





from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )













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

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

class    _UpperCAmelCase							:
      def __init__(		self      ,       a__       ):
         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
            A_     :				Optional[Any]			      =	deepcopy(a__       )
         elif os.path.exists(a__       ):
            with io.open(a__      ,       """r"""      ,       encoding="""utf-8"""       ) as f:
               A_     :				str			      =	json.load(a__       )
         else:
            try:
               A_     :				Dict			      =	baseaa.urlsafe_baadecode(a__       ).decode("""utf-8"""       )
               A_     :				List[Any]			      =	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}"""       )

         A_     :				Any			      =	config

         self.set_stage_and_offload()
      def 		_lowerCamelCase      (		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.
         A_     :				List[str]			      =	self.get_value("""zero_optimization.stage"""      ,       -1       )

         # offload
         A_     :				Any			      =	False
         if self.is_zeroa() or self.is_zeroa():
            A_     :				Optional[int]			      =	set(["""cpu""", """nvme"""]       )
            A_     :				Dict			      =	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:
               A_     :				Tuple			      =	True
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	self.config

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

         return config, ds_key
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=None       ):
         A_      ,				A_     :				Union[str, Any]			      =	self.find_config_node(a__       )
         if config is None:
            return default
         return config.get(a__      ,       a__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=False       ):
         A_     :				Union[str, Any]			      =	self.config

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






      def 		_lowerCamelCase      (		self       ):
         return self._offload






class    _UpperCAmelCase							:
      def __init__(		self      ,       a__       ):
         A_     :				Any			      =	engine






      def 		_lowerCamelCase      (		self      ,       a__      ,       **a__       ):
         # runs backpropagation and handles mixed precision
         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    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__       ):
         super().__init__(a__      ,       device_placement=a__      ,       scaler=a__       )
         A_     :				Dict			      =	hasattr(self.optimizer      ,       """overflow"""       )
      def 		_lowerCamelCase      (		self      ,       a__=None       ):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
      def 		_lowerCamelCase      (		self       ):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






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






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __init__(		self      ,       a__      ,       a__       ):
         super().__init__(a__      ,       a__       )






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






class    _UpperCAmelCase							:
      def __init__(		self      ,       a__      ,       a__=0.001      ,       a__=0      ,       **a__       ):
         A_     :				List[str]			      =	params
         A_     :				Any			      =	lr
         A_     :				int			      =	weight_decay
         A_     :				Optional[int]			      =	kwargs






class    _UpperCAmelCase							:
      def __init__(		self      ,       a__      ,       a__=None      ,       a__=0      ,       **a__       ):
         A_     :				Union[str, Any]			      =	optimizer
         A_     :				int			      =	total_num_steps
         A_     :				Any			      =	warmup_num_steps
         A_     :				int			      =	kwargs






