

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_							:					List[str]		 =   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_							:					Optional[Any]		 =   list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
UpperCAmelCase_							:					Dict		 =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				List[Any]:
	with open(a__			,"""rb"""       ) as f:
		__A :			Tuple		    =			Image.open(a__       )
		return im.convert("""RGB"""       )






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






def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				Tuple:
	__A :			int		    =			torch.stack([example["""pixel_values"""] for example in examples]       )
	__A :			Dict		    =			torch.tensor([example["""labels"""] for example in examples]       )
	return {"pixel_values": pixel_values, "labels": labels}
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	# 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 :			Any		    =			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 :			Any		    =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
	else:
		__A    ,				__A    ,				__A :			int		    =			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"""			,a__			,a__       )

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

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

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

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

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

	def val_transforms(a__    :   Union[str, Any]       ):
		__A :			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:
			__A :			Any		    =			(
			    dataset["""train"""].shuffle(seed=training_args.seed       ).select(range(data_args.max_train_samples       )       )
			)
		# Set the training transforms
		dataset["train"].set_transform(a__       )

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

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

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

	# Write model card and (optionally) push to hub
	__A :			List[str]		    =			{
	    """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(**a__       )
	else:
		trainer.create_model_card(**a__       )


if __name__ == "__main__":
			main()








from math import sqrt
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 1000000       )					->				int:
	__A :			int		    =			0
	__A :			int		    =			0
	__A :			int

	while num_cuboids <= limit:
		max_cuboid_size += 1
		for sum_shortest_sides in range(2			,2 * max_cuboid_size + 1       ):
			if sqrt(sum_shortest_sides**2 + max_cuboid_size**2       ).is_integer():
				num_cuboids += (
				    min(a__			,sum_shortest_sides // 2       )
				    - max(1			,sum_shortest_sides - max_cuboid_size       )
				    + 1
				)

	return max_cuboid_size


if __name__ == "__main__":
			print(f"""{solution() = }""")





