






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_ :						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     _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 						_A (		self				:					Dict	):
		UpperCamelCase     :Dict          =	{}
		if self.train_dir is not None:
			UpperCamelCase     :Any          =	self.train_dir
		if self.validation_dir is not None:
			UpperCamelCase     :List[Any]          =	self.validation_dir
		UpperCamelCase     :Optional[Any]          =	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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



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

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :Any          =	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     :Tuple          =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
	else:
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :str          =	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"""	,			__magic_name__	,			__magic_name__					)

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

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

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

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

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

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

	# Compute absolute learning rate
	UpperCamelCase     :Optional[Any]          =	(
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		UpperCamelCase     :Optional[int]          =	training_args.base_learning_rate * total_train_batch_size / 256

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

	# Training
	if training_args.do_train:
		UpperCamelCase     :Optional[int]          =	None
		if training_args.resume_from_checkpoint is not None:
			UpperCamelCase     :int          =	training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			UpperCamelCase     :Any          =	last_checkpoint
		UpperCamelCase     :int          =	trainer.train(resume_from_checkpoint=__magic_name__					)
		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     :List[str]          =	trainer.evaluate()
		trainer.log_metrics("""eval"""	,			__magic_name__					)
		trainer.save_metrics("""eval"""	,			__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int					)		->			Union[str, Any]:






	"""simple docstring"""



	main()


if __name__ == "__main__":
	main()








import re
import string

import numpy as np

import datasets


UpperCAmelCase_ :						Dict      =      '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''

UpperCAmelCase_ :						Any      =      '''
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    25.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    50.0


    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results["exact_match"], 1))
    75.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["the cat", "theater", "YELLING", "agent007"]
    >>> preds = ["cat?", "theater", "yelling", "agent"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results["exact_match"], 1))
    100.0

    >>> exact_match = datasets.load_metric("exact_match")
    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]
    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results["exact_match"], 1))
    33.3

'''

UpperCAmelCase_ :						Tuple      =      '''
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION      )
class     _SCREAMING_SNAKE_CASE       (			datasets.Metric      ):





	def 						_A (		self				:					Optional[int]	):
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(
		        {
		            """predictions""": datasets.Value("""string""" ,  id="""sequence"""	),
		            """references""": datasets.Value("""string""" ,  id="""sequence"""	),
		        }	) ,  reference_urls=[] ,  )





	def 						_A (		self				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					List[str]=None ,  __lowerCamelCase				:					Optional[int]=False ,  __lowerCamelCase				:					int=False ,  __lowerCamelCase				:					Optional[int]=False ,  ):
		if regexes_to_ignore is not None:
			for s in regexes_to_ignore:
				UpperCamelCase     :str          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in predictions]	)
				UpperCamelCase     :Tuple          =	np.array([re.sub(__lowerCamelCase ,  """""" ,  __lowerCamelCase	) for x in references]	)
		else:
			UpperCamelCase     :Any          =	np.asarray(__lowerCamelCase	)
			UpperCamelCase     :str          =	np.asarray(__lowerCamelCase	)

		if ignore_case:
			UpperCamelCase     :Tuple          =	np.char.lower(__lowerCamelCase	)
			UpperCamelCase     :Any          =	np.char.lower(__lowerCamelCase	)

		if ignore_punctuation:
			UpperCamelCase     :Optional[int]          =	string.punctuation.maketrans("""""" ,  """""" ,  string.punctuation	)
			UpperCamelCase     :Optional[Any]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :List[str]          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		if ignore_numbers:
			UpperCamelCase     :Tuple          =	string.digits.maketrans("""""" ,  """""" ,  string.digits	)
			UpperCamelCase     :Dict          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)
			UpperCamelCase     :Tuple          =	np.char.translate(__lowerCamelCase ,  table=__lowerCamelCase	)

		UpperCamelCase     :int          =	predictions == references

		return {"exact_match": np.mean(__lowerCamelCase	) * 100}
