
"""simple docstring"""



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			:       Any     ,					__UpperCamelCase			:       Optional[int]     )		->Tuple:






        '''simple docstring'''






        if isinstance(__UpperCamelCase     ,					__UpperCamelCase     ):
            # 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(__UpperCamelCase     )
        elif os.path.exists(__UpperCamelCase     ):
            with io.open(__UpperCamelCase     ,					"""r"""     ,					encoding="""utf-8"""     ) as f:
                _UpperCAmelCase          =   json.load(__UpperCamelCase     )
        else:
            try:
                _UpperCAmelCase          =   baseaa.urlsafe_baadecode(__UpperCamelCase     ).decode("""utf-8"""     )
                _UpperCAmelCase          =   json.loads(__UpperCamelCase     )
            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      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''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      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Optional[int]     )		->str:






        '''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(__UpperCamelCase     )
            if config is None:
                return None, ds_key

        return config, ds_key







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Dict=None     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.find_config_node(__UpperCamelCase     )
        if config is None:
            return default
        return config.get(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       List[Any]=False     )		->List[Any]:






        '''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(__UpperCamelCase     )
            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(__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       str     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_value(__UpperCamelCase     )
        return False if value is None else bool(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.get_value(__UpperCamelCase     )
        return False if value is None else not bool(__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     )		->List[Any]:






        '''simple docstring'''






        return self._stage == 2







    def      _snake_case					(			self			:       List[Any]     )		->Dict:






        '''simple docstring'''






        return self._stage == 3







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        return self._offload







class 		a_						:







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       Any     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   engine







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					**__UpperCamelCase			:       Optional[Any]     )		->str:






        '''simple docstring'''






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

        # 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_						( _UpperCAmelCase	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(__UpperCamelCase     ,					device_placement=__UpperCamelCase     ,					scaler=__UpperCamelCase     )
        _UpperCAmelCase          =   hasattr(self.optimizer     ,					"""overflow"""     )







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       str=None     )		->Optional[int]:






        '''simple docstring'''






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







    def      _snake_case					(			self			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






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







    @property
    def      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






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







class 		a_						( _UpperCAmelCase	):







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Optional[Any]     )		->List[Any]:






        '''simple docstring'''






        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     )		->Any:






        '''simple docstring'''






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







class 		a_						:







    def __init__(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       int=0.0_0_1     ,					__UpperCamelCase			:       Dict=0     ,					**__UpperCamelCase			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   params
        _UpperCAmelCase          =   lr
        _UpperCAmelCase          =   weight_decay
        _UpperCAmelCase          =   kwargs







class 		a_						:







    def __init__(			self			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       List[Any]=0     ,					**__UpperCamelCase			:       List[str]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   optimizer
        _UpperCAmelCase          =   total_num_steps
        _UpperCAmelCase          =   warmup_num_steps
        _UpperCAmelCase          =   kwargs

"""simple docstring"""



import re

from filelock import FileLock


try:
 import nltk

 a					:	str							=		True
except (ImportError, ModuleNotFoundError):
 a					:	List[str]							=		False

if NLTK_AVAILABLE:
 with FileLock('''.lock''') as lock:
  nltk.download('''punkt''', quiet=True)


def  _UpperCamelCase	(						_A      )    ->  str:





    """simple docstring"""

    re.sub("""<n>"""					,      """"""					,      _A      )  # remove pegasus newline char
    assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
    return "\n".join(nltk.sent_tokenize(_A      )      )