

'''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							:				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")

SCREAMING_SNAKE_CASE							:				Dict     	=			list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 							_UpperCamelCase				(   lowerCAmelCase__:					str   )      -> Optional[Any]:
							with open(lowerCAmelCase__		,'rb'   ) as f:
														SCREAMING_SNAKE_CASE_				=				Image.open(lowerCAmelCase__   )
														return im.convert('RGB'   )






@dataclass
class    snake_case     :






				"""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   a__					(    self       )     ->							int:
											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    snake_case     :






				"""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 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple   )      -> List[str]:
							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 							_UpperCamelCase				(   )      -> Tuple:
							# 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.

							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(lowerCAmelCase__:					Optional[int]   ):
														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(lowerCAmelCase__:					int   ):
														SCREAMING_SNAKE_CASE_				=				[
														    _train_transforms(pil_img.convert('RGB'   )   ) for pil_img in example_batch['image']
														]
														return example_batch

							def val_transforms(lowerCAmelCase__:					str   ):
														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()








'''simple docstring'''

from math import factorial




def 							_UpperCamelCase				(   lowerCAmelCase__:					int = 100   )      -> int:
							return sum(map(lowerCAmelCase__		,str(factorial(lowerCAmelCase__   )   )   )   )


if __name__ == "__main__":
							print(solution(int(input("Enter the Number: ").strip())))





