
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__		:List[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")

lowercase__		:Dict   =    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase__		:List[str]   =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					with open(lowerCAmelCase__ ,			'''rb'''		) as f:
										lowercase							=						Image.open(lowerCAmelCase__		)
										return im.convert('''RGB'''		)







@dataclass
class 				lowercase		:
							lowercase_       :       Optional[str]       					=field(
							    default=SCREAMING_SNAKE_CASE__       ,  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=SCREAMING_SNAKE_CASE__       ,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
							lowercase_       :       Optional[str]       					=field(default=SCREAMING_SNAKE_CASE__       ,  metadata={'''help''': '''A folder containing the training data.'''}		)
							lowercase_       :       Optional[str]       					=field(default=SCREAMING_SNAKE_CASE__       ,  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=SCREAMING_SNAKE_CASE__       ,  metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }       ,  )
							lowercase_       :       Optional[int]       					=field(
							    default=SCREAMING_SNAKE_CASE__       ,  metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }       ,  )




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



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						torch.stack([example['''pixel_values'''] for example in examples]		)
					lowercase							=						torch.tensor([example['''labels'''] for example in examples]		)
					return {"pixel_values": pixel_values, "labels": labels}



def        UpperCamelCase    (							):
					'''simple docstring'''
					# 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.

					lowercase							=						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							=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
					else:
										lowercase	,						lowercase	,						lowercase							=						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							=						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							=						None
					if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
										lowercase							=						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							=						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							=						{}
										if data_args.train_dir is not None:
															lowercase							=						os.path.join(data_args.train_dir ,			'''**'''		)
										if data_args.validation_dir is not None:
															lowercase							=						os.path.join(data_args.validation_dir ,			'''**'''		)
										lowercase							=						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							=						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							=						dataset['''train'''].train_test_split(data_args.train_val_split		)
										lowercase							=						split['''train''']
										lowercase							=						split['''test''']

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

					# Load the accuracy metric from the datasets package
					lowercase							=						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							=						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							=						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							=						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							=						image_processor.size['''shortest_edge''']
					else:
										lowercase							=						(image_processor.size['''height'''], image_processor.size['''width'''])
					lowercase							=						Normalize(mean=image_processor.image_mean ,			std=image_processor.image_std		)
					lowercase							=						Compose(
					    [
					        RandomResizedCrop(lowerCAmelCase__		),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        normalize,
					    ]		)
					lowercase							=						Compose(
					    [
					        Resize(lowerCAmelCase__		),
					        CenterCrop(lowerCAmelCase__		),
					        ToTensor(),
					        normalize,
					    ]		)

					def train_transforms(lowerCAmelCase__		):
										lowercase							=						[
										    _train_transforms(pil_img.convert('''RGB'''		)		) for pil_img in example_batch['''image''']
										]
										return example_batch

					def val_transforms(lowerCAmelCase__		):
										lowercase							=						[_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							=						(
															    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							=						(
															    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							=						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							=						None
										if training_args.resume_from_checkpoint is not None:
															lowercase							=						training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowercase							=						last_checkpoint
										lowercase							=						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							=						trainer.evaluate()
										trainer.log_metrics('''eval''' ,			lowerCAmelCase__		)
										trainer.save_metrics('''eval''' ,			lowerCAmelCase__		)

					# Write model card and (optionally) push to hub
					lowercase							=						{
					    '''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 math import ceil



def    __UpperCAmelCase (	__a      :			int = 1_001	)			->     int:

					"""simple docstring"""

					_a	:    List[Any]         =		1

					for i in range(1		,int(ceil(n / 2.0	)	)	):
										_a	:    Optional[Any]         =		2 * i + 1
										_a	:    Optional[int]         =		2 * i
										_a	:    str         =		total + 4 * odd**2 - 6 * even

					return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									a__				=		int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print('''Invalid entry - please enter a number''')



