






import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator


def __A					(   __lowerCAmelCase			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    fname.split(os.path.sep			)[-1]
						return re.search(R'^(.*)_\d+\.jpg$'				,	__lowerCAmelCase			).groups()[0]





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	def __init__(							self ,					UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase=None					):
							"""simple docstring"""







							_UpperCAmelCase             =    file_names
							_UpperCAmelCase             =    image_transform
							_UpperCAmelCase             =    label_to_id

	def __len__(							self					):
							"""simple docstring"""







							return len(self.file_names					)

	def __getitem__(							self ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase             =    self.file_names[idx]
							_UpperCAmelCase             =    PIL.Image.open(UpperCAmelCase					)
							_UpperCAmelCase             =    raw_image.convert('RGB'					)
							if self.image_transform is not None:
													_UpperCAmelCase             =    self.image_transform(UpperCAmelCase					)
							_UpperCAmelCase             =    extract_label(UpperCAmelCase					)
							if self.label_to_id is not None:
													_UpperCAmelCase             =    self.label_to_id[label]
							return {"image": image, "label": label}



def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Optional[int]:





						"""simple docstring"""



						_UpperCAmelCase             =    Accelerator(cpu=args.cpu				,	mixed_precision=args.mixed_precision			)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_UpperCAmelCase             =    config['lr']
						_UpperCAmelCase             =    int(config['num_epochs']			)
						_UpperCAmelCase             =    int(config['seed']			)
						_UpperCAmelCase             =    int(config['batch_size']			)
						_UpperCAmelCase             =    config['image_size']
						if not isinstance(__lowerCAmelCase				,	(list, tuple)			):
												_UpperCAmelCase             =    (image_size, image_size)

						# Grab all the image filenames
						_UpperCAmelCase             =    [os.path.join(args.data_dir				,	__lowerCAmelCase			) for fname in os.listdir(args.data_dir			) if fname.endswith('.jpg'			)]

						# Build the label correspondences
						_UpperCAmelCase             =    [extract_label(__lowerCAmelCase			) for fname in file_names]
						_UpperCAmelCase             =    list(set(__lowerCAmelCase			)			)
						id_to_label.sort()
						_UpperCAmelCase             =    {lbl: i for i, lbl in enumerate(__lowerCAmelCase			)}

						# Set the seed before splitting the data.
						np.random.seed(__lowerCAmelCase			)
						torch.manual_seed(__lowerCAmelCase			)
						torch.cuda.manual_seed_all(__lowerCAmelCase			)

						# Split our filenames between train and validation
						_UpperCAmelCase             =    np.random.permutation(len(__lowerCAmelCase			)			)
						_UpperCAmelCase             =    int(0.8 * len(__lowerCAmelCase			)			)
						_UpperCAmelCase             =    random_perm[:cut]
						_UpperCAmelCase             =    random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						_UpperCAmelCase             =    Compose([RandomResizedCrop(__lowerCAmelCase				,	scale=(0.5, 1.0)			), ToTensor()]			)
						_UpperCAmelCase             =    PetsDataset(
						    [file_names[i] for i in train_split]				,	image_transform=__lowerCAmelCase				,	label_to_id=__lowerCAmelCase			)

						# For evaluation, we use a deterministic Resize
						_UpperCAmelCase             =    Compose([Resize(__lowerCAmelCase			), ToTensor()]			)
						_UpperCAmelCase             =    PetsDataset([file_names[i] for i in eval_split]				,	image_transform=__lowerCAmelCase				,	label_to_id=__lowerCAmelCase			)

						# Instantiate dataloaders.
						_UpperCAmelCase             =    DataLoader(__lowerCAmelCase				,	shuffle=__lowerCAmelCase				,	batch_size=__lowerCAmelCase				,	num_workers=4			)
						_UpperCAmelCase             =    DataLoader(__lowerCAmelCase				,	shuffle=__lowerCAmelCase				,	batch_size=__lowerCAmelCase				,	num_workers=4			)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_UpperCAmelCase             =    create_model('resnet50d'				,	pretrained=__lowerCAmelCase				,	num_classes=len(__lowerCAmelCase			)			)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						_UpperCAmelCase             =    model.to(accelerator.device			)

						# Freezing the base model
						for param in model.parameters():
												_UpperCAmelCase             =    False
						for param in model.get_classifier().parameters():
												_UpperCAmelCase             =    True

						# We normalize the batches of images to be a bit faster.
						_UpperCAmelCase             =    torch.tensor(model.default_cfg['mean']			)[None, :, None, None].to(accelerator.device			)
						_UpperCAmelCase             =    torch.tensor(model.default_cfg['std']			)[None, :, None, None].to(accelerator.device			)

						# Instantiate optimizer
						_UpperCAmelCase             =    torch.optim.Adam(params=model.parameters()				,	lr=lr / 25			)

