code: 
  
  
  '''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__	:      Tuple				=						logging.get_logger(__name__)
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[Any]:
       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_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  List[Any]:
           # set global weakref object
           set_hf_deepspeed_config(self			)
           dep_version_check('accelerate'			)
           dep_version_check('deepspeed'			)
           super().__init__(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           super().__init__(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Any              =	None
           __lowerCamelCase     :			List[Any]              =	[]
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           if self._dtype is None:
                raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype'			)
           return self._dtype
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Tuple:
           __lowerCamelCase     :			List[Any]              =	self.get_value(SCREAMING_SNAKE_CASE_			)
           if val is None:
                return False
           else:
                return val == "auto"
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=None    ,  SCREAMING_SNAKE_CASE_=True			)       ->  Any:
           __lowerCamelCase       , __lowerCamelCase     :			int              =	self.find_config_node(SCREAMING_SNAKE_CASE_			)
           if config is None:
                return
  
           if config.get(SCREAMING_SNAKE_CASE_			) == "auto":
                __lowerCamelCase     :			Any              =	hf_val
                return
  
           if not must_match:
                return
  
           __lowerCamelCase     :			int              =	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						:      Dict							  =    partialmethod(_UpperCAmelCase      ,	must_match=_UpperCAmelCase						)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           __lowerCamelCase     :			int              =	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:
                __lowerCamelCase     :			int              =	'apex' if args.fpaa_backend == 'apex' else 'amp'
           else:
                __lowerCamelCase     :			Optional[Any]              =	None
  
           if args.save_on_each_node:
                # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
                __lowerCamelCase     :			Union[str, Any]              =	self.config.get('checkpoint'    ,  {}			)
                __lowerCamelCase     :			str              =	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'			):
                __lowerCamelCase     :			Dict              =	torch.bfloataa
           elif self.is_false('fp16.enabled'			):
                __lowerCamelCase     :			List[str]              =	torch.floataa
           else:
                __lowerCamelCase     :			List[str]              =	torch.floataa
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
           __lowerCamelCase     :			int              =	[
               'zero_optimization.reduce_bucket_size',
               'zero_optimization.stage3_prefetch_bucket_size',
               'zero_optimization.stage3_param_persistence_threshold',
           ]
           __lowerCamelCase     :			Optional[Any]              =	[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'			):
                     __lowerCamelCase     :			Union[str, Any]              =	model.config.hidden_size
                elif hasattr(model.config    ,  'hidden_sizes'			):
                     # if there are many hidden sizes pick the largest one
                     __lowerCamelCase     :			Optional[Any]              =	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:
                __lowerCamelCase     :			Optional[int]              =	'\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__	:      Union[str, Any]				=						None
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Union[str, Any]							)  ->	str:
       # 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)
       __lowerCamelCase     :			Optional[int]              =	weakref.ref(UpperCAmelCase_							)
  
  
  
  def     UpperCAmelCase__      (			)  ->	List[str]:
       # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
       global _hf_deepspeed_config_weak_ref
       __lowerCamelCase     :			Any              =	None
  
  
  
  def     UpperCAmelCase__      (			)  ->	List[str]:
       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     UpperCAmelCase__      (			)  ->	List[Any]:
       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     UpperCAmelCase__      (			UpperCAmelCase_					:       Tuple   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       str   ,					UpperCAmelCase_					:       int   ,					UpperCAmelCase_					:       str							)  ->	Any:
       from accelerate.utils import DummyOptim, DummyScheduler
  
       __lowerCamelCase     :			Union[str, Any]              =	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)
  
       __lowerCamelCase     :			str              =	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.'							)
            __lowerCamelCase     :			List[Any]              =	DummyOptim(params=UpperCAmelCase_							)
       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.
            __lowerCamelCase     :			str              =	trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            __lowerCamelCase     :			List[str]              =	True
  
       __lowerCamelCase     :			Optional[Any]              =	None
       if "scheduler" in config:
            __lowerCamelCase     :			int              =	DummyScheduler(UpperCAmelCase_							)
       else:
            if isinstance(UpperCAmelCase_   ,					UpperCAmelCase_							):
                 raise ValueError(
                     'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
                     'Please configure a scheduler in the DeepSpeed config.'							)
            __lowerCamelCase     :			str              =	trainer.create_scheduler(num_training_steps=UpperCAmelCase_   ,					optimizer=UpperCAmelCase_							)
  
       return optimizer, lr_scheduler
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Optional[Any]=False							)  ->	List[Any]:
       from deepspeed.utils import logger as ds_logger
  
       __lowerCamelCase     :			Tuple              =	trainer.model
       __lowerCamelCase     :			Union[str, Any]              =	trainer.args
  
       __lowerCamelCase     :			str              =	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(UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
  
       # 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'							)
            __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	None, None
            __lowerCamelCase     :			Dict              =	None
       else:
            __lowerCamelCase     :			Dict              =	None  # important for when deepspeed_init is used as re-init
            __lowerCamelCase     :			Union[str, Any]              =	list(filter(lambda UpperCAmelCase_							: p.requires_grad   ,					model.parameters()							)							)
            __lowerCamelCase       , __lowerCamelCase     :			Optional[int]              =	deepspeed_optim_sched(
                UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_   ,					UpperCAmelCase_							)
  
       # keep for quick debug:
       # from pprint import pprint; pprint(config)
  
       return optimizer, lr_scheduler
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Dict							)  ->	Union[str, Any]:
       # 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
  
       __lowerCamelCase     :			Union[str, Any]              =	sorted(glob.glob(F'{checkpoint_path}/global_step*'							)							)
  
       if len(UpperCAmelCase_							) > 0:
            logger.info(F'Attempting to resume from {checkpoint_path}'							)
            # this magically updates self.optimizer and self.lr_scheduler
            __lowerCamelCase       , __lowerCamelCase     :			Any              =	deepspeed_engine.load_checkpoint(
                UpperCAmelCase_   ,					load_optimizer_states=UpperCAmelCase_   ,					load_lr_scheduler_states=UpperCAmelCase_							)
            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: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  