




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
__A								=       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")

__A								=       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__A								=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        __lowerCAmelCase					:

			"""simple docstring"""







			snake_case_  						=			field(
			    default='''cifar10'''     ,							metadata={'''help''': '''Name of a dataset from the datasets package'''}					)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}					)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}     ,							)
			snake_case_  						=			field(default=__magic_name__     ,							metadata={'''help''': '''A folder containing the training data.'''}					)
			snake_case_  						=			field(default=__magic_name__     ,							metadata={'''help''': '''A folder containing the validation data.'''}					)
			snake_case_  						=			field(
			    default=0.1_5     ,							metadata={'''help''': '''Percent to split off of train for validation.'''}					)
			snake_case_  						=			field(default=32     ,							metadata={'''help''': '''The size of the square patches to use for masking.'''}					)
			snake_case_  						=			field(
			    default=0.6     ,							metadata={'''help''': '''Percentage of patches to mask.'''}     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    }     ,							)


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__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        __lowerCAmelCase					:

			"""simple docstring"""







			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
			            '''checkpoint identifier on the hub. '''
			            '''Don\'t set if you want to train a model from scratch.'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__magic_name__					)}     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
			snake_case_  						=			field(
			    default=__magic_name__     ,							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=__magic_name__     ,							metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}     ,							)
			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=__magic_name__     ,							metadata={'''help''': '''Name or path of preprocessor config.'''}					)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={
			        '''help''': (
			            '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
			        )
			    }     ,							)
			snake_case_  						=			field(
			    default=__magic_name__     ,							metadata={'''help''': '''Stride to use for the encoder.'''}     ,							)







class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__=192     ,		lowerCamelCase__=32     ,		lowerCamelCase__=4     ,		lowerCamelCase__=0.6				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       input_size
							__lowerCamelCase									=       mask_patch_size
							__lowerCamelCase									=       model_patch_size
							__lowerCamelCase									=       mask_ratio

							if self.input_size % self.mask_patch_size != 0:
											raise ValueError('Input size must be divisible by mask patch size'				)
							if self.mask_patch_size % self.model_patch_size != 0:
											raise ValueError('Mask patch size must be divisible by model patch size'				)

							__lowerCamelCase									=       self.input_size // self.mask_patch_size
							__lowerCamelCase									=       self.mask_patch_size // self.model_patch_size

							__lowerCamelCase									=       self.rand_size**2
							__lowerCamelCase									=       int(np.ceil(self.token_count * self.mask_ratio				)				)






			def __call__( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       np.random.permutation(self.token_count				)[: self.mask_count]
							__lowerCamelCase									=       np.zeros(self.token_count     ,		dtype=lowerCamelCase__				)
							__lowerCamelCase									=       1

							__lowerCamelCase									=       mask.reshape((self.rand_size, self.rand_size)				)
							__lowerCamelCase									=       mask.repeat(self.scale     ,		axis=0				).repeat(self.scale     ,		axis=1				)

							return torch.tensor(mask.flatten()				)







def      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       torch.stack([example['pixel_values'] for example in examples] )
				__lowerCamelCase									=       torch.stack([example['mask'] for example in examples] )
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				__lowerCamelCase									=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				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_mim'					,	UpperCamelCase__					,	UpperCamelCase__ )

				# 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(UpperCamelCase__ )
				transformers.utils.logging.set_verbosity(UpperCamelCase__ )
				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					,	UpperCamelCase__ ) 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']

				# Create config
				# 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_or_path:
								__lowerCamelCase									=       AutoConfig.from_pretrained(model_args.config_name_or_path					,	**UpperCamelCase__ )
				elif model_args.model_name_or_path:
								__lowerCamelCase									=       AutoConfig.from_pretrained(model_args.model_name_or_path					,	**UpperCamelCase__ )
				else:
								__lowerCamelCase									=       CONFIG_MAPPING[model_args.model_type]()
								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}""" )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(UpperCamelCase__					,	'decoder_type' ):
								__lowerCamelCase									=       'simmim'

