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          =						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'''






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



						"""simple docstring"""





						A_						:							Tuple      = {}
						if self.train_dir is not None:
								A_						:							Tuple      = self.train_dir
						if self.validation_dir is not None:
								A_						:							Dict      = self.validation_dir
						A_						:							Optional[int]      = data_files if data_files else None





@dataclass
class UpperCAmelCase		:


				'''simple docstring'''






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





@dataclass
class UpperCAmelCase		(   __A    ):


				'''simple docstring'''






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




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							List[str]      = torch.stack([example['pixel_values'] for example in examples]      )
		return {"pixel_values": pixel_values}




def 							UpperCamelCase		(			):
		'''simple docstring'''




		A_						:							int      = 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.
				A_						:							Optional[int]      = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
		else:
				A_						:							List[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'		,__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()

		A_						:							Tuple      = 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.
		A_						:							Any      = None
		if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
				A_						:							Optional[Any]      = 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.
		A_						:							int      = 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.
		A_						:							Tuple      = 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:
				A_						:							Optional[int]      = ds["train"].train_test_split(data_args.train_val_split      )
				A_						:							Tuple      = split["train"]
				A_						:							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.
		A_						:							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:
				A_						:							Optional[int]      = ViTMAEConfig.from_pretrained(model_args.config_name		,**__lowercase      )
		elif model_args.model_name_or_path:
				A_						:							Tuple      = ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,**__lowercase      )
		else:
				A_						:							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:
				A_						:							Tuple      = ViTImageProcessor.from_pretrained(model_args.image_processor_name		,**__lowercase      )
		elif model_args.model_name_or_path:
				A_						:							int      = ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,**__lowercase      )
		else:
				A_						:							List[Any]      = ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				A_						:							List[str]      = 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'      )
				A_						:							Optional[Any]      = ViTMAEForPreTraining(__lowercase      )

		if training_args.do_train:
				A_						:							Any      = ds["train"].column_names
		else:
				A_						:							Union[str, Any]      = ds["validation"].column_names

		if data_args.image_column_name is not None:
				A_						:							Any      = data_args.image_column_name
		elif "image" in column_names:
				A_						:							List[Any]      = "image"
		elif "img" in column_names:
				A_						:							List[Any]      = "img"
		else:
				A_						:							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:
				A_						:							List[Any]      = image_processor.size["shortest_edge"]
		else:
				A_						:							Optional[int]      = (image_processor.size["height"], image_processor.size["width"])
		A_						:							List[str]      = Compose(
		    [
		        Lambda(lambda __lowercase      : 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(__lowercase		:					Optional[Any]      ):
				A_						:							Union[str, Any]      = [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:
						A_						:							Union[str, Any]      = 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:
						A_						:							Any      = (
						    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
		A_						:							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:
				A_						:							Dict      = training_args.base_learning_rate * total_train_batch_size / 2_56

		# Initialize our trainer
		A_						:							str      = 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:
				A_						:							Dict      = None
				if training_args.resume_from_checkpoint is not None:
						A_						:							str      = training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						A_						:							Any      = last_checkpoint
				A_						:							Dict      = 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:
				A_						:							Dict      = trainer.evaluate()
				trainer.log_metrics('eval'		,__lowercase      )
				trainer.save_metrics('eval'		,__lowercase      )

		# Write model card and (optionally) push to hub
		A_						:							Any      = {
		    "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 							UpperCamelCase		(			__lowercase		:					List[Any]      ):
		'''simple docstring'''




		main()


if __name__ == "__main__":
	main()










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			Dict       			=  ["""pixel_values"""]






		def __init__(						self				:     Tuple   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Dict[str, int] = None   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Union[int, float] = 1 / 2_55   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     bool = True   ,  **UpperCAmelCase				:     Any   ,  ):
				super().__init__(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						size if size is not None else {"height": 3_84, "width": 3_84}
				SCREAMING_SNAKE_CASE_				:str				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						do_resize
				SCREAMING_SNAKE_CASE_				:int				     =						size
				SCREAMING_SNAKE_CASE_				:int				     =						resample
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_std if image_std is not None else OPENAI_CLIP_STD
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_convert_rgb






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Dict[str, int]   ,  UpperCAmelCase				:     PILImageResampling = PILImageResampling.BICUBIC   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Any   ,  ):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)
				if "height" not in size or "width" not in size:
						raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(size["height"], size["width"])
				return resize(UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Union[int, float]   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     List[str]   ,  ):
				return rescale(UpperCAmelCase   ,  scale=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Union[float, List[float]]   ,  UpperCAmelCase				:     Optional[Union[str, ChannelDimension]] = None   ,  **UpperCAmelCase				:     Optional[Any]   ,  ):
				return normalize(UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase   ,  data_format=UpperCAmelCase   ,  **UpperCAmelCase)







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     ImageInput   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Dict[str, int]] = None   ,  UpperCAmelCase				:     PILImageResampling = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[float] = None   ,  UpperCAmelCase				:     Optional[bool] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[float, List[float]]] = None   ,  UpperCAmelCase				:     Optional[Union[str, TensorType]] = None   ,  UpperCAmelCase				:     bool = None   ,  UpperCAmelCase				:     ChannelDimension = ChannelDimension.FIRST   ,  **UpperCAmelCase				:     Dict   ,  ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_				:Tuple				     =						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						do_rescale if do_rescale is not None else self.do_rescale
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						rescale_factor if rescale_factor is not None else self.rescale_factor
				SCREAMING_SNAKE_CASE_				:Dict				     =						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_mean if image_mean is not None else self.image_mean
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_std if image_std is not None else self.image_std
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_				:Tuple				     =						get_size_dict(UpperCAmelCase   ,  default_to_square=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						make_list_of_images(UpperCAmelCase)

				if not valid_images(UpperCAmelCase):
						raise ValueError(
						    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
						    "torch.Tensor, tf.Tensor or jax.ndarray.")

				if do_resize and size is None or resample is None:
						raise ValueError("Size and resample must be specified if do_resize is True.")

				if do_rescale and rescale_factor is None:
						raise ValueError("Rescale factor must be specified if do_rescale is True.")

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError("Image mean and std must be specified if do_normalize is True.")

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[convert_to_rgb(UpperCAmelCase) for image in images]

				# All transformations expect numpy arrays.
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[to_numpy_array(UpperCAmelCase) for image in images]

				if do_resize:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						[self.resize(image=UpperCAmelCase   ,  size=UpperCAmelCase   ,  resample=UpperCAmelCase) for image in images]

				if do_rescale:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.rescale(image=UpperCAmelCase   ,  scale=UpperCAmelCase) for image in images]

				if do_normalize:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[self.normalize(image=UpperCAmelCase   ,  mean=UpperCAmelCase   ,  std=UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[to_channel_dimension_format(UpperCAmelCase   ,  UpperCAmelCase) for image in images]

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						BatchFeature(data={"pixel_values": images}   ,  tensor_type=UpperCAmelCase)

				return encoded_outputs






