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
  
  snake_case   :   Dict    		=		logging.get_logger(__name__)
  
  
  def        A	(							)						->		List[str]:
  
         """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__   (							lowercase__):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  List[str]							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             set_hf_deepspeed_config(self   )
             dep_version_check('accelerate'   )
             dep_version_check('deepspeed'   )
             super().__init__(_A   )
  
  
  
  class 					UpperCamelCase__   (							lowercase__):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  List[Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(_A   )
             __magic_name__										=						None
             __magic_name__										=						[]
  
  
  
  
  
  
  
      def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
             '''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   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.get_value(_A   )
             if val is None:
                    return False
             else:
                    return val == "auto"
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  str=None							,						UpperCamelCase_   :  Any=True   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						self.find_config_node(_A   )
             if config is None:
                    return
  
             if config.get(_A   ) == "auto":
                    __magic_name__										=						hf_val
                    return
  
             if not must_match:
                    return
  
             __magic_name__										=						config.get(_A   )
             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											=   partialmethod(lowercase__       ,  must_match=lowercase__)
  
  
  
  
  
  
  
      def 							a__ (		self   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						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'							,						_A							,						'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:
                    __magic_name__										=						'apex' if args.fpaa_backend == 'apex' else 'amp'
             else:
                    __magic_name__										=						None
  
             if args.save_on_each_node:
                    # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
                    __magic_name__										=						self.config.get('checkpoint'							,						{}   )
                    __magic_name__										=						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'   ):
                    __magic_name__										=						torch.bfloataa
             elif self.is_false('fp16.enabled'   ):
                    __magic_name__										=						torch.floataa
             else:
                    __magic_name__										=						torch.floataa
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						[
                 'zero_optimization.reduce_bucket_size',
                 'zero_optimization.stage3_prefetch_bucket_size',
                 'zero_optimization.stage3_param_persistence_threshold',
             ]
             __magic_name__										=						[x for x in hidden_size_based_keys if self.is_auto(_A   )]
  
             if len(_A   ) > 0:
                    if hasattr(model.config							,						'hidden_size'   ):
                           __magic_name__										=						model.config.hidden_size
                    elif hasattr(model.config							,						'hidden_sizes'   ):
                           # if there are many hidden sizes pick the largest one
                           __magic_name__										=						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'							,						1_0 * hidden_size   )
  
          # scheduler
             self.fill_match('scheduler.params.total_num_steps'							,						_A							,						'num_training_steps (calculated)'   )
             self.fill_match('scheduler.params.warmup_num_steps'							,						args.get_warmup_steps(_A   )							,						'warmup_steps'   )
  
             if len(self.mismatches   ) > 0:
                    __magic_name__										=						'\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[Any]    		=		None
  
  
  def        A	(							__snake_case:   List[Any]     )						->		str:
  
         """simple docstring"""
  
  
  
  
  
         global _hf_deepspeed_config_weak_ref
         # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
         __magic_name__										=						weakref.ref(__snake_case     )
  
  
  def        A	(							)						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         global _hf_deepspeed_config_weak_ref
         __magic_name__										=						None
  
  
  def        A	(							)						->		Optional[Any]:
  
         """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        A	(							)						->		List[Any]:
  
         """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        A	(							__snake_case:   Optional[int]   ,    __snake_case:   str   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Optional[Any]   ,    __snake_case:   str     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         from accelerate.utils import DummyOptim, DummyScheduler
  
         __magic_name__										=						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)
  
         __magic_name__										=						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.'     )
                __magic_name__										=						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.
                __magic_name__										=						trainer.create_optimizer()
                # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
                __magic_name__										=						True
  
         __magic_name__										=						None
         if "scheduler" in config:
                __magic_name__										=						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.'     )
                __magic_name__										=						trainer.create_scheduler(num_training_steps=__snake_case   ,    optimizer=__snake_case     )
  
         return optimizer, lr_scheduler
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Dict=False     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         from deepspeed.utils import logger as ds_logger
  
         __magic_name__										=						trainer.model
         __magic_name__										=						trainer.args
  
         __magic_name__										=						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'     )
                __magic_name__										=						None, None
                __magic_name__										=						None
         else:
                __magic_name__										=						None  # important for when deepspeed_init is used as re-init
                __magic_name__										=						list(filter(lambda __snake_case     : p.requires_grad   ,    model.parameters()     )     )
                __magic_name__										=						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	(							__snake_case:   List[Any]   ,    __snake_case:   Optional[Any]     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         import glob
  
         __magic_name__										=						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
                __magic_name__										=						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}"""     )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						np.where(remainder == value     )
                       # if we need_to_add less than what's in inds
                       # we draw randomly from them.
                       # if we need to add more, we add them all and
                       # go to the next value
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 2:
                raise ValueError('Minimum class count error'     )
         if n_train < n_classes:
                raise ValueError(
                    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
         if n_test < n_classes:
                raise ValueError(
                    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                       train.extend(perm_indices_class_i[: n_i[i]]     )
                       test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test