


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
snake_case							:       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''')

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






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							with open(__lowerCAmelCase					,    'rb'   ) as f:
														a__             =     Image.open(__lowerCAmelCase   )
														return im.convert('RGB'   )







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






def 							__lowercase   ( __lowerCAmelCase							:				Dict   ):
							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 							__lowercase   ( ):
							# 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'					,    __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()

							a__             =     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.
							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=__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.
							a__             =     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:
														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(__lowerCAmelCase   ):
														a__             =     str(__lowerCAmelCase   )
														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(__lowerCAmelCase							:				List[Any]   ):
														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(__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					,    )
							a__             =     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					,    )
							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(__lowerCAmelCase   ),
							        RandomHorizontalFlip(),
							        ToTensor(),
							        normalize,
							    ]   )
							a__             =     Compose(
							    [
							        Resize(__lowerCAmelCase   ),
							        CenterCrop(__lowerCAmelCase   ),
							        ToTensor(),
							        normalize,
							    ]   )

							def train_transforms(__lowerCAmelCase							:				List[Any]   ):
														a__             =     [
														    _train_transforms(pil_img.convert('RGB'   )   ) for pil_img in example_batch['image']
														]
														return example_batch

							def val_transforms(__lowerCAmelCase							:				Any   ):
														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(__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:
																					a__             =     (
																					    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
							a__             =     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:
														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=__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:
														a__             =     trainer.evaluate()
														trainer.log_metrics('eval'					,    __lowerCAmelCase   )
														trainer.save_metrics('eval'					,    __lowerCAmelCase   )

							# 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(**__lowerCAmelCase   )
							else:
														trainer.create_model_card(**__lowerCAmelCase   )


if __name__ == "__main__":
						main()








import math






def 							__lowercase   ( __lowerCAmelCase							:				int   ):
							a__             =     [True] * n
							a__             =     False
							a__             =     False
							a__             =     True

							for i in range(3					,    int(n**0.5 + 1   )					,    2   ):
														a__             =     i * 2
														while index < n:
																					a__             =     False
																					a__             =     index + i

							a__             =     [2]

							for i in range(3					,    __lowerCAmelCase					,    2   ):
														if is_prime[i]:
																					primes.append(__lowerCAmelCase   )

							return primes






def 							__lowercase   ( __lowerCAmelCase							:				int = 9_9_9_9_6_6_6_6_3_3_3_3   ):
							a__             =     math.floor(math.sqrt(__lowerCAmelCase   )   ) + 1_0_0
							a__             =     prime_sieve(__lowerCAmelCase   )

							a__             =     0
							a__             =     0
							a__             =     primes[prime_index]

							while (last_prime**2) <= limit:
														a__             =     primes[prime_index + 1]

														a__             =     last_prime**2
														a__             =     next_prime**2

														# Get numbers divisible by lps(current)
														a__             =     lower_bound + last_prime
														while upper_bound > current <= limit:
																					matches_sum += current
																					current += last_prime

														# Reset the upper_bound
														while (upper_bound - next_prime) > limit:
																					upper_bound -= next_prime

														# Add the numbers divisible by ups(current)
														a__             =     upper_bound - next_prime
														while current > lower_bound:
																					matches_sum += current
																					current -= next_prime

														# Remove the numbers divisible by both ups and lps
														a__             =     0
														while upper_bound > current <= limit:
																					if current <= lower_bound:
																												# Increment the current number
																												current += last_prime * next_prime
																												continue

																					if current > limit:
																												break

																					# Remove twice since it was added by both ups and lps
																					matches_sum -= current * 2

																					# Increment the current number
																					current += last_prime * next_prime

														# Setup for next pair
														a__             =     next_prime
														prime_index += 1

							return matches_sum


if __name__ == "__main__":
						print(solution())




