


"""simple docstring"""






from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowercase  		=			datasets.utils.logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE_  (   folder_based_builder.FolderBasedBuilderConfig):




							'''simple docstring'''





							__magic_name__					:     bool				  =  None
							__magic_name__					:     bool				  =  None


class 			SCREAMING_SNAKE_CASE_  (   folder_based_builder.FolderBasedBuilder):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  datasets.Image()
							__magic_name__					:     List[Any]				  =  '''image'''
							__magic_name__					:     Union[str, Any]				  =  ImageFolderConfig
							__magic_name__					:     List[str]  # definition at the bottom of the script
							__magic_name__					:     str				  =  ImageClassification(image_column='''image'''	,      label_column='''label''')


lowercase  		=			[
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
lowercase  		=			IMAGE_EXTENSIONS













'''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							)
