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






	def       _a(self				: Tuple    ) ->			int:
							_lowercase       :	int           =   {}
							if self.train_dir is not None:
													_lowercase       :	Any           =   self.train_dir
							if self.validation_dir is not None:
													_lowercase       :	Union[str, Any]           =   self.validation_dir
							_lowercase       :	Tuple           =   data_files if data_files else None




@dataclass
class   __lowercase						:
	_A       =   field(
	    default=__snake_case   , metadata={
	        "help": (
	            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
	        )
	    }   , )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}       )
	_A       =   field(
	    default=__snake_case   , 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=__snake_case   , 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=__snake_case   , metadata={"help": "Name or path of preprocessor config."}       )
	_A       =   field(
	    default=__snake_case   , metadata={
	        "help": (
	            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
	            "with private models)."
	        )
	    }   , )
	_A       =   field(
	    default=0.75   , metadata={"help": "The ratio of the number of masked tokens in the input sequence."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "Whether or not to train with normalized pixel values as target."}       )




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



def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			List[Any]:






						'''simple docstring'''

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



def 		UpperCamelCase     (       )   ->			Optional[Any]:






						'''simple docstring'''

						_lowercase       :	Tuple           =   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.
												_lowercase      ,    _lowercase      ,    _lowercase       :	Optional[Any]           =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
						else:
												_lowercase      ,    _lowercase      ,    _lowercase       :	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" ,			_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       :	Tuple           =   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       :	List[Any]           =   None
						if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
												_lowercase       :	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.
						_lowercase       :	Dict           =   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       :	Optional[Any]           =   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       :	int           =   ds["train"].train_test_split(data_args.train_val_split  )
												_lowercase       :	Any           =   split["train"]
												_lowercase       :	Optional[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.
						_lowercase       :	List[str]           =   {
						    "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:
												_lowercase       :	str           =   ViTMAEConfig.from_pretrained(model_args.config_name ,			**_UpperCAmelCase  )
						elif model_args.model_name_or_path:
												_lowercase       :	Tuple           =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path ,			**_UpperCAmelCase  )
						else:
												_lowercase       :	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:
												_lowercase       :	Union[str, Any]           =   ViTImageProcessor.from_pretrained(model_args.image_processor_name ,			**_UpperCAmelCase  )
						elif model_args.model_name_or_path:
												_lowercase       :	Tuple           =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path ,			**_UpperCAmelCase  )
						else:
												_lowercase       :	str           =   ViTImageProcessor()

						# create model
						if model_args.model_name_or_path:
												_lowercase       :	Optional[Any]           =   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"  )
												_lowercase       :	Any           =   ViTMAEForPreTraining(_UpperCAmelCase  )

						if training_args.do_train:
												_lowercase       :	Optional[Any]           =   ds["train"].column_names
						else:
												_lowercase       :	Any           =   ds["validation"].column_names

						if data_args.image_column_name is not None:
												_lowercase       :	Optional[Any]           =   data_args.image_column_name
						elif "image" in column_names:
												_lowercase       :	int           =   "image"
						elif "img" in column_names:
												_lowercase       :	int           =   "img"
						else:
												_lowercase       :	Tuple           =   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:
												_lowercase       :	Optional[Any]           =   image_processor.size["shortest_edge"]
						else:
												_lowercase       :	Union[str, Any]           =   (image_processor.size["height"], image_processor.size["width"])
						_lowercase       :	Union[str, Any]           =   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  :   Union[str, Any]  ):
												_lowercase       :	Union[str, Any]           =   [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:
																		_lowercase       :	List[Any]           =   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       :	int           =   (
																		    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
						_lowercase       :	Tuple           =   (
						    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
						)
						if training_args.base_learning_rate is not None:
												_lowercase       :	Tuple           =   training_args.base_learning_rate * total_train_batch_size / 256

						# Initialize our trainer
						_lowercase       :	Tuple           =   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       :	Tuple           =   None
												if training_args.resume_from_checkpoint is not None:
																		_lowercase       :	Tuple           =   training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		_lowercase       :	str           =   last_checkpoint
												_lowercase       :	List[str]           =   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       :	int           =   trainer.evaluate()
												trainer.log_metrics("eval" ,			_UpperCAmelCase  )
												trainer.save_metrics("eval" ,			_UpperCAmelCase  )

						# Write model card and (optionally) push to hub
						_lowercase       :	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(**_UpperCAmelCase  )
						else:
												trainer.create_model_card(**_UpperCAmelCase  )



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any]  )   ->			Union[str, Any]:






						'''simple docstring'''

						main()


if __name__ == "__main__":
							main()






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from ..image_utils import load_image


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase_						:     int	       =							logging.get_logger(__name__)


UpperCamelCase_						:     List[Any]	       =							Dict[str, Any]
UpperCamelCase_						:     Optional[int]	       =							List[Prediction]




