


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
lowerCAmelCase__  :Dict             =							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''')

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

def 	lowerCAmelCase__			(      a__:		str	)  ->		List[str]:


							'''simple docstring'''





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

@dataclass
class 		__a				:
		_a     :  Optional[str]           =     field(
		    default=UpperCAmelCase   ,				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=UpperCAmelCase   ,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}      )
		_a     :  Optional[str]           =     field(default=UpperCAmelCase   ,				metadata={'help': 'A folder containing the training data.'}      )
		_a     :  Optional[str]           =     field(default=UpperCAmelCase   ,				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=UpperCAmelCase   ,				metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of training examples to this '
		            'value if set.'
		        )
		    }   ,				)
		_a     :  Optional[int]           =     field(
		    default=UpperCAmelCase   ,				metadata={
		        'help': (
		            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
		            'value if set.'
		        )
		    }   ,				)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""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				:
		_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=UpperCAmelCase   ,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase      )}   ,				)
		_a     :  Optional[str]           =     field(
		    default=UpperCAmelCase   ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
		_a     :  Optional[str]           =     field(
		    default=UpperCAmelCase   ,				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=UpperCAmelCase   ,				metadata={'help': 'Name or path of preprocessor config.'}      )
		_a     :  bool           =     field(
		    default=UpperCAmelCase   ,				metadata={
		        'help': (
		            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
		            'with private models).'
		        )
		    }   ,				)
		_a     :  bool           =     field(
		    default=UpperCAmelCase   ,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}   ,				)



def 	lowerCAmelCase__			(      a__:		List[Any]	)  ->		int:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      )  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      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					=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
							else:
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      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'      ,  a__      ,  a__	)

							# 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					=      training_args.get_process_log_level()
							logger.setLevel(a__	)
							transformers.utils.logging.set_verbosity(a__	)
							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					=      None
							if os.path.isdir(training_args.output_dir	) and training_args.do_train and not training_args.overwrite_output_dir:
														_UpperCAmelCase					=      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					=      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					=      {}
														if data_args.train_dir is not None:
																					_UpperCAmelCase					=      os.path.join(data_args.train_dir      ,  '**'	)
														if data_args.validation_dir is not None:
																					_UpperCAmelCase					=      os.path.join(data_args.validation_dir      ,  '**'	)
														_UpperCAmelCase					=      load_dataset(
														    'imagefolder'      ,  data_files=a__      ,  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					=      None if 'validation' in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split      ,  a__	) and data_args.train_val_split > 0.0:
														_UpperCAmelCase					=      dataset['train'].train_test_split(data_args.train_val_split	)
														_UpperCAmelCase					=      split['train']
														_UpperCAmelCase					=      split['test']

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

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

							_UpperCAmelCase					=      AutoConfig.from_pretrained(
							    model_args.config_name or model_args.model_name_or_path      ,  num_labels=len(a__	)      ,  labelaid=a__      ,  idalabel=a__      ,  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					=      AutoModelForImageClassification.from_pretrained(
							    model_args.model_name_or_path      ,  from_tf=bool('.ckpt' in model_args.model_name_or_path	)      ,  config=a__      ,  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					=      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					=      image_processor.size['shortest_edge']
							else:
														_UpperCAmelCase					=      (image_processor.size['height'], image_processor.size['width'])
							_UpperCAmelCase					=      Normalize(mean=image_processor.image_mean      ,  std=image_processor.image_std	)
							_UpperCAmelCase					=      Compose(
							    [
							        RandomResizedCrop(a__	),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]	)
							_UpperCAmelCase					=      Compose(
							    [
							        Resize(a__	),
							        CenterCrop(a__	),
							        ToTensor(),
							        normalize,
							    ]	)

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

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

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

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

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

							# Write model card and (optionally) push to hub
							_UpperCAmelCase					=      {
							    '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(**a__	)
							else:
														trainer.create_model_card(**a__	)


if __name__ == "__main__":
		main()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__  :List[str]             =							{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Any             =							[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		lowerCAmelCase__  :str             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
