



'''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
__lowerCAmelCase						      =       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''')

__lowerCAmelCase						      =       list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCAmelCase						      =       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				List[Any]:
		with open(lowerCAmelCase_						,      'rb'  ) as f:
				_a					:				Optional[Any]				=							Image.open(lowerCAmelCase_  )
				return im.convert('RGB'  )

@dataclass
class      __magic_name__						:
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={
							        'help': 'Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'
							    }      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}       )
							lowerCAmelCase	:   Optional[str]           = field(default=_UpperCamelCase      ,						metadata={'help': 'A folder containing the training data.'}       )
							lowerCAmelCase	:   Optional[str]           = field(default=_UpperCamelCase      ,						metadata={'help': 'A folder containing the validation data.'}       )
							lowerCAmelCase	:   Optional[float]           = field(
							    default=0.15      ,						metadata={'help': 'Percent to split off of train for validation.'}       )
							lowerCAmelCase	:   Optional[int]           = field(
							    default=_UpperCamelCase      ,						metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }      ,						)
							lowerCAmelCase	:   Optional[int]           = field(
							    default=_UpperCamelCase      ,						metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }      ,						)







							def 							__lowercase      (							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      __magic_name__						:
							lowerCAmelCase	:   str           = field(
							    default='google/vit-base-patch16-224-in21k'      ,						metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_UpperCamelCase       )}      ,						)
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowerCAmelCase	:   Optional[str]           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}       )
							lowerCAmelCase	:   str           = field(
							    default='main'      ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}      ,						)
							lowerCAmelCase	:   str           = field(default=_UpperCamelCase      ,						metadata={'help': 'Name or path of preprocessor config.'}       )
							lowerCAmelCase	:   bool           = field(
							    default=_UpperCamelCase      ,						metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }      ,						)
							lowerCAmelCase	:   bool           = field(
							    default=_UpperCamelCase      ,						metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}      ,						)



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				Dict:
		_a					:				Tuple				=							torch.stack([example['pixel_values'] for example in examples]  )
		_a					:				Tuple				=							torch.tensor([example['labels'] for example in examples]  )
		return {"pixel_values": pixel_values, "labels": labels}



def 					__lowerCamelCase					(				)      ->				int:
		# 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					:				Tuple				=							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					:				str				=							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				_a	, _a	, _a					:				List[str]				=							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					:				Optional[Any]				=							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					:				Tuple				=							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					:				Any				=							{}
				if data_args.train_dir is not None:
						_a					:				int				=							os.path.join(data_args.train_dir						,      '**'  )
				if data_args.validation_dir is not None:
						_a					:				int				=							os.path.join(data_args.validation_dir						,      '**'  )
				_a					:				Any				=							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					:				Any				=							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					:				Union[str, Any]				=							dataset['train'].train_test_split(data_args.train_val_split  )
				_a					:				List[Any]				=							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					:				Union[str, Any]				=							dataset['train'].features['labels'].names
		_a	, _a					:				Any				=							{}, {}
		for i, label in enumerate(lowerCAmelCase_  ):
				_a					:				List[str]				=							str(lowerCAmelCase_  )
				_a					:				Any				=							label

		# Load the accuracy metric from the datasets package
		_a					:				List[Any]				=							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  )

		_a					:				Optional[int]				=							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					:				Optional[int]				=							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					:				List[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					:				str				=							image_processor.size['shortest_edge']
		else:
				_a					:				Union[str, Any]				=							(image_processor.size['height'], image_processor.size['width'])
		_a					:				Any				=							Normalize(mean=image_processor.image_mean						,      std=image_processor.image_std  )
		_a					:				Dict				=							Compose(
		    [
		        RandomResizedCrop(lowerCAmelCase_  ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        normalize,
		    ]  )
		_a					:				Union[str, Any]				=							Compose(
		    [
		        Resize(lowerCAmelCase_  ),
		        CenterCrop(lowerCAmelCase_  ),
		        ToTensor(),
		        normalize,
		    ]  )

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

		def val_transforms(lowerCAmelCase_  ):
				_a					:				Dict				=							[_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					:				Tuple				=							(
						    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					:				Any				=							(
						    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					:				str				=							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					:				Optional[Any]				=							None
				if training_args.resume_from_checkpoint is not None:
						_a					:				Dict				=							training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_a					:				Optional[Any]				=							last_checkpoint
				_a					:				Optional[Any]				=							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					:				List[str]				=							trainer.evaluate()
				trainer.log_metrics('eval'						,      lowerCAmelCase_  )
				trainer.save_metrics('eval'						,      lowerCAmelCase_  )