						# Instantiate learning rate scheduler
						_UpperCAmelCase             =    OneCycleLR(optimizer=__lowerCAmelCase				,	max_lr=__lowerCAmelCase				,	epochs=__lowerCAmelCase				,	steps_per_epoch=len(__lowerCAmelCase			)			)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase             =    accelerator.prepare(
						    __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)

						# Now we train the model
						for epoch in range(__lowerCAmelCase			):
												model.train()
												for step, batch in enumerate(__lowerCAmelCase			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		_UpperCAmelCase             =    {k: v.to(accelerator.device			) for k, v in batch.items()}
																		_UpperCAmelCase             =    (batch['image'] - mean) / std
																		_UpperCAmelCase             =    model(__lowerCAmelCase			)
																		_UpperCAmelCase             =    torch.nn.functional.cross_entropy(__lowerCAmelCase				,	batch['label']			)
																		accelerator.backward(__lowerCAmelCase			)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												_UpperCAmelCase             =    0
												_UpperCAmelCase             =    0
												for _, batch in enumerate(__lowerCAmelCase			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		_UpperCAmelCase             =    {k: v.to(accelerator.device			) for k, v in batch.items()}
																		_UpperCAmelCase             =    (batch['image'] - mean) / std
																		with torch.no_grad():
																								_UpperCAmelCase             =    model(__lowerCAmelCase			)
																		_UpperCAmelCase             =    outputs.argmax(dim=-1			)
																		_UpperCAmelCase      ,				_UpperCAmelCase             =    accelerator.gather_for_metrics((predictions, batch['label'])			)
																		_UpperCAmelCase             =    predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												_UpperCAmelCase             =    accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""			)


def __A					(   )->						Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    argparse.ArgumentParser(description='Simple example of training script.'			)
						parser.add_argument('--data_dir'				,	required=__lowerCAmelCase				,	help='The data folder on disk.'			)
						parser.add_argument(
						    '--mixed_precision'				,	type=__lowerCAmelCase				,	default=__lowerCAmelCase				,	choices=['no', 'fp16', 'bf16', 'fp8']				,	help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'				,	)
						parser.add_argument(
						    '--checkpointing_steps'				,	type=__lowerCAmelCase				,	default=__lowerCAmelCase				,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'				,	)
						parser.add_argument('--cpu'				,	action='store_true'				,	help='If passed, will train on the CPU.'			)
						_UpperCAmelCase             =    parser.parse_args()
						_UpperCAmelCase             =    {'lr': 3E-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
						training_function(__lowerCAmelCase				,	__lowerCAmelCase			)


if __name__ == "__main__":
		main()








import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


_a   			=						'''none'''
_a   			=						'''none'''


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    value.split(__lowerCAmelCase			)
						return len(__lowerCAmelCase			) > 1 and any((len(__lowerCAmelCase			) == 3 for x in split)			)


def __A					(   __lowerCAmelCase			)->						List[str]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												return value
						if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												return float(__lowerCAmelCase			)
						if not isinstance(__lowerCAmelCase				,	__lowerCAmelCase			):
												raise ValueError('Argument value is not a string. Can\'t parse it as float'			)
						_UpperCAmelCase             =    value

						try:
												# Example: 1,000.7
												if "." in sanitized and "," in sanitized:
																		return float(sanitized.replace(','				,	''			)			)
												# 1,000
												if "," in sanitized and _split_thousands(','				,	__lowerCAmelCase			):
																		return float(sanitized.replace(','				,	''			)			)
												# 5,5556
												if "," in sanitized and sanitized.count(','			) == 1 and not _split_thousands(','				,	__lowerCAmelCase			):
																		return float(sanitized.replace(','				,	'.'			)			)
												# 0.0.0.1
												if sanitized.count('.'			) > 1:
																		return float(sanitized.replace('.'				,	''			)			)
												# 0,0,0,1
												if sanitized.count(','			) > 1:
																		return float(sanitized.replace(','				,	''			)			)
												return float(__lowerCAmelCase			)
						except ValueError:
												# Avoid adding the sanitized value in the error message.
												raise ValueError('Unable to convert value to float'			)


def __A					(   __lowerCAmelCase			)->						Any:





						"""simple docstring"""



						if answer is None:
												return None
						try:
												_UpperCAmelCase             =    convert_to_float(__lowerCAmelCase			)
												if isinstance(__lowerCAmelCase				,	__lowerCAmelCase			) and math.isnan(__lowerCAmelCase			):
																		return None
												return value
						except ValueError:
												return answer.lower()


_a   			=						{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2
	UpperCamelCase__			=   3
	UpperCamelCase__			=   4
	UpperCamelCase__			=   5





class __lowerCamelCase				(   enum.Enum):





	"""simple docstring"""






	UpperCamelCase__			=   0
	UpperCamelCase__			=   1
	UpperCamelCase__			=   2





@dataclasses.dataclass
class __lowerCamelCase				:





	"""simple docstring"""






	UpperCamelCase__			=   42
	UpperCamelCase__			=   42
	UpperCamelCase__			=   42



_a   			=						re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)


def __A					(   __lowerCAmelCase			)->						Optional[Any]:





