
"""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
UpperCAmelCase__   :					Tuple												=				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__   :					str												=				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase__   :					Dict												=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def lowercase_						(     _snake_case		):
							with open(_snake_case     ,"""rb"""		) as f:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Image.open(_snake_case		)
														return im.convert("""RGB"""		)



@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		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).'''
				    }				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(default=a__				,      metadata={'''help''': '''A folder containing the training data.'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(default=a__				,      metadata={'''help''': '''A folder containing the validation data.'''}						)
				__UpperCamelCase   :		Optional[float]       =     field(
				    default=0.15				,      metadata={'''help''': '''Percent to split off of train for validation.'''}						)
				__UpperCamelCase   :		Optional[int]       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }				,      )
				__UpperCamelCase   :		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 			__magic_name__					(self					)      ->	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 			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[str]       =     field(
				    default=a__				,      metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(a__						)}				,      )
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
				__UpperCamelCase   :		Optional[str]       =     field(
				    default=a__				,      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=a__				,      metadata={'''help''': '''Name or path of preprocessor config.'''}						)
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}				,      )






def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.stack([example["""pixel_values"""] for example in examples]		)
							SCREAMING_SNAKE_CASE__		: int     =     torch.tensor([example["""labels"""] for example in examples]		)
							return {"pixel_values": pixel_values, "labels": labels}

def lowercase_						(     ):
							# 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.

							SCREAMING_SNAKE_CASE__		: Tuple     =     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.
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
							else:
														SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     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"""     ,_snake_case     ,_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()

							SCREAMING_SNAKE_CASE__		: int     =     training_args.get_process_log_level()
							logger.setLevel(_snake_case		)
							transformers.utils.logging.set_verbosity(_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.
							SCREAMING_SNAKE_CASE__		: List[Any]     =     None
							if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
														SCREAMING_SNAKE_CASE__		: List[str]     =     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:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     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:
														SCREAMING_SNAKE_CASE__		: Any     =     {}
														if data_args.train_dir is not None:
																					SCREAMING_SNAKE_CASE__		: Any     =     os.path.join(data_args.train_dir     ,"""**"""		)
														if data_args.validation_dir is not None:
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(data_args.validation_dir     ,"""**"""		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     load_dataset(
														    """imagefolder"""     ,data_files=_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.
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     None if """validation""" in dataset.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split     ,_snake_case		) and data_args.train_val_split > 0.0:
														SCREAMING_SNAKE_CASE__		: List[str]     =     dataset["""train"""].train_test_split(data_args.train_val_split		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     split["""train"""]
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     split["""test"""]

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

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

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

							def train_transforms(_snake_case		):
														SCREAMING_SNAKE_CASE__		: int     =     [
														    _train_transforms(pil_img.convert("""RGB"""		)		) for pil_img in example_batch["""image"""]
														]
														return example_batch

							def val_transforms(_snake_case		):
														SCREAMING_SNAKE_CASE__		: Dict     =     [_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:
																					SCREAMING_SNAKE_CASE__		: Tuple     =     (
																					    dataset["""train"""].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
																					)
														# Set the training transforms
														dataset["train"].set_transform(_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:
																					SCREAMING_SNAKE_CASE__		: Any     =     (
																					    dataset["""validation"""].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
																					)
														# Set the validation transforms
														dataset["validation"].set_transform(_snake_case		)

							# Initalize our trainer
							SCREAMING_SNAKE_CASE__		: List[str]     =     Trainer(
							    model=_snake_case     ,args=_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=_snake_case     ,tokenizer=_snake_case     ,data_collator=_snake_case     ,)

							# Training
							if training_args.do_train:
														SCREAMING_SNAKE_CASE__		: Dict     =     None
														if training_args.resume_from_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__		: Dict     =     training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					SCREAMING_SNAKE_CASE__		: List[str]     =     last_checkpoint
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     trainer.train(resume_from_checkpoint=_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:
														SCREAMING_SNAKE_CASE__		: Tuple     =     trainer.evaluate()
														trainer.log_metrics("""eval"""     ,_snake_case		)
														trainer.save_metrics("""eval"""     ,_snake_case		)

							# Write model card and (optionally) push to hub
							SCREAMING_SNAKE_CASE__		: List[str]     =     {
							    """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(**_snake_case		)
							else:
														trainer.create_model_card(**_snake_case		)


if __name__ == "__main__":
					main()






"""simple docstring"""




import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


UpperCAmelCase__   :					Any												=				logging.getLogger()



@unittest.skip('''Temporarily disable the doc tests.'''						)
@require_torch
@require_tf
@slow
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     [file for file in os.listdir(SCREAMING_SNAKE_CASE__					) if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)]

											if identifier is not None:
																		SCREAMING_SNAKE_CASE__		: int     =     [file for file in files if identifier in file]

											if n_identifier is not None:
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									for n_ in n_identifier:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [file for file in files if n_ not in file]
																		else:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     [file for file in files if n_identifier not in file]

											SCREAMING_SNAKE_CASE__		: int     =     ignore_files or []
											ignore_files.append("""__init__.py"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [file for file in files if file not in ignore_files]

											for file in files:
																		# Open all files
																		print("""Testing"""     , SCREAMING_SNAKE_CASE__					)

																		if only_modules:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     file.split("""."""					)[0]
																									try:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     getattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.DocTestSuite(SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: int     =     unittest.TextTestRunner().run(SCREAMING_SNAKE_CASE__					)
																																self.assertIs(len(result.failures					)     , 0					)
																									except AttributeError:
																																logger.info(F'''{module_identifier} is not a module.'''					)
																		else:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.testfile(str("""..""" / directory / file					)     , optionflags=doctest.ELLIPSIS					)
																									self.assertIs(result.failed     , 0					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """modeling"""
											SCREAMING_SNAKE_CASE__		: int     =     [
											    """modeling_ctrl.py""",
											    """modeling_tf_ctrl.py""",
											]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """tokenization"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """configuration"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     ["""configuration""", """modeling""", """tokenization"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , n_identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""docs/source"""					)
											SCREAMING_SNAKE_CASE__		: Any     =     ["""favicon.ico"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__     , only_modules=SCREAMING_SNAKE_CASE__					)




