
"""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__      :    Union[str, 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 	lowerCAmelCase_						:


				"""simple docstring"""



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



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




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






@dataclass
class 	lowerCAmelCase_						:


				"""simple docstring"""



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






@dataclass
class 	lowerCAmelCase_						(UpperCAmelCase__  ):


				"""simple docstring"""



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






def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   torch.stack([example["""pixel_values"""] for example in examples]						)
							return {"pixel_values": pixel_values}

def lowercase_  (   ):
							# 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__						:       List[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.
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[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"""   ,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__						:       str           =   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__						:       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.
							SCREAMING_SNAKE_CASE__						:       Union[str, 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.
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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__						:       List[str]           =   ds['''train'''].train_test_split(data_args.train_val_split						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   split['''train''']
														SCREAMING_SNAKE_CASE__						:       List[str]           =   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__						:       int           =   {
							    '''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__						:       Optional[int]           =   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__						:       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__						:       Union[str, Any]           =   ViTImageProcessor.from_pretrained(model_args.image_processor_name   ,**lowerCAmelCase_						)
							elif model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__						:       int           =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path   ,**lowerCAmelCase_						)
							else:
														SCREAMING_SNAKE_CASE__						:       int           =   ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														SCREAMING_SNAKE_CASE__						:       str           =   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__						:       str           =   ViTMAEForPreTraining(lowerCAmelCase_						)

							if training_args.do_train:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   ds['''train'''].column_names
							else:
														SCREAMING_SNAKE_CASE__						:       int           =   ds['''validation'''].column_names

							if data_args.image_column_name is not None:
														SCREAMING_SNAKE_CASE__						:       Dict           =   data_args.image_column_name
							elif "image" in column_names:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   '''image'''
							elif "img" in column_names:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   '''img'''
							else:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   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__						:       Optional[int]           =   image_processor.size['''shortest_edge''']
							else:
														SCREAMING_SNAKE_CASE__						:       str           =   (image_processor.size['''height'''], image_processor.size['''width'''])
							SCREAMING_SNAKE_CASE__						:       List[str]           =   Compose(
							    [
							        Lambda(lambda _snake_case						: 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(_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [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__						:       List[str]           =   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__						:       Optional[Any]           =   (
																					    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__						:       Tuple           =   (
							    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__						:       Tuple           =   training_args.base_learning_rate * total_train_batch_size / 256

							# Initialize our trainer
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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__						:       str           =   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__						:       Any           =   last_checkpoint
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   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__						:       Tuple           =   trainer.evaluate()
														trainer.log_metrics("""eval"""   ,lowerCAmelCase_						)
														trainer.save_metrics("""eval"""   ,lowerCAmelCase_						)

							# Write model card and (optionally) push to hub
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {
							    '''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 lowercase_  (   _snake_case						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
					main()








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel