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
  
  __lowerCAmelCase							:			Union[str, 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			( __lowerCamelCase       ):
   def __init__(       self   :str		, lowercase_   :Union[str, Any]							)->							List[Any]:
       set_hf_deepspeed_config(self							)
       dep_version_check("accelerate"							)
       dep_version_check("deepspeed"							)
       super().__init__(lowercase_							)
  
  
  
  
  
  
  class      UpperCAmelCase			( __lowerCamelCase       ):
   def __init__(       self   :str		, lowercase_   :List[Any]							)->							Tuple:
       super().__init__(lowercase_							)
       A__       =    None
       A__       =    []
   def   UpperCAmelCase_							(       self   :Dict							)->							Optional[Any]:
  
  
  
  
  
  
       if self._dtype is None:
           raise ValueError("trainer_config_process() wasn\'t called yet to tell dtype"							)
       return self._dtype
   def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :str							)->							List[Any]:
       A__       =    self.get_value(lowercase_							)
       if val is None:
           return False
       else:
           return val == "auto"
   def   UpperCAmelCase_							(       self   :int		, lowercase_   :List[str]		, lowercase_   :str		, lowercase_   :Any=None		, lowercase_   :Optional[Any]=True							)->							Any:
       A__,			A__       =    self.find_config_node(lowercase_							)
       if config is None:
           return
  
       if config.get(lowercase_							) == "auto":
           A__       =    hf_val
           return
  
       if not must_match:
           return
  
       A__       =    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}"							)
  
   __lowercase     	=       partialmethod(__lowerCamelCase    , must_match=__lowerCamelCase       )
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :Any							)->							Optional[int]:
       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"		, 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:
           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   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any		, lowercase_   :str		, lowercase_   :Any							)->							List[Any]:
       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(lowercase_							)]
  
       if len(lowercase_							) > 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"		, 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:
           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							:			Tuple				=None
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple ):
      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      A__       =    weakref.ref(_lowerCamelCase )
  
  
  
  
  
  def 		UpperCamelCase       (							):
      global _hf_deepspeed_config_weak_ref
      A__       =    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       (							_lowerCamelCase      :      int		, _lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      Union[str, Any] ):
      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=_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.
          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(_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." )
          A__       =    trainer.create_scheduler(num_training_steps=_lowerCamelCase		, optimizer=_lowerCamelCase )
  
      return optimizer, lr_scheduler
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      str=False ):
      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(_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" )
          A__,			A__       =    None, None
          A__       =    None
      else:
          A__       =    None  # important for when deepspeed_init is used as re-init
          A__       =    list(filter(lambda _lowerCamelCase : p.requires_grad		, model.parameters() ) )
          A__,			A__       =    deepspeed_optim_sched(
              _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )
  
      # keep for quick debug:
      # from pprint import pprint; pprint(config)
  
      return optimizer, lr_scheduler
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Dict ):
      import glob
  
      A__       =    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
          A__,			A__       =    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}" )
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
      A__       =    min(img.size )
  
      if s < target_image_size:
          raise ValueError(F"min dim for image {s} < {target_image_size}" )
  
      A__       =    target_image_size / s
      A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
      A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
      A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
      A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
      return img
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    2.0 * x - 1.0
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
      A__       =    x.detach().cpu()
      if process:
          A__       =    post_process_tensor(_lowerCamelCase )
      A__       =    x.numpy()
      if process:
          A__       =    (2_55 * x).astype(np.uinta )
      A__       =    Image.fromarray(_lowerCamelCase )
      if not x.mode == mode:
          A__       =    x.convert(_lowerCamelCase )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
      A__       =    (x + 1.0) / 2.0
      A__       =    x.permute(1		, 2		, 0 )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    post_process_tensor(x.squeeze() )
      return x.permute(2		, 0		, 1 ).unsqueeze(0 )
  
  
  
  