
# 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    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = True				,			__UpperCamelCase = False  ):
									_lowercase					       =					scheduler
									_lowercase					       =					optimizers if isinstance(__UpperCamelCase				,			(list, tuple)  ) else [optimizers]
									_lowercase					       =					split_batches
									_lowercase					       =					step_with_optimizer
									_lowercase					       =					GradientState()






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									if not self.step_with_optimizer:
															# No link between scheduler and optimizer -> just step
															self.scheduler.step(*__UpperCamelCase				,			**__UpperCamelCase  )
															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(*__UpperCamelCase				,			**__UpperCamelCase  )
									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(__UpperCamelCase  ):
																					# 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(*__UpperCamelCase				,			**__UpperCamelCase  )
																					else:
																											self.scheduler.step(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									return self.scheduler.get_last_lr()






			def    UpperCamelCase_				(      self  ):
									return self.scheduler.state_dict()






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									self.scheduler.load_state_dict(__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									return self.scheduler.get_lr()






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.scheduler.print_lr(*__UpperCamelCase				,			**__UpperCamelCase  )

def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# bit count represents no. of bits in the gray code
						if bit_count < 0:
												raise ValueError("""The given input must be positive"""       )

						# get the generated string sequence
						_lowercase					       =					gray_code_sequence_string(SCREAMING_SNAKE_CASE_       )
						#
						# convert them to integers
						for i in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_lowercase					       =					int(sequence[i]		,   2       )

						return sequence




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int       )  ->     list:

						# The approach is a recursive one
						# Base case achieved when either n = 0 or n=1
						if bit_count == 0:
												return ["0"]

						if bit_count == 1:
												return ["0", "1"]

						_lowercase					       =					1 << bit_count  # defines the length of the sequence
						# 1<< n is equivalent to 2^n

						# recursive answer will generate answer for n-1 bits
						_lowercase					       =					gray_code_sequence_string(bit_count - 1       )

						_lowercase					       =					[]

						# append 0 to first half of the smaller sequence generated
						for i in range(seq_len // 2       ):
												_lowercase					       =					"""0""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						# append 1 to second half ... start from the end of the list
						for i in reversed(range(seq_len // 2       )       ):
												_lowercase					       =					"""1""" + smaller_sequence[i]
												sequence.append(SCREAMING_SNAKE_CASE_       )

						return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()