






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					:   Dict							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]						):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_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__			:   List[str]       				=  deepcopy(SCREAMING_SNAKE_CASE						)
      elif os.path.exists(SCREAMING_SNAKE_CASE						):
           with io.open(SCREAMING_SNAKE_CASE							,   "r"							,   encoding="utf-8"						) as f:
                UpperCamelCase__			:   Tuple       				=  json.load(SCREAMING_SNAKE_CASE						)
      else:
           try:
                UpperCamelCase__			:   Dict       				=  baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE						).decode("utf-8"						)
                UpperCamelCase__			:   str       				=  json.loads(SCREAMING_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__			:   Union[str, Any]       				=  config

      self.set_stage_and_offload()







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.get_value("zero_optimization.stage"							,   -1						)

      # offload
      UpperCamelCase__			:   Optional[Any]       				=  False
      if self.is_zeroa() or self.is_zeroa():
           UpperCamelCase__			:   List[str]       				=  set(["cpu", "nvme"]						)
           UpperCamelCase__			:   str       				=  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__			:   Dict       				=  True







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.config

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

      return config, ds_key







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__			:   str       				=  self.find_config_node(SCREAMING_SNAKE_CASE						)
      if config is None:
           return default
      return config.get(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   str=False						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.config

      # find the config node of interest if it exists
      UpperCamelCase__			:   int       				=  ds_key_long.split("."						)
      for node in nodes:
           UpperCamelCase__			:   Tuple       				=  config
           UpperCamelCase__			:   List[Any]       				=  config.get(SCREAMING_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(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  self.get_value(SCREAMING_SNAKE_CASE						)
      return False if value is None else bool(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.get_value(SCREAMING_SNAKE_CASE						)
      return False if value is None else not bool(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return self._stage == 2







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      return self._stage == 3







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      return self._offload







class 			__a  :







 def __init__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  engine







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   **SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      self.engine.backward(SCREAMING_SNAKE_CASE							,   **SCREAMING_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 			__a  (    A__       ):







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      super().__init__(SCREAMING_SNAKE_CASE							,   device_placement=SCREAMING_SNAKE_CASE							,   scaler=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  hasattr(self.optimizer							,   "overflow"						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







class 			__a  (    A__       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      super().__init__(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






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







class 			__a  :







 def __init__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=0.0_0_1							,   SCREAMING_SNAKE_CASE					:   Dict=0							,   **SCREAMING_SNAKE_CASE					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  params
      UpperCamelCase__			:   Optional[int]       				=  lr
      UpperCamelCase__			:   int       				=  weight_decay
      UpperCamelCase__			:   List[str]       				=  kwargs







class 			__a  :







 def __init__(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any=None							,   SCREAMING_SNAKE_CASE					:   str=0							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  optimizer
      UpperCamelCase__			:   List[str]       				=  total_num_steps
      UpperCamelCase__			:   List[str]       				=  warmup_num_steps
      UpperCamelCase__			:   Dict       				=  kwargs







lowerCamelCase	:							List[Any]				=[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	int:
     UpperCamelCase__			:   Optional[int]       				=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
     UpperCamelCase__			:   Any       				=  0
     UpperCamelCase__			:   Any       				=  0
     while place < len(__lowerCAmelCase			):
          if (place + 1 < len(__lowerCAmelCase			)) and (vals[roman[place]] < vals[roman[place + 1]]):
               total += vals[roman[place + 1]] - vals[roman[place]]
               place += 2
          else:
               total += vals[roman[place]]
               place += 1
     return total







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[int]       				=  []
     for arabic, roman in ROMAN:
          ((UpperCamelCase__)				,     (UpperCamelCase__))			:   Optional[int]       				=  divmod(__lowerCAmelCase					,    __lowerCAmelCase			)
          result.append(roman * factor			)
          if number == 0:
               break
     return "".join(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()