



'''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__ :			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 		_UpperCamelCase						:


			'''simple docstring'''

			_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=0.1_5					,      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 UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	{}
					if self.train_dir is not None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.train_dir
					if self.validation_dir is not None:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.validation_dir
					__SCREAMING_SNAKE_CASE :	str					 =	data_files if data_files else None






@dataclass
class 		_UpperCamelCase						:


			'''simple docstring'''

			_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=0.7_5					,      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 		_UpperCamelCase						(							__a					):


			'''simple docstring'''

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


def        lowerCAmelCase_     (			_lowerCamelCase:     List[str]      ):
		__SCREAMING_SNAKE_CASE :	Dict					 =	torch.stack([example["""pixel_values"""] for example in examples]      )
		return {"pixel_values": pixel_values}


def        lowerCAmelCase_     (			):
		# 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.

		__SCREAMING_SNAKE_CASE :	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.
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
		else:
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	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""" ,   lowerCAmelCase__ ,   lowerCAmelCase__      )

		# 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()

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

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

		if training_args.do_train:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	ds["""train"""].column_names
		else:
				__SCREAMING_SNAKE_CASE :	Dict					 =	ds["""validation"""].column_names

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

		def preprocess_images(_lowerCamelCase:     Dict      ):
				__SCREAMING_SNAKE_CASE :	Tuple					 =	[transforms(lowerCAmelCase__      ) 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:
						__SCREAMING_SNAKE_CASE :	Dict					 =	ds["""train"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
				# Set the training transforms
				ds["train"].set_transform(lowerCAmelCase__      )

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

		# Compute absolute learning rate
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				__SCREAMING_SNAKE_CASE :	str					 =	training_args.base_learning_rate * total_train_batch_size / 2_56

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

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

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


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()







'''simple docstring'''




from unittest.mock import Mock, patch

from file_transfer.send_file import send_file


@patch("""socket.socket"""      )
@patch("""builtins.open"""      )
def        lowerCAmelCase_     (			_lowerCamelCase:     Dict ,   _lowerCamelCase:     List[Any]      ):
		# ===== initialization =====
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	Mock()
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	conn, Mock()
		__SCREAMING_SNAKE_CASE :	str					 =	iter([1, None]      )
		__SCREAMING_SNAKE_CASE :	Any					 =	lambda _lowerCamelCase      : next(_lowerCamelCase      )

		# ===== invoke =====
		send_file(filename="""mytext.txt""" ,   testing=_lowerCamelCase      )

		# ===== ensurance =====
		sock.assert_called_once()
		sock.return_value.bind.assert_called_once()
		sock.return_value.listen.assert_called_once()
		sock.return_value.accept.assert_called_once()
		conn.recv.assert_called_once()

		file.return_value.__enter__.assert_called_once()
		file.return_value.__enter__.return_value.read.assert_called()

		conn.send.assert_called_once()
		conn.close.assert_called_once()
		sock.return_value.shutdown.assert_called_once()
		sock.return_value.close.assert_called_once()