


'''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}'							)










'''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()






