





'''simple docstring'''

# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")

class       UpperCAmelCase :




		def __init__(self      :	str							,			A__      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[int] = True							,			A__      :	Any = False				)					-> Any:
						lowercase         = scheduler
						lowercase         = optimizers if isinstance(a_							,			(list, tuple)				) else [optimizers]
						lowercase         = split_batches
						lowercase         = step_with_optimizer
						lowercase         = GradientState()




		def 				UpperCAmelCase__   (self      :	Optional[Any]							,			*A__      :	str							,			**A__      :	str				)					-> int:
						if not self.step_with_optimizer:
										# No link between scheduler and optimizer -> just step
										self.scheduler.step(*a_							,			**a_				)
										return

						# Otherwise, first make sure the optimizer was stepped.
						if not self.gradient_state.sync_gradients:
										if self.gradient_state.adjust_scheduler:
														self.scheduler._step_count += 1
										return

						for opt in self.optimizers:
										if opt.step_was_skipped:
														return
						if self.split_batches:
										# Split batches -> the training dataloader batch size is not changed so one step per training step
										self.scheduler.step(*a_							,			**a_				)
						else:
										# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
										# num_processes steps per training step
										lowercase         = AcceleratorState().num_processes
										for _ in range(a_				):
														# Special case when using OneCycle and `drop_last` was not used
														if hasattr(self.scheduler							,			"total_steps"				):
																		if self.scheduler._step_count <= self.scheduler.total_steps:
																						self.scheduler.step(*a_							,			**a_				)
														else:
																		self.scheduler.step(*a_							,			**a_				)




		def 				UpperCAmelCase__   (self      :	Dict				)					-> Optional[int]:
						return self.scheduler.get_last_lr()




		def 				UpperCAmelCase__   (self      :	Any				)					-> List[Any]:
						return self.scheduler.state_dict()




		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	Tuple				)					-> str:
						self.scheduler.load_state_dict(a_				)




		def 				UpperCAmelCase__   (self      :	Optional[Any]				)					-> Union[str, Any]:
						return self.scheduler.get_lr()





		def 				UpperCAmelCase__   (self      :	Tuple							,			*A__      :	List[Any]							,			**A__      :	str				)					-> Tuple:
						return self.scheduler.print_lr(*a_							,			**a_				)











'''simple docstring'''

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




