



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

_UpperCamelCase							    =						logging.get_logger(__name__)

def 	a_					(							)				->				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  lowerCamelCase_   (    a_					):
  """simple docstring"""




  def __init__(			self				:     Dict      ,     _a				:     str       ) -> Union[str, Any]:
    set_hf_deepspeed_config(self       )
    dep_version_check('accelerate'       )
    dep_version_check('deepspeed'       )
    super().__init__(snake_case__       )






class  lowerCamelCase_   (    a_					):
  """simple docstring"""




  def __init__(			self				:     Tuple      ,     _a				:     Union[str, Any]       ) -> Dict:
    super().__init__(snake_case__       )
    __lowerCamelCase	:	int   	=			None
    __lowerCamelCase	:	Optional[Any]   	=			[]




  def 						_lowercase					(			self				:     Dict       ) -> int:


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




  def 						_lowercase					(			self				:     Tuple      ,     _a				:     int       ) -> Dict:
    __lowerCamelCase	:	Optional[int]   	=			self.get_value(snake_case__       )
    if val is None:
      return False
    else:
      return val == "auto"




  def 						_lowercase					(			self				:     Dict      ,     _a				:     Optional[int]      ,     _a				:     Dict      ,     _a				:     Optional[int]=None      ,     _a				:     List[str]=True       ) -> str:
    __lowerCamelCase	,__lowerCamelCase	:	str   	=			self.find_config_node(snake_case__       )
    if config is None:
      return

    if config.get(snake_case__       ) == "auto":
      __lowerCamelCase	:	Optional[Any]   	=			hf_val
      return

    if not must_match:
      return

    __lowerCamelCase	:	Optional[Any]   	=			config.get(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}'       )

  a_							 =partialmethod(a_       ,       must_match=a_					)




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     Union[str, Any]       ) -> Tuple:
    __lowerCamelCase	:	str   	=			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'      ,     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	:	Any   	=			'apex' if args.fpaa_backend == 'apex' else 'amp'
    else:
      __lowerCamelCase	:	int   	=			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	:	Optional[Any]   	=			self.config.get('checkpoint'      ,     {}       )
      __lowerCamelCase	:	Optional[Any]   	=			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	:	List[Any]   	=			torch.bfloataa
    elif self.is_false('fp16.enabled'       ):
      __lowerCamelCase	:	Optional[Any]   	=			torch.floataa
    else:
      __lowerCamelCase	:	Union[str, Any]   	=			torch.floataa




  def 						_lowercase					(			self				:     str      ,     _a				:     Tuple      ,     _a				:     List[Any]      ,     _a				:     List[str]       ) -> Tuple:
    __lowerCamelCase	:	List[Any]   	=			[
        'zero_optimization.reduce_bucket_size',
        'zero_optimization.stage3_prefetch_bucket_size',
        'zero_optimization.stage3_param_persistence_threshold',
    ]
    __lowerCamelCase	:	Dict   	=			[x for x in hidden_size_based_keys if self.is_auto(snake_case__       )]

    if len(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	:	Tuple   	=			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'      ,     snake_case__      ,     'num_training_steps (calculated)'       )
    self.fill_match('scheduler.params.warmup_num_steps'      ,     args.get_warmup_steps(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\'.'       )




_UpperCamelCase							    =						None

def 	a_					(							_lowerCAmelCase	)				->				Optional[int]:
  # 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[Any]   	=			weakref.ref(_lowerCAmelCase	)

def 	a_					(							)				->				Optional[Any]:
  # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  global _hf_deepspeed_config_weak_ref
  __lowerCamelCase	:	Dict   	=			None

def 	a_					(							)				->				Optional[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().is_zeroa()
  else:
    return False

def 	a_					(							)				->				Tuple:
  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_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[Any]:
  from accelerate.utils import DummyOptim, DummyScheduler

  __lowerCamelCase	:	List[str]   	=			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	:	List[Any]   	=			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	:	Any   	=			DummyOptim(params=_lowerCAmelCase	)
  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	:	List[str]   	=			trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    __lowerCamelCase	:	str   	=			True

  __lowerCamelCase	:	str   	=			None
  if "scheduler" in config:
    __lowerCamelCase	:	Any   	=			DummyScheduler(_lowerCAmelCase	)
  else:
    if isinstance(_lowerCAmelCase	,_lowerCAmelCase	):
      raise ValueError(
          'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
          'Please configure a scheduler in the DeepSpeed config.'	)
    __lowerCamelCase	:	List[str]   	=			trainer.create_scheduler(num_training_steps=_lowerCAmelCase	,optimizer=_lowerCAmelCase	)

  return optimizer, lr_scheduler

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase=False	)				->				Optional[Any]:
  from deepspeed.utils import logger as ds_logger

  __lowerCamelCase	:	Union[str, Any]   	=			trainer.model
  __lowerCamelCase	:	List[Any]   	=			trainer.args

  __lowerCamelCase	:	Any   	=			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(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  # 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[Any]   	=			None, None
    __lowerCamelCase	:	Optional[Any]   	=			None
  else:
    __lowerCamelCase	:	List[str]   	=			None  # important for when deepspeed_init is used as re-init
    __lowerCamelCase	:	Optional[int]   	=			list(filter(lambda _lowerCAmelCase	: p.requires_grad	,model.parameters()	)	)
    __lowerCamelCase	,__lowerCamelCase	:	List[Any]   	=			deepspeed_optim_sched(
        _lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

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

  return optimizer, lr_scheduler


def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				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	:	str   	=			sorted(glob.glob(F'{checkpoint_path}/global_step*'	)	)

  if len(_lowerCAmelCase	) > 0:
    logger.info(F'Attempting to resume from {checkpoint_path}'	)
    # this magically updates self.optimizer and self.lr_scheduler
    __lowerCamelCase	,__lowerCamelCase	:	Union[str, Any]   	=			deepspeed_engine.load_checkpoint(
        _lowerCAmelCase	,load_optimizer_states=_lowerCAmelCase	,load_lr_scheduler_states=_lowerCAmelCase	)
    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'''





from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
_UpperCamelCase							    =						load_iris()
iris.keys()


print(f'''Target names: \n {iris.target_names} ''')
print(f'''\n Features: \n {iris.feature_names}''')

# Train set e Test set
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

_UpperCamelCase							    =						KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
_UpperCamelCase							    =						[[1, 2, 1, 4], [2, 3, 4, 5]]

_UpperCamelCase							    =						knn.predict(X_new)

print(
    f'''\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'''
    f''' {iris["target_names"][prediction]}'''
)