@add_end_docstrings(__snake_case       )
class   __lowercase						(						__snake_case       ):






	def __init__(self				: Dict      ,	*snake_case				: List[Any]      ,	**snake_case				: Tuple    ) ->			Any:
							super().__init__(*snake_case      ,	**snake_case    )

							if self.framework == "tf":
													raise ValueError(F"""The {self.__class__} is only available in PyTorch."""    )

							requires_backends(self      ,	"vision"    )
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )






	def       _a(self				: Optional[int]      ,	**snake_case				: Union[str, Any]    ) ->			Any:
							_lowercase       :	List[Any]           =   {}
							if "threshold" in kwargs:
													_lowercase       :	Optional[Any]           =   kwargs["threshold"]
							return {}, {}, postprocess_kwargs






	def __call__(self				: str      ,	*snake_case				: Dict      ,	**snake_case				: Optional[int]    ) ->			Union[Predictions, List[Prediction]]:
							return super().__call__(*snake_case      ,	**snake_case    )






	def       _a(self				: Tuple      ,	snake_case				: str    ) ->			Dict:
							_lowercase       :	List[str]           =   load_image(snake_case    )
							_lowercase       :	str           =   torch.IntTensor([[image.height, image.width]]    )
							_lowercase       :	int           =   self.image_processor(images=[image]      ,	return_tensors="pt"    )
							if self.tokenizer is not None:
													_lowercase       :	List[Any]           =   self.tokenizer(text=inputs["words"]      ,	boxes=inputs["boxes"]      ,	return_tensors="pt"    )
							_lowercase       :	str           =   target_size
							return inputs






	def       _a(self				: List[Any]      ,	snake_case				: Any    ) ->			str:
							_lowercase       :	Dict           =   model_inputs.pop("target_size"    )
							_lowercase       :	str           =   self.model(**snake_case    )
							_lowercase       :	Union[str, Any]           =   outputs.__class__({"target_size": target_size, **outputs}    )
							if self.tokenizer is not None:
													_lowercase       :	str           =   model_inputs["bbox"]
							return model_outputs






	def       _a(self				: str      ,	snake_case				: Tuple      ,	snake_case				: Dict=0.9    ) ->			List[str]:
							_lowercase       :	Optional[Any]           =   model_outputs["target_size"]
							if self.tokenizer is not None:
													# This is a LayoutLMForTokenClassification variant.
													# The OCR got the boxes and the model classified the words.
													_lowercase      ,    _lowercase       :	str           =   target_size[0].tolist()

													def unnormalize(snake_case				: str    ):
																			return self._get_bounding_box(
																			    torch.Tensor(
																			        [
																			            (width * bbox[0] / 1000),
																			            (height * bbox[1] / 1000),
																			            (width * bbox[2] / 1000),
																			            (height * bbox[3] / 1000),
																			        ]    )    )

													_lowercase      ,    _lowercase       :	Any           =   model_outputs["logits"].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
													_lowercase       :	Optional[Any]           =   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
													_lowercase       :	Dict           =   [unnormalize(snake_case    ) for bbox in model_outputs["bbox"].squeeze(0    )]
													_lowercase       :	Tuple           =   ["score", "label", "box"]
													_lowercase       :	int           =   [dict(zip(snake_case      ,	snake_case    )    ) for vals in zip(scores.tolist()      ,	snake_case      ,	snake_case    ) if vals[0] > threshold]
							else:
													# This is a regular ForObjectDetectionModel
													_lowercase       :	str           =   self.image_processor.post_process_object_detection(snake_case      ,	snake_case      ,	snake_case    )
													_lowercase       :	List[Any]           =   raw_annotations[0]
													_lowercase       :	str           =   raw_annotation["scores"]
													_lowercase       :	Optional[int]           =   raw_annotation["labels"]
													_lowercase       :	Dict           =   raw_annotation["boxes"]

													_lowercase       :	int           =   scores.tolist()
													_lowercase       :	List[str]           =   [self.model.config.idalabel[label.item()] for label in labels]
													_lowercase       :	List[Any]           =   [self._get_bounding_box(snake_case    ) for box in boxes]

													# {"scores": [...], ...} --> [{"score":x, ...}, ...]
													_lowercase       :	Union[str, Any]           =   ["score", "label", "box"]
													_lowercase       :	Tuple           =   [
													    dict(zip(snake_case      ,	snake_case    )    )
													    for vals in zip(raw_annotation["scores"]      ,	raw_annotation["labels"]      ,	raw_annotation["boxes"]    )
													]

							return annotation






	def       _a(self				: int      ,	snake_case				: "torch.Tensor"    ) ->			Dict[str, int]:
							if self.framework != "pt":
													raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
							_lowercase      ,    _lowercase      ,    _lowercase      ,    _lowercase       :	Optional[int]           =   box.int().tolist()
							_lowercase       :	Tuple           =   {
							    "xmin": xmin,
							    "ymin": ymin,
							    "xmax": xmax,
							    "ymax": ymax,
							}
							return bbox




