code: 
  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
  
  _snake_case			:       Dict      =						logging.get_logger(__name__)
  
  def    __snake_case       (  ):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				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      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :Optional[Any]		,						lowerCAmelCase__       :str    )     ->Optional[Any]:
  										# set global weakref object
  										set_hf_deepspeed_config(self    )
  										dep_version_check("accelerate"    )
  										dep_version_check("deepspeed"    )
  										super().__init__(lowerCAmelCase__    )
  
  
  
  
  
  class      UpperCamelCase_			(			__a		):
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :Dict		,						lowerCAmelCase__       :str    )     ->Any:
  										super().__init__(lowerCAmelCase__    )
  										lowercase						    =			None
  										lowercase						    =			[]
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]    )     ->Optional[Any]:
  										if self._dtype is None:
  														raise ValueError("trainer_config_process() wasn't called yet to tell dtype"    )
  										return self._dtype
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :Tuple    )     ->Dict:
  										lowercase						    =			self.get_value(lowerCAmelCase__    )
  										if val is None:
  														return False
  										else:
  														return val == "auto"
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :List[str]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :str=None		,						lowerCAmelCase__       :List[str]=True    )     ->Tuple:
  										lowercase    ,  lowercase						    =			self.find_config_node(lowerCAmelCase__    )
  										if config is None:
  														return
  
  										if config.get(lowerCAmelCase__    ) == "auto":
  														lowercase						    =			hf_val
  														return
  
  										if not must_match:
  														return
  
  										lowercase						    =			config.get(lowerCAmelCase__    )
  										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}'''    )
  
  						UpperCamelCase			:     Dict							      =				partialmethod(__a		,						must_match=__a		)
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :List[str]		,						lowerCAmelCase__       :Dict    )     ->List[str]:
  										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"		,						lowerCAmelCase__		,						"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 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]		,						lowerCAmelCase__       :List[Any]		,						lowerCAmelCase__       :Optional[Any]		,						lowerCAmelCase__       :int    )     ->List[Any]:
  										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(lowerCAmelCase__    )]
  
  										if len(lowerCAmelCase__    ) > 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"		,						lowerCAmelCase__		,						"num_training_steps (calculated)"    )
  										self.fill_match("scheduler.params.warmup_num_steps"		,						args.get_warmup_steps(lowerCAmelCase__    )		,						"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\'.'''    )
  
  
  
  
  _snake_case			:       Optional[int]      =						None
  
  def    __snake_case       (  __magic_name__	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				global _hf_deepspeed_config_weak_ref
  				# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  				lowercase						    =			weakref.ref(__magic_name__	)
  
  def    __snake_case       (  ):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				global _hf_deepspeed_config_weak_ref
  				lowercase						    =			None
  
  def    __snake_case       (  ):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				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    __snake_case       (  ):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				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    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				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=__magic_name__	)
  				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(__magic_name__	)
  				else:
  								if isinstance(__magic_name__		,						__magic_name__	):
  												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=__magic_name__		,						optimizer=__magic_name__	)
  
  				return optimizer, lr_scheduler
  
  def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__=False	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				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(__magic_name__		,						__magic_name__		,						__magic_name__	)
  
  				# 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 __magic_name__	: p.requires_grad		,						model.parameters()	)	)
  								lowercase    ,  lowercase						    =			deepspeed_optim_sched(
  								    __magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__		,						__magic_name__	)
  
  				# keep for quick debug:
  				# from pprint import pprint; pprint(config)
  
  				return optimizer, lr_scheduler
  
  
  
  
  def    __snake_case       (  __magic_name__		,						__magic_name__	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				import glob
  
  				lowercase						    =			sorted(glob.glob(F'''{checkpoint_path}/global_step*'''	)	)
  
  				if len(__magic_name__	) > 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(
  								    __magic_name__		,						load_optimizer_states=__magic_name__		,						load_lr_scheduler_states=__magic_name__	)
  								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}'''	)
  
style_context: 
  from __future__ import annotations
  
  import random
  import string
  
  
  
  
  
  class      UpperCamelCase_			:
  
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						def __init__( self       :Dict		,						lowerCAmelCase__       :str | None = None    )     ->None:
  										lowercase						    =			passcode or self.__passcode_creator()
  										lowercase						    =			self.__make_key_list()
  										lowercase						    =			self.__make_shift_key()
  
  
  
  
  
  						def __str__( self       :Dict    )     ->str:
  										return "".join(self.__passcode    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :list[int]    )     ->list[int]:
  										for i in range(1		,						len(lowerCAmelCase__    )		,						2    ):
  														iterlist[i] *= -1
  										return iterlist
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :int    )     ->list[str]:
  										lowercase						    =			string.ascii_letters + string.digits
  										lowercase						    =			[random.choice(lowerCAmelCase__    ) for _ in range(random.randint(10		,						20    )    )]
  										return password
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]    )     ->list[str]:
  										lowercase						    =			(
  										    string.ascii_letters + string.digits + string.punctuation + " \t\n"
  										)
  
  										lowercase						    =			[]
  
  										# creates points known as breakpoints to break the key_list_options at those
  										# points and pivot each substring
  										lowercase						    =			sorted(set(self.__passcode    )    )
  										lowercase						    =			[]
  
  										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
  										for i in key_list_options:
  														temp_list.extend(lowerCAmelCase__    )
  
  														# checking breakpoints at which to pivot temporary sublist and add it into
  														# keys_l
  														if i in breakpoints or i == key_list_options[-1]:
  																		keys_l.extend(temp_list[::-1]    )
  																		temp_list.clear()
  
          # returning a shuffled keys_l to prevent brute force guessing of shift key
  										return keys_l
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Optional[int]    )     ->int:
  										lowercase						    =			sum(self.__neg_pos([ord(lowerCAmelCase__    ) for x in self.__passcode]    )    )
  										return num if num > 0 else len(self.__passcode    )
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :str		,						lowerCAmelCase__       :str    )     ->str:
  										lowercase						    =			""
  
  										# decoding shift like Caesar cipher algorithm implementing negative shift or
  										# reverse shift or left shift
  										for i in encoded_message:
  														lowercase						    =			self.__key_list.index(lowerCAmelCase__    )
  														decoded_message += self.__key_list[
  														    (position - self.__shift_key) % -len(self.__key_list    )
  														]
  
  										return decoded_message
  
  
  
  
  
  						def 					SCREAMING_SNAKE_CASE( self       :Dict		,						lowerCAmelCase__       :str    )     ->str:
  										lowercase						    =			""
  
  										# encoding shift like Caesar cipher algorithm implementing positive shift or
  										# forward shift or right shift
  										for i in plaintext:
  														lowercase						    =			self.__key_list.index(lowerCAmelCase__    )
  														encoded_message += self.__key_list[
  														    (position + self.__shift_key) % len(self.__key_list    )
  														]
  
  										return encoded_message
  
  
  
  
  def    __snake_case       (  __magic_name__ = "Hello, this is a modified Caesar cipher"	):
  
  
  
  
  
  
  
  				'''simple docstring'''
  
  
  				lowercase						    =			ShuffledShiftCipher()
  				return cipa.decrypt(cipa.encrypt(__magic_name__	)	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  