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
SCREAMING_SNAKE_CASE		:     Optional[Any]         =	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")

SCREAMING_SNAKE_CASE		:     Any         =	list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE		:     Optional[int]         =	tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						with open(_SCREAMING_SNAKE_CASE		, """rb"""			) as f:
												UpperCamelCase_		:							int  =		Image.open(_SCREAMING_SNAKE_CASE			)
												return im.convert("""RGB"""			)

@dataclass
class        UpperCamelCase						:
						a__		:Optional[str]      =			field(
						    default=__a	,	metadata={
						        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
						    }	,	)
						a__		:Optional[str]      =			field(
						    default=__a	,	metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
						a__		:Optional[str]      =			field(default=__a	,	metadata={'''help''': '''A folder containing the training data.'''}				)
						a__		:Optional[str]      =			field(default=__a	,	metadata={'''help''': '''A folder containing the validation data.'''}				)
						a__		:Optional[float]      =			field(
						    default=0.1_5	,	metadata={'''help''': '''Percent to split off of train for validation.'''}				)
						a__		:Optional[int]      =			field(
						    default=__a	,	metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
						            '''value if set.'''
						        )
						    }	,	)
						a__		:Optional[int]      =			field(
						    default=__a	,	metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
						            '''value if set.'''
						        )
						    }	,	)





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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Dict  =		torch.stack([example["""pixel_values"""] for example in examples]			)
						UpperCamelCase_		:							Union[str, Any]  =		torch.tensor([example["""labels"""] for example in examples]			)
						return {"pixel_values": pixel_values, "labels": labels}






def     lowerCAmelCase_			(   ):
						# 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.

						UpperCamelCase_		:							Any  =		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.
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Union[str, Any]  =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
						else:
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							List[str]  =		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"""		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						# 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()

						UpperCamelCase_		:							List[str]  =		training_args.get_process_log_level()
						logger.setLevel(_SCREAMING_SNAKE_CASE			)
						transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE			)
						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.
						UpperCamelCase_		:							List[str]  =		None
						if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
												UpperCamelCase_		:							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."""			)

    # 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:
												UpperCamelCase_		:							List[str]  =		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:
												UpperCamelCase_		:							int  =		{}
												if data_args.train_dir is not None:
																		UpperCamelCase_		:							Optional[Any]  =		os.path.join(data_args.train_dir		, """**"""			)
												if data_args.validation_dir is not None:
																		UpperCamelCase_		:							Tuple  =		os.path.join(data_args.validation_dir		, """**"""			)
												UpperCamelCase_		:							Optional[int]  =		load_dataset(
												    """imagefolder"""		, data_files=_SCREAMING_SNAKE_CASE		, 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.
						UpperCamelCase_		:							Tuple  =		None if """validation""" in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split		, _SCREAMING_SNAKE_CASE			) and data_args.train_val_split > 0.0:
												UpperCamelCase_		:							List[str]  =		dataset["""train"""].train_test_split(data_args.train_val_split			)
												UpperCamelCase_		:							List[Any]  =		split["""train"""]
												UpperCamelCase_		:							Union[str, Any]  =		split["""test"""]

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

						# Load the accuracy metric from the datasets package
						UpperCamelCase_		:							List[str]  =		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(_SCREAMING_SNAKE_CASE     :				Dict			):
												return metric.compute(predictions=np.argmax(p.predictions		, axis=1			)		, references=p.label_ids			)

						UpperCamelCase_		:							List[Any]  =		AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path		, num_labels=len(_SCREAMING_SNAKE_CASE			)		, labelaid=_SCREAMING_SNAKE_CASE		, idalabel=_SCREAMING_SNAKE_CASE		, 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		, )
						UpperCamelCase_		:							str  =		AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path		, from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)		, config=_SCREAMING_SNAKE_CASE		, 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		, )
						UpperCamelCase_		:							List[str]  =		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:
												UpperCamelCase_		:							str  =		image_processor.size["""shortest_edge"""]
						else:
												UpperCamelCase_		:							List[Any]  =		(image_processor.size["""height"""], image_processor.size["""width"""])
						UpperCamelCase_		:							List[str]  =		Normalize(mean=image_processor.image_mean		, std=image_processor.image_std			)
						UpperCamelCase_		:							Optional[int]  =		Compose(
						    [
						        RandomResizedCrop(_SCREAMING_SNAKE_CASE			),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]			)
						UpperCamelCase_		:							Dict  =		Compose(
						    [
						        Resize(_SCREAMING_SNAKE_CASE			),
						        CenterCrop(_SCREAMING_SNAKE_CASE			),
						        ToTensor(),
						        normalize,
						    ]			)

						def train_transforms(_SCREAMING_SNAKE_CASE     :				Optional[Any]			):
												UpperCamelCase_		:							int  =		[
												    _train_transforms(pil_img.convert("""RGB"""			)			) for pil_img in example_batch["""image"""]
												]
												return example_batch

						def val_transforms(_SCREAMING_SNAKE_CASE     :				Tuple			):
												UpperCamelCase_		:							Optional[int]  =		[_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:
																		UpperCamelCase_		:							int  =		(
																		    dataset["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
																		)
												# Set the training transforms
												dataset["train"].set_transform(_SCREAMING_SNAKE_CASE			)

