



"""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     =							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     =							list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCamelCase     =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> Tuple:






	"""simple docstring"""





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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					Optional[str]							  =     field(
			    default=_lowerCAmelCase       ,		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=_lowerCAmelCase       ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
			A__	:					Optional[str]							  =     field(default=_lowerCAmelCase       ,		metadata={"help": "A folder containing the training data."}      )
			A__	:					Optional[str]							  =     field(default=_lowerCAmelCase       ,		metadata={"help": "A folder containing the validation data."}      )
			A__	:					Optional[float]							  =     field(
			    default=0.15       ,		metadata={"help": "Percent to split off of train for validation."}      )
			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }       ,		)
			A__	:					Optional[int]							  =     field(
			    default=_lowerCAmelCase       ,		metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }       ,		)


			def   snake_case__	(      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       UpperCamelCase__			:

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



def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any]     )	-> Dict:






	"""simple docstring"""





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

def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					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__											=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
	else:
		A__							,							A__							,							A__											=					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",	UpperCAmelCase_,	UpperCAmelCase_     )

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

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

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

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

	def train_transforms(UpperCAmelCase_ :       Tuple     ):
		A__											=					[
		    _train_transforms(pil_img.convert("RGB"     )     ) for pil_img in example_batch["image"]
		]
		return example_batch

	def val_transforms(UpperCAmelCase_ :       Any     ):
		A__											=					[_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__											=					(
			    dataset["train"].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
			)
		# Set the training transforms
		dataset["train"].set_transform(UpperCAmelCase_     )

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

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

	# Training
	if training_args.do_train:
		A__											=					None
		if training_args.resume_from_checkpoint is not None:
			A__											=					training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			A__											=					last_checkpoint
		A__											=					trainer.train(resume_from_checkpoint=UpperCAmelCase_     )
		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__											=					trainer.evaluate()
		trainer.log_metrics("eval",	UpperCAmelCase_     )
		trainer.save_metrics("eval",	UpperCAmelCase_     )

	# Write model card and (optionally) push to hub
	A__											=					{
	    "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(**UpperCAmelCase_     )
	else:
		trainer.create_model_card(**UpperCAmelCase_     )


if __name__ == "__main__":
			main()











from __future__ import annotations

from cmath import sqrt

def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int						)			->    tuple[complex, complex]:

   if a == 0:
      raise ValueError("Coefficient 'a' must not be zero."						)
   A_       :					Tuple        =      b * b - 4 * a * c

   A_       :					Any        =      (-b + sqrt(_lowerCAmelCase						)) / (2 * a)
   A_       :					Union[str, Any]        =      (-b - sqrt(_lowerCAmelCase						)) / (2 * a)

   return (
       root_a.real if not root_a.imag else root_a,
       root_a.real if not root_a.imag else root_a,
   )

def   __snake_case    (       )			->    List[Any]:
   A_   ,    A_       :					Optional[Any]        =      quadratic_roots(a=5					,					b=6					,					c=1						)
   print(f"The solutions are: {solutiona} and {solutiona}"						)


if __name__ == "__main__":
 main()
