_name__    ,  **__magic_name__      )

       # 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__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  __magic_name__      ):
       super().__init__(__magic_name__    ,  device_placement=__magic_name__    ,  scaler=__magic_name__      )
       lowerCamelCase		:	Dict							=      hasattr(self.optimizer    ,  """overflow"""      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__=None      ):
       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
      def 						UpperCamelCase__							(      self      ):
       pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






      @property
      def 						UpperCamelCase__							(      self      ):
       if self.__has_overflow__:
        return self.optimizer.overflow
       return False





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  __magic_name__    ,  __magic_name__      ):
       super().__init__(__magic_name__    ,  __magic_name__      )






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





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=0.001    ,  __magic_name__=0    ,  **__magic_name__      ):
       lowerCamelCase		:	List[Any]							=      params
       lowerCamelCase		:	Dict							=      lr
       lowerCamelCase		:	Optional[int]							=      weight_decay
       lowerCamelCase		:	int							=      kwargs





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=None    ,  __magic_name__=0    ,  **__magic_name__      ):
       lowerCamelCase		:	Dict							=      optimizer
       lowerCamelCase		:	Dict							=      total_num_steps
       lowerCamelCase		:	List[str]							=      warmup_num_steps
       lowerCamelCase		:	str							=      kwargs






def     _a					(						lowerCamelCase							):

 if p < 2:
  raise ValueError("""p should not be less than 2!"""							)
 elif p == 2:
  return True

 lowerCamelCase		:	Any							=      4
 lowerCamelCase		:	List[str]							=      (1 << p) - 1
 for _ in range(p - 2							):
  lowerCamelCase		:	List[Any]							=      ((s * s) - 2) % m
 return s == 0


if __name__ == "__main__":
      print(lucas_lehmer_test(7))
      print(lucas_lehmer_test(1_1))




