
"""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
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 				A__ :
		A_		: str            =			field(
		    default='cifar10' ,  metadata={'help': 'Name of a dataset from the datasets package'})
		A_		: Optional[Any]            =			field(
		    default=__snake_case ,  metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
		A_		: str            =			field(
		    default=__snake_case ,  metadata={'help': 'The column name of the images in the files.'})
		A_		: Optional[Any]            =			field(default=__snake_case ,  metadata={'help': 'A folder containing the training data.'})
		A_		: List[str]            =			field(default=__snake_case ,  metadata={'help': 'A folder containing the validation data.'})
		A_		: int            =			field(
		    default=0.15 ,  metadata={'help': 'Percent to split off of train for validation.'})
		A_		: Tuple            =			field(
		    default=__snake_case ,  metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    } ,  )
		A_		: List[str]            =			field(
		    default=__snake_case ,  metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    } ,  )

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase		    =   {}
			if self.train_dir is not None:
				__lowerCAmelCase		    =   self.train_dir
			if self.validation_dir is not None:
				__lowerCAmelCase		    =   self.validation_dir
			__lowerCAmelCase		    =   data_files if data_files else None




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




@dataclass
class 				A__ (			__snake_case):
		A_		: Tuple            =			field(
		    default=1e-3 ,  metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'})



def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase		    =   torch.stack([example['pixel_values'] for example in examples]	)
	return {"pixel_values": pixel_values}
def 					__lowerCAmelCase       ():
	__lowerCAmelCase		    =   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.
		__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase		    =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
	else:
		__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase		    =   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()

	__lowerCAmelCase		    =   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.
	__lowerCAmelCase		    =   None
	if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
		__lowerCAmelCase		    =   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.
	__lowerCAmelCase		    =   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.
	__lowerCAmelCase		    =   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:
		__lowerCAmelCase		    =   ds['train'].train_test_split(data_args.train_val_split	)
		__lowerCAmelCase		    =   split['train']
		__lowerCAmelCase		    =   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.
	__lowerCAmelCase		    =   {
	    '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:
		__lowerCAmelCase		    =   ViTMAEConfig.from_pretrained(model_args.config_name ,						**lowercase__	)
	elif model_args.model_name_or_path:
		__lowerCAmelCase		    =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,						**lowercase__	)
	else:
		__lowerCAmelCase		    =   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:
		__lowerCAmelCase		    =   ViTImageProcessor.from_pretrained(model_args.image_processor_name ,						**lowercase__	)
	elif model_args.model_name_or_path:
		__lowerCAmelCase		    =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,						**lowercase__	)
	else:
		__lowerCAmelCase		    =   ViTImageProcessor()

	# create model
	if model_args.model_name_or_path:
		__lowerCAmelCase		    =   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'	)
		__lowerCAmelCase		    =   ViTMAEForPreTraining(lowercase__	)

	if training_args.do_train:
		__lowerCAmelCase		    =   ds['train'].column_names
	else:
		__lowerCAmelCase		    =   ds['validation'].column_names

	if data_args.image_column_name is not None:
		__lowerCAmelCase		    =   data_args.image_column_name
	elif "image" in column_names:
		__lowerCAmelCase		    =   'image'
	elif "img" in column_names:
		__lowerCAmelCase		    =   'img'
	else:
		__lowerCAmelCase		    =   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:
		__lowerCAmelCase		    =   image_processor.size['shortest_edge']
	else:
		__lowerCAmelCase		    =   (image_processor.size['height'], image_processor.size['width'])
	__lowerCAmelCase		    =   Compose(
	    [
	        Lambda(lambda _UpperCamelCase	: 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(_UpperCamelCase	):
		__lowerCAmelCase		    =   [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:
			__lowerCAmelCase		    =   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:
			__lowerCAmelCase		    =   (
			    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
	__lowerCAmelCase		    =   (
	    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
	)
	if training_args.base_learning_rate is not None:
		__lowerCAmelCase		    =   training_args.base_learning_rate * total_train_batch_size / 256

