




"""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   :  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')

__A   :  Optional[int]					=      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__A   :  Any					=      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


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




@dataclass
class       lowerCAmelCase__       :


				"""simple docstring"""





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





def 						snake_case__       (					_lowerCamelCase				)			->str:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				torch.stack([example["pixel_values"] for example in examples]				)
		__lowercase				:		Optional[int]												=				torch.tensor([example["labels"] for example in examples]				)
		return {"pixel_values": pixel_values, "labels": labels}

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






		"""simple docstring"""


		__lowercase				:		Optional[int]												=				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.
				__lowercase						,__lowercase						,__lowercase				:		Tuple												=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
		else:
				__lowercase						,__lowercase						,__lowercase				:		List[Any]												=				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",  _lowerCamelCase,  _lowerCamelCase				)

		# 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()

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

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

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

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

		def val_transforms(_lowerCamelCase				):
				__lowercase				:		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:
						__lowercase				:		int												=				(
						    dataset["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
						)
				# Set the training transforms
				dataset["train"].set_transform(_lowerCamelCase				)

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

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

		# Training
		if training_args.do_train:
				__lowercase				:		Any												=				None
				if training_args.resume_from_checkpoint is not None:
						__lowercase				:		Optional[int]												=				training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						__lowercase				:		List[str]												=				last_checkpoint
				__lowercase				:		Optional[Any]												=				trainer.train(resume_from_checkpoint=_lowerCamelCase				)
				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:
				__lowercase				:		List[str]												=				trainer.evaluate()
				trainer.log_metrics("eval",  _lowerCamelCase				)
				trainer.save_metrics("eval",  _lowerCamelCase				)

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


if __name__ == "__main__":
						main()






"""simple docstring"""







from collections import deque

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		Any												=				len(_lowerCamelCase				)
		__lowercase				:		str												=				deque()
		__lowercase				:		Tuple												=				[False for _ in range(_lowerCamelCase				)]
		__lowercase				:		Optional[int]												=				[-1 for _ in range(_lowerCamelCase				)]
		__lowercase				:		Dict												=				index_of[:]

		def strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		List[str]												=				index  # the number when this node is seen
				__lowercase				:		List[str]												=				index  # lowest rank node reachable from here
				index += 1
				stack.append(_lowerCamelCase				)
				__lowercase				:		List[Any]												=				True

				for w in g[v]:
						if index_of[w] == -1:
								__lowercase				:		Optional[int]												=				strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)
						elif on_stack[w]:
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)

				if lowlink_of[v] == index_of[v]:
						__lowercase				:		Any												=				[]
						__lowercase				:		List[Any]												=				stack.pop()
						__lowercase				:		Any												=				False
						component.append(_lowerCamelCase				)
						while w != v:
								__lowercase				:		List[str]												=				stack.pop()
								__lowercase				:		Dict												=				False
								component.append(_lowerCamelCase				)
						components.append(_lowerCamelCase				)
				return index

		__lowercase				:		Tuple												=				[]
		for v in range(_lowerCamelCase				):
				if index_of[v] == -1:
						strong_connect(_lowerCamelCase,  0,  _lowerCamelCase				)

		return components

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		int												=				[[] for _ in range(_lowerCamelCase				)]
		for u, v in edges:
				g[u].append(_lowerCamelCase				)
		return g


if __name__ == "__main__":
						# Test
						__A   :  int					=      7
						__A   :  int					=      [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__A   :  List[Any]					=      [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__A   :  List[str]					=      [(u, v) for u, v in zip(source, target)]
						__A   :  List[str]					=      create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
