


from math import factorial


class        _a							:

							def __init__(							self:	Dict    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Union[str, Any]							)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      real
									if isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											lowercase__            =      [1] * rank
									else:
											lowercase__            =      rank

							def __repr__(							self:	Union[str, Any]							)			->   Optional[int]:





									"""simple docstring"""







									return (
									    f'{self.real}+'
									    f'{"+".join(str(UpperCamelCase_							)+"E"+str(n+1							)for n,dual in enumerate(self.duals							)							)}'
									)

							def       lowerCamelCase_ (							self:	List[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      self.duals.copy()
									while cur[-1] == 0:
											cur.pop(-1							)
									return Dual(self.real    ,						UpperCamelCase_							)

							def __add__(							self:	Optional[int]    ,						UpperCamelCase_:	Optional[Any]							)			->   str:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											return Dual(self.real + other    ,						self.duals							)
									lowercase__            =      self.duals.copy()
									lowercase__            =      other.duals.copy()
									if len(UpperCamelCase_							) > len(UpperCamelCase_							):
											o_dual.extend([1] * (len(UpperCamelCase_							) - len(UpperCamelCase_							))							)
									elif len(UpperCamelCase_							) < len(UpperCamelCase_							):
											s_dual.extend([1] * (len(UpperCamelCase_							) - len(UpperCamelCase_							))							)
									lowercase__            =      []
									for i in range(len(UpperCamelCase_							)							):
											new_duals.append(s_dual[i] + o_dual[i]							)
									return Dual(self.real + other.real    ,						UpperCamelCase_							)

							_lowercase	:			int        = __add__

							def __sub__(							self:	Optional[Any]    ,						UpperCamelCase_:	List[str]							)			->   List[str]:





									"""simple docstring"""







									return self + other * -1

							def __mul__(							self:	List[Any]    ,						UpperCamelCase_:	List[Any]							)			->   List[Any]:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											lowercase__            =      []
											for i in self.duals:
													new_duals.append(i * other							)
											return Dual(self.real * other    ,						UpperCamelCase_							)
									lowercase__            =      [0] * (len(self.duals							) + len(other.duals							) + 1)
									for i, item in enumerate(self.duals							):
											for j, jtem in enumerate(other.duals							):
													new_duals[i + j + 1] += item * jtem
									for k in range(len(self.duals							)							):
											new_duals[k] += self.duals[k] * other.real
									for index in range(len(other.duals							)							):
											new_duals[index] += other.duals[index] * self.real
									return Dual(self.real * other.real    ,						UpperCamelCase_							)

							_lowercase	:			Optional[int]        = __mul__

							def __truediv__(							self:	List[str]    ,						UpperCamelCase_:	Tuple							)			->   Union[str, Any]:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											lowercase__            =      []
											for i in self.duals:
													new_duals.append(i / other							)
											return Dual(self.real / other    ,						UpperCamelCase_							)
									raise ValueError

							def __floordiv__(							self:	Tuple    ,						UpperCamelCase_:	Optional[int]							)			->   Tuple:





									"""simple docstring"""







									if not isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											lowercase__            =      []
											for i in self.duals:
													new_duals.append(i // other							)
											return Dual(self.real // other    ,						UpperCamelCase_							)
									raise ValueError


							def __pow__(							self:	Dict    ,						UpperCamelCase_:	Optional[Any]							)			->   Dict:





									"""simple docstring"""







									if n < 0 or isinstance(UpperCamelCase_    ,						UpperCamelCase_							):
											raise ValueError('''power must be a positive integer'''							)
									if n == 0:
											return 1
									if n == 1:
											return self
									lowercase__            =      self
									for _ in range(n - 1							):
											x *= self
									return x
def 			_a (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		if not callable(SCREAMING_SNAKE_CASE     ):
				raise ValueError('''differentiate() requires a function as input for func'''     )
		if not isinstance(SCREAMING_SNAKE_CASE						,				(float, int)     ):
				raise ValueError('''differentiate() requires a float as input for position'''     )
		if not isinstance(SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):
				raise ValueError('''differentiate() requires an int as input for order'''     )
		lowercase__            =      Dual(SCREAMING_SNAKE_CASE						,				1     )
		lowercase__            =      func(SCREAMING_SNAKE_CASE     )
		if order == 0:
				return result.real
		return result.duals[order - 1] * factorial(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			def 			_a (      SCREAMING_SNAKE_CASE     ):


					"""simple docstring"""




					return y**2 * y**4
			print(differentiate(f, 9, 2))






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 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




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


@dataclass
class        _a							:
							_lowercase	:			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).'''
							    }     ,	)
							_lowercase	:			Optional[str]        = field(
							    default=UpperCamelCase__     ,	metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the training data.'''}			)
							_lowercase	:			Optional[str]        = field(default=UpperCamelCase__     ,	metadata={'''help''': '''A folder containing the validation data.'''}			)
							_lowercase	:			Optional[float]        = field(
							    default=0.15     ,	metadata={'''help''': '''Percent to split off of train for validation.'''}			)
							_lowercase	:			Optional[int]        = field(
							    default=UpperCamelCase__     ,	metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }     ,	)
							_lowercase	:			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       lowerCamelCase_ (							self:	Any							)			->   int:





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


		"""simple docstring"""




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


		"""simple docstring"""




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

		# 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()

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

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

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

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

		def train_transforms(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [
				    _train_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']
				]
				return example_batch

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

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

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

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

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


if __name__ == "__main__":
			main()


