


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       (		__A			)						->					Dict:



       '''simple docstring'''





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


class     lowercase__  (					__SCREAMING_SNAKE_CASE		):






  def __init__(					self      :					int		,						_lowercase      :					List[Any]		,						_lowercase      :					Dict=None		,						_lowercase      :					Optional[int]=None ):




         """simple docstring"""







         UpperCAmelCase__										=     file_names
         UpperCAmelCase__										=     image_transform
         UpperCAmelCase__										=     label_to_id






  def __len__(					self      :					List[str] ):




         """simple docstring"""







         return len(self.file_names )






  def __getitem__(					self      :					List[str]		,						_lowercase      :					Any ):




         """simple docstring"""







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





def __UpperCAmelCase       (		__A ,						__A			)						->					int:



       '''simple docstring'''





       if args.with_tracking:
              UpperCAmelCase__										=     Accelerator(
                  cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with="all" ,						project_dir=args.project_dir			)
       else:
              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(__A ,						(list, tuple)			):
              UpperCAmelCase__										=     (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__										=     args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     UpperCAmelCase__										=     int(args.checkpointing_steps			)
              else:
                     raise ValueError(
                         F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""			)
       else:
              UpperCAmelCase__										=     None

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

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

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

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

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

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

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

       # Instantiate dataloaders.
       UpperCAmelCase__										=     DataLoader(__A ,						shuffle=__A ,						batch_size=__A ,						num_workers=4			)
       UpperCAmelCase__										=     DataLoader(__A ,						shuffle=__A ,						batch_size=__A ,						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=__A ,						num_classes=len(__A			)			)

       # 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 / 2_5			)

       # Instantiate learning rate scheduler
       UpperCAmelCase__										=     OneCycleLR(optimizer=__A ,						max_lr=__A ,						epochs=__A ,						steps_per_epoch=len(__A			)			)

       # 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(
           __A ,						__A ,						__A ,						__A ,						__A			)
       # We need to keep track of how many total steps we have iterated over
       UpperCAmelCase__										=     0
       # We also need to keep track of the starting epoch so files are named properly
       UpperCAmelCase__										=     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__										=     os.path.basename(args.resume_from_checkpoint			)
              else:
                     # Get the most recent checkpoint
                     UpperCAmelCase__										=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
                     dirs.sort(key=os.path.getctime			)
                     UpperCAmelCase__										=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              UpperCAmelCase__										=     os.path.splitext(__A			)[0]

              if "epoch" in training_difference:
                     UpperCAmelCase__										=     int(training_difference.replace("epoch_" ,						""			)			) + 1
                     UpperCAmelCase__										=     None
              else:
                     UpperCAmelCase__										=     int(training_difference.replace("step_" ,						""			)			)
                     UpperCAmelCase__										=     resume_step // len(__A			)
                     resume_step -= starting_epoch * len(__A			)

    # Now we train the model
       for epoch in range(__A ,						__A			):
              model.train()
              if args.with_tracking:
                     UpperCAmelCase__										=     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__										=     accelerator.skip_first_batches(__A ,						__A			)
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     UpperCAmelCase__										=     train_dataloader
              for batch in active_dataloader:
                     # 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(__A			)
                     UpperCAmelCase__										=     torch.nn.functional.cross_entropy(__A ,						batch["label"]			)
                     # We keep track of the loss at each epoch
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     accelerator.backward(__A			)
                     optimizer.step()
                     lr_scheduler.step()
                     optimizer.zero_grad()
                     overall_step += 1
                     if isinstance(__A ,						__A			):
                            UpperCAmelCase__										=     F"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          UpperCAmelCase__										=     os.path.join(args.output_dir ,						__A			)
                                   accelerator.save_state(__A			)
              model.eval()
              UpperCAmelCase__										=     0
              UpperCAmelCase__										=     0
              for step, batch in enumerate(__A			):
                     # 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(__A			)
                     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}: {1_0_0 * eval_metric:.2f}"""			)
              if args.with_tracking:
                     accelerator.log(
                         {
                             "accuracy": 1_0_0 * eval_metric,
                             "train_loss": total_loss.item() / len(__A			),
                             "epoch": epoch,
                         } ,						step=__A ,						)
              if checkpointing_steps == "epoch":
                     UpperCAmelCase__										=     F"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            UpperCAmelCase__										=     os.path.join(args.output_dir ,						__A			)
                     accelerator.save_state(__A			)

       if args.with_tracking:
              accelerator.end_training()





def __UpperCAmelCase       (		)						->					Any:



