






'''simple docstring'''




from math import cos, sin

def       __lowercase		(    __lowercase ) ->		list[list[float]]:

      '''simple docstring'''


      _A      =			float(__lowercase )
      return [[scaling_factor * int(x == y ) for x in range(2 )] for y in range(2 )]

def       __lowercase		(    __lowercase ) ->		list[list[float]]:

      '''simple docstring'''


      _A  ,			_A      =			cos(__lowercase ), sin(__lowercase )
      return [[c, -s], [s, c]]

def       __lowercase		(    __lowercase ) ->		list[list[float]]:

      '''simple docstring'''


      _A  ,			_A      =			cos(__lowercase ), sin(__lowercase )
      _A      =			c * s
      return [[c * c, cs], [cs, s * s]]

def       __lowercase		(    __lowercase ) ->		list[list[float]]:

      '''simple docstring'''


      _A  ,			_A      =			cos(__lowercase ), sin(__lowercase )
      _A      =			c * s
      return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")


print(F"""{reflection(45) = }""")













'''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

lowerCamelCase_							      =      logging.get_logger(__name__)

def       __lowercase		(    ) ->		Optional[Any]:

      '''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    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





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





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase	)
         _A      =			None
         _A      =			[]

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





         _A      =			self.get_value(__UpperCAmelCase	)
         if val is None:
               return False
         else:
               return val == "auto"

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			Union[str, Any]=True	):





         '''simple docstring'''





         _A  ,			_A      =			self.find_config_node(__UpperCAmelCase	)
         if config is None:
               return

         if config.get(__UpperCAmelCase	) == "auto":
               _A      =			hf_val
               return

         if not must_match:
               return

         _A      =			config.get(__UpperCAmelCase	)
         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}'''	)

   snake_case					       =		partialmethod(snake_case_							,					must_match=snake_case_		)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			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"   ,   __UpperCAmelCase   ,   "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:
               _A      =			"apex" if args.fpaa_backend == "apex" else "amp"
         else:
               _A      =			None

         if args.save_on_each_node:
               # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
               _A      =			self.config.get("checkpoint"   ,   {}	)
               _A      =			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"	):
               _A      =			torch.bfloataa
         elif self.is_false("fp16.enabled"	):
               _A      =			torch.floataa
         else:
               _A      =			torch.floataa

   def 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _A      =			[
             "zero_optimization.reduce_bucket_size",
             "zero_optimization.stage3_prefetch_bucket_size",
             "zero_optimization.stage3_param_persistence_threshold",
         ]
         _A      =			[x for x in hidden_size_based_keys if self.is_auto(__UpperCAmelCase	)]

         if len(__UpperCAmelCase	) > 0:
               if hasattr(model.config   ,   "hidden_size"	):
                     _A      =			model.config.hidden_size
               elif hasattr(model.config   ,   "hidden_sizes"	):
                     # if there are many hidden sizes pick the largest one
                     _A      =			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"   ,   __UpperCAmelCase   ,   "num_training_steps (calculated)"	)
         self.fill_match("scheduler.params.warmup_num_steps"   ,   args.get_warmup_steps(__UpperCAmelCase	)   ,   "warmup_steps"	)

         if len(self.mismatches	) > 0:
               _A      =			"\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\'.'''	)





lowerCamelCase_							      =      None

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      _A      =			weakref.ref(__lowercase )

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      _A      =			None

def       __lowercase		(    ) ->		Optional[int]:

      '''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		(    ) ->		str:

      '''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		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      from accelerate.utils import DummyOptim, DummyScheduler

      _A      =			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)

      _A      =			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." )
            _A      =			DummyOptim(params=__lowercase )
      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.
            _A      =			trainer.create_optimizer()
            # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
            _A      =			True

      _A      =			None
      if "scheduler" in config:
            _A      =			DummyScheduler(__lowercase )
      else:
            if isinstance(__lowercase     ,						__lowercase ):
                  raise ValueError(
                      "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                      "Please configure a scheduler in the DeepSpeed config." )
            _A      =			trainer.create_scheduler(num_training_steps=__lowercase     ,						optimizer=__lowercase )

      return optimizer, lr_scheduler

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=False ) ->		Tuple:

      '''simple docstring'''


      from deepspeed.utils import logger as ds_logger

      _A      =			trainer.model
      _A      =			trainer.args

      _A      =			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(__lowercase     ,						__lowercase     ,						__lowercase )

      # 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" )
            _A  ,			_A      =			None, None
            _A      =			None
      else:
            _A      =			None  # important for when deepspeed_init is used as re-init
            _A      =			list(filter(lambda __lowercase : p.requires_grad     ,						model.parameters() ) )
            _A  ,			_A      =			deepspeed_optim_sched(
                __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase )

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

      return optimizer, lr_scheduler


def       __lowercase		(    __lowercase     ,						__lowercase ) ->		Union[str, Any]:

      '''simple docstring'''


      import glob

      _A      =			sorted(glob.glob(F'''{checkpoint_path}/global_step*''' ) )

      if len(__lowercase ) > 0:
            logger.info(F'''Attempting to resume from {checkpoint_path}''' )
            # this magically updates self.optimizer and self.lr_scheduler
            _A  ,			_A      =			deepspeed_engine.load_checkpoint(
                __lowercase     ,						load_optimizer_states=__lowercase     ,						load_lr_scheduler_states=__lowercase )
            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}''' )





