





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__						:			List[str]									=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      =				field(
				    default="""cifar10"""    ,					metadata={"""help""": """Name of a dataset from the datasets package"""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """The column name of the images in the files."""}    )
				__A      =				field(default=a    ,					metadata={"""help""": """A folder containing the training data."""}    )
				__A      =				field(default=a    ,					metadata={"""help""": """A folder containing the validation data."""}    )
				__A      =				field(
				    default=0.1_5    ,					metadata={"""help""": """Percent to split off of train for validation."""}    )
				__A      =				field(
				    default=a    ,					metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }    ,					)
				__A      =				field(
				    default=a    ,					metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }    ,					)


				def _snake_case       (		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      =				field(
				    default=a    ,					metadata={
				        """help""": (
				            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
				        )
				    }    ,					)
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}    )
				__A      =				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"""
				        )
				    }    ,					)
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
				__A      =				field(
				    default="""main"""    ,					metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,					)
				__A      =				field(default=a    ,					metadata={"""help""": """Name or path of preprocessor config."""}    )
				__A      =				field(
				    default=a    ,					metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }    ,					)
				__A      =				field(
				    default=0.7_5    ,					metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}    )
				__A      =				field(
				    default=a    ,					metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}    )




@dataclass
class       __A     (						a    ):
				__A      =				field(
				    default=1E-3    ,					metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}    )





def      _lowercase					(						_UpperCAmelCase				)						->  Optional[int]:
				lowerCamelCase	 =torch.stack([example["""pixel_values"""] for example in examples]				)
				return {"pixel_values": pixel_values}





def      _lowercase					(						)						->  List[Any]:
				# 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.

				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"""			,      _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"""]

				# 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			,      **_UpperCAmelCase				)
				elif model_args.model_name_or_path:
								lowerCamelCase	 =ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,      **_UpperCAmelCase				)
				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			,      **_UpperCAmelCase				)
				elif model_args.model_name_or_path:
								lowerCamelCase	 =ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,      **_UpperCAmelCase				)
				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=_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	 =ViTMAEForPreTraining(_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 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(_UpperCAmelCase			,      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(_UpperCAmelCase				) 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(_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				)

				# 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 / 2_56

				# 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	 ={
				    """tasks""": """masked-auto-encoding""",
				    """dataset""": data_args.dataset_name,
				    """tags""": ["""masked-auto-encoding"""],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**_UpperCAmelCase				)
				else:
								trainer.create_model_card(**_UpperCAmelCase				)





def      _lowercase					(						_UpperCAmelCase				)						->  Any:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()












from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch

@require_torch
@require_tf
class       __A     (						a    ):


				def _snake_case       (		self							):
								lowerCamelCase	 =SMALL_MODEL_IDENTIFIER
								lowerCamelCase	 ="""pt"""
								lowerCamelCase	 ="""tf"""


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =AutoModel.from_pretrained(self.test_model							)
								model_pt.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								lowerCamelCase	 =TFAutoModel.from_pretrained(self.test_model   , from_pt=UpperCAmelCase_							)
								model_tf.save_pretrained(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""mock_framework"""

								# Framework provided - return whatever the user provides
								lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model   , UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_   , UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
												self._setup_pt_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
												self._setup_tf_ckpt(UpperCAmelCase_							)
												lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# PyTorch not in environment -> use TensorFlow
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_tf							)

								# Both in environment -> use PyTorch
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)
												self.assertEqual(UpperCAmelCase_   , self.framework_pt							)

								# Both not in environment -> raise error
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								lowerCamelCase	 =MagicMock(return_value=UpperCAmelCase_							)
								with patch("""transformers.onnx.features.is_tf_available"""   , UpperCAmelCase_							), patch(
								    """transformers.onnx.features.is_torch_available"""   , UpperCAmelCase_							):
												with self.assertRaises(UpperCAmelCase_							):
																lowerCamelCase	 =FeaturesManager.determine_framework(self.test_model							)





