


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






       '''simple docstring'''


       __lowercase						=						{}
       if self.train_dir is not None:
        __lowercase						=						self.train_dir
       if self.validation_dir is not None:
        __lowercase						=						self.validation_dir
       __lowercase						=						data_files if data_files else None






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






@dataclass
class  lowerCamelCase__    (		_a     ):
      a			:  float           =		field(
          default=1e-3      , metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}     )






def lowerCAmelCase_   (					UpperCamelCase__						:			Optional[int]			):






 """simple docstring"""







 __lowercase						=						torch.stack([example["""pixel_values"""] for example in examples]			)
 return {"pixel_values": pixel_values}


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						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						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
 else:
  __lowercase				,		__lowercase				,		__lowercase						=						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"""					, UpperCamelCase__					, UpperCamelCase__			)

 # 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						=						training_args.get_process_log_level()
 logger.setLevel(UpperCamelCase__			)
 transformers.utils.logging.set_verbosity(UpperCamelCase__			)
 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						=						None
 if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
  __lowercase						=						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						=						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						=						None if """validation""" in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split					, UpperCamelCase__			) and data_args.train_val_split > 0.0:
  __lowercase						=						ds["""train"""].train_test_split(data_args.train_val_split			)
  __lowercase						=						split["""train"""]
  __lowercase						=						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						=						{
     """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						=						ViTMAEConfig.from_pretrained(model_args.config_name					, **UpperCamelCase__			)
 elif model_args.model_name_or_path:
  __lowercase						=						ViTMAEConfig.from_pretrained(model_args.model_name_or_path					, **UpperCamelCase__			)
 else:
  __lowercase						=						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						=						ViTImageProcessor.from_pretrained(model_args.image_processor_name					, **UpperCamelCase__			)
 elif model_args.model_name_or_path:
  __lowercase						=						ViTImageProcessor.from_pretrained(model_args.model_name_or_path					, **UpperCamelCase__			)
 else:
  __lowercase						=						ViTImageProcessor()

 # create model
 if model_args.model_name_or_path:
  __lowercase						=						ViTMAEForPreTraining.from_pretrained(
      model_args.model_name_or_path					, from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)					, config=UpperCamelCase__					, 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						=						ViTMAEForPreTraining(UpperCamelCase__			)

 if training_args.do_train:
  __lowercase						=						ds["""train"""].column_names
 else:
  __lowercase						=						ds["""validation"""].column_names

 if data_args.image_column_name is not None:
  __lowercase						=						data_args.image_column_name
 elif "image" in column_names:
  __lowercase						=						"""image"""
 elif "img" in column_names:
  __lowercase						=						"""img"""
 else:
  __lowercase						=						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						=						image_processor.size["""shortest_edge"""]
 else:
  __lowercase						=						(image_processor.size["""height"""], image_processor.size["""width"""])
 __lowercase						=						Compose(
     [
         Lambda(lambda UpperCamelCase__			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
         RandomResizedCrop(UpperCamelCase__					, 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__						:			Optional[int]			):
  __lowercase						=						[transforms(UpperCamelCase__			) 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						=						ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
  # Set the training transforms
  ds["train"].set_transform(UpperCamelCase__			)

 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						=						(
       ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
   )
  # Set the validation transforms
  ds["validation"].set_transform(UpperCamelCase__			)

 # Compute absolute learning rate
 __lowercase						=						(
     training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
 )
 if training_args.base_learning_rate is not None:
  __lowercase						=						training_args.base_learning_rate * total_train_batch_size / 256

 # Initialize our trainer
 __lowercase						=						Trainer(
     model=UpperCamelCase__					, args=UpperCamelCase__					, train_dataset=ds["""train"""] if training_args.do_train else None					, eval_dataset=ds["""validation"""] if training_args.do_eval else None					, tokenizer=UpperCamelCase__					, data_collator=UpperCamelCase__					, )

 # Training
 if training_args.do_train:
  __lowercase						=						None
  if training_args.resume_from_checkpoint is not None:
   __lowercase						=						training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __lowercase						=						last_checkpoint
  __lowercase						=						trainer.train(resume_from_checkpoint=UpperCamelCase__			)
  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						=						trainer.evaluate()
  trainer.log_metrics("""eval"""					, UpperCamelCase__			)
  trainer.save_metrics("""eval"""					, UpperCamelCase__			)

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


def lowerCAmelCase_   (					UpperCamelCase__						:			List[Any]			):






 """simple docstring"""







 main()


if __name__ == "__main__":
       main()




"""simple docstring"""

import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase__					=logging.get_logger(__name__)





@dataclass
class  lowerCamelCase__    :
      a			:  str           =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
      a			:  str           =		field(
          metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
      a			:  int           =		field(
          default=1_28      , metadata={
              """help""": (
                  """The maximum total input sequence length after tokenization. Sequences longer """
                  """than this will be truncated, sequences shorter will be padded."""
              )
          }      , )
      a			:  bool           =		field(
          default=_a      , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.task_name.lower()






class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		"""train"""
      a			:  List[str]           =		"""dev"""
      a			:  Optional[int]           =		"""test"""






class  lowerCamelCase__    (		_a     ):
      a			:  GlueDataTrainingArguments
      a			:  str
      a			:  List[InputFeatures]
      def __init__(		self						:						Optional[int]   ,							A_						:						GlueDataTrainingArguments   ,							A_						:						PreTrainedTokenizerBase   ,							A_						:						Optional[int] = None   ,							A_						:						Union[str, Split] = Split.train   ,							A_						:						Optional[str] = None   ,							):






       '''simple docstring'''


       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""   ,							A_   ,							)
       __lowercase						=						args
       __lowercase						=						glue_processors[args.task_name]()
       __lowercase						=						glue_output_modes[args.task_name]
       if isinstance(A_   ,							A_				):
        try:
         __lowercase						=						Split[mode]
        except KeyError:
         raise KeyError("""mode is not a valid split name"""				)
        # Load data features from cache or dataset file
       __lowercase						=						os.path.join(
           cache_dir if cache_dir is not None else args.data_dir   ,							F'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''   ,							)
       __lowercase						=						self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        __lowercase				,		__lowercase						=						label_list[2], label_list[1]
       __lowercase						=						label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       __lowercase						=						cached_features_file + """.lock"""
       with FileLock(A_				):
        if os.path.exists(A_				) and not args.overwrite_cache:
         __lowercase						=						time.time()
         __lowercase						=						torch.load(A_				)
         logger.info(
             F'''Loading features from cached file {cached_features_file} [took %.3f s]'''   ,							time.time() - start				)
        else:
         logger.info(F'''Creating features from dataset file at {args.data_dir}'''				)

         if mode == Split.dev:
          __lowercase						=						self.processor.get_dev_examples(args.data_dir				)
         elif mode == Split.test:
          __lowercase						=						self.processor.get_test_examples(args.data_dir				)
         else:
          __lowercase						=						self.processor.get_train_examples(args.data_dir				)
         if limit_length is not None:
          __lowercase						=						examples[:limit_length]
         __lowercase						=						glue_convert_examples_to_features(
             A_   ,							A_   ,							max_length=args.max_seq_length   ,							label_list=A_   ,							output_mode=self.output_mode   ,							)
         __lowercase						=						time.time()
         torch.save(self.features   ,							A_				)
         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
         logger.info(
             F'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''				)
      def __len__(		self						:						int				):






       '''simple docstring'''


       return len(self.features				)
      def __getitem__(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       return self.features[i]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return self.label_list
