
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a									=				{
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
        "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
        "MODEL_FOR_BACKBONE_MAPPING",
        "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
        "MODEL_FOR_CAUSAL_LM_MAPPING",
        "MODEL_FOR_CTC_MAPPING",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
        "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
        "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
        "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
        "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
        "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
        "MODEL_FOR_MASKED_LM_MAPPING",
        "MODEL_FOR_MASK_GENERATION_MAPPING",
        "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
        "MODEL_FOR_OBJECT_DETECTION_MAPPING",
        "MODEL_FOR_PRETRAINING_MAPPING",
        "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
        "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
        "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
        "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
        "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
        "MODEL_FOR_TEXT_ENCODING_MAPPING",
        "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
        "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
        "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
        "MODEL_FOR_VISION_2_SEQ_MAPPING",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
        "MODEL_MAPPING",
        "MODEL_WITH_LM_HEAD_MAPPING",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
        "AutoModel",
        "AutoBackbone",
        "AutoModelForAudioClassification",
        "AutoModelForAudioFrameClassification",
        "AutoModelForAudioXVector",
        "AutoModelForCausalLM",
        "AutoModelForCTC",
        "AutoModelForDepthEstimation",
        "AutoModelForImageClassification",
        "AutoModelForImageSegmentation",
        "AutoModelForInstanceSegmentation",
        "AutoModelForMaskGeneration",
        "AutoModelForTextEncoding",
        "AutoModelForMaskedImageModeling",
        "AutoModelForMaskedLM",
        "AutoModelForMultipleChoice",
        "AutoModelForNextSentencePrediction",
        "AutoModelForObjectDetection",
        "AutoModelForPreTraining",
        "AutoModelForQuestionAnswering",
        "AutoModelForSemanticSegmentation",
        "AutoModelForSeq2SeqLM",
        "AutoModelForSequenceClassification",
        "AutoModelForSpeechSeq2Seq",
        "AutoModelForTableQuestionAnswering",
        "AutoModelForTokenClassification",
        "AutoModelForUniversalSegmentation",
        "AutoModelForVideoClassification",
        "AutoModelForVision2Seq",
        "AutoModelForVisualQuestionAnswering",
        "AutoModelForDocumentQuestionAnswering",
        "AutoModelWithLMHead",
        "AutoModelForZeroShotImageClassification",
        "AutoModelForZeroShotObjectDetection",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
        "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
        "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
        "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
        "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
        "TF_MODEL_FOR_MASKED_LM_MAPPING",
        "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
        "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
        "TF_MODEL_FOR_PRETRAINING_MAPPING",
        "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
        "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
        "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
        "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
        "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
        "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
        "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
        "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
        "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
        "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
        "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
        "TF_MODEL_MAPPING",
        "TF_MODEL_WITH_LM_HEAD_MAPPING",
        "TFAutoModel",
        "TFAutoModelForAudioClassification",
        "TFAutoModelForCausalLM",
        "TFAutoModelForImageClassification",
        "TFAutoModelForMaskedImageModeling",
        "TFAutoModelForMaskedLM",
        "TFAutoModelForMaskGeneration",
        "TFAutoModelForMultipleChoice",
        "TFAutoModelForNextSentencePrediction",
        "TFAutoModelForPreTraining",
        "TFAutoModelForDocumentQuestionAnswering",
        "TFAutoModelForQuestionAnswering",
        "TFAutoModelForSemanticSegmentation",
        "TFAutoModelForSeq2SeqLM",
        "TFAutoModelForSequenceClassification",
        "TFAutoModelForSpeechSeq2Seq",
        "TFAutoModelForTableQuestionAnswering",
        "TFAutoModelForTextEncoding",
        "TFAutoModelForTokenClassification",
        "TFAutoModelForVision2Seq",
        "TFAutoModelForZeroShotImageClassification",
        "TFAutoModelWithLMHead",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a									=				[
        "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
        "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
        "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
        "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
        "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
        "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
        "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
        "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
        "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
        "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
        "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
        "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
        "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
        "FLAX_MODEL_MAPPING",
        "FlaxAutoModel",
        "FlaxAutoModelForCausalLM",
        "FlaxAutoModelForImageClassification",
        "FlaxAutoModelForMaskedLM",
        "FlaxAutoModelForMultipleChoice",
        "FlaxAutoModelForNextSentencePrediction",
        "FlaxAutoModelForPreTraining",
        "FlaxAutoModelForQuestionAnswering",
        "FlaxAutoModelForSeq2SeqLM",
        "FlaxAutoModelForSequenceClassification",
        "FlaxAutoModelForSpeechSeq2Seq",
        "FlaxAutoModelForTokenClassification",
        "FlaxAutoModelForVision2Seq",
    ]


