



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



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str	)->  Tuple:
				with open(UpperCamelCase_ ,       '''rb'''	) as f:
								A__		 =					Image.open(UpperCamelCase_	)
								return im.convert('''RGB'''	)



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




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







def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  Union[str, Any]:
				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 		lowerCAmelCase__    (					)->  Optional[Any]:
				# 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''' ,       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()

				A__		 =					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.
				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=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.
				A__		 =					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:
								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(UpperCamelCase_	):
								A__		 =					str(UpperCamelCase_	)
								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(UpperCamelCase_				:       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(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 ,       )
				A__		 =					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 ,       )
				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(UpperCamelCase_	),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        normalize,
				    ]	)
				A__		 =					Compose(
				    [
				        Resize(UpperCamelCase_	),
				        CenterCrop(UpperCamelCase_	),
				        ToTensor(),
				        normalize,
				    ]	)

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

				def val_transforms(UpperCamelCase_				:       List[str]	):
								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(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:
												A__		 =					(
												    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
				A__		 =					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:
								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=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:
								A__		 =					trainer.evaluate()
								trainer.log_metrics('''eval''' ,       UpperCamelCase_	)
								trainer.save_metrics('''eval''' ,       UpperCamelCase_	)

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


if __name__ == "__main__":
			main()





import numpy as np



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return 1 / (1 + np.exp(-vector	))



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.array	)->  np.array:
    return vector * sigmoid(1.702 * vector	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
