




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

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




class    __lowerCAmelCase       :






       def __init__(   self:					Optional[int]       ,					_lowerCAmelCase:					Optional[int]				):
         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
           lowercase    :int      	=						deepcopy(_lowerCAmelCase				)
         elif os.path.exists(_lowerCAmelCase				):
           with io.open(_lowerCAmelCase       ,					"r"       ,					encoding="utf-8"				) as f:
             lowercase    :Tuple      	=						json.load(_lowerCAmelCase				)
         else:
           try:
             lowercase    :str      	=						baseaa.urlsafe_baadecode(_lowerCAmelCase				).decode("utf-8"				)
             lowercase    :int      	=						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}"				)

         lowercase    :Optional[int]      	=						config

         self.set_stage_and_offload()






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         # 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.
         lowercase    :Optional[Any]      	=						self.get_value("zero_optimization.stage"       ,					-1				)

         # offload
         lowercase    :Optional[int]      	=						False
         if self.is_zeroa() or self.is_zeroa():
           lowercase    :Dict      	=						set(["cpu", "nvme"]				)
           lowercase    :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:
             lowercase    :Any      	=						True






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					_lowerCAmelCase:					Dict				):
         lowercase    :List[Any]      	=						self.config

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

         return config, ds_key






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					Tuple=None				):
         lowercase	,    lowercase    :Dict      	=						self.find_config_node(_lowerCAmelCase				)
         if config is None:
           return default
         return config.get(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					List[Any]       ,					_lowerCAmelCase:					Dict=False				):
         lowercase    :int      	=						self.config

         # find the config node of interest if it exists
         lowercase    :int      	=						ds_key_long.split("."				)
         for node in nodes:
           lowercase    :Dict      	=						config
           lowercase    :Union[str, Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Any				):
         lowercase    :str      	=						self.get_value(_lowerCAmelCase				)
         return False if value is None else bool(_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					_lowerCAmelCase:					List[Any]				):
         lowercase    :str      	=						self.get_value(_lowerCAmelCase				)
         return False if value is None else not bool(_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         return self._stage == 2






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         return self._stage == 3






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         return self._offload







class    __lowerCAmelCase       :






       def __init__(   self:					Optional[Any]       ,					_lowerCAmelCase:					Tuple				):
         lowercase    :List[Any]      	=						engine






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					int       ,					**_lowerCAmelCase:					List[Any]				):
         # 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    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					Tuple				):
         super().__init__(_lowerCAmelCase       ,					device_placement=_lowerCAmelCase       ,					scaler=_lowerCAmelCase				)
         lowercase    :Optional[int]      	=						hasattr(self.optimizer       ,					"overflow"				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					int=None				):
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






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






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







class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					Dict       ,					_lowerCAmelCase:					Optional[int]       ,					_lowerCAmelCase:					List[str]				):
         super().__init__(_lowerCAmelCase       ,					_lowerCAmelCase				)






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







class    __lowerCAmelCase       :






       def __init__(   self:					int       ,					_lowerCAmelCase:					List[str]       ,					_lowerCAmelCase:					Tuple=0.0_01       ,					_lowerCAmelCase:					str=0       ,					**_lowerCAmelCase:					int				):
         lowercase    :Dict      	=						params
         lowercase    :Optional[int]      	=						lr
         lowercase    :Any      	=						weight_decay
         lowercase    :Optional[Any]      	=						kwargs







class    __lowerCAmelCase       :






       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					Dict       ,					_lowerCAmelCase:					Any=None       ,					_lowerCAmelCase:					Any=0       ,					**_lowerCAmelCase:					List[str]				):
         lowercase    :Optional[int]      	=						optimizer
         lowercase    :List[Any]      	=						total_num_steps
         lowercase    :Union[str, Any]      	=						warmup_num_steps
         lowercase    :Dict      	=						kwargs









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



