

'''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       :      str  	=			logging.get_logger(__name__)





def 				lowercase (					):



     '''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   ,      snake_case      ):


       '''simple docstring'''

       set_hf_deepspeed_config(self      )
       dep_version_check("accelerate"      )
       dep_version_check("deepspeed"      )
       super().__init__(snake_case      )







class   UpperCamelCase__   (       lowercase__       ):


  """simple docstring"""

  def __init__(			self   ,      snake_case      ):


       '''simple docstring'''

       super().__init__(snake_case      )
       UpperCAmelCase		:							Tuple        =       None
       UpperCAmelCase		:							Any        =       []







  def 				A_	(			self      ):


       '''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   ,      snake_case      ):


       '''simple docstring'''

       UpperCAmelCase		:							Dict        =       self.get_value(snake_case      )
       if val is None:
            return False
       else:
            return val == "auto"







  def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case=None   ,      snake_case=True      ):


       '''simple docstring'''

       UpperCAmelCase		:							List[str]        =       self.find_config_node(snake_case      )
       if config is None:
            return

       if config.get(snake_case      ) == "auto":
            UpperCAmelCase		:							Any        =       hf_val
            return

       if not must_match:
            return

       UpperCAmelCase		:							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}"      )

  SCREAMING_SNAKE_CASE__  :     List[Any]       =							partialmethod(lowercase__							,							must_match=lowercase__       )







  def 				A_	(			self   ,      snake_case      ):


       '''simple docstring'''

       UpperCAmelCase		:							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:
            UpperCAmelCase		:							Tuple        =       "apex" if args.fpaa_backend == "apex" else "amp"
       else:
            UpperCAmelCase		:							str        =       None

       if args.save_on_each_node:
            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
            UpperCAmelCase		:							Optional[int]        =       self.config.get("checkpoint"   ,      {}      )
            UpperCAmelCase		:							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"      ):
            UpperCAmelCase		:							Union[str, Any]        =       torch.bfloataa
       elif self.is_false("fp16.enabled"      ):
            UpperCAmelCase		:							Dict        =       torch.floataa
       else:
            UpperCAmelCase		:							List[Any]        =       torch.floataa







  def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


       '''simple docstring'''

       UpperCAmelCase		:							Union[str, Any]        =       [
           "zero_optimization.reduce_bucket_size",
           "zero_optimization.stage3_prefetch_bucket_size",
           "zero_optimization.stage3_param_persistence_threshold",
       ]
       UpperCAmelCase		:							List[str]        =       [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"      ):
                 UpperCAmelCase		:							Dict        =       model.config.hidden_size
            elif hasattr(model.config   ,      "hidden_sizes"      ):
                 # if there are many hidden sizes pick the largest one
                 UpperCAmelCase		:							List[str]        =       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"   ,      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:
            UpperCAmelCase		:							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       :      Optional[Any]  	=			None





def 				lowercase (					__magic_name__							):



     '''simple docstring'''







     global _hf_deepspeed_config_weak_ref
     # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
     UpperCAmelCase		:							str        =       weakref.ref(__magic_name__							)





def 				lowercase (					):



     '''simple docstring'''







     global _hf_deepspeed_config_weak_ref
     UpperCAmelCase		:							List[Any]        =       None





def 				lowercase (					):



     '''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 				lowercase (					):



     '''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 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__			,   __magic_name__							):



     '''simple docstring'''







     from accelerate.utils import DummyOptim, DummyScheduler

     UpperCAmelCase		:							Tuple        =       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)

     UpperCAmelCase		:							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."							)
          UpperCAmelCase		:							List[str]        =       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.
          UpperCAmelCase		:							Tuple        =       trainer.create_optimizer()
          # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
          UpperCAmelCase		:							Any        =       True

     UpperCAmelCase		:							Optional[Any]        =       None
     if "scheduler" in config:
          UpperCAmelCase		:							Optional[Any]        =       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."							)
          UpperCAmelCase		:							int        =       trainer.create_scheduler(num_training_steps=__magic_name__			,   optimizer=__magic_name__							)

     return optimizer, lr_scheduler





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=False							):



     '''simple docstring'''







     from deepspeed.utils import logger as ds_logger

     UpperCAmelCase		:							Any        =       trainer.model
     UpperCAmelCase		:							int        =       trainer.args

     UpperCAmelCase		:							Optional[int]        =       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"							)
          UpperCAmelCase		:							Dict        =       None, None
          UpperCAmelCase		:							List[Any]        =       None
     else:
          UpperCAmelCase		:							int        =       None  # important for when deepspeed_init is used as re-init
          UpperCAmelCase		:							Any        =       list(filter(lambda __magic_name__							: p.requires_grad			,   model.parameters()							)							)
          UpperCAmelCase		:							Tuple        =       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 				lowercase (					__magic_name__			,   __magic_name__							):



     '''simple docstring'''







     import glob

     UpperCAmelCase		:							Optional[Any]        =       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
          UpperCAmelCase		:							str        =       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}"							)



'''simple docstring'''

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

# Load iris file
a       :      List[Any]  	=			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
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

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