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 lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    with open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ) as f:
        __UpperCamelCase					:Dict        =		Image.open(SCREAMING_SNAKE_CASE     )
        return im.convert('''RGB'''     )


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    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)	->     Optional[Any]:
        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'''






    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  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:List[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					:str        =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
    else:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					: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'''  ,      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					:int        =		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					:Optional[Any]        =		None
    if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
        __UpperCamelCase					:Optional[int]        =		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[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:
        __UpperCamelCase					:str        =		{}
        if data_args.train_dir is not None:
            __UpperCamelCase					:Tuple        =		os.path.join(data_args.train_dir  ,      '''**'''     )
        if data_args.validation_dir is not None:
            __UpperCamelCase					:Dict        =		os.path.join(data_args.validation_dir  ,      '''**'''     )
        __UpperCamelCase					:Any        =		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					:Optional[Any]        =		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					:Union[str, Any]        =		dataset['''train'''].train_test_split(data_args.train_val_split     )
        __UpperCamelCase					:Tuple        =		split['''train''']
        __UpperCamelCase					:Dict        =		split['''test''']

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

    # Load the accuracy metric from the datasets package
    __UpperCamelCase					:Dict        =		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     ):
        return metric.compute(predictions=np.argmax(p.predictions  ,      axis=1     )  ,      references=p.label_ids     )

    __UpperCamelCase					:Dict        =		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					:Tuple        =		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					: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					:List[Any]        =		image_processor.size['''shortest_edge''']
    else:
        __UpperCamelCase					:Optional[Any]        =		(image_processor.size['''height'''], image_processor.size['''width'''])
    __UpperCamelCase					:Optional[int]        =		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     ):
        __UpperCamelCase					:Optional[Any]        =		[
            _train_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']
        ]
        return example_batch

    def val_transforms(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:Optional[Any]        =		[_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					:Any        =		(
                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					:int        =		(
                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					:List[Any]        =		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					:Tuple        =		None
        if training_args.resume_from_checkpoint is not None:
            __UpperCamelCase					:Optional[int]        =		training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            __UpperCamelCase					:List[Any]        =		last_checkpoint
        __UpperCamelCase					:Optional[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					:Optional[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					:Union[str, Any]        =		{
        '''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

import pytest

from transformers.dynamic_module_utils import get_imports


__lowercase										= '''
import os
'''

__lowercase										= '''
def foo():
    import os
    return False
'''

__lowercase										= '''
def foo():
    def bar():
        if True:
            import os
        return False
    return bar()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

def foo():
    try:
        import bar
    except ImportError:
        raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except (ImportError, AttributeError):
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except ImportError as e:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
except:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    raise ValueError()
'''

__lowercase										= '''
import os

try:
    import bar
    import baz
except ImportError:
    x = 1
    raise ValueError()
'''

__lowercase										= [
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]



@pytest.mark.parametrize('''case'''  ,      SCREAMING_SNAKE_CASE     )
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		os.path.join(SCREAMING_SNAKE_CASE  ,      '''test_file.py'''     )
    with open(SCREAMING_SNAKE_CASE  ,      '''w'''     ) as _tmp_file:
        _tmp_file.write(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		get_imports(SCREAMING_SNAKE_CASE     )
    assert parsed_imports == ["os"]





