
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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   fname.split(os.path.sep							)[-1]
	return re.search(r'^(.*)_\d+\.jpg$'			,							__SCREAMING_SNAKE_CASE							).groups()[0]







class 	A_       (      __lowerCamelCase       ):




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   snake_case=None		,   snake_case=None	):
		lowercase					  =   file_names
		lowercase					  =   image_transform
		lowercase					  =   label_to_id





	def __len__(	self	):
		return len(self.file_names	)





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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							):
	# Initialize accelerator
	lowercase					  =   Accelerator(cpu=args.cpu			,							mixed_precision=args.mixed_precision							)

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

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

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

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

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

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

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

	# Instantiate dataloaders.
	lowercase					  =   DataLoader(__SCREAMING_SNAKE_CASE			,							shuffle=__SCREAMING_SNAKE_CASE			,							batch_size=__SCREAMING_SNAKE_CASE			,							num_workers=4							)
	lowercase					  =   DataLoader(__SCREAMING_SNAKE_CASE			,							shuffle=__SCREAMING_SNAKE_CASE			,							batch_size=__SCREAMING_SNAKE_CASE			,							num_workers=4							)

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

	# 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).
	lowercase					  =   model.to(accelerator.device							)

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

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

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

	# Instantiate learning rate scheduler
	lowercase					  =   OneCycleLR(optimizer=__SCREAMING_SNAKE_CASE			,							max_lr=__SCREAMING_SNAKE_CASE			,							epochs=__SCREAMING_SNAKE_CASE			,							steps_per_epoch=len(__SCREAMING_SNAKE_CASE							)							)

	# 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.
	lowercase  , lowercase  , lowercase  , lowercase  , lowercase					  =   accelerator.prepare(
	    __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)

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

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

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






def 							UpperCAmelCase_		(							):
	lowercase					  =   argparse.ArgumentParser(description='Simple example of training script.'							)
	parser.add_argument('--data_dir'			,							required=__SCREAMING_SNAKE_CASE			,							help='The data folder on disk.'							)
	parser.add_argument(
	    '--mixed_precision'			,							type=__SCREAMING_SNAKE_CASE			,							default=__SCREAMING_SNAKE_CASE			,							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=__SCREAMING_SNAKE_CASE			,							default=__SCREAMING_SNAKE_CASE			,							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.'							)
	lowercase					  =   parser.parse_args()
	lowercase					  =   {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
	training_function(__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	main()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     				=			{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPRContextEncoderTokenizerFast''',
	    '''DPRQuestionEncoderTokenizerFast''',
	    '''DPRReaderTokenizerFast''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DPRContextEncoder''',
	    '''DPRPretrainedContextEncoder''',
	    '''DPRPreTrainedModel''',
	    '''DPRPretrainedQuestionEncoder''',
	    '''DPRPretrainedReader''',
	    '''DPRQuestionEncoder''',
	    '''DPRReader''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase     				=			[
	    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDPRContextEncoder''',
	    '''TFDPRPretrainedContextEncoder''',
	    '''TFDPRPretrainedQuestionEncoder''',
	    '''TFDPRPretrainedReader''',
	    '''TFDPRQuestionEncoder''',
	    '''TFDPRReader''',
	]


if TYPE_CHECKING:
	from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
	from .tokenization_dpr import (
	    DPRContextEncoderTokenizer,
	    DPRQuestionEncoderTokenizer,
	    DPRReaderOutput,
	    DPRReaderTokenizer,
	)

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_dpr_fast import (
		    DPRContextEncoderTokenizerFast,
		    DPRQuestionEncoderTokenizerFast,
		    DPRReaderTokenizerFast,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dpr import (
		    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DPRContextEncoder,
		    DPRPretrainedContextEncoder,
		    DPRPreTrainedModel,
		    DPRPretrainedQuestionEncoder,
		    DPRPretrainedReader,
		    DPRQuestionEncoder,
		    DPRReader,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_dpr import (
		    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDPRContextEncoder,
		    TFDPRPretrainedContextEncoder,
		    TFDPRPretrainedQuestionEncoder,
		    TFDPRPretrainedReader,
		    TFDPRQuestionEncoder,
		    TFDPRReader,
		)

else:
	import sys

	UpperCAmelCase     				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

