


"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
lowercase_			     =  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-classification/requirements.txt")

lowercase_			     =  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase_			     =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      str:


		with open(lowerCAmelCase_    ,					'''rb'''			) as f:
				__a           =			Image.open(lowerCAmelCase_			)
				return im.convert('''RGB'''			)
@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Dict										=				field(
		    default=_lowerCamelCase     ,			metadata={
		        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
		    }     ,			)
		__UpperCAmelCase		:			str										=				field(
		    default=_lowerCamelCase     ,			metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
		__UpperCAmelCase		:			Dict										=				field(default=_lowerCamelCase     ,			metadata={'help': 'A folder containing the training data.'}       )
		__UpperCAmelCase		:			Dict										=				field(default=_lowerCamelCase     ,			metadata={'help': 'A folder containing the validation data.'}       )
		__UpperCAmelCase		:			str										=				field(
		    default=0.15     ,			metadata={'help': 'Percent to split off of train for validation.'}       )
		__UpperCAmelCase		:			Dict										=				field(
		    default=_lowerCamelCase     ,			metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }     ,			)
		__UpperCAmelCase		:			Optional[int]										=				field(
		    default=_lowerCamelCase     ,			metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }     ,			)

		def 			__UpperCAmelCase       (  self      ):
				if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
						raise ValueError(
						    '''You must specify either a dataset name from the hub or a train and/or validation directory.'''      )







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			str										=				field(
		    default='google/vit-base-patch16-224-in21k'     ,			metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     ,			)
		__UpperCAmelCase		:			Optional[Any]										=				field(
		    default=_lowerCamelCase     ,			metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_lowerCamelCase       )}     ,			)
		__UpperCAmelCase		:			str										=				field(
		    default=_lowerCamelCase     ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
		__UpperCAmelCase		:			List[str]										=				field(
		    default=_lowerCamelCase     ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}       )
		__UpperCAmelCase		:			Any										=				field(
		    default='main'     ,			metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,			)
		__UpperCAmelCase		:			int										=				field(default=_lowerCamelCase     ,			metadata={'help': 'Name or path of preprocessor config.'}       )
		__UpperCAmelCase		:			Union[str, Any]										=				field(
		    default=_lowerCamelCase     ,			metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }     ,			)
		__UpperCAmelCase		:			int										=				field(
		    default=_lowerCamelCase     ,			metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}     ,			)







def        lowercase							(		lowerCAmelCase__ :     List[Any]			) ->      List[Any]:
		__a           =			torch.stack([example['''pixel_values'''] for example in examples]			)
		__a           =			torch.tensor([example['''labels'''] for example in examples]			)
		return {"pixel_values": pixel_values, "labels": labels}

def        lowercase							(		) ->      Tuple:
		__a           =			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.
				__a           =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
		else:
				__a           =			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_image_classification'''    ,					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()

		__a           =			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.
		__a           =			None
		if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
				__a           =			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.'''			)

    # Set seed before initializing model.
		set_seed(training_args.seed			)

		# Initialize our dataset and prepare it for the 'image-classification' task.
		if data_args.dataset_name is not None:
				__a           =			load_dataset(
				    data_args.dataset_name    ,					data_args.dataset_config_name    ,					cache_dir=model_args.cache_dir    ,					task='''image-classification'''    ,					use_auth_token=True if model_args.use_auth_token else None    ,					)
		else:
				__a           =			{}
				if data_args.train_dir is not None:
						__a           =			os.path.join(data_args.train_dir    ,					'''**'''			)
				if data_args.validation_dir is not None:
						__a           =			os.path.join(data_args.validation_dir    ,					'''**'''			)
				__a           =			load_dataset(
				    '''imagefolder'''    ,					data_files=lowerCAmelCase_    ,					cache_dir=model_args.cache_dir    ,					task='''image-classification'''    ,					)

		# If we don't have a validation split, split off a percentage of train as validation.
		__a           =			None if """validation""" in dataset.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split    ,					lowerCAmelCase_			) and data_args.train_val_split > 0.0:
				__a           =			dataset["""train"""].train_test_split(data_args.train_val_split			)
				__a           =			split["""train"""]
				__a           =			split["""test"""]

		# Prepare label mappings.
		# We'll include these in the model's config to get human readable labels in the Inference API.
		__a           =			dataset["""train"""].features["""labels"""].names
		__a           =			{}, {}
		for i, label in enumerate(lowerCAmelCase_			):
				__a           =			str(lowerCAmelCase_			)
				__a           =			label

		# Load the accuracy metric from the datasets package
		__a           =			evaluate.load('''accuracy'''			)

		# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(lowerCAmelCase__ :     Any			):
				return metric.compute(predictions=np.argmax(p.predictions    ,					axis=1			)    ,					references=p.label_ids			)

		__a           =			AutoConfig.from_pretrained(
		    model_args.config_name or model_args.model_name_or_path    ,					num_labels=len(lowerCAmelCase_			)    ,					labelaid=lowerCAmelCase_    ,					idalabel=lowerCAmelCase_    ,					finetuning_task='''image-classification'''    ,					cache_dir=model_args.cache_dir    ,					revision=model_args.model_revision    ,					use_auth_token=True if model_args.use_auth_token else None    ,					)
		__a           =			AutoModelForImageClassification.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    ,					ignore_mismatched_sizes=model_args.ignore_mismatched_sizes    ,					)
		__a           =			AutoImageProcessor.from_pretrained(
		    model_args.image_processor_name or model_args.model_name_or_path    ,					cache_dir=model_args.cache_dir    ,					revision=model_args.model_revision    ,					use_auth_token=True if model_args.use_auth_token else None    ,					)

