
"""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
_SCREAMING_SNAKE_CASE	       =      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	       =      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_SCREAMING_SNAKE_CASE	       =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							with open(SCREAMING_SNAKE_CASE							,    "rb" ) as f:
														__snake_case     			=       Image.open(SCREAMING_SNAKE_CASE )
														return im.convert("RGB" )







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					Optional[str]     							=   field(
					    default=lowercase__      ,		metadata={
					        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
					    }      ,		)
					_SCREAMING_SNAKE_CASE       :					Optional[str]     							=   field(
					    default=lowercase__      ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
					_SCREAMING_SNAKE_CASE       :					Optional[str]     							=   field(default=lowercase__      ,		metadata={'help': 'A folder containing the training data.'}   )
					_SCREAMING_SNAKE_CASE       :					Optional[str]     							=   field(default=lowercase__      ,		metadata={'help': 'A folder containing the validation data.'}   )
					_SCREAMING_SNAKE_CASE       :					Optional[float]     							=   field(
					    default=0.15      ,		metadata={'help': 'Percent to split off of train for validation.'}   )
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   field(
					    default=lowercase__      ,		metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }      ,		)
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   field(
					    default=lowercase__      ,		metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }      ,		)





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




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Union[str, Any]:







							"""simple docstring"""


							__snake_case     			=       torch.stack([example["pixel_values"] for example in examples] )
							__snake_case     			=       torch.tensor([example["labels"] for example in examples] )
							return {"pixel_values": pixel_values, "labels": labels}
def   __UpperCamelCase   (					)  ->     Tuple:







							"""simple docstring"""


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

							__snake_case     			=       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.
							__snake_case     			=       None
							if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
														__snake_case     			=       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:
														__snake_case     			=       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:
														__snake_case     			=       {}
														if data_args.train_dir is not None:
																					__snake_case     			=       os.path.join(data_args.train_dir							,    "**" )
														if data_args.validation_dir is not None:
																					__snake_case     			=       os.path.join(data_args.validation_dir							,    "**" )
														__snake_case     			=       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.
							__snake_case     			=       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:
														__snake_case     			=       dataset["train"].train_test_split(data_args.train_val_split )
														__snake_case     			=       split["train"]
														__snake_case     			=       split["test"]

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

							# Load the accuracy metric from the datasets package
							__snake_case     			=       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 )

							__snake_case     			=       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							,    )
							__snake_case     			=       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							,    )
							__snake_case     			=       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:
														__snake_case     			=       image_processor.size["shortest_edge"]
							else:
														__snake_case     			=       (image_processor.size["height"], image_processor.size["width"])
							__snake_case     			=       Normalize(mean=image_processor.image_mean							,    std=image_processor.image_std )
							__snake_case     			=       Compose(
							    [
							        RandomResizedCrop(SCREAMING_SNAKE_CASE ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ] )
							__snake_case     			=       Compose(
							    [
							        Resize(SCREAMING_SNAKE_CASE ),
							        CenterCrop(SCREAMING_SNAKE_CASE ),
							        ToTensor(),
							        normalize,
							    ] )

							def train_transforms(SCREAMING_SNAKE_CASE ):
														__snake_case     			=       [
														    _train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
														]
														return example_batch

							def val_transforms(SCREAMING_SNAKE_CASE ):
														__snake_case     			=       [_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:
																					__snake_case     			=       (
																					    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:
																					__snake_case     			=       (
																					    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
							__snake_case     			=       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:
														__snake_case     			=       None
														if training_args.resume_from_checkpoint is not None:
																					__snake_case     			=       training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__snake_case     			=       last_checkpoint
														__snake_case     			=       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:
														__snake_case     			=       trainer.evaluate()
														trainer.log_metrics("eval"							,    SCREAMING_SNAKE_CASE )
														trainer.save_metrics("eval"							,    SCREAMING_SNAKE_CASE )

							# Write model card and (optionally) push to hub
							__snake_case     			=       {
							    "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()


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
