


import gc
import threading
import time

import psutil
import torch







class        UpperCamelCase__   :

					def __init__(     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			psutil.Process()
										lowercase_  							=			False

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										lowercase_  							=			-1

										while True:
															lowercase_  							=			max(self.process.memory_info().rss							,					self.cpu_memory_peak       )

															# can't sleep or will not catch the peak right (this comment is here on purpose)
															if not self.peak_monitoring:
																				break

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			True
										lowercase_  							=			threading.Thread(target=self.peak_monitor       )
										lowercase_  							=			True
										self.thread.start()







					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			False
										self.thread.join()
										return self.cpu_memory_peak



a					  =     PeakCPUMemory()


def 			UpperCAmelCase_				(      ):
					# Time
					lowercase_  							=			{"""time""": time.time()}

					gc.collect()
					torch.cuda.empty_cache()

					# CPU mem
					lowercase_  							=			psutil.Process().memory_info().rss
					cpu_peak_tracker.start()

					# GPU mem
					for i in range(torch.cuda.device_count()  ):
										lowercase_  							=			torch.cuda.memory_allocated(UpperCAmelCase__  )
					torch.cuda.reset_peak_memory_stats()

					return measures


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# Time
					lowercase_  							=			{"""time""": time.time() - start_measures["""time"""]}

					gc.collect()
					torch.cuda.empty_cache()

					# CPU mem
					lowercase_  							=			(psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
					lowercase_  							=			(cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

					# GPU mem
					for i in range(torch.cuda.device_count()  ):
										lowercase_  							=			(torch.cuda.memory_allocated(UpperCAmelCase__  ) - start_measures[str(UpperCAmelCase__  )]) / 2**2_0
										lowercase_  							=			(torch.cuda.max_memory_allocated(UpperCAmelCase__  ) - start_measures[str(UpperCAmelCase__  )]) / 2**2_0

					return measures



def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					print(F'''{description}:'''  )
					print(F'''- Time: {measures["time"]:.2f}s'''  )
					for i in range(torch.cuda.device_count()  ):
										print(F'''- GPU {i} allocated: {measures[str(UpperCAmelCase__  )]:.2f}MiB'''  )
										lowercase_  							=			measures[F'''{i}-peak''']
										print(F'''- GPU {i} peak: {peak:.2f}MiB'''  )
					print(F'''- CPU RAM allocated: {measures["cpu"]:.2f}MiB'''  )
					print(F'''- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB'''  )






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        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default='cifar10'							,				metadata={'help': 'Name of a dataset from the datasets package'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   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\'.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the training data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the validation data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[float]						=   field(
					    default=0.1_5							,				metadata={'help': 'Percent to split off of train for validation.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(default=32							,				metadata={'help': 'The size of the square patches to use for masking.'}   )
					__SCREAMING_SNAKE_CASE       :  float						=   field(
					    default=0.6							,				metadata={'help': 'Percentage of patches to mask.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				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'''






										lowercase_  							=			{}
										if self.train_dir is not None:
															lowercase_  							=			self.train_dir
										if self.validation_dir is not None:
															lowercase_  							=			self.validation_dir
										lowercase_  							=			data_files if data_files else None







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   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.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__magic_name__   )}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   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'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default='main'							,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(default=__magic_name__							,				metadata={'help': 'Name or path of preprocessor config.'}   )
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Stride to use for the encoder.'}							,				)







class        UpperCamelCase__   :

					def __init__(     self       :   Dict							,					UpperCamelCase__       :   List[Any]=192							,					UpperCamelCase__       :   Tuple=32							,					UpperCamelCase__       :   Dict=4							,					UpperCamelCase__       :   str=0.6       ):






										'''simple docstring'''






										lowercase_  							=			input_size
										lowercase_  							=			mask_patch_size
										lowercase_  							=			model_patch_size
										lowercase_  							=			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"""       )

										lowercase_  							=			self.input_size // self.mask_patch_size
										lowercase_  							=			self.mask_patch_size // self.model_patch_size

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







					def __call__(     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			np.random.permutation(self.token_count       )[: self.mask_count]
										lowercase_  							=			np.zeros(self.token_count							,					dtype=UpperCamelCase__       )
										lowercase_  							=			1

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

										return torch.tensor(mask.flatten()       )



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			torch.stack([example["""pixel_values"""] for example in examples]  )
					lowercase_  							=			torch.stack([example["""mask"""] for example in examples]  )
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def 			UpperCAmelCase_				(      ):
					# 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.

					lowercase_  							=			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.
										lowercase_							,					lowercase_							,					lowercase_  							=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
					else:
										lowercase_							,					lowercase_							,					lowercase_  							=			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()

					lowercase_  							=			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.
					lowercase_  							=			None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										lowercase_  							=			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.
					lowercase_  							=			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.
					lowercase_  							=			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:
										lowercase_  							=			ds["""train"""].train_test_split(data_args.train_val_split  )
										lowercase_  							=			split["""train"""]
										lowercase_  							=			split["""test"""]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					lowercase_  							=			{
					    """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:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.config_name_or_path						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			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"""  ):
										lowercase_  							=			"""simmim"""

					# adapt config
					lowercase_  							=			model_args.image_size if model_args.image_size is not None else config.image_size
					lowercase_  							=			model_args.patch_size if model_args.patch_size is not None else config.patch_size
					lowercase_  							=			(
					    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:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.image_processor_name						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										lowercase_  							=			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										lowercase_  							=			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"""  )
										lowercase_  							=			AutoModelForMaskedImageModeling.from_config(UpperCAmelCase__  )

					if training_args.do_train:
										lowercase_  							=			ds["""train"""].column_names
					else:
										lowercase_  							=			ds["""validation"""].column_names

					if data_args.image_column_name is not None:
										lowercase_  							=			data_args.image_column_name
					elif "image" in column_names:
										lowercase_  							=			"""image"""
					elif "img" in column_names:
										lowercase_  							=			"""img"""
					else:
										lowercase_  							=			column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					lowercase_  							=			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
					lowercase_  							=			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__  ):
										lowercase_  							=			[transforms(UpperCAmelCase__  ) for image in examples[image_column_name]]
										lowercase_  							=			[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:
															lowercase_  							=			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:
															lowercase_  							=			(
															    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
					lowercase_  							=			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:
										lowercase_  							=			None
										if training_args.resume_from_checkpoint is not None:
															lowercase_  							=			training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowercase_  							=			last_checkpoint
										lowercase_  							=			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:
										lowercase_  							=			trainer.evaluate()
										trainer.log_metrics("""eval"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""eval"""						,							UpperCAmelCase__  )

					# Write model card and (optionally) push to hub
					lowercase_  							=			{
					    """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()


