



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__		 =					deepcopy(a__)
         elif os.path.exists(a__):
             with io.open(a__ ,						'''r''' ,						encoding='''utf-8''') as f:
                 A__		 =					json.load(a__)
         else:
             try:
                 A__		 =					baseaa.urlsafe_baadecode(a__).decode('''utf-8''')
                 A__		 =					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__		 =					config

         self.set_stage_and_offload()




     def 		snake_case_		(				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__		 =					self.get_value('''zero_optimization.stage''' ,						-1)

         # offload
         A__		 =					False
         if self.is_zeroa() or self.is_zeroa():
             A__		 =					set(['''cpu''', '''nvme'''])
             A__		 =					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__		 =					True




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.config

         # find the config node of interest if it exists
         A__		 =					ds_key_long.split('''.''')
         A__		 =					nodes.pop()
         for node in nodes:
             A__		 =					config.get(a__)
             if config is None:
                 return None, ds_key

         return config, ds_key




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         A__ ,	A__		 =					self.find_config_node(a__)
         if config is None:
             return default
         return config.get(a__ ,						a__)




     def 		snake_case_		(				self ,						a__ ,						a__=False):
         A__		 =					self.config

         # find the config node of interest if it exists
         A__		 =					ds_key_long.split('''.''')
         for node in nodes:
             A__		 =					config
             A__		 =					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 		snake_case_		(				self ,						a__):
         A__		 =					self.get_value(a__)
         return False if value is None else bool(a__)




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.get_value(a__)
         return False if value is None else not bool(a__)




     def 		snake_case_		(				self):
         return self._stage == 2




     def 		snake_case_		(				self):
         return self._stage == 3




     def 		snake_case_		(				self):
         return self._offload







class     _UpperCAmelCase			:




     def __init__(				self ,						a__):
         A__		 =					engine




     def 		snake_case_		(				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			(       A__     ):




     def __init__(				self ,						a__):
         super().__init__(a__ ,						device_placement=a__ ,						scaler=a__)
         A__		 =					hasattr(self.optimizer ,						'''overflow''')




     def 		snake_case_		(				self ,						a__=None):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




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




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







class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						a__ ,						a__):
         super().__init__(a__ ,						a__)




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







class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						a__=0.0_0_1 ,						a__=0 ,						**a__):
         A__		 =					params
         A__		 =					lr
         A__		 =					weight_decay
         A__		 =					kwargs







class     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						a__=None ,						a__=0 ,						**a__):
         A__		 =					optimizer
         A__		 =					total_num_steps
         A__		 =					warmup_num_steps
         A__		 =					kwargs





def 		lowerCAmelCase__    (					UpperCamelCase_				:       float ,       UpperCamelCase_				:       int	)->  float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase_	) ,       UpperCamelCase_	)
    return number - int(UpperCamelCase_	)


if __name__ == "__main__":
   print(decimal_isolate(1.53, 0))
   print(decimal_isolate(35.345, 1))
   print(decimal_isolate(35.345, 2))
   print(decimal_isolate(35.345, 3))
   print(decimal_isolate(-14.789, 3))
   print(decimal_isolate(0, 2))
   print(decimal_isolate(-14.123, 1))
   print(decimal_isolate(-14.123, 2))
   print(decimal_isolate(-14.123, 3))
