"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__A			   = logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")


@dataclass
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Optional[str]						    = field(
        default="cifar10"    ,metadata={"help": "Name of a dataset from the datasets package"}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=__lowerCamelCase    ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=__lowerCamelCase    ,metadata={"help": "The column name of the images in the files."}  )
    _UpperCAmelCase	:Optional[str]						    = field(default=__lowerCamelCase    ,metadata={"help": "A folder containing the training data."}  )
    _UpperCAmelCase	:Optional[str]						    = field(default=__lowerCamelCase    ,metadata={"help": "A folder containing the validation data."}  )
    _UpperCAmelCase	:Optional[float]						    = field(
        default=0.15    ,metadata={"help": "Percent to split off of train for validation."}  )
    _UpperCAmelCase	:Optional[int]						    = field(
        default=__lowerCamelCase    ,metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of training examples to this "
                "value if set."
            )
        }    ,)
    _UpperCAmelCase	:Optional[int]						    = field(
        default=__lowerCamelCase    ,metadata={
            "help": (
                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                "value if set."
            )
        }    ,)






    def _snake_case			(   self							):
       lowercase__:   List[Any]					=      {}
       if self.train_dir is not None:
          lowercase__:   Any					=      self.train_dir
       if self.validation_dir is not None:
          lowercase__:   Dict					=      self.validation_dir
       lowercase__:   Union[str, Any]					=      data_files if data_files else None



@dataclass
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:str						    = field(
        default=__lowerCamelCase    ,metadata={
            "help": (
                "The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
            )
        }    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=__lowerCamelCase    ,metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}  )
    _UpperCAmelCase	:Optional[str]						    = field(
        default=__lowerCamelCase    ,metadata={
            "help": (
                "Override some existing default config settings when a model is trained from scratch. Example: "
                "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
            )
        }    ,)
    _UpperCAmelCase	:Optional[str]						    = field(
        default=__lowerCamelCase    ,metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}  )
    _UpperCAmelCase	:str						    = field(
        default="main"    ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,)
    _UpperCAmelCase	:str						    = field(default=__lowerCamelCase    ,metadata={"help": "Name or path of preprocessor config."}  )
    _UpperCAmelCase	:bool						    = field(
        default=__lowerCamelCase    ,metadata={
            "help": (
                "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                "with private models)."
            )
        }    ,)
    _UpperCAmelCase	:float						    = field(
        default=0.75    ,metadata={"help": "The ratio of the number of masked tokens in the input sequence."}  )
    _UpperCAmelCase	:bool						    = field(
        default=__lowerCamelCase    ,metadata={"help": "Whether or not to train with normalized pixel values as target."}  )



@dataclass
class     UpperCAmelCase						(__lowerCamelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:float						    = field(
        default=1E-3    ,metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}  )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   Any					=      torch.stack([example['''pixel_values'''] for example in examples] )
   return {"pixel_values": pixel_values}



def SCREAMING_SNAKE_CASE__			(   )  ->  List[Any]:
   lowercase__:   Dict					=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
   if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowercase__, lowercase__, lowercase__:   Union[str, Any]					=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
   else:
      lowercase__, lowercase__, lowercase__:   List[Any]					=      parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry('''run_mae'''       ,    A__       ,    A__ )

   # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,    datefmt='''%m/%d/%Y %H:%M:%S'''       ,    handlers=[logging.StreamHandler(sys.stdout )]       ,    )

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   lowercase__:   Dict					=      training_args.get_process_log_level()
   logger.setLevel(A__ )
   transformers.utils.logging.set_verbosity(A__ )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
       + F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
   logger.info(F"""Training/evaluation parameters {training_args}""" )

   # Detecting last checkpoint.
   lowercase__:   Tuple					=      None
   if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
      lowercase__:   Any					=      get_last_checkpoint(training_args.output_dir )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
         raise ValueError(
             F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
             '''Use --overwrite_output_dir to overcome.''' )
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
             '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )

    # Initialize our dataset.
   lowercase__:   Any					=      load_dataset(
       data_args.dataset_name       ,    data_args.dataset_config_name       ,    data_files=data_args.data_files       ,    cache_dir=model_args.cache_dir       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )

   # If we don't have a validation split, split off a percentage of train as validation.
   lowercase__:   List[str]					=      None if '''validation''' in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split       ,    A__ ) and data_args.train_val_split > 0.0:
      lowercase__:   Tuple					=      ds['''train'''].train_test_split(data_args.train_val_split )
      lowercase__:   Optional[int]					=      split['''train''']
      lowercase__:   List[str]					=      split['''test''']

