





"""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
a		:				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''')

a		:				Any  			=    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a		:				Tuple  			=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	with open(_lowercase					, "rb"      ) as f:
		a   :	List[str]			=       Image.open(_lowercase      )
		return im.convert("RGB"      )


@dataclass
class  __UpperCamelCase       :
							lowerCamelCase     :      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)."""
							    }	, )
							lowerCamelCase     :      Optional[str]         =field(
							    default=a__	, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
							lowerCamelCase     :      Optional[str]         =field(default=a__	, metadata={"""help""": """A folder containing the training data."""}		)
							lowerCamelCase     :      Optional[str]         =field(default=a__	, metadata={"""help""": """A folder containing the validation data."""}		)
							lowerCamelCase     :      Optional[float]         =field(
							    default=0.15	, metadata={"""help""": """Percent to split off of train for validation."""}		)
							lowerCamelCase     :      Optional[int]         =field(
							    default=a__	, metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }	, )
							lowerCamelCase     :      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 					__a     (     self   )			->				List[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  __UpperCamelCase       :
							lowerCamelCase     :      str         =field(
							    default="""google/vit-base-patch16-224-in21k"""	, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	, )
							lowerCamelCase     :      Optional[str]         =field(
							    default=a__	, metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a__		)}	, )
							lowerCamelCase     :      Optional[str]         =field(
							    default=a__	, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}		)
							lowerCamelCase     :      Optional[str]         =field(
							    default=a__	, metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}		)
							lowerCamelCase     :      str         =field(
							    default="""main"""	, metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}	, )
							lowerCamelCase     :      str         =field(default=a__	, metadata={"""help""": """Name or path of preprocessor config."""}		)
							lowerCamelCase     :      bool         =field(
							    default=a__	, metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }	, )
							lowerCamelCase     :      bool         =field(
							    default=a__	, metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}	, )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Dict:






	'''simple docstring'''






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






def    _SCREAMING_SNAKE_CASE	(			)				->Any:






	'''simple docstring'''






	a   :	str			=       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.
		a, a, a   :	Union[str, Any]			=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
	else:
		a, a, a   :	int			=       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"					, _lowercase					, _lowercase      )

	# 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()

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

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

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

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

	def train_transforms(_lowercase		:					List[str]      ):
		a   :	List[Any]			=       [
		    _train_transforms(pil_img.convert("RGB"      )      ) for pil_img in example_batch["image"]
		]
		return example_batch

	def val_transforms(_lowercase		:					List[Any]      ):
		a   :	Union[str, 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:
			a   :	Optional[int]			=       (
			    dataset["train"].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
			)
		# Set the training transforms
		dataset["train"].set_transform(_lowercase      )

	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:
			a   :	Any			=       (
			    dataset["validation"].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
			)
		# Set the validation transforms
		dataset["validation"].set_transform(_lowercase      )

	# Initalize our trainer
	a   :	Union[str, Any]			=       Trainer(
	    model=_lowercase					, args=_lowercase					, train_dataset=dataset["train"] if training_args.do_train else None					, eval_dataset=dataset["validation"] if training_args.do_eval else None					, compute_metrics=_lowercase					, tokenizer=_lowercase					, data_collator=_lowercase					, )

	# Training
	if training_args.do_train:
		a   :	List[str]			=       None
		if training_args.resume_from_checkpoint is not None:
			a   :	List[str]			=       training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			a   :	Dict			=       last_checkpoint
		a   :	List[str]			=       trainer.train(resume_from_checkpoint=_lowercase      )
		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:
		a   :	Dict			=       trainer.evaluate()
		trainer.log_metrics("eval"					, _lowercase      )
		trainer.save_metrics("eval"					, _lowercase      )

	# Write model card and (optionally) push to hub
	a   :	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(**_lowercase      )
	else:
		trainer.create_model_card(**_lowercase      )


if __name__ == "__main__":
		main()









"""simple docstring"""




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