	# Initialize our trainer
	__lowerCAmelCase		    =   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:
		__lowerCAmelCase		    =   None
		if training_args.resume_from_checkpoint is not None:
			__lowerCAmelCase		    =   training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			__lowerCAmelCase		    =   last_checkpoint
		__lowerCAmelCase		    =   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:
		__lowerCAmelCase		    =   trainer.evaluate()
		trainer.log_metrics('eval' ,						lowercase__	)
		trainer.save_metrics('eval' ,						lowercase__	)

	# Write model card and (optionally) push to hub
	__lowerCAmelCase		    =   {
	    '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 					__lowerCAmelCase       (_UpperCamelCase	):
	main()


if __name__ == "__main__":
							main()

"""simple docstring"""







import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCamelCase__           =	data_utils.TransfoXLTokenizer
lowerCamelCase__           =	data_utils.TransfoXLCorpus
lowerCamelCase__           =	data_utils
lowerCamelCase__           =	data_utils
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(_UpperCamelCase ,						'rb'	) as fp:
			__lowerCAmelCase    :		int		    =   pickle.load(_UpperCamelCase ,						encoding='latin1'	)
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__lowerCAmelCase    :		Any		    =   pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
		print(F"Save vocabulary to {pytorch_vocab_dump_path}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   corpus.vocab.__dict__
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   corpus.__dict__
		corpus_dict_no_vocab.pop('vocab' ,						_UpperCamelCase	)
		__lowerCAmelCase    :		List[str]		    =   pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F"Save dataset to {pytorch_dataset_dump_path}"	)
		torch.save(_UpperCamelCase ,						_UpperCamelCase	)

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__lowerCAmelCase    :		Tuple		    =   os.path.abspath(_UpperCamelCase	)
		__lowerCAmelCase    :		List[Any]		    =   os.path.abspath(_UpperCamelCase	)

		print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."	)
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__lowerCAmelCase    :		Any		    =   TransfoXLConfig()
		else:
			__lowerCAmelCase    :		str		    =   TransfoXLConfig.from_json_file(_UpperCamelCase	)
		print(F"Building PyTorch model from configuration: {config}"	)
		__lowerCAmelCase    :		Optional[Any]		    =   TransfoXLLMHeadModel(_UpperCamelCase	)

		__lowerCAmelCase    :		Dict		    =   load_tf_weights_in_transfo_xl(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		# Save pytorch-model
		__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		__lowerCAmelCase    :		Tuple		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
		print(F"Save PyTorch model to {os.path.abspath(_UpperCamelCase	)}"	)
		torch.save(model.state_dict() ,						_UpperCamelCase	)
		print(F"Save configuration file to {os.path.abspath(_UpperCamelCase	)}"	)
		with open(_UpperCamelCase ,						'w' ,						encoding='utf-8'	) as f:
			f.write(config.to_json_string()	)


if __name__ == "__main__":
							lowerCamelCase__           =	argparse.ArgumentParser()
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
							)
							parser.add_argument(
							    """--tf_checkpoint_path""",
							    default="""""",
							    type=str,
							    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
							)
							parser.add_argument(
							    """--transfo_xl_config_file""",
							    default="""""",
							    type=str,
							    help=(
							        """An optional config json file corresponding to the pre-trained BERT model. \n"""
							        """This specifies the model architecture."""
							    ),
							)
							parser.add_argument(
							    """--transfo_xl_dataset_file""",
							    default="""""",
							    type=str,
							    help="""An optional dataset file to be converted in a vocabulary.""",
							)
							lowerCamelCase__           =	parser.parse_args()
							convert_transfo_xl_checkpoint_to_pytorch(
							    args.tf_checkpoint_path,
							    args.transfo_xl_config_file,
							    args.pytorch_dump_folder_path,
							    args.transfo_xl_dataset_file,
							)