"""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

__UpperCamelCase       :    List[Any]         =		logging.get_logger(__name__)


def   __UpperCAmelCase     (	):
   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__   ):
    def __init__(	self			:       Any   ,						_lowercase			:       Dict		)						->    Tuple:
       # set global weakref object
       set_hf_deepspeed_config(self		)
       dep_version_check("accelerate"		)
       dep_version_check("deepspeed"		)
       super().__init__(_lowercase		)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[str]   ,						_lowercase			:       Any		)						->    List[str]:
       super().__init__(_lowercase		)
       _lowercase							     =       None
       _lowercase							     =       []
    def 		_lowerCamelCase      (	self			:       Any		)						->    Union[str, Any]:
       if self._dtype is None:
          raise ValueError("trainer_config_process() wasn't called yet to tell dtype"		)
       return self._dtype
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       Any		)						->    Optional[int]:
       _lowercase							     =       self.get_value(_lowercase		)
       if val is None:
          return False
       else:
          return val == "auto"
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Dict   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]=None   ,						_lowercase			:       Dict=True		)						->    List[str]:
       _lowercase     ,  _lowercase							     =       self.find_config_node(_lowercase		)
       if config is None:
          return

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

       if not must_match:
          return

       _lowercase							     =       config.get(_lowercase		)
       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(lowercase__,    must_match=lowercase__   )
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       List[Any]		)						->    int:
       _lowercase							     =       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"   ,						_lowercase   ,						"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:
          _lowercase							     =       "apex" if args.fpaa_backend == "apex" else "amp"
       else:
          _lowercase							     =       None

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




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

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

       if len(self.mismatches		) > 0:
          _lowercase							     =       "\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       :    Union[str, Any]         =		None


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any]       ):
   # 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)
   _lowercase							     =       weakref.ref(_snake_case       )


def   __UpperCAmelCase     (	):
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   _lowercase							     =       None


def   __UpperCAmelCase     (	):
   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     (	):
   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     (	_snake_case		:       List[str],				_snake_case		:       List[Any],				_snake_case		:       Any,				_snake_case		:       Dict,				_snake_case		:       Optional[int]       ):
   from accelerate.utils import DummyOptim, DummyScheduler

   _lowercase							     =       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)

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

   _lowercase							     =       None
   if "scheduler" in config:
      _lowercase							     =       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."       )
      _lowercase							     =       trainer.create_scheduler(num_training_steps=_snake_case,				optimizer=_snake_case       )

   return optimizer, lr_scheduler


def   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       Optional[int],				_snake_case		:       str=False       ):
   from deepspeed.utils import logger as ds_logger

   _lowercase							     =       trainer.model
   _lowercase							     =       trainer.args

   _lowercase							     =       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"       )
      _lowercase     ,  _lowercase							     =       None, None
      _lowercase							     =       None
   else:
      _lowercase							     =       None  # important for when deepspeed_init is used as re-init
      _lowercase							     =       list(filter(lambda _snake_case       : p.requires_grad,				model.parameters()       )       )
      _lowercase     ,  _lowercase							     =       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   __UpperCAmelCase     (	_snake_case		:       Union[str, Any],				_snake_case		:       Tuple       ):
   # 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

   _lowercase							     =       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
      _lowercase     ,  _lowercase							     =       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}"""       )
"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_convnext import ConvNextImageProcessor


__UpperCamelCase       :    str         =		logging.get_logger(__name__)
class   UpperCAmelCase_	(				lowercase__   ):
    def __init__(	self			:       List[Any]   ,						*_lowercase			:       Optional[Any]   ,						**_lowercase			:       str		)						->    None:
       warnings.warn(
           "The class ConvNextFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use ConvNextImageProcessor instead."   ,						_lowercase   ,						)
       super().__init__(*_lowercase   ,						**_lowercase		)