if TYPE_CHECKING:
    from .auto_factory import get_values
    from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
    from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
    from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
    from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
    from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_auto import (
            MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
            MODEL_FOR_AUDIO_XVECTOR_MAPPING,
            MODEL_FOR_BACKBONE_MAPPING,
            MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
            MODEL_FOR_CAUSAL_LM_MAPPING,
            MODEL_FOR_CTC_MAPPING,
            MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
            MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
            MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
            MODEL_FOR_MASK_GENERATION_MAPPING,
            MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            MODEL_FOR_MASKED_LM_MAPPING,
            MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            MODEL_FOR_OBJECT_DETECTION_MAPPING,
            MODEL_FOR_PRETRAINING_MAPPING,
            MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_TEXT_ENCODING_MAPPING,
            MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
            MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
            MODEL_FOR_VISION_2_SEQ_MAPPING,
            MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
            MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
            MODEL_MAPPING,
            MODEL_WITH_LM_HEAD_MAPPING,
            AutoBackbone,
            AutoModel,
            AutoModelForAudioClassification,
            AutoModelForAudioFrameClassification,
            AutoModelForAudioXVector,
            AutoModelForCausalLM,
            AutoModelForCTC,
            AutoModelForDepthEstimation,
            AutoModelForDocumentQuestionAnswering,
            AutoModelForImageClassification,
            AutoModelForImageSegmentation,
            AutoModelForInstanceSegmentation,
            AutoModelForMaskedImageModeling,
            AutoModelForMaskedLM,
            AutoModelForMaskGeneration,
            AutoModelForMultipleChoice,
            AutoModelForNextSentencePrediction,
            AutoModelForObjectDetection,
            AutoModelForPreTraining,
            AutoModelForQuestionAnswering,
            AutoModelForSemanticSegmentation,
            AutoModelForSeqaSeqLM,
            AutoModelForSequenceClassification,
            AutoModelForSpeechSeqaSeq,
            AutoModelForTableQuestionAnswering,
            AutoModelForTextEncoding,
            AutoModelForTokenClassification,
            AutoModelForUniversalSegmentation,
            AutoModelForVideoClassification,
            AutoModelForVisionaSeq,
            AutoModelForVisualQuestionAnswering,
            AutoModelForZeroShotImageClassification,
            AutoModelForZeroShotObjectDetection,
            AutoModelWithLMHead,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_auto import (
            TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_MASK_GENERATION_MAPPING,
            TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
            TF_MODEL_FOR_MASKED_LM_MAPPING,
            TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            TF_MODEL_FOR_PRETRAINING_MAPPING,
            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
            TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
            TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
            TF_MODEL_MAPPING,
            TF_MODEL_WITH_LM_HEAD_MAPPING,
            TFAutoModel,
            TFAutoModelForAudioClassification,
            TFAutoModelForCausalLM,
            TFAutoModelForDocumentQuestionAnswering,
            TFAutoModelForImageClassification,
            TFAutoModelForMaskedImageModeling,
            TFAutoModelForMaskedLM,
            TFAutoModelForMaskGeneration,
            TFAutoModelForMultipleChoice,
            TFAutoModelForNextSentencePrediction,
            TFAutoModelForPreTraining,
            TFAutoModelForQuestionAnswering,
            TFAutoModelForSemanticSegmentation,
            TFAutoModelForSeqaSeqLM,
            TFAutoModelForSequenceClassification,
            TFAutoModelForSpeechSeqaSeq,
            TFAutoModelForTableQuestionAnswering,
            TFAutoModelForTextEncoding,
            TFAutoModelForTokenClassification,
            TFAutoModelForVisionaSeq,
            TFAutoModelForZeroShotImageClassification,
            TFAutoModelWithLMHead,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_auto import (
            FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
            FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
            FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
            FLAX_MODEL_MAPPING,
            FlaxAutoModel,
            FlaxAutoModelForCausalLM,
            FlaxAutoModelForImageClassification,
            FlaxAutoModelForMaskedLM,
            FlaxAutoModelForMultipleChoice,
            FlaxAutoModelForNextSentencePrediction,
            FlaxAutoModelForPreTraining,
            FlaxAutoModelForQuestionAnswering,
            FlaxAutoModelForSeqaSeqLM,
            FlaxAutoModelForSequenceClassification,
            FlaxAutoModelForSpeechSeqaSeq,
            FlaxAutoModelForTokenClassification,
            FlaxAutoModelForVisionaSeq,
        )

else:
    import sys

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










'''simple docstring'''

import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm






def 						_UpperCAmelCase     (	a		:						Tuple    ,      a		:						int				)      ->      List[str]:





