






'''simple docstring'''



import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
				import torch

__A       =      logging.get_logger(__name__)





def 	_A						(							):
						return importlib.util.find_spec("""deepspeed"""		) is not None


if is_accelerate_available() and is_deepspeed_available():
				from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
				# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
				# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
				from builtins import object as DeepSpeedConfig





class      A							(    UpperCamelCase_			):



			def __init__(   self    ,       lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									set_hf_deepspeed_config(self    )
									dep_version_check("""accelerate"""    )
									dep_version_check("""deepspeed"""    )
									super().__init__(__SCREAMING_SNAKE_CASE    )





class      A							(    UpperCamelCase_			):



			def __init__(   self    ,       lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									super().__init__(__SCREAMING_SNAKE_CASE    )
									lowercase__								=    None
									lowercase__								=    []



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									if self._dtype is None:
															raise ValueError("""trainer_config_process() wasn\'t called yet to tell dtype"""    )
									return self._dtype



			def A__	(   self    ,       lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_value(__SCREAMING_SNAKE_CASE    )
									if val is None:
															return False
									else:
															return val == "auto"



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__=None    ,       lowerCamelCase__=True    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.find_config_node(__SCREAMING_SNAKE_CASE    )
									if config is None:
															return

									if config.get(__SCREAMING_SNAKE_CASE    ) == "auto":
															lowercase__								=    hf_val
															return

									if not must_match:
															return

									lowercase__								=    config.get(__SCREAMING_SNAKE_CASE    )
									if ds_val is not None and ds_val != hf_val:
															self.mismatches.append(F'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''    )

			lowerCamelCase							:							int    						=					partialmethod(UpperCamelCase_						,  must_match=UpperCamelCase_			)



			def A__	(   self    ,       lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    """train_micro_batch_size_per_gpu"""    ,       args.per_device_train_batch_size    ,       """per_device_train_batch_size"""    )
									self.fill_match("""gradient_accumulation_steps"""    ,       args.gradient_accumulation_steps    ,       """gradient_accumulation_steps"""    )
									self.fill_match("""train_batch_size"""    ,       __SCREAMING_SNAKE_CASE    ,       """train_batch_size (calculated)"""    )
									self.fill_match("""gradient_clipping"""    ,       args.max_grad_norm    ,       """max_grad_norm"""    )

									self.fill_match("""optimizer.params.lr"""    ,       args.learning_rate    ,       """learning_rate"""    )
									self.fill_match("""optimizer.params.betas"""    ,       [args.adam_betaa, args.adam_betaa]    ,       """adam_beta1+adam_beta2"""    )
									self.fill_match("""optimizer.params.eps"""    ,       args.adam_epsilon    ,       """adam_epsilon"""    )
									self.fill_match("""optimizer.params.weight_decay"""    ,       args.weight_decay    ,       """weight_decay"""    )

									self.fill_only("""scheduler.params.warmup_min_lr"""    ,       0    )  # not a trainer arg
									self.fill_match("""scheduler.params.warmup_max_lr"""    ,       args.learning_rate    ,       """learning_rate"""    )
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
															lowercase__								=    """apex""" if args.fpaa_backend == """apex""" else """amp"""
									else:
															lowercase__								=    None

									if args.save_on_each_node:
															# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
															lowercase__								=    self.config.get("""checkpoint"""    ,       {}    )
															lowercase__								=    args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    """fp16.enabled"""    ,       ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == """amp""")    ,       """fp16|fp16_full_eval+fp16_backend(amp)"""    ,       )

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match("""amp.enabled"""    ,       fpaa_backend == """apex"""    ,       """fp16+fp16_backend(apex)"""    )
									self.fill_match("""amp.opt_level"""    ,       args.fpaa_opt_level    ,       """fp16_opt_level"""    )

									self.fill_match("""bf16.enabled"""    ,       (args.bfaa or args.bfaa_full_eval)    ,       """bf16|bf16_full_eval"""    )

