



"""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							:				Optional[Any]   ,	_lowercase							:				Union[str, Any]		)							->					Any:
									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
															snake_case   :			List[str]						    = deepcopy(_lowercase		)
									elif os.path.exists(_lowercase		):
															with io.open(_lowercase   ,	"r"   ,	encoding="utf-8"		) as f:
																					snake_case   :			Optional[Any]						    = json.load(_lowercase		)
									else:
															try:
																					snake_case   :			str						    = baseaa.urlsafe_baadecode(_lowercase		).decode("utf-8"		)
																					snake_case   :			List[Any]						    = 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}'''		)

									snake_case   :			Optional[Any]						    = config

									self.set_stage_and_offload()



			def 				__lowercase					(			self							:				List[str]		)							->					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.
									snake_case   :			Optional[Any]						    = self.get_value("zero_optimization.stage"   ,	-1		)

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



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				List[Any]		)							->					Any:
									snake_case   :			Optional[Any]						    = self.config

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

									return config, ds_key



			def 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				Dict   ,	_lowercase							:				List[Any]=None		)							->					List[Any]:
									snake_case    ,			snake_case   :			Union[str, Any]						    = self.find_config_node(_lowercase		)
									if config is None:
															return default
									return config.get(_lowercase   ,	_lowercase		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[Any]=False		)							->					Tuple:
									snake_case   :			List[str]						    = self.config

									# find the config node of interest if it exists
									snake_case   :			int						    = ds_key_long.split("."		)
									for node in nodes:
															snake_case   :			Union[str, Any]						    = config
															snake_case   :			str						    = 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 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[int]		)							->					Optional[int]:
									snake_case   :			Tuple						    = self.get_value(_lowercase		)
									return False if value is None else bool(_lowercase		)



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = self.get_value(_lowercase		)
									return False if value is None else not bool(_lowercase		)



			def 				__lowercase					(			self							:				List[str]		)							->					int:
									return self._stage == 2



			def 				__lowercase					(			self							:				str		)							->					List[str]:
									return self._stage == 3



			def 				__lowercase					(			self							:				Optional[int]		)							->					List[str]:
									return self._offload



class       _a  :



			def __init__(			self							:				int   ,	_lowercase							:				Optional[Any]		)							->					Dict:
									snake_case   :			Optional[Any]						    = engine



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	**_lowercase							:				Dict		)							->					Optional[int]:
									# runs backpropagation and handles mixed precision
									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  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				List[Any]   ,	_lowercase							:				List[Any]		)							->					str:
									super().__init__(_lowercase   ,	device_placement=_lowercase   ,	scaler=_lowercase		)
									snake_case   :			Any						    = hasattr(self.optimizer   ,	"overflow"		)



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[Any]=None		)							->					int:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



			def 				__lowercase					(			self							:				int		)							->					Optional[Any]:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



			@property
			def 				__lowercase					(			self							:				Any		)							->					Optional[int]:
									if self.__has_overflow__:
															return self.optimizer.overflow
									return False



class       _a  ( SCREAMING_SNAKE_CASE__):



			def __init__(			self							:				List[Any]   ,	_lowercase							:				str   ,	_lowercase							:				Dict		)							->					List[Any]:
									super().__init__(_lowercase   ,	_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					Optional[Any]:
									pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



class       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				Any   ,	_lowercase							:				int=0.001   ,	_lowercase							:				Tuple=0   ,	**_lowercase							:				List[str]		)							->					Union[str, Any]:
									snake_case   :			Union[str, Any]						    = params
									snake_case   :			Optional[int]						    = lr
									snake_case   :			List[Any]						    = weight_decay
									snake_case   :			Any						    = kwargs



class       _a  :



			def __init__(			self							:				Optional[int]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				List[str]=0   ,	**_lowercase							:				int		)							->					int:
									snake_case   :			Union[str, Any]						    = optimizer
									snake_case   :			Tuple						    = total_num_steps
									snake_case   :			int						    = warmup_num_steps
									snake_case   :			List[str]						    = kwargs








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A		=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['ViTFeatureExtractor']
			A		=						['ViTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ViTForImageClassification',
			    'ViTForMaskedImageModeling',
			    'ViTModel',
			    'ViTPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'TFViTForImageClassification',
			    'TFViTModel',
			    'TFViTPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'FlaxViTForImageClassification',
			    'FlaxViTModel',
			    'FlaxViTPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



