


'''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
__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		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''')

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







def       a_				(	UpperCamelCase_ ):
							with open(UpperCamelCase_						,  "rb" ) as f:
														A_			    =		Image.open(UpperCamelCase_ )
														return im.convert("RGB" )






@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       Optional[str]   						=field(
						    default=lowercase				,			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=lowercase				,			metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}						)
						_UpperCAmelCase		:       Optional[str]   						=field(default=lowercase				,			metadata={"help": "A folder containing the training data."}						)
						_UpperCAmelCase		:       Optional[str]   						=field(default=lowercase				,			metadata={"help": "A folder containing the validation data."}						)
						_UpperCAmelCase		:       Optional[float]   						=field(
						    default=0.1_5				,			metadata={"help": "Percent to split off of train for validation."}						)
						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }				,			)
						_UpperCAmelCase		:       Optional[int]   						=field(
						    default=lowercase				,			metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
						            "value if set."
						        )
						    }				,			)






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







def       a_				(	UpperCamelCase_ ):
							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       a_				(	):
							# See all possible arguments in src/transformers/training_args.py
							# or by passing the --help flag to this script.
							# We now keep distinct sets of args, for a cleaner separation of concerns.

							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_ ):
														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_ ):
														A_			    =		[
														    _train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
														]
														return example_batch

							def val_transforms(UpperCamelCase_ ):
														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()









'''simple docstring'''
def       a_				(	UpperCamelCase_ ):

							if length <= 0 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														raise ValueError("Length must be a positive integer." )
							return [n * (2 * n - 1) for n in range(UpperCamelCase_ )]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=10))





