





'''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__							:							Optional[Any] =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      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default="cifar10"			,       metadata={"help": "Name of a dataset from the datasets package"}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default=_a			,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(
     default=_a			,       metadata={"help": "The column name of the images in the files."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(default=_a			,       metadata={"help": "A folder containing the training data."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[str]					=field(default=_a			,       metadata={"help": "A folder containing the validation data."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[float]					=field(
     default=0.15			,       metadata={"help": "Percent to split off of train for validation."}       )
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=field(
     default=_a			,       metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     }			,       )
 SCREAMING_SNAKE_CASE_	:					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        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  {}
        if self.train_dir is not None:
               __UpperCamelCase       =  self.train_dir
        if self.validation_dir is not None:
               __UpperCamelCase       =  self.validation_dir
        __UpperCamelCase       =  data_files if data_files else None






@dataclass
class      snake_case     :




 """simple docstring"""



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






@dataclass
class      snake_case     (    _a       ):




 """simple docstring"""



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






def 				lowercase__   (   __lowercase						:  List[str]     ) ->      Dict:



       """simple docstring"""






       __UpperCamelCase       =  torch.stack([example['pixel_values'] for example in examples]     )
       return {"pixel_values": pixel_values}
def 				lowercase__   (   ) ->      Tuple:



       """simple docstring"""






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

       # 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()

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

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

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

       if data_args.image_column_name is not None:
              __UpperCamelCase       =  data_args.image_column_name
       elif "image" in column_names:
              __UpperCamelCase       =  """image"""
       elif "img" in column_names:
              __UpperCamelCase       =  """img"""
       else:
              __UpperCamelCase       =  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:
              __UpperCamelCase       =  image_processor.size["""shortest_edge"""]
       else:
              __UpperCamelCase       =  (image_processor.size["""height"""], image_processor.size["""width"""])
       __UpperCamelCase       =  Compose(
           [
               Lambda(lambda __lowercase     : img.convert('RGB'     ) if img.mode != "RGB" else img     ),
               RandomResizedCrop(__lowercase				,  scale=(0.2, 1.0)				,  interpolation=InterpolationMode.BICUBIC     ),
               RandomHorizontalFlip(),
               ToTensor(),
               Normalize(mean=image_processor.image_mean				,  std=image_processor.image_std     ),
           ]     )

       def preprocess_images(__lowercase						:  Union[str, Any]     ):
              __UpperCamelCase       =  [transforms(__lowercase     ) 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:
                     __UpperCamelCase       =  ds["""train"""].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
              # Set the training transforms
              ds["train"].set_transform(__lowercase     )

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

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

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

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

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



       """simple docstring"""






       main()


if __name__ == "__main__":
    main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