				# adapt config
				__lowerCamelCase									=       model_args.image_size if model_args.image_size is not None else config.image_size
				__lowerCamelCase									=       model_args.patch_size if model_args.patch_size is not None else config.patch_size
				__lowerCamelCase									=       (
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        'image_size': model_args.image_size,
				        'patch_size': model_args.patch_size,
				        'encoder_stride': model_args.encoder_stride,
				    } )

				# create image processor
				if model_args.image_processor_name:
								__lowerCamelCase									=       AutoImageProcessor.from_pretrained(model_args.image_processor_name					,	**UpperCamelCase__ )
				elif model_args.model_name_or_path:
								__lowerCamelCase									=       AutoImageProcessor.from_pretrained(model_args.model_name_or_path					,	**UpperCamelCase__ )
				else:
								__lowerCamelCase									=       {
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								__lowerCamelCase									=       IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								__lowerCamelCase									=       AutoModelForMaskedImageModeling.from_pretrained(
								    model_args.model_name_or_path					,	from_tf=bool('.ckpt' in model_args.model_name_or_path )					,	config=UpperCamelCase__					,	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									=       AutoModelForMaskedImageModeling.from_config(UpperCamelCase__ )

				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 SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				__lowerCamelCase									=       Compose(
				    [
				        Lambda(lambda UpperCamelCase__ : img.convert('RGB' ) if img.mode != "RGB" else img ),
				        RandomResizedCrop(model_args.image_size					,	scale=(0.67, 1.0)					,	ratio=(3.0 / 4.0, 4.0 / 3.0) ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean					,	std=image_processor.image_std ),
				    ] )

				# create mask generator
				__lowerCamelCase									=       MaskGenerator(
				    input_size=model_args.image_size					,	mask_patch_size=data_args.mask_patch_size					,	model_patch_size=model_args.patch_size					,	mask_ratio=data_args.mask_ratio					,	)

				def preprocess_images(UpperCamelCase__							: Union[str, Any] ):
								__lowerCamelCase									=       [transforms(UpperCamelCase__ ) for image in examples[image_column_name]]
								__lowerCamelCase									=       [mask_generator() for i in range(len(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(UpperCamelCase__ )

				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(UpperCamelCase__ )

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

				# 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=UpperCamelCase__ )
								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'					,	UpperCamelCase__ )
								trainer.save_metrics('eval'					,	UpperCamelCase__ )

				# Write model card and (optionally) push to hub
				__lowerCamelCase									=       {
				    'finetuned_from': model_args.model_name_or_path,
				    'tasks': 'masked-image-modeling',
				    'dataset': data_args.dataset_name,
				    'tags': ['masked-image-modeling'],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**UpperCamelCase__ )
				else:
								trainer.create_model_card(**UpperCamelCase__ )


if __name__ == "__main__":
					main()








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''lxmert'''
			snake_case_  						=			{}


			def __init__( self     ,		lowerCamelCase__=30_522     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=9_500     ,		lowerCamelCase__=1_600     ,		lowerCamelCase__=400     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=512     ,		lowerCamelCase__=2     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=9     ,		lowerCamelCase__=5     ,		lowerCamelCase__=5     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=4     ,		lowerCamelCase__=6.67     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       num_qa_labels
							__lowerCamelCase									=       num_object_labels
							__lowerCamelCase									=       num_attr_labels
							__lowerCamelCase									=       l_layers
							__lowerCamelCase									=       x_layers
							__lowerCamelCase									=       r_layers
							__lowerCamelCase									=       visual_feat_dim
							__lowerCamelCase									=       visual_pos_dim
							__lowerCamelCase									=       visual_loss_normalizer
							__lowerCamelCase									=       task_matched
							__lowerCamelCase									=       task_mask_lm
							__lowerCamelCase									=       task_obj_predict
							__lowerCamelCase									=       task_qa
							__lowerCamelCase									=       visual_obj_loss
							__lowerCamelCase									=       visual_attr_loss
							__lowerCamelCase									=       visual_feat_loss
							__lowerCamelCase									=       {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
							super().__init__(**lowerCamelCase__				)


