





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        __SCREAMING_SNAKE_CASE					:
							snake_case     :      Optional[str]						     =       field(
							    default="""cifar10"""   ,		metadata={"""help""": """Name of a dataset from the datasets package"""}       )
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}       )
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """The column name of the images in the files."""}       )
							snake_case     :      Optional[str]						     =       field(default=_a   ,		metadata={"""help""": """A folder containing the training data."""}       )
							snake_case     :      Optional[str]						     =       field(default=_a   ,		metadata={"""help""": """A folder containing the validation data."""}       )
							snake_case     :      Optional[float]						     =       field(
							    default=0.15   ,		metadata={"""help""": """Percent to split off of train for validation."""}       )
							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."""
							        )
							    }   ,		)
							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  ):
														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        __SCREAMING_SNAKE_CASE					:
							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."""
							        )
							    }   ,		)
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}       )
							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"""
							        )
							    }   ,		)
							snake_case     :      Optional[str]						     =       field(
							    default=_a   ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}       )
							snake_case     :      str						     =       field(
							    default="""main"""   ,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}   ,		)
							snake_case     :      str						     =       field(default=_a   ,		metadata={"""help""": """Name or path of preprocessor config."""}       )
							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)."""
							        )
							    }   ,		)
							snake_case     :      float						     =       field(
							    default=0.75   ,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}       )
							snake_case     :      bool						     =       field(
							    default=_a   ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}       )






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






def     _UpperCamelCase     (a__   :Optional[Any]       ):






							"""simple docstring"""





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





def     _UpperCamelCase     ():






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

							UpperCamelCase__				     =			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.
							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   ,			a__       ) 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   ,			**a__       )
							elif model_args.model_name_or_path:
														UpperCamelCase__				     =			ViTMAEConfig.from_pretrained(model_args.model_name_or_path   ,			**a__       )
							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   ,			**a__       )
							elif model_args.model_name_or_path:
														UpperCamelCase__				     =			ViTImageProcessor.from_pretrained(model_args.model_name_or_path   ,			**a__       )
							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=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"""       )
														UpperCamelCase__				     =			ViTMAEForPreTraining(a__       )

							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 a__       : 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(a__   :Optional[Any]       ):
														UpperCamelCase__				     =			[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:
																					UpperCamelCase__				     =			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:
																					UpperCamelCase__				     =			(
																					    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
							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=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:
														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=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:
														UpperCamelCase__				     =			trainer.evaluate()
														trainer.log_metrics("""eval"""   ,			a__       )
														trainer.save_metrics("""eval"""   ,			a__       )

							# 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(**a__       )
							else:
														trainer.create_model_card(**a__       )





def     _UpperCamelCase     (a__   :Dict       ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
					main()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__											= {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= ["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= [
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					UpperCamelCase__											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




