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
_lowerCamelCase   :				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''')

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):




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


@dataclass
class   lowercase      :
							lowercase__  :    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)."""
							    }			,    )
							lowercase__  :    Optional[str]									=							field(
							    default=a_			,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}       )
							lowercase__  :    Optional[str]									=							field(default=a_			,    metadata={"""help""": """A folder containing the training data."""}       )
							lowercase__  :    Optional[str]									=							field(default=a_			,    metadata={"""help""": """A folder containing the validation data."""}       )
							lowercase__  :    Optional[float]									=							field(
							    default=0.15			,    metadata={"""help""": """Percent to split off of train for validation."""}       )
							lowercase__  :    Optional[int]									=							field(
							    default=a_			,    metadata={
							        """help""": (
							            """For debugging purposes or quicker training, truncate the number of training examples to this """
							            """value if set."""
							        )
							    }			,    )
							lowercase__  :    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 		__snake_case(  self				:   Tuple )   ->			Dict:



												'''simple docstring'''

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict					):
					SCREAMING_SNAKE_CASE           =    torch.stack([example["pixel_values"] for example in examples]					)
					SCREAMING_SNAKE_CASE           =    torch.tensor([example["labels"] for example in examples]					)
					return {"pixel_values": pixel_values, "labels": labels}





def 		__lowerCamelCase							():
					SCREAMING_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.
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
					else:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_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" ,	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()

					SCREAMING_SNAKE_CASE           =    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.
					SCREAMING_SNAKE_CASE           =    None
					if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
										SCREAMING_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:
										SCREAMING_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:
										SCREAMING_SNAKE_CASE           =    {}
										if data_args.train_dir is not None:
															SCREAMING_SNAKE_CASE           =    os.path.join(data_args.train_dir ,	"**"					)
										if data_args.validation_dir is not None:
															SCREAMING_SNAKE_CASE           =    os.path.join(data_args.validation_dir ,	"**"					)
										SCREAMING_SNAKE_CASE           =    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.
					SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    dataset["train"].train_test_split(data_args.train_val_split					)
										SCREAMING_SNAKE_CASE           =    split["train"]
										SCREAMING_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.
					SCREAMING_SNAKE_CASE           =    dataset["train"].features["labels"].names
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    {}, {}
					for i, label in enumerate(lowerCamelCase__					):
										SCREAMING_SNAKE_CASE           =    str(lowerCamelCase__					)
										SCREAMING_SNAKE_CASE           =    label

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

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

					def train_transforms(UpperCAmelCase__       :							Union[str, Any]					):
										SCREAMING_SNAKE_CASE           =    [
										    _train_transforms(pil_img.convert("RGB"					)					) for pil_img in example_batch["image"]
										]
										return example_batch

					def val_transforms(UpperCAmelCase__       :							Dict					):
										SCREAMING_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:
															SCREAMING_SNAKE_CASE           =    (
															    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:
															SCREAMING_SNAKE_CASE           =    (
															    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
					SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    None
										if training_args.resume_from_checkpoint is not None:
															SCREAMING_SNAKE_CASE           =    training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															SCREAMING_SNAKE_CASE           =    last_checkpoint
										SCREAMING_SNAKE_CASE           =    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:
										SCREAMING_SNAKE_CASE           =    trainer.evaluate()
										trainer.log_metrics("eval" ,	lowerCamelCase__					)
										trainer.save_metrics("eval" ,	lowerCamelCase__					)

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


if __name__ == "__main__":
							main()





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