		# Define torchvision transforms to be applied to each image.
		if "shortest_edge" in image_processor.size:
				__a           =			image_processor.size["""shortest_edge"""]
		else:
				__a           =			(image_processor.size["""height"""], image_processor.size["""width"""])
		__a           =			Normalize(mean=image_processor.image_mean    ,					std=image_processor.image_std			)
		__a           =			Compose(
		    [
		        RandomResizedCrop(lowerCAmelCase_			),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]			)
		__a           =			Compose(
		    [
		        Resize(lowerCAmelCase_			),
		        CenterCrop(lowerCAmelCase_			),
		        ToTensor(),
		        normalize,
		    ]			)

		def train_transforms(lowerCAmelCase__ :     str			):
				__a           =			[
				    _train_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch["""image"""]
				]
				return example_batch

		def val_transforms(lowerCAmelCase__ :     str			):
				__a           =			[_val_transforms(pil_img.convert('''RGB'''			)			) for pil_img in example_batch["""image"""]]
				return example_batch

		if training_args.do_train:
				if "train" not in dataset:
						raise ValueError('''--do_train requires a train dataset'''			)
				if data_args.max_train_samples is not None:
						__a           =			(
						    dataset["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
						)
				# Set the training transforms
				dataset["train"].set_transform(lowerCAmelCase_			)

		if training_args.do_eval:
				if "validation" not in dataset:
						raise ValueError('''--do_eval requires a validation dataset'''			)
				if data_args.max_eval_samples is not None:
						__a           =			(
						    dataset["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
						)
				# Set the validation transforms
				dataset["validation"].set_transform(lowerCAmelCase_			)

		# Initalize our trainer
		__a           =			Trainer(
		    model=lowerCAmelCase_    ,					args=lowerCAmelCase_    ,					train_dataset=dataset['''train'''] if training_args.do_train else None    ,					eval_dataset=dataset['''validation'''] if training_args.do_eval else None    ,					compute_metrics=lowerCAmelCase_    ,					tokenizer=lowerCAmelCase_    ,					data_collator=lowerCAmelCase_    ,					)

		# Training
		if training_args.do_train:
				__a           =			None
				if training_args.resume_from_checkpoint is not None:
						__a           =			training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						__a           =			last_checkpoint
				__a           =			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:
				__a           =			trainer.evaluate()
				trainer.log_metrics('''eval'''    ,					lowerCAmelCase_			)
				trainer.save_metrics('''eval'''    ,					lowerCAmelCase_			)

		# Write model card and (optionally) push to hub
		__a           =			{
		    """finetuned_from""": model_args.model_name_or_path,
		    """tasks""": """image-classification""",
		    """dataset""": data_args.dataset_name,
		    """tags""": ["""image-classification""", """vision"""],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**lowerCAmelCase_			)
		else:
				trainer.create_model_card(**lowerCAmelCase_			)


if __name__ == "__main__":
				main()











'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							: Any						 =	logging.get_logger(__name__)

A_							: Any						 =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  """markuplm"""







 def __init__(							self  ,a_=30_522  ,a_=768  ,a_=12  ,a_=12  ,a_=3_072  ,a_="gelu"  ,a_=0.1  ,a_=0.1  ,a_=512  ,a_=2  ,a_=0.02  ,a_=1E-1_2  ,a_=0  ,a_=0  ,a_=2  ,a_=256  ,a_=1_024  ,a_=216  ,a_=1_001  ,a_=32  ,a_=50  ,a_="absolute"  ,a_=True  ,a_=None  ,**a_  ,)		->   Union[str, Any]:
        super().__init__(
            pad_token_id=a_  ,bos_token_id=a_  ,eos_token_id=a_  ,**a_  ,)
        _UpperCAmelCase  :						Optional[int]			     = vocab_size
        _UpperCAmelCase  :						Tuple			     = hidden_size
        _UpperCAmelCase  :						str			     = num_hidden_layers
        _UpperCAmelCase  :						Dict			     = num_attention_heads
        _UpperCAmelCase  :						int			     = hidden_act
        _UpperCAmelCase  :						Optional[Any]			     = intermediate_size
        _UpperCAmelCase  :						Tuple			     = hidden_dropout_prob
        _UpperCAmelCase  :						List[str]			     = attention_probs_dropout_prob
        _UpperCAmelCase  :						Optional[int]			     = max_position_embeddings
        _UpperCAmelCase  :						Tuple			     = type_vocab_size
        _UpperCAmelCase  :						Dict			     = initializer_range
        _UpperCAmelCase  :						List[Any]			     = layer_norm_eps
        _UpperCAmelCase  :						Optional[Any]			     = position_embedding_type
        _UpperCAmelCase  :						Any			     = use_cache
        _UpperCAmelCase  :						List[Any]			     = classifier_dropout
        # additional properties
        _UpperCAmelCase  :						Dict			     = max_depth
        _UpperCAmelCase  :						Union[str, Any]			     = max_xpath_tag_unit_embeddings
        _UpperCAmelCase  :						Optional[int]			     = max_xpath_subs_unit_embeddings
        _UpperCAmelCase  :						List[Any]			     = tag_pad_id
        _UpperCAmelCase  :						Tuple			     = subs_pad_id
        _UpperCAmelCase  :						List[str]			     = xpath_unit_hidden_size