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowercase__:   Any					=      {
       '''cache_dir''': model_args.cache_dir,
       '''revision''': model_args.model_revision,
       '''use_auth_token''': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      lowercase__:   Tuple					=      ViTMAEConfig.from_pretrained(model_args.config_name       ,    **A__ )
   elif model_args.model_name_or_path:
      lowercase__:   int					=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path       ,    **A__ )
   else:
      lowercase__:   str					=      ViTMAEConfig()
      logger.warning('''You are instantiating a new config instance from scratch.''' )
      if model_args.config_overrides is not None:
         logger.info(F"""Overriding config: {model_args.config_overrides}""" )
         config.update_from_string(model_args.config_overrides )
         logger.info(F"""New config: {config}""" )

    # adapt config
   config.update(
       {
           '''mask_ratio''': model_args.mask_ratio,
           '''norm_pix_loss''': model_args.norm_pix_loss,
       } )

   # create image processor
   if model_args.image_processor_name:
      lowercase__:   Union[str, Any]					=      ViTImageProcessor.from_pretrained(model_args.image_processor_name       ,    **A__ )
   elif model_args.model_name_or_path:
      lowercase__:   Tuple					=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path       ,    **A__ )
   else:
      lowercase__:   Optional[Any]					=      ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      lowercase__:   Union[str, Any]					=      ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path       ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path )       ,    config=A__       ,    cache_dir=model_args.cache_dir       ,    revision=model_args.model_revision       ,    use_auth_token=True if model_args.use_auth_token else None       ,    )
   else:
      logger.info('''Training new model from scratch''' )
      lowercase__:   Union[str, Any]					=      ViTMAEForPreTraining(A__ )

   if training_args.do_train:
      lowercase__:   int					=      ds['''train'''].column_names
   else:
      lowercase__:   List[str]					=      ds['''validation'''].column_names

   if data_args.image_column_name is not None:
      lowercase__:   Optional[int]					=      data_args.image_column_name
   elif "image" in column_names:
      lowercase__:   Any					=      '''image'''
   elif "img" in column_names:
      lowercase__:   Optional[Any]					=      '''img'''
   else:
      lowercase__:   Optional[int]					=      column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      lowercase__:   Optional[int]					=      image_processor.size['''shortest_edge''']
   else:
      lowercase__:   List[str]					=      (image_processor.size['''height'''], image_processor.size['''width'''])
   lowercase__:   List[Any]					=      Compose(
       [
           Lambda(lambda __UpperCAmelCase : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
           RandomResizedCrop(A__       ,    scale=(0.2, 1.0)       ,    interpolation=InterpolationMode.BICUBIC ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean       ,    std=image_processor.image_std ),
       ] )

   def preprocess_images(__UpperCAmelCase ):
      lowercase__:   Optional[Any]					=      [transforms(A__ ) for image in examples[image_column_name]]
      return examples

   if training_args.do_train:
      if "train" not in ds:
         raise ValueError('''--do_train requires a train dataset''' )
      if data_args.max_train_samples is not None:
         lowercase__:   Optional[Any]					=      ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
      # Set the training transforms
      ds["train"].set_transform(A__ )

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError('''--do_eval requires a validation dataset''' )
      if data_args.max_eval_samples is not None:
         lowercase__:   Union[str, Any]					=      (
             ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
         )
      # Set the validation transforms
      ds["validation"].set_transform(A__ )

   # Compute absolute learning rate
   lowercase__:   int					=      (
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      lowercase__:   Optional[Any]					=      training_args.base_learning_rate * total_train_batch_size / 2_5_6

   # Initialize our trainer
   lowercase__:   int					=      Trainer(
       model=A__       ,    args=A__       ,    train_dataset=ds['''train'''] if training_args.do_train else None       ,    eval_dataset=ds['''validation'''] if training_args.do_eval else None       ,    tokenizer=A__       ,    data_collator=A__       ,    )