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true("""bf16.enabled"""    ):
															lowercase__								=    torch.bfloataa
									elif self.is_false("""fp16.enabled"""    ):
															lowercase__								=    torch.floataa
									else:
															lowercase__								=    torch.floataa



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    [
									    """zero_optimization.reduce_bucket_size""",
									    """zero_optimization.stage3_prefetch_bucket_size""",
									    """zero_optimization.stage3_param_persistence_threshold""",
									]
									lowercase__								=    [x for x in hidden_size_based_keys if self.is_auto(__SCREAMING_SNAKE_CASE    )]

									if len(__SCREAMING_SNAKE_CASE    ) > 0:
															if hasattr(model.config    ,       """hidden_size"""    ):
																					lowercase__								=    model.config.hidden_size
															elif hasattr(model.config    ,       """hidden_sizes"""    ):
																					# if there are many hidden sizes pick the largest one
																					lowercase__								=    max(model.config.hidden_sizes    )
															else:
																					raise ValueError(
																					    """The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, """
																					    """therefore it\'s not possible to automatically fill out the following `auto` entries """
																					    F'''in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing '''
																					    """`auto` values for these keys with an integer value of your choice."""    )

															self.fill_only("""zero_optimization.reduce_bucket_size"""    ,       hidden_size * hidden_size    )
															if self.is_zeroa():
																					# automatically assign the optimal config values based on model config
																					self.fill_only("""zero_optimization.stage3_prefetch_bucket_size"""    ,       0.9 * hidden_size * hidden_size    )
																					self.fill_only("""zero_optimization.stage3_param_persistence_threshold"""    ,       10 * hidden_size    )

        # scheduler
									self.fill_match("""scheduler.params.total_num_steps"""    ,       __SCREAMING_SNAKE_CASE    ,       """num_training_steps (calculated)"""    )
									self.fill_match("""scheduler.params.warmup_num_steps"""    ,       args.get_warmup_steps(__SCREAMING_SNAKE_CASE    )    ,       """warmup_steps"""    )

									if len(self.mismatches    ) > 0:
															lowercase__								=    """\n""".join(self.mismatches    )
															raise ValueError(
															    """Please correct the following DeepSpeed config values that mismatch TrainingArguments"""
															    F''' values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to \'auto\'.'''    )




__A       =      None





def 	_A						(							lowercase__		):
						# this is a special weakref global object to allow us to get to Deepspeed config from APIs
						# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
						global _hf_deepspeed_config_weak_ref
						# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
						lowercase__								=    weakref.ref(snake_case__		)





def 	_A						(							):
						# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
						global _hf_deepspeed_config_weak_ref
						lowercase__								=    None





def 	_A						(							):
						if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
												return _hf_deepspeed_config_weak_ref().is_zeroa()
						else:
												return False





def 	_A						(							):
						if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
												return _hf_deepspeed_config_weak_ref().config
						else:
												return None





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__		):
						from accelerate.utils import DummyOptim, DummyScheduler

						lowercase__								=    hf_deepspeed_config.config

						# Optimizer + Scheduler
						# Currently supported combos:
						# 1. DS scheduler + DS optimizer: Yes
						# 2. HF scheduler + HF optimizer: Yes
						# 3. DS scheduler + HF optimizer: Yes
						# 4. HF scheduler + DS optimizer: No
						#
						# Unless Offload is enabled in which case it's:
						# 1. DS scheduler + DS optimizer: Yes
						# 2. HF scheduler + HF optimizer: Mostly*
						# 3. DS scheduler + HF optimizer: Mostly*
						# 4. HF scheduler + DS optimizer: No
						#
						# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

						lowercase__								=    None
						if "optimizer" in config:
												if args.adafactor:
																		raise ValueError(
																		    """--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. """
																		    """Only one optimizer can be configured."""		)
												lowercase__								=    DummyOptim(params=snake_case__		)
						else:
												if hf_deepspeed_config.is_offload():
																		logger.info(
																		    """Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"""
																		    """ custom optimizer has both CPU and GPU implementation (except LAMB)"""		)

												# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
												# But trainer uses AdamW by default.
												lowercase__								=    trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												lowercase__								=    True

						lowercase__								=    None
						if "scheduler" in config:
												lowercase__								=    DummyScheduler(snake_case__		)
						else:
												if isinstance(snake_case__						,						snake_case__		):
																		raise ValueError(
																		    """Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. """
																		    """Please configure a scheduler in the DeepSpeed config."""		)
												lowercase__								=    trainer.create_scheduler(num_training_steps=snake_case__						,						optimizer=snake_case__		)

						return optimizer, lr_scheduler





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__=False		):
						from deepspeed.utils import logger as ds_logger

						lowercase__								=    trainer.model
						lowercase__								=    trainer.args

						lowercase__								=    trainer.accelerator.state.deepspeed_plugin.hf_ds_config

						# resume config update - some bits like `model` and `num_training_steps` only become available during train
						hf_deepspeed_config.trainer_config_finalize(snake_case__						,						snake_case__						,						snake_case__		)

						# set the Deepspeed log level consistent with the Trainer
						ds_logger.setLevel(args.get_process_log_level()		)

						if inference:
												# only Z3 makes sense for the inference
												if not hf_deepspeed_config.is_zeroa():
																		raise ValueError("""ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"""		)

												# in case the training config is re-used for inference
												hf_deepspeed_config.del_config_sub_tree("""optimizer"""		)
												hf_deepspeed_config.del_config_sub_tree("""lr_scheduler"""		)
												lowercase__	,  lowercase__								=    None, None
												lowercase__								=    None
						else:
												lowercase__								=    None  # important for when deepspeed_init is used as re-init
												lowercase__								=    list(filter(lambda lowercase__		: p.requires_grad						,						model.parameters()		)		)
												lowercase__	,  lowercase__								=    deepspeed_optim_sched(
												    snake_case__						,						snake_case__						,						snake_case__						,						snake_case__						,						snake_case__		)

						# keep for quick debug:
						# from pprint import pprint; pprint(config)

						return optimizer, lr_scheduler







def 	_A						(							lowercase__						,						lowercase__		):
						# it's possible that the user is trying to resume from model_path, which doesn't necessarily
						# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
						# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
						# path contains what looks like a deepspeed checkpoint
						import glob

						lowercase__								=    sorted(glob.glob(f'''{checkpoint_path}/global_step*'''		)		)

						if len(snake_case__		) > 0:
												logger.info(f'''Attempting to resume from {checkpoint_path}'''		)
												# this magically updates self.optimizer and self.lr_scheduler
												lowercase__	,  lowercase__								=    deepspeed_engine.load_checkpoint(
												    snake_case__						,						load_optimizer_states=snake_case__						,						load_lr_scheduler_states=snake_case__		)
												if load_path is None:
																		raise ValueError(f'''[deepspeed] failed to resume from checkpoint {checkpoint_path}'''		)
						else:
												raise ValueError(f'''Can\'t find a valid checkpoint at {checkpoint_path}'''		)








'''simple docstring'''



def 	_A						(							lowercase__		):
						lowercase__								=    1.3
						lowercase__								=    len(lowercase__		)
						lowercase__								=    False

						while not completed:
												# Update the gap value for a next comb
												lowercase__								=    int(gap / shrink_factor		)
												if gap <= 1:
																		lowercase__								=    True

												lowercase__								=    0
												while index + gap < len(lowercase__		):
																		if data[index] > data[index + gap]:
																								# Swap values
																								lowercase__	,  lowercase__								=    data[index + gap], data[index]
																								lowercase__								=    False
																		index += 1

						return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				print(comb_sort(unsorted))






