




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
UpperCAmelCase__				     = 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")

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







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							with open(UpperCamelCase__				,       '''rb'''				) as f:
														__lowercase      =    Image.open(UpperCamelCase__				)
														return im.convert('''RGB'''				)







@dataclass
class a :
						"""simple docstring"""

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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


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







def 		_A(       UpperCamelCase__							:			List[str]				)    -> List[Any]:
							'''simple docstring'''


							__lowercase      =    torch.stack([example['''pixel_values'''] for example in examples]				)
							__lowercase      =    torch.tensor([example['''labels'''] for example in examples]				)
							return {"pixel_values": pixel_values, "labels": labels}







def 		_A(       )    -> Tuple:
							'''simple docstring'''


							__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_image_classification'''				,       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.'''				)

    # 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:
														__lowercase      =    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:
														__lowercase      =    {}
														if data_args.train_dir is not None:
																					__lowercase      =    os.path.join(data_args.train_dir				,       '''**'''				)
														if data_args.validation_dir is not None:
																					__lowercase      =    os.path.join(data_args.validation_dir				,       '''**'''				)
														__lowercase      =    load_dataset(
														    '''imagefolder'''				,       data_files=UpperCamelCase__				,       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.
							__lowercase      =    None if '''validation''' in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split				,       UpperCamelCase__				) and data_args.train_val_split > 0.0:
														__lowercase      =    dataset['''train'''].train_test_split(data_args.train_val_split				)
														__lowercase      =    split['''train''']
														__lowercase      =    split['''test''']

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

							# Load the accuracy metric from the datasets package
							__lowercase      =    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(UpperCamelCase__							:			Optional[int]				):
														return metric.compute(predictions=np.argmax(p.predictions				,       axis=1				)				,       references=p.label_ids				)

							__lowercase      =    AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path				,       num_labels=len(UpperCamelCase__				)				,       labelaid=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       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				,       )
							__lowercase      =    AutoModelForImageClassification.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				,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes				,       )
							__lowercase      =    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:
														__lowercase      =    image_processor.size['''shortest_edge''']
							else:
														__lowercase      =    (image_processor.size['''height'''], image_processor.size['''width'''])
							__lowercase      =    Normalize(mean=image_processor.image_mean				,       std=image_processor.image_std				)
							__lowercase      =    Compose(
							    [
							        RandomResizedCrop(UpperCamelCase__				),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]				)
							__lowercase      =    Compose(
							    [
							        Resize(UpperCamelCase__				),
							        CenterCrop(UpperCamelCase__				),
							        ToTensor(),
							        normalize,
							    ]				)

							def train_transforms(UpperCamelCase__							:			int				):
														__lowercase      =    [
														    _train_transforms(pil_img.convert('''RGB'''				)				) for pil_img in example_batch['''image''']
														]
														return example_batch

							def val_transforms(UpperCamelCase__							:			Any				):
														__lowercase      =    [_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:
																					__lowercase      =    (
																					    dataset['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
																					)
														# Set the training transforms
														dataset["train"].set_transform(UpperCamelCase__				)

							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:
																					__lowercase      =    (
																					    dataset['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
																					)
														# Set the validation transforms
														dataset["validation"].set_transform(UpperCamelCase__				)

							# Initalize our trainer
							__lowercase      =    Trainer(
							    model=UpperCamelCase__				,       args=UpperCamelCase__				,       train_dataset=dataset['''train'''] if training_args.do_train else None				,       eval_dataset=dataset['''validation'''] if training_args.do_eval else None				,       compute_metrics=UpperCamelCase__				,       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''': '''image-classification''',
							    '''dataset''': data_args.dataset_name,
							    '''tags''': ['''image-classification''', '''vision'''],
							}
							if training_args.push_to_hub:
														trainer.push_to_hub(**UpperCamelCase__				)
							else:
														trainer.create_model_card(**UpperCamelCase__				)


if __name__ == "__main__":
			main()












import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()

													__lowercase      =    BlipImageProcessor()
													__lowercase      =    GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''				)

													__lowercase      =    BlipaProcessor(lowerCamelCase__				,							lowerCamelCase__				)

													processor.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							**lowerCamelCase__ :     Any				)    ->						str:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).tokenizer






						def     UpperCAmelCase_      ( self :     Dict				,							**lowerCamelCase__ :     List[str]				)    ->						Dict:
													"""simple docstring"""


													return AutoProcessor.from_pretrained(self.tmpdirname				,							**lowerCamelCase__				).image_processor






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						str:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Any				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta				)]

													__lowercase      =    [Image.fromarray(np.moveaxis(lowerCamelCase__				,							0				,							-1				)				) for x in image_inputs]

													return image_inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    BlipaProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()				)
													processor.save_pretrained(self.tmpdirname				)

													__lowercase      =    self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				)
													__lowercase      =    self.get_image_processor(do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													__lowercase      =    BlipaProcessor.from_pretrained(
													    self.tmpdirname				,							bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				,							do_normalize=lowerCamelCase__				,							padding_value=1.0				)

													self.assertEqual(processor.tokenizer.get_vocab()				,							tokenizer_add_kwargs.get_vocab()				)
													self.assertIsInstance(processor.tokenizer				,							lowerCamelCase__				)

													self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()				)
													self.assertIsInstance(processor.image_processor				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    image_processor(lowerCamelCase__				,							return_tensors='''np'''				)
													__lowercase      =    processor(images=lowerCamelCase__				,							return_tensors='''np'''				)

													for key in input_feat_extract.keys():
																				self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1e-2				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''

													__lowercase      =    processor(text=lowerCamelCase__				)

													__lowercase      =    tokenizer(lowerCamelCase__				,							return_token_type_ids=lowerCamelCase__				)

													for key in encoded_tok.keys():
																				self.assertListEqual(encoded_tok[key]				,							encoded_processor[key]				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)

													# test if it raises when no input is passed
													with pytest.raises(lowerCamelCase__				):
																				processor()






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													__lowercase      =    processor.batch_decode(lowerCamelCase__				)
													__lowercase      =    tokenizer.batch_decode(lowerCamelCase__				)

													self.assertListEqual(lowerCamelCase__				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_image_processor()
													__lowercase      =    self.get_tokenizer()

													__lowercase      =    BlipaProcessor(tokenizer=lowerCamelCase__				,							image_processor=lowerCamelCase__				)

													__lowercase      =    '''lower newer'''
													__lowercase      =    self.prepare_image_inputs()

													__lowercase      =    processor(text=lowerCamelCase__				,							images=lowerCamelCase__				)

													# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
													self.assertListEqual(list(inputs.keys()				)				,							['''pixel_values''', '''input_ids''', '''attention_mask''']				)






