




'''simple docstring'''



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

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







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Dict  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         # 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
         _SCREAMING_SNAKE_CASE	=				deepcopy(__lowerCamelCase   )
      elif os.path.exists(__lowerCamelCase   ):
         with io.open(__lowerCamelCase  ,				"r"  ,				encoding="utf-8"   ) as f:
            _SCREAMING_SNAKE_CASE	=				json.load(__lowerCamelCase   )
      else:
         try:
            _SCREAMING_SNAKE_CASE	=				baseaa.urlsafe_baadecode(__lowerCamelCase   ).decode("utf-8"   )
            _SCREAMING_SNAKE_CASE	=				json.loads(__lowerCamelCase   )
         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}"""   )

      _SCREAMING_SNAKE_CASE	=				config

      self.set_stage_and_offload()



   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      # 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.
      _SCREAMING_SNAKE_CASE	=				self.get_value("zero_optimization.stage"  ,				-1   )

      # offload
      _SCREAMING_SNAKE_CASE	=				False
      if self.is_zeroa() or self.is_zeroa():
         _SCREAMING_SNAKE_CASE	=				set(["cpu", "nvme"]   )
         _SCREAMING_SNAKE_CASE	=				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:
            _SCREAMING_SNAKE_CASE	=				True



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.config

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

      return config, ds_key



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     int=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				self.find_config_node(__lowerCamelCase   )
      if config is None:
         return default
      return config.get(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[Any]=False   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.config

      # find the config node of interest if it exists
      _SCREAMING_SNAKE_CASE	=				ds_key_long.split("."   )
      for node in nodes:
         _SCREAMING_SNAKE_CASE	=				config
         _SCREAMING_SNAKE_CASE	=				config.get(__lowerCamelCase   )
         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(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_value(__lowerCamelCase   )
      return False if value is None else bool(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.get_value(__lowerCamelCase   )
      return False if value is None else not bool(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self._stage == 2



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      return self._stage == 3



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self._offload







class 		lowercase_   :


   """simple docstring"""

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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				engine



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      # runs backpropagation and handles mixed precision
      self.engine.backward(__lowerCamelCase  ,				**__lowerCamelCase   )

      # 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 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase  ,				device_placement=__lowerCamelCase  ,				scaler=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				hasattr(self.optimizer  ,				"overflow"   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Dict=None   ):

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



   def 							lowerCAmelCase_      (   self :     Any   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      if self.__has_overflow__:
         return self.optimizer.overflow
      return False







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Any  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase  ,				__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

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







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Optional[int]=0.0_0_1  ,				__lowerCamelCase :     Dict=0  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				params
      _SCREAMING_SNAKE_CASE	=				lr
      _SCREAMING_SNAKE_CASE	=				weight_decay
      _SCREAMING_SNAKE_CASE	=				kwargs







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple=None  ,				__lowerCamelCase :     List[Any]=0  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				optimizer
      _SCREAMING_SNAKE_CASE	=				total_num_steps
      _SCREAMING_SNAKE_CASE	=				warmup_num_steps
      _SCREAMING_SNAKE_CASE	=				kwargs







'''simple docstring'''



from collections.abc import Sequence
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Sequence[int] | None = None  )			->			int:
   if nums is None or not nums:
      raise ValueError("Input sequence should not be empty"  )

   _SCREAMING_SNAKE_CASE	=				nums[0]
   for i in range(1	,					len(__A  )  ):
      _SCREAMING_SNAKE_CASE	=				nums[i]
      _SCREAMING_SNAKE_CASE	=				max(__A	,					ans + num	,					__A  )

   return ans


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Try on a sample input from the user
 lowerCamelCase_			=						int(input('Enter number of elements : ').strip())
 lowerCamelCase_			=						list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
 print(max_subsequence_sum(array))

