
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  :					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')

lowercase  :					Dict													=       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase  :					Any													=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Dict:






							'''simple docstring'''





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




@dataclass
class  lowerCamelCase__							:




	'''simple docstring'''




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




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






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		)					->    str:






							'''simple docstring'''





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

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

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

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

							__UpperCamelCase	:   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						,  )
							__UpperCamelCase	:   List[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						,  )
							__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	:   Dict			   =					image_processor.size["shortest_edge"]
							else:
														__UpperCamelCase	:   str			   =					(image_processor.size["height"], image_processor.size["width"])
							__UpperCamelCase	:   Union[str, Any]			   =					Normalize(mean=image_processor.image_mean						,  std=image_processor.image_std)
							__UpperCamelCase	:   List[Any]			   =					Compose(
							    [
							        RandomResizedCrop(_lowerCamelCase),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ])
							__UpperCamelCase	:   List[str]			   =					Compose(
							    [
							        Resize(_lowerCamelCase),
							        CenterCrop(_lowerCamelCase),
							        ToTensor(),
							        normalize,
							    ])

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

							def val_transforms(_lowerCamelCase :			int):
														__UpperCamelCase	:   List[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	:   List[str]			   =					(
																					    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:
																					__UpperCamelCase	:   Tuple			   =					(
																					    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
							__UpperCamelCase	:   Union[str, Any]			   =					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:
														__UpperCamelCase	:   Tuple			   =					None
														if training_args.resume_from_checkpoint is not None:
																					__UpperCamelCase	:   int			   =					training_args.resume_from_checkpoint
														elif last_checkpoint is not None:
																					__UpperCamelCase	:   Optional[Any]			   =					last_checkpoint
														__UpperCamelCase	:   str			   =					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:
														__UpperCamelCase	:   str			   =					trainer.evaluate()
														trainer.log_metrics("eval"						,  _lowerCamelCase)
														trainer.save_metrics("eval"						,  _lowerCamelCase)

							# Write model card and (optionally) push to hub
							__UpperCamelCase	:   Tuple			   =					{
							    "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()

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:int						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					{}







	def    _lowerCamelCase				(					self		:str						)		-> None:
								print(self.vertex						)
								for i in self.vertex:
															print(a     ,   " -> "     ,   " -> ".join([str(a						) for j in self.vertex[i]]						)						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:int     ,   a		:int						)		-> None:
								# check if vertex is already present,
								if from_vertex in self.vertex:
															self.vertex[from_vertex].append(a						)
								else:
															# else make a new vertex
															__UpperCamelCase	:   Optional[Any]			   =					[to_vertex]







	def    _lowerCamelCase				(					self		:Tuple						)		-> None:
								# visited array for storing already visited nodes
								__UpperCamelCase	:   Dict			   =					[False] * len(self.vertex						)

								# call the recursive helper function
								for i in range(len(self.vertex						)						):
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)







	def    _lowerCamelCase				(					self		:Any     ,   a		:int     ,   a		:list						)		-> None:
								# mark start vertex as visited
								__UpperCamelCase	:   int			   =					True

								print(a     ,   end=" "						)

								# Recur for all the vertices that are adjacent to this node
								for i in self.vertex:
															if not visited[i]:
																						self.dfs_recursive(a     ,   a						)


if __name__ == "__main__":
		lowercase  :					Dict													=       Graph()
		g.add_edge(0, 1)
		g.add_edge(0, 2)
		g.add_edge(1, 2)
		g.add_edge(2, 0)
		g.add_edge(2, 3)
		g.add_edge(3, 3)

		g.print_graph()
		print('DFS:')
		g.dfs()

		# OUTPUT:
		# 0  ->  1 -> 2
		# 1  ->  2
		# 2  ->  0 -> 3
		# 3  ->  3
		# DFS:
		#  0 1 2 3