   # Training
   if training_args.do_train:
      lowercase__:   Optional[int]					=      None
      if training_args.resume_from_checkpoint is not None:
         lowercase__:   Tuple					=      training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         lowercase__:   List[str]					=      last_checkpoint
      lowercase__:   int					=      trainer.train(resume_from_checkpoint=A__ )
      trainer.save_model()
      trainer.log_metrics('''train'''       ,    train_result.metrics )
      trainer.save_metrics('''train'''       ,    train_result.metrics )
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      lowercase__:   Optional[Any]					=      trainer.evaluate()
      trainer.log_metrics('''eval'''       ,    A__ )
      trainer.save_metrics('''eval'''       ,    A__ )

   # Write model card and (optionally) push to hub
   lowercase__:   List[Any]					=      {
       '''tasks''': '''masked-auto-encoding''',
       '''dataset''': data_args.dataset_name,
       '''tags''': ['''masked-auto-encoding'''],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**A__ )
   else:
      trainer.create_model_card(**A__ )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  int:
   main()


if __name__ == "__main__":
       main()






import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
       import torch.distributed.checkpoint as dist_cp
       from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
       from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
       from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCAmelCase_									=	get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					Union[str, Any]  ,			A__       :					str  ,			A__       :					Any  ,			A__       :					Dict  ,			A__       :					Any=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						model.state_dict()
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     if accelerator.process_index == 0:
                            logger.info(f'Saving model to {output_model_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Saving model to {output_model_file}'	)
                     torch.save(A__  ,			A__	)
                     logger.info(f'Model saved to {output_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving model to {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": state_dict}

                     dist_cp.save_state_dict(
                         state_dict=A__  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Model saved to {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					Dict  ,			A__       :					int  ,			A__       :					List[str]  ,			A__       :					Any=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if type(A__	) != FSDP and accelerator.process_index != 0:
                            if not fsdp_plugin.sync_module_states:
                                   raise ValueError(
                                       """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
                                       """initializing FSDP object"""	)
                            return
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                     __lowerCamelCase   =						(
                         f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
                         if model_index == 0
                         else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                         if f'{MODEL_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading model from {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""model""": model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=A__  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			planner=DefaultLoadPlanner()  ,			)
                     __lowerCamelCase   =						state_dict["""model"""]
                     logger.info(f'Model loaded from {ckpt_dir}'	)
              model.load_state_dict(A__	)




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[str]  ,			A__       :					str  ,			A__       :					Dict  ,			A__       :					Optional[Any]  ,			A__       :					Optional[int]=0	):





       '''simple docstring'''



       os.makedirs(A__  ,			exist_ok=A__	)
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              __lowerCamelCase   =						FSDP.optim_state_dict(A__  ,			A__	)
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     if accelerator.process_index == 0:
                            __lowerCamelCase   =						(
                                f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                            )
                            __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                            logger.info(f'Saving Optimizer state to {output_optimizer_file}'	)
                            torch.save(A__  ,			A__	)
                            logger.info(f'Optimizer state saved in {output_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving Optimizer state to {ckpt_dir}'	)
                     dist_cp.save_state_dict(
                         state_dict={"""optimizer""": optim_state}  ,			storage_writer=dist_cp.FileSystemWriter(A__	)  ,			planner=DefaultSavePlanner()  ,			)
                     logger.info(f'Optimizer state saved in {ckpt_dir}'	)




def 					lowerCamelCase__       (						A__       :					int  ,			A__       :					List[str]  ,			A__       :					int  ,			A__       :					Any  ,			A__       :					Union[str, Any]  ,			A__       :					List[Any]=0	):





       '''simple docstring'''



       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           A__  ,			fsdp_plugin.state_dict_type  ,			fsdp_plugin.state_dict_config  ,			fsdp_plugin.optim_state_dict_config	):
              if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                     __lowerCamelCase   =						None
                     # below check should work but currently it isn't working (mostly opytorch issue),
                     # in the meantime disabling it at the cost of excess memory usage
                     # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                     __lowerCamelCase   =						(
                         f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
                     )
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						(
                         os.path.join(A__  ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
                         if f'{OPTIMIZER_NAME}' not in input_dir
                         else input_dir
                     )
                     logger.info(f'Loading Optimizer from {ckpt_dir}'	)
                     __lowerCamelCase   =						load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,			optimizer_key="""optimizer"""  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			)
                     __lowerCamelCase   =						optim_state["""optimizer"""]
                     logger.info(f'Optimizer loaded from {ckpt_dir}'	)
              __lowerCamelCase   =						FSDP.optim_state_dict_to_load(A__  ,			A__  ,			A__	)
              optimizer.load_state_dict(A__	)