						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:
																		UpperCamelCase_		:							List[Any]  =		(
																		    dataset["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
																		)
												# Set the validation transforms
												dataset["validation"].set_transform(_SCREAMING_SNAKE_CASE			)

						# Initalize our trainer
						UpperCamelCase_		:							Optional[int]  =		Trainer(
						    model=_SCREAMING_SNAKE_CASE		, args=_SCREAMING_SNAKE_CASE		, train_dataset=dataset["""train"""] if training_args.do_train else None		, eval_dataset=dataset["""validation"""] if training_args.do_eval else None		, compute_metrics=_SCREAMING_SNAKE_CASE		, tokenizer=_SCREAMING_SNAKE_CASE		, data_collator=_SCREAMING_SNAKE_CASE		, )

						# Training
						if training_args.do_train:
												UpperCamelCase_		:							Any  =		None
												if training_args.resume_from_checkpoint is not None:
																		UpperCamelCase_		:							Dict  =		training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		UpperCamelCase_		:							Union[str, Any]  =		last_checkpoint
												UpperCamelCase_		:							List[Any]  =		trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE			)
												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:
												UpperCamelCase_		:							Any  =		trainer.evaluate()
												trainer.log_metrics("""eval"""		, _SCREAMING_SNAKE_CASE			)
												trainer.save_metrics("""eval"""		, _SCREAMING_SNAKE_CASE			)

						# Write model card and (optionally) push to hub
						UpperCamelCase_		:							Dict  =		{
						    """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(**_SCREAMING_SNAKE_CASE			)
						else:
												trainer.create_model_card(**_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
	main()







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	"▁"

SCREAMING_SNAKE_CASE		:     Dict         =	{"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE		:     List[Any]         =	{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

SCREAMING_SNAKE_CASE		:     Any         =	{
    "google/reformer-crime-and-punishment": 524288,
}

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			VOCAB_FILES_NAMES
						a__		:List[Any]      =			PRETRAINED_VOCAB_FILES_MAP
						a__		:Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__		:List[Any]      =			['''input_ids''', '''attention_mask''']





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase="</s>"			,     __UpperCamelCase="<unk>"			,     __UpperCamelCase=[]			,     __UpperCamelCase = None			,     **__UpperCamelCase			,     )				->				None:
												UpperCamelCase_		:							List[str]  =		{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=__UpperCamelCase			,     unk_token=__UpperCamelCase			,     additional_special_tokens=__UpperCamelCase			,     sp_model_kwargs=self.sp_model_kwargs			,     **__UpperCamelCase			,     )

												UpperCamelCase_		:							int  =		vocab_file
												UpperCamelCase_		:							str  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(__UpperCamelCase						)





						@property
						def        A_							(self						)				->				Dict:
												return self.sp_model.get_piece_size()





						def        A_							(self						)				->				Dict[str, int]:
												UpperCamelCase_		:							str  =		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
												vocab.update(self.added_tokens_encoder						)
												return vocab





						def __getstate__(self						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.__dict__.copy()
												UpperCamelCase_		:							List[str]  =		None
												return state





						def __setstate__(self			,     __UpperCamelCase						)				->				List[Any]:
												UpperCamelCase_		:							List[Any]  =		d

												# for backward compatibility
												if not hasattr(self			,     """sp_model_kwargs"""						):
																		UpperCamelCase_		:							Any  =		{}

												UpperCamelCase_		:							Dict  =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
												self.sp_model.Load(self.vocab_file						)





						def        A_							(self			,     __UpperCamelCase						)				->				List[str]:
												return self.sp_model.encode(__UpperCamelCase			,     out_type=__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												return self.sp_model.piece_to_id(__UpperCamelCase						)





						def        A_							(self			,     __UpperCamelCase						)				->				Dict:
												if index < self.sp_model.get_piece_size():
																		UpperCamelCase_		:							Union[str, Any]  =		self.sp_model.IdToPiece(__UpperCamelCase						)
												return token





						def        A_							(self			,     __UpperCamelCase						)				->				int:
												UpperCamelCase_		:							Dict  =		[]
												UpperCamelCase_		:							Union[str, Any]  =		""""""
												for token in tokens:
																		# make sure that special tokens are not decoded using sentencepiece model
																		if token in self.all_special_tokens:
																								out_string += self.sp_model.decode(__UpperCamelCase						) + token
																								UpperCamelCase_		:							Optional[Any]  =		[]
																		else:
																								current_sub_tokens.append(__UpperCamelCase						)
												out_string += self.sp_model.decode(__UpperCamelCase						)
												return out_string.strip()





						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase = None						)				->				Tuple[str]:
												if not os.path.isdir(__UpperCamelCase						):
																		logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
																		return
												UpperCamelCase_		:							Tuple  =		os.path.join(
												    __UpperCamelCase			,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file			,     __UpperCamelCase						)
												elif not os.path.isfile(self.vocab_file						):
																		with open(__UpperCamelCase			,     """wb"""						) as fi:
																								UpperCamelCase_		:							int  =		self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase						)

												return (out_vocab_file,)






