# 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   lowercase      :




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   bool = False )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    scheduler
												SCREAMING_SNAKE_CASE           =    optimizers if isinstance(_UpperCamelCase		,							(list, tuple) ) else [optimizers]
												SCREAMING_SNAKE_CASE           =    split_batches
												SCREAMING_SNAKE_CASE           =    step_with_optimizer
												SCREAMING_SNAKE_CASE           =    GradientState()




							def 		__snake_case(  self				:   Tuple		,							*_UpperCamelCase				:   Tuple		,							**_UpperCamelCase				:   Optional[Any] )   ->			Tuple:



												'''simple docstring'''

												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
																	SCREAMING_SNAKE_CASE           =    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 		__snake_case(  self				:   Union[str, Any] )   ->			List[Any]:



												'''simple docstring'''

												return self.scheduler.get_last_lr()




							def 		__snake_case(  self				:   Dict )   ->			Optional[int]:



												'''simple docstring'''

												return self.scheduler.state_dict()




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Tuple )   ->			Optional[int]:



												'''simple docstring'''

												self.scheduler.load_state_dict(_UpperCamelCase )




							def 		__snake_case(  self				:   int )   ->			List[Any]:



												'''simple docstring'''

												return self.scheduler.get_lr()





							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   Optional[Any]		,							**_UpperCamelCase				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												return self.scheduler.print_lr(*_UpperCamelCase		,							**_UpperCamelCase )





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