						"""simple docstring"""



						return list(_TOKENIZER.findall(x.lower()			)			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						if operator == _Operator.EQUALS:
												return src == tgt
						elif operator == _Operator.GREATER:
												return src > tgt
						elif operator == _Operator.LESSER:
												return src < tgt
						raise ValueError(F"""Unknown operator: {operator}"""			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						_UpperCAmelCase             =    table['types']
						return _TYPE_CONVERTER[types[column]](__lowerCAmelCase			)


def __A					(   __lowerCAmelCase			)->						str:





						"""simple docstring"""



						return isinstance(__lowerCAmelCase				,	__lowerCAmelCase			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						for cond in conditions:
												_UpperCAmelCase             =    row[cond.column]

												_UpperCAmelCase             =    _parse_value(__lowerCAmelCase				,	cond.column				,	cond.cmp_value			)

												if _is_string(__lowerCAmelCase			) and _is_string(__lowerCAmelCase			):
																		_UpperCAmelCase             =    _normalize_for_match(__lowerCAmelCase			)
																		_UpperCAmelCase             =    _normalize_for_match(__lowerCAmelCase			)

												if not isinstance(__lowerCAmelCase				,	type(__lowerCAmelCase			)			):
																		raise ValueError('Type difference {} != {}'.format(type(__lowerCAmelCase			)				,	type(__lowerCAmelCase			)			)			)

												if not _compare(cond.operator				,	__lowerCAmelCase				,	__lowerCAmelCase			):
																		return False
						return True


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						str:





						"""simple docstring"""



						if not answer_coordinates:
												if aggregation_op == _Aggregation.COUNT:
																		return 0.0
												else:
																		return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
						if aggregation_op == _Aggregation.COUNT:
												return float(len(__lowerCAmelCase			)			)

						# If we have just one answer, if float returns it or try a conversion.
						_UpperCAmelCase             =    [table['rows'][i][j] for (i, j) in answer_coordinates]
						if len(__lowerCAmelCase			) == 1:
												try:
																		return convert_to_float(values[0]			)
												except ValueError as e:
																		if aggregation_op != _Aggregation.NONE:
																								raise e

						if aggregation_op == _Aggregation.NONE:
												return None

						# Other aggregation only support numeric values. Bail out if we have strings.
						if not all((isinstance(__lowerCAmelCase				,	(int, float)			) for v in values)			):
												return None

						if aggregation_op == _Aggregation.SUM:
												return float(sum(__lowerCAmelCase			)			)
						elif aggregation_op == _Aggregation.AVERAGE:
												return sum(__lowerCAmelCase			) / len(__lowerCAmelCase			)
						else:
												raise ValueError(F"""Unknown aggregation: {aggregation_op}"""			)


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    sql_query['agg']
						if aggregation_op_index >= 3:
												_UpperCAmelCase             =    _Aggregation(__lowerCAmelCase			)
						else:
												_UpperCAmelCase             =    _Aggregation.NONE

						_UpperCAmelCase             =    sql_query['sel']
						_UpperCAmelCase             =    [
						    _Condition(__lowerCAmelCase				,	_Operator(__lowerCAmelCase			)				,	__lowerCAmelCase			)
						    for column, operator, cmp_value in zip(
						        sql_query['conds']['column_index']				,	sql_query['conds']['operator_index']				,	sql_query['conds']['condition']			)
						]

						_UpperCAmelCase             =    []
						for row in range(len(table['rows']			)			):
												if _respect_conditions(__lowerCAmelCase				,	table['rows'][row]				,	__lowerCAmelCase			):
																		indices.append((row, target_column)			)

						if not indices:
												return [], aggregation_op

						if len(__lowerCAmelCase			) == 1:
												return indices, aggregation_op

						# Parsing of MIN/MAX.
						if aggregation_op_index in (1, 2):
												_UpperCAmelCase             =    {2: min, 1: max}
												_UpperCAmelCase             =    [(table['rows'][i][j], index) for index, (i, j) in enumerate(__lowerCAmelCase			)]
												_UpperCAmelCase             =    functools.reduce(operators[sql_query['agg']]				,	__lowerCAmelCase			)

												_UpperCAmelCase             =    [indices[reduced[1]]]
												return ret, _Aggregation.NONE

						return indices, aggregation_op


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Dict:





						"""simple docstring"""



						if float_answer is not None:
												return [str(__lowerCAmelCase			)]
						return [str(table['real_rows'][r][c]			) for r, c in answer_coordinates]


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase			)->						List:





						"""simple docstring"""



						_UpperCAmelCase      ,				_UpperCAmelCase             =    _get_answer_coordinates(__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_float_answer(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						_UpperCAmelCase             =    _get_answer_text(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)
						# keep the original data the same with TaPas
						if len(__lowerCAmelCase			) == 0:
												_UpperCAmelCase             =    [EMPTY_ANSWER]
						return answer_text
