






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





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





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





def 		_UpperCAmelCase   (							__A							:   List[Any]      ):
				a_		:  List[str]											=      torch.stack([example['''pixel_values'''] for example in examples]      )
				return {"pixel_values": pixel_values}





def 		_UpperCAmelCase   (							):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				a_		:  Tuple											=      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.
								a_				,			a_				,			a_		:  Union[str, Any]											=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								a_				,			a_				,			a_		:  int											=      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'''						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE      )

				# 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()

				a_		:  int											=      training_args.get_process_log_level()
				logger.setLevel(_SCREAMING_SNAKE_CASE      )
				transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE      )
				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.
				a_		:  Optional[int]											=      None
				if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
								a_		:  Optional[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.
				a_		:  str											=      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.
				a_		:  Dict											=      None if '''validation''' in ds.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split						,		_SCREAMING_SNAKE_CASE      ) and data_args.train_val_split > 0.0:
								a_		:  Dict											=      ds['''train'''].train_test_split(data_args.train_val_split      )
								a_		:  Any											=      split['''train''']
								a_		:  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.
				a_		:  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:
								a_		:  int											=      ViTMAEConfig.from_pretrained(model_args.config_name						,		**_SCREAMING_SNAKE_CASE      )
				elif model_args.model_name_or_path:
								a_		:  List[str]											=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path						,		**_SCREAMING_SNAKE_CASE      )
				else:
								a_		:  Union[str, Any]											=      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:
								a_		:  List[str]											=      ViTImageProcessor.from_pretrained(model_args.image_processor_name						,		**_SCREAMING_SNAKE_CASE      )
				elif model_args.model_name_or_path:
								a_		:  Dict											=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path						,		**_SCREAMING_SNAKE_CASE      )
				else:
								a_		:  Optional[Any]											=      ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								a_		:  List[str]											=      ViTMAEForPreTraining.from_pretrained(
								    model_args.model_name_or_path						,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )						,		config=_SCREAMING_SNAKE_CASE						,		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'''      )
								a_		:  List[Any]											=      ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE      )

				if training_args.do_train:
								a_		:  Union[str, Any]											=      ds['''train'''].column_names
				else:
								a_		:  Optional[int]											=      ds['''validation'''].column_names

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

				def preprocess_images(__A							:   str      ):
								a_		:  List[Any]											=      [transforms(_SCREAMING_SNAKE_CASE      ) 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:
												a_		:  Dict											=      ds['''train'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
								# Set the training transforms
								ds["train"].set_transform(_SCREAMING_SNAKE_CASE      )

				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:
												a_		:  List[Any]											=      (
												    ds['''validation'''].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
												)
								# Set the validation transforms
								ds["validation"].set_transform(_SCREAMING_SNAKE_CASE      )

				# Compute absolute learning rate
				a_		:  List[Any]											=      (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								a_		:  List[Any]											=      training_args.base_learning_rate * total_train_batch_size / 2_56

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

				# Training
				if training_args.do_train:
								a_		:  Union[str, Any]											=      None
								if training_args.resume_from_checkpoint is not None:
												a_		:  Tuple											=      training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												a_		:  Optional[int]											=      last_checkpoint
								a_		:  List[Any]											=      trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE      )
								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:
								a_		:  Optional[int]											=      trainer.evaluate()
								trainer.log_metrics('''eval'''						,		_SCREAMING_SNAKE_CASE      )
								trainer.save_metrics('''eval'''						,		_SCREAMING_SNAKE_CASE      )

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





def 		_UpperCAmelCase   (							__A							:   List[Any]      ):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase       		=      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase       		=      [
		    'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'GitForCausalLM',
		    'GitModel',
		    'GitPreTrainedModel',
		    'GitVisionModel',
		]

if TYPE_CHECKING:
		from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
		from .processing_git import GitProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_git import (
				    GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GitForCausalLM,
				    GitModel,
				    GitPreTrainedModel,
				    GitVisionModel,
				)

else:
		import sys

		__lowerCAmelCase       		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




