




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		:			int     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       lowercase       ):
               # 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     =	deepcopy(lowercase       )
           elif os.path.exists(lowercase       ):
               with io.open(lowercase     ,       '''r'''     ,       encoding='''utf-8'''       ) as f:
                   UpperCAmelCase     =	json.load(lowercase       )
           else:
               try:
                   UpperCAmelCase     =	baseaa.urlsafe_baadecode(lowercase       ).decode('''utf-8'''       )
                   UpperCAmelCase     =	json.loads(lowercase       )
               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     =	config

           self.set_stage_and_offload()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_value('''zero_optimization.stage'''     ,       -1       )

           # offload
           UpperCAmelCase     =	False
           if self.is_zeroa() or self.is_zeroa():
               UpperCAmelCase     =	set(['''cpu''', '''nvme''']       )
               UpperCAmelCase     =	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     =	True
       def 					A   (			self		:			Dict     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.config

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

           return config, ds_key
       def 					A   (			self		:			str     ,       lowercase		:			Dict     ,       lowercase		:			List[Any]=None       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.find_config_node(lowercase       )
           if config is None:
               return default
           return config.get(lowercase     ,       lowercase       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.config

           # find the config node of interest if it exists
           UpperCAmelCase     =	ds_key_long.split('''.'''       )
           for node in nodes:
               UpperCAmelCase     =	config
               UpperCAmelCase     =	config.get(lowercase       )
               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(lowercase       )
       def 					A   (			self		:			Dict     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_value(lowercase       )
           return False if value is None else bool(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_value(lowercase       )
           return False if value is None else not bool(lowercase       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           return self._stage == 2
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           return self._stage == 3







       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return self._offload






class     _a						:
       def __init__(			self		:			Tuple     ,       lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	engine







       def 					A   (			self		:			List[Any]     ,       lowercase		:			Dict     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           self.engine.backward(lowercase     ,       **lowercase       )

           # 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		:			Optional[int]     ,       lowercase		:			str       ):



           '''simple docstring'''

           super().__init__(lowercase     ,       device_placement=lowercase     ,       scaler=lowercase       )
           UpperCAmelCase     =	hasattr(self.optimizer     ,       '''overflow'''       )
       def 					A   (			self		:			Optional[int]     ,       lowercase		:			Optional[Any]=None       ):



           '''simple docstring'''

           pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

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







       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

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






class     _a						(			__a				):
       def __init__(			self		:			str     ,       lowercase		:			Optional[int]     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           super().__init__(lowercase     ,       lowercase       )







       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

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






class     _a						:
       def __init__(			self		:			Tuple     ,       lowercase		:			List[str]     ,       lowercase		:			Dict=0.001     ,       lowercase		:			int=0     ,       **lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	params
           UpperCAmelCase     =	lr
           UpperCAmelCase     =	weight_decay
           UpperCAmelCase     =	kwargs






class     _a						:
       def __init__(			self		:			str     ,       lowercase		:			List[str]     ,       lowercase		:			List[Any]=None     ,       lowercase		:			Dict=0     ,       **lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	optimizer
           UpperCAmelCase     =	total_num_steps
           UpperCAmelCase     =	warmup_num_steps
           UpperCAmelCase     =	kwargs










'''simple docstring'''







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int					,     _a			:							int					,     _a			:							list[int]  ):

    # 1. Validate that path exists between current and next vertices
    if graph[path[curr_ind - 1]][next_ver] == 0:
        return False

    # 2. Validate that next vertex is not already in path
    return not any(vertex == next_ver for vertex in path  )





def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							list[int]					,     _a			:							int  ):

    # Base Case
    if curr_ind == len(_a  ):
        # return whether path exists between current and starting vertices
        return graph[path[curr_ind - 1]][path[0]] == 1

    # Recursive Step
    for next_ver in range(0					,     len(_a  )  ):
        if valid_connection(_a					,     _a					,     _a					,     _a  ):
            # Insert current vertex  into path as next transition
            UpperCAmelCase     =	next_ver
            # Validate created path
            if util_hamilton_cycle(_a					,     _a					,     curr_ind + 1  ):
                return True
            # Backtrack
            UpperCAmelCase     =	-1
    return False







def 		snake_case_							(_a			:							list[list[int]]					,     _a			:							int = 0  ):
    UpperCAmelCase     =	[-1] * (len(_a  ) + 1)
    # initialize start and end of path with starting index
    UpperCAmelCase     =	UpperCAmelCase     =	start_index
    # evaluate and if we find answer return path either return empty array
    return path if util_hamilton_cycle(_a					,     _a					,     1  ) else []