       '''simple docstring'''





       UpperCAmelCase__										=     argparse.ArgumentParser(description="Simple example of training script."			)
       parser.add_argument("--data_dir" ,						required=__A ,						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=__A ,						default=__A ,						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=__A ,						default=__A ,						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=__A ,						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=__A ,						default=__A ,						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=__A ,						default="logs" ,						help="Location on where to store experiment tracking logs` and relevent project information" ,						)
       UpperCAmelCase__										=     parser.parse_args()
       UpperCAmelCase__										=     {"lr": 3E-2, "num_epochs": 3, "seed": 4_2, "batch_size": 6_4, "image_size": 2_2_4}
       training_function(__A ,						__A			)


if __name__ == "__main__":
    main()









import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





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



       '''simple docstring'''





       if (
           (cp >= 0x4_E_0_0 and cp <= 0x9_F_F_F)
           or (cp >= 0x3_4_0_0 and cp <= 0x4_D_B_F)  #
           or (cp >= 0x2_0_0_0_0 and cp <= 0x2_A_6_D_F)  #
           or (cp >= 0x2_A_7_0_0 and cp <= 0x2_B_7_3_F)  #
           or (cp >= 0x2_B_7_4_0 and cp <= 0x2_B_8_1_F)  #
           or (cp >= 0x2_B_8_2_0 and cp <= 0x2_C_E_A_F)  #
           or (cp >= 0xF_9_0_0 and cp <= 0xF_A_F_F)
           or (cp >= 0x2_F_8_0_0 and cp <= 0x2_F_A_1_F)  #
       ):  #
              return True

       return False





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



       '''simple docstring'''





       for char in word:
              UpperCAmelCase__										=     ord(__A			)
              if not _is_chinese_char(__A			):
                     return 0
       return 1





def __UpperCAmelCase       (		__A			)						->					str:



       '''simple docstring'''





       UpperCAmelCase__										=     set()

       for token in tokens:
              UpperCAmelCase__										=     len(__A			) > 1 and is_chinese(__A			)
              if chinese_word:
                     word_set.add(__A			)
       UpperCAmelCase__										=     list(__A			)
       return word_list





def __UpperCAmelCase       (		__A ,						__A			)						->					Optional[Any]:



       '''simple docstring'''





       if not chinese_word_set:
              return bert_tokens
       UpperCAmelCase__										=     max([len(__A			) for w in chinese_word_set]			)

       UpperCAmelCase__										=     bert_tokens
       UpperCAmelCase__						,			UpperCAmelCase__										=     0, len(__A			)
       while start < end:
              UpperCAmelCase__										=     True
              if is_chinese(bert_word[start]			):
                     UpperCAmelCase__										=     min(end - start ,						__A			)
                     for i in range(__A ,						1 ,						-1			):
                            UpperCAmelCase__										=     "".join(bert_word[start : start + i]			)
                            if whole_word in chinese_word_set:
                                   for j in range(start + 1 ,						start + i			):
                                          UpperCAmelCase__										=     "##" + bert_word[j]
                                   UpperCAmelCase__										=     start + i
                                   UpperCAmelCase__										=     False
                                   break
              if single_word:
                     start += 1
       return bert_word





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					List[Any]:



       '''simple docstring'''





       UpperCAmelCase__										=     []

       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     ltp_tokenizer.seg(lines[i : i + 1_0_0]			)[0]
              UpperCAmelCase__										=     [get_chinese_word(__A			) for r in res]
              ltp_res.extend(__A			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for i in range(0 ,						len(__A			) ,						1_0_0			):
              UpperCAmelCase__										=     bert_tokenizer(lines[i : i + 1_0_0] ,						add_special_tokens=__A ,						truncation=__A ,						max_length=5_1_2			)
              bert_res.extend(res["input_ids"]			)
       assert len(__A			) == len(__A			)

       UpperCAmelCase__										=     []
       for input_ids, chinese_word in zip(__A ,						__A			):
              UpperCAmelCase__										=     []
              for id in input_ids:
                     UpperCAmelCase__										=     bert_tokenizer._convert_id_to_token(__A			)
                     input_tokens.append(__A			)
              UpperCAmelCase__										=     add_sub_symbol(__A ,						