		# Write model card and (optionally) push to hub
		_a					:				Tuple				=							{
		    '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 __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__lowerCAmelCase						      =       200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase						      =       50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase						      =       0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_000))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, float]:
		_a					:				List[Any]				=							len([g for position, g in enumerate(lowerCAmelCase_  ) if g == main_target[position]]  )
		return (item, float(lowerCAmelCase_  ))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				tuple[str, str]:
		_a					:				Dict				=							random.randint(0						,      len(lowerCAmelCase_  ) - 1  )
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		_a					:				Optional[int]				=							parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				str:
		_a					:				Optional[Any]				=							list(lowerCAmelCase_  )
		if random.uniform(0						,      1  ) < MUTATION_PROBABILITY:
				_a					:				Optional[int]				=							random.choice(lowerCAmelCase_  )
		return "".join(lowerCAmelCase_  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )      ->				list[str]:
		_a					:				List[str]				=							[]
		# Generate more children proportionally to the fitness score.
		_a					:				Tuple				=							int(parent_a[1] * 100  ) + 1
		_a					:				Tuple				=							10 if child_n >= 10 else child_n
		for _ in range(lowerCAmelCase_  ):
				_a					:				Any				=							population_score[random.randint(0						,      lowerCAmelCase_  )][0]

				_a	, _a					:				Tuple				=							crossover(parent_a[0]						,      lowerCAmelCase_  )
				# Append new string to the population list.
				pop.append(mutate(lowerCAmelCase_						,      lowerCAmelCase_  )  )
				pop.append(mutate(lowerCAmelCase_						,      lowerCAmelCase_  )  )
		return pop



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_ = True  )      ->				tuple[int, int, str]:

		# Verify if N_POPULATION is bigger than N_SELECTED
		if N_POPULATION < N_SELECTED:
				_a					:				Dict				=							f"""{N_POPULATION} must be bigger than {N_SELECTED}"""
				raise ValueError(lowerCAmelCase_  )
		# Verify that the target contains no genes besides the ones inside genes variable.
		_a					:				Optional[int]				=							sorted({c for c in target if c not in genes}  )
		if not_in_genes_list:
				_a					:				List[Any]				=							f"""{not_in_genes_list} is not in genes list, evolution cannot converge"""
				raise ValueError(lowerCAmelCase_  )

		# Generate random starting population.
		_a					:				Union[str, Any]				=							[]
		for _ in range(lowerCAmelCase_  ):
				population.append(''.join([random.choice(lowerCAmelCase_  ) for i in range(len(lowerCAmelCase_  )  )]  )  )

		# Just some logs to know what the algorithms is doing.
		_a	, _a					:				Union[str, Any]				=							0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(lowerCAmelCase_  )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				_a					:				Optional[Any]				=							[evaluate(lowerCAmelCase_						,      lowerCAmelCase_  ) for item in population]

				# Check if there is a matching evolution.
				_a					:				Tuple				=							sorted(lowerCAmelCase_						,      key=lambda lowerCAmelCase_  : x[1]						,      reverse=lowerCAmelCase_  )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    f"""\nGeneration: {generation}"""
						    f"""\nTotal Population:{total_population}"""
						    f"""\nBest score: {population_score[0][1]}"""
						    f"""\nBest string: {population_score[0][0]}"""  )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				_a					:				Dict				=							population[: int(N_POPULATION / 3  )]
				population.clear()
				population.extend(lowerCAmelCase_  )
				# Normalize population score to be between 0 and 1.
				_a					:				Tuple				=							[
				    (item, score / len(lowerCAmelCase_  )) for item, score in population_score
				]

				# This is selection
				for i in range(lowerCAmelCase_  ):
						population.extend(select(population_score[int(lowerCAmelCase_  )]						,      lowerCAmelCase_						,      lowerCAmelCase_  )  )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(lowerCAmelCase_  ) > N_POPULATION:
								break


if __name__ == "__main__":
							__lowerCAmelCase						      =       (
							    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
							)
							__lowerCAmelCase						      =       list(
							    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
							    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
							)
							__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase						      =       basic(target_str, genes_list)
							print(
							    f"""\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}"""
							)




