






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    :    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""")

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple		)  ->   List[str]:

						"""simple docstring"""



						with open(UpperCamelCase_    ,       'rb'		) as f:
												lowerCAmelCase__    						=						Image.open(UpperCamelCase_		)
												return im.convert('RGB'		)


@dataclass
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       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).'''
						    }		,					)
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(
						    default=__lowercase		,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(default=__lowercase		,					metadata={'''help''': '''A folder containing the training data.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[str]   =					field(default=__lowercase		,					metadata={'''help''': '''A folder containing the validation data.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[float]   =					field(
						    default=0.1_5		,					metadata={'''help''': '''Percent to split off of train for validation.'''})
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					field(
						    default=__lowercase		,					metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
						            '''value if set.'''
						        )
						    }		,					)
						_SCREAMING_SNAKE_CASE	:       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       UpperCamelCase__      (	self   ):






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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]		)  ->   List[Any]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    )  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						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.
												lowerCAmelCase__    						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowerCAmelCase__    						=						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'    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# 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()

						lowerCAmelCase__    						=						training_args.get_process_log_level()
						logger.setLevel(UpperCamelCase_		)
						transformers.utils.logging.set_verbosity(UpperCamelCase_		)
						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.
						lowerCAmelCase__    						=						None
						if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
												lowerCAmelCase__    						=						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:
												lowerCAmelCase__    						=						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:
												lowerCAmelCase__    						=						{}
												if data_args.train_dir is not None:
																		lowerCAmelCase__    						=						os.path.join(data_args.train_dir    ,       '**'		)
												if data_args.validation_dir is not None:
																		lowerCAmelCase__    						=						os.path.join(data_args.validation_dir    ,       '**'		)
												lowerCAmelCase__    						=						load_dataset(
												    'imagefolder'    ,       data_files=UpperCamelCase_    ,       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.
						lowerCAmelCase__    						=						None if '''validation''' in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split    ,       UpperCamelCase_		) and data_args.train_val_split > 0.0:
												lowerCAmelCase__    						=						dataset['''train'''].train_test_split(data_args.train_val_split		)
												lowerCAmelCase__    						=						split['''train''']
												lowerCAmelCase__    						=						split['''test''']

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

						# Load the accuracy metric from the datasets package
						lowerCAmelCase__    						=						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(UpperCamelCase_       :					Dict		):
												return metric.compute(predictions=np.argmax(p.predictions    ,       axis=1		)    ,       references=p.label_ids		)

						lowerCAmelCase__    						=						AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path    ,       num_labels=len(UpperCamelCase_		)    ,       labelaid=UpperCamelCase_    ,       idalabel=UpperCamelCase_    ,       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    ,       )
						lowerCAmelCase__    						=						AutoModelForImageClassification.from_pretrained(
						    model_args.model_name_or_path    ,       from_tf=bool('.ckpt' in model_args.model_name_or_path		)    ,       config=UpperCamelCase_    ,       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    ,       )
						lowerCAmelCase__    						=						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:
												lowerCAmelCase__    						=						image_processor.size['''shortest_edge''']
						else:
												lowerCAmelCase__    						=						(image_processor.size['''height'''], image_processor.size['''width'''])
						lowerCAmelCase__    						=						Normalize(mean=image_processor.image_mean    ,       std=image_processor.image_std		)
						lowerCAmelCase__    						=						Compose(
						    [
						        RandomResizedCrop(UpperCamelCase_		),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]		)
						lowerCAmelCase__    						=						Compose(
						    [
						        Resize(UpperCamelCase_		),
						        CenterCrop(UpperCamelCase_		),
						        ToTensor(),
						        normalize,
						    ]		)

						def train_transforms(UpperCamelCase_       :					Union[str, Any]		):
												lowerCAmelCase__    						=						[
												    _train_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['''image''']
												]
												return example_batch

						def val_transforms(UpperCamelCase_       :					List[Any]		):
												lowerCAmelCase__    						=						[_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:
																		lowerCAmelCase__    						=						(
																		    dataset['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
																		)
												# Set the training transforms
												dataset["train"].set_transform(UpperCamelCase_		)

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

						# Initalize our trainer
						lowerCAmelCase__    						=						Trainer(
						    model=UpperCamelCase_    ,       args=UpperCamelCase_    ,       train_dataset=dataset['train'] if training_args.do_train else None    ,       eval_dataset=dataset['validation'] if training_args.do_eval else None    ,       compute_metrics=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       data_collator=UpperCamelCase_    ,       )

						# Training
						if training_args.do_train:
												lowerCAmelCase__    						=						None
												if training_args.resume_from_checkpoint is not None:
																		lowerCAmelCase__    						=						training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		lowerCAmelCase__    						=						last_checkpoint
												lowerCAmelCase__    						=						trainer.train(resume_from_checkpoint=UpperCamelCase_		)
												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:
												lowerCAmelCase__    						=						trainer.evaluate()
												trainer.log_metrics('eval'    ,       UpperCamelCase_		)
												trainer.save_metrics('eval'    ,       UpperCamelCase_		)

						# Write model card and (optionally) push to hub
						lowerCAmelCase__    						=						{
						    '''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(**UpperCamelCase_		)
						else:
												trainer.create_model_card(**UpperCamelCase_		)


if __name__ == "__main__":
					main()













import numpy as np
from PIL import Image







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						lowerCAmelCase__    						=						np.zeros((maxpool_shape, maxpool_shape)		)

						while i < arr.shape[0]:
												if i + size > arr.shape[0]:
																		# if the end of the matrix is reached, break
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the maximum of the pooling matrix
																		lowerCAmelCase__    						=						np.max(arr[i : i + size, j : j + size]		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1

												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr







def 	_UpperCamelCase   (    UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					int		)  ->   np.ndarray:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('The input array is not a square matrix'		)
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0

						# compute the shape of the output matrix
						lowerCAmelCase__    						=						(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						lowerCAmelCase__    						=						np.zeros((avgpool_shape, avgpool_shape)		)

						while i < arr.shape[0]:
												# if the end of the matrix is reached, break
												if i + size > arr.shape[0]:
																		break
												while j < arr.shape[1]:
																		# if the end of the matrix is reached, break
																		if j + size > arr.shape[1]:
																								break
																		# compute the average of the pooling matrix
																		lowerCAmelCase__    						=						int(np.average(arr[i : i + size, j : j + size]		)		)
																		# shift the pooling matrix by stride of column pixels
																		j += stride
																		mat_j += 1

												# shift the pooling matrix by stride of row pixels
												i += stride
												mat_i += 1
												# reset the column index to 0
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						0

						return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""avgpooling""", verbose=True)

					# Loading the image
					__snake_case    :    List[str]         =     Image.open("""path_to_image""")

					# Converting the image to numpy array and maxpooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

					# Converting the image to numpy array and averagepooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()