						"""simple docstring"""
						if isinstance(a    ,      (list, tuple)				):
												return honor_type(a    ,      (move_to_device(a    ,      a				) for t in state)				)
						elif isinstance(a    ,      a				):
												return type(a				)({k: move_to_device(a    ,      a				) for k, v in state.items()}				)
						elif isinstance(a    ,      torch.Tensor				):
												return state.to(a				)
						return state






class 				__magic_name__  (						torch.optim.Optimizer					):







	"""simple docstring"""



	def __init__(				self	,   _lowercase	,   _lowercase=True	,   _lowercase=None							)    ->	int:
							lowercase_   : List[str]   	=      optimizer
							lowercase_   : Optional[int]   	=      scaler
							lowercase_   : Union[str, Any]   	=      AcceleratorState()
							lowercase_   : Optional[Any]   	=      GradientState()
							lowercase_   : Union[str, Any]   	=      device_placement
							lowercase_   : List[str]   	=      False
							lowercase_   : Optional[Any]   	=      None

							# Handle device placement
							if device_placement:
													lowercase_   : str   	=      self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_lowercase	,   self.accelerator_state.device							)
													else:
																			lowercase_   : List[str]   	=      move_to_device(_lowercase	,   self.accelerator_state.device							)
													self.optimizer.load_state_dict(_lowercase							)
	@property
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.state
	@state.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Union[str, Any]:
							lowercase_   : Optional[Any]   	=      state
	@property
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.param_groups
	@param_groups.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Dict   	=      param_groups
	@property
	def 							lowerCamelCase__		(				self							)    ->	int:
							return self.optimizer.defaults
	@defaults.setter
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							lowercase_   : List[Any]   	=      defaults
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Tuple:
							self.optimizer.add_param_group(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Any:
							if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
													xm.send_cpu_data_to_device(_lowercase	,   self.accelerator_state.device							)
							self.optimizer.load_state_dict(_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	Any:
							return self.optimizer.state_dict()
	def 							lowerCamelCase__		(				self	,   _lowercase=None							)    ->	Optional[Any]:
							if self.gradient_state.sync_gradients:
													lowercase_   : Tuple   	=      'set_to_none' in inspect.signature(self.optimizer.zero_grad							).parameters
													if accept_arg:
																			if set_to_none is None:
																									lowercase_   : Optional[int]   	=      False
																			self.optimizer.zero_grad(set_to_none=_lowercase							)
													else:
																			if set_to_none is not None:
																									raise ValueError('`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.'							)
																			self.optimizer.zero_grad()
	def 							lowerCamelCase__		(				self	,   _lowercase=None							)    ->	Optional[Any]:
							if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																			lowercase_   : Union[str, Any]   	=      {'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer	,   optimizer_args=_lowercase							)
													elif self.scaler is not None:
																			lowercase_   : Optional[Any]   	=      False
																			if self._last_scale is None:
																									# `get_scale` is an async operation requiring full synchronization
																									# on CPU and GPUs before finishing. As a result, we store away
																									# the prior one to reduce the call overhead
																									lowercase_   : Tuple   	=      self.scaler.get_scale()
																									lowercase_   : int   	=      True
																			self.scaler.step(self.optimizer	,   _lowercase							)
																			self.scaler.update()
																			lowercase_   : List[str]   	=      self.scaler.get_scale()
																			if not new_scale:
																									# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																									lowercase_   : Optional[int]   	=      scale_after < self._last_scale
																			lowercase_   : List[str]   	=      scale_after

													else:
																			self.optimizer.step(_lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							for param_group in self.optimizer.param_groups:
													lowercase_   : Dict   	=      [parameters_map.get(_lowercase	,   _lowercase							) for p in param_group['params']]
	@property
	def 							lowerCamelCase__		(				self							)    ->	List[Any]:
							warnings.warn(
							    'The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '
							    '`optimizer.step_was_skipped` instead.'	,   _lowercase	,   )
							return self._is_overflow
	@property
	def 							lowerCamelCase__		(				self							)    ->	str:
							return self._is_overflow
	def __getstate__(				self							)    ->	str:
							return self.__dict__.copy()







	def __setstate__(				self	,   _lowercase							)    ->	Union[str, Any]:
							self.__dict__.update(_lowercase							)
