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 lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







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


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=None				,				__lowercase=None) ->			List[str]:
        __UpperCamelCase  :int    						=	file_names
        __UpperCamelCase  :Dict    						=	image_transform
        __UpperCamelCase  :List[str]    						=	label_to_id







    def __len__(			self) ->			Tuple:
        return len(self.file_names)







    def __getitem__(			self				,				__lowercase) ->			Tuple:
        __UpperCamelCase  :Union[str, Any]    						=	self.file_names[idx]
        __UpperCamelCase  :str    						=	PIL.Image.open(__lowercase)
        __UpperCamelCase  :Any    						=	raw_image.convert('''RGB''')
        if self.image_transform is not None:
            __UpperCamelCase  :int    						=	self.image_transform(__lowercase)
        __UpperCamelCase  :Optional[int]    						=	extract_label(__lowercase)
        if self.label_to_id is not None:
            __UpperCamelCase  :Tuple    						=	self.label_to_id[label]
        return {"image": image, "label": label}



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if args.with_tracking:
        __UpperCamelCase  :List[str]    						=	Accelerator(
            cpu=args.cpu						,    mixed_precision=args.mixed_precision						,    log_with='''all'''						,    project_dir=args.project_dir			)
    else:
        __UpperCamelCase  :Dict    						=	Accelerator(cpu=args.cpu						,    mixed_precision=args.mixed_precision			)

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

    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps						,    '''isdigit'''			):
        if args.checkpointing_steps == "epoch":
            __UpperCamelCase  :str    						=	args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCamelCase  :Union[str, Any]    						=	int(args.checkpointing_steps			)
        else:
            raise ValueError(
                f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
    else:
        __UpperCamelCase  :str    						=	None

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __UpperCamelCase  :Optional[Any]    						=	os.path.split(SCREAMING_SNAKE_CASE			)[-1].split('''.'''			)[0]
        accelerator.init_trackers(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Grab all the image filenames
    __UpperCamelCase  :Optional[Any]    						=	[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
    __UpperCamelCase  :str    						=	[extract_label(SCREAMING_SNAKE_CASE			) for fname in file_names]
    __UpperCamelCase  :Optional[Any]    						=	list(set(SCREAMING_SNAKE_CASE			)			)
    id_to_label.sort()
    __UpperCamelCase  :Dict    						=	{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
    __UpperCamelCase  :int    						=	np.random.permutation(len(SCREAMING_SNAKE_CASE			)			)
    __UpperCamelCase  :Optional[Any]    						=	int(0.8 * len(SCREAMING_SNAKE_CASE			)			)
    __UpperCamelCase  :str    						=	random_perm[:cut]
    __UpperCamelCase  :Any    						=	random_perm[cut:]

    # For training we use a simple RandomResizedCrop
    __UpperCamelCase  :str    						=	Compose([RandomResizedCrop(SCREAMING_SNAKE_CASE						,    scale=(0.5, 1.0)			), ToTensor()]			)
    __UpperCamelCase  :Optional[Any]    						=	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
    __UpperCamelCase  :int    						=	Compose([Resize(SCREAMING_SNAKE_CASE			), ToTensor()]			)
    __UpperCamelCase  :Optional[Any]    						=	PetsDataset([file_names[i] for i in eval_split]						,    image_transform=SCREAMING_SNAKE_CASE						,    label_to_id=SCREAMING_SNAKE_CASE			)

    # Instantiate dataloaders.
    __UpperCamelCase  :Union[str, Any]    						=	DataLoader(SCREAMING_SNAKE_CASE						,    shuffle=SCREAMING_SNAKE_CASE						,    batch_size=SCREAMING_SNAKE_CASE						,    num_workers=4			)
    __UpperCamelCase  :List[str]    						=	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)
    __UpperCamelCase  :Tuple    						=	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).
    __UpperCamelCase  :List[str]    						=	model.to(accelerator.device			)

    # Freezing the base model
    for param in model.parameters():
        __UpperCamelCase  :str    						=	False
    for param in model.get_classifier().parameters():
        __UpperCamelCase  :Union[str, Any]    						=	True

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

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

    # Instantiate learning rate scheduler
    __UpperCamelCase  :str    						=	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.
    __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :str    						=	accelerator.prepare(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase  :Dict    						=	0
    # We also need to keep track of the starting epoch so files are named properly
    __UpperCamelCase  :List[Any]    						=	0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""			)
            accelerator.load_state(args.resume_from_checkpoint			)
            __UpperCamelCase  :Optional[Any]    						=	os.path.basename(args.resume_from_checkpoint			)
        else:
            # Get the most recent checkpoint
            __UpperCamelCase  :str    						=	[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
            dirs.sort(key=os.path.getctime			)
            __UpperCamelCase  :Tuple    						=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCamelCase  :Dict    						=	os.path.splitext(SCREAMING_SNAKE_CASE			)[0]

        if "epoch" in training_difference:
            __UpperCamelCase  :Dict    						=	int(training_difference.replace('''epoch_'''						,    ''''''			)			) + 1
            __UpperCamelCase  :Optional[int]    						=	None
        else:
            __UpperCamelCase  :Dict    						=	int(training_difference.replace('''step_'''						,    ''''''			)			)
            __UpperCamelCase  :List[Any]    						=	resume_step // len(SCREAMING_SNAKE_CASE			)
            resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE			)

    # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        model.train()
        if args.with_tracking:
            __UpperCamelCase  :Dict    						=	0
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __UpperCamelCase  :List[str]    						=	accelerator.skip_first_batches(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCamelCase  :Tuple    						=	train_dataloader
        for batch in active_dataloader:
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            __UpperCamelCase  :str    						=	{k: v.to(accelerator.device			) for k, v in batch.items()}
            __UpperCamelCase  :int    						=	(batch['''image'''] - mean) / std
            __UpperCamelCase  :Optional[Any]    						=	model(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :List[Any]    						=	torch.nn.functional.cross_entropy(SCREAMING_SNAKE_CASE						,    batch['''label''']			)
            # We keep track of the loss at each epoch
            if args.with_tracking:
                total_loss += loss.detach().float()
            accelerator.backward(SCREAMING_SNAKE_CASE			)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()
            overall_step += 1
            if isinstance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
                __UpperCamelCase  :List[Any]    						=	f"""step_{overall_step}"""
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCamelCase  :int    						=	os.path.join(args.output_dir						,    SCREAMING_SNAKE_CASE			)
                    accelerator.save_state(SCREAMING_SNAKE_CASE			)
        model.eval()
        __UpperCamelCase  :Optional[Any]    						=	0
        __UpperCamelCase  :int    						=	0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            __UpperCamelCase  :Union[str, Any]    						=	{k: v.to(accelerator.device			) for k, v in batch.items()}
            __UpperCamelCase  :List[Any]    						=	(batch['''image'''] - mean) / std
            with torch.no_grad():
                __UpperCamelCase  :Any    						=	model(SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[Any]    						=	outputs.argmax(dim=-1			)
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	accelerator.gather_for_metrics((predictions, batch['''label'''])			)
            __UpperCamelCase  :List[str]    						=	predictions == references
            num_elems += accurate_preds.shape[0]
            accurate += accurate_preds.long().sum()

        __UpperCamelCase  :Optional[Any]    						=	accurate.item() / num_elems
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}: {100 * eval_metric:.2f}"""			)
        if args.with_tracking:
            accelerator.log(
                {
                    '''accuracy''': 100 * eval_metric,
                    '''train_loss''': total_loss.item() / len(SCREAMING_SNAKE_CASE			),
                    '''epoch''': epoch,
                }						,    step=SCREAMING_SNAKE_CASE						,    )
        if checkpointing_steps == "epoch":
            __UpperCamelCase  :str    						=	f"""epoch_{epoch}"""
            if args.output_dir is not None:
                __UpperCamelCase  :Union[str, Any]    						=	os.path.join(args.output_dir						,    SCREAMING_SNAKE_CASE			)
            accelerator.save_state(SCREAMING_SNAKE_CASE			)

    if args.with_tracking:
        accelerator.end_training()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	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('''--fp16'''						,    action='''store_true'''						,    help='''If passed, will use FP16 training.'''			)
    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('''--cpu'''						,    action='''store_true'''						,    help='''If passed, will train on the CPU.'''			)
    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(
        '''--output_dir'''						,    type=SCREAMING_SNAKE_CASE						,    default='''.'''						,    help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''						,    )
    parser.add_argument(
        '''--resume_from_checkpoint'''						,    type=SCREAMING_SNAKE_CASE						,    default=SCREAMING_SNAKE_CASE						,    help='''If the training should continue from a checkpoint folder.'''						,    )
    parser.add_argument(
        '''--with_tracking'''						,    action='''store_true'''						,    help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''						,    )
    parser.add_argument(
        '''--project_dir'''						,    type=SCREAMING_SNAKE_CASE						,    default='''logs'''						,    help='''Location on where to store experiment tracking logs` and relevent project information'''						,    )
    __UpperCamelCase  :List[Any]    						=	parser.parse_args()
    __UpperCamelCase  :List[Any]    						=	{'''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()






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin


@require_jieba
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   CpmAntTokenizer
    a__			:		Optional[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			Any:
        super().setUp()

        __UpperCamelCase  :Optional[int]    						=	[
            '''<d>''',
            '''</d>''',
            '''<s>''',
            '''</s>''',
            '''</_>''',
            '''<unk>''',
            '''<pad>''',
            '''</n>''',
            '''我''',
            '''是''',
            '''C''',
            '''P''',
            '''M''',
            '''A''',
            '''n''',
            '''t''',
        ]
        __UpperCamelCase  :Optional[int]    						=	os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file'''])
        with open(self.vocab_file				,				'''w'''				,				encoding='''utf-8''') as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]))







    @tooslow
    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Tuple    						=	CpmAntTokenizer.from_pretrained('''openbmb/cpm-ant-10b''')
        __UpperCamelCase  :Dict    						=	'''今天天气真好！'''
        __UpperCamelCase  :Tuple    						=	['''今天''', '''天气''', '''真''', '''好''', '''！''']
        __UpperCamelCase  :Optional[Any]    						=	tokenizer.tokenize(__lowercase)
        self.assertListEqual(__lowercase				,				__lowercase)
        __UpperCamelCase  :int    						=	'''今天天气真好！'''
        __UpperCamelCase  :List[str]    						=	[tokenizer.bos_token] + tokens

        __UpperCamelCase  :List[str]    						=	[6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)				,				__lowercase)

        __UpperCamelCase  :Dict    						=	tokenizer.decode(__lowercase)
        self.assertEqual(__lowercase				,				__lowercase)





