






import warnings

from .state import AcceleratorState, GradientState


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


class        _snake_case    :






	def __init__(		self       ,UpperCamelCase       ,UpperCamelCase       ,UpperCamelCase = True       ,UpperCamelCase = False							)    ->     Union[str, Any]:
					snake_case__   :Union[str, Any]       = scheduler
					snake_case__   :List[Any]       = optimizers if isinstance(_SCREAMING_SNAKE_CASE       ,(list, tuple)							) else [optimizers]
					snake_case__   :Union[str, Any]       = split_batches
					snake_case__   :int       = step_with_optimizer
					snake_case__   :int       = GradientState()






	def  lowerCAmelCase_				(		self       ,*UpperCamelCase       ,**UpperCamelCase							)    ->     Union[str, Any]:
					if not self.step_with_optimizer:
									# No link between scheduler and optimizer -> just step
									self.scheduler.step(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)
									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(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)
					else:
									# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
									# num_processes steps per training step
									snake_case__   :str       = AcceleratorState().num_processes
									for _ in range(_SCREAMING_SNAKE_CASE							):
													# 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(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)
													else:
																	self.scheduler.step(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)






	def  lowerCAmelCase_				(		self							)    ->     List[Any]:
					return self.scheduler.get_last_lr()






	def  lowerCAmelCase_				(		self							)    ->     int:
					return self.scheduler.state_dict()






	def  lowerCAmelCase_				(		self       ,UpperCamelCase							)    ->     Dict:
					self.scheduler.load_state_dict(_SCREAMING_SNAKE_CASE							)






	def  lowerCAmelCase_				(		self							)    ->     Any:
					return self.scheduler.get_lr()






	def  lowerCAmelCase_				(		self       ,*UpperCamelCase       ,**UpperCamelCase							)    ->     str:
					return self.scheduler.print_lr(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)





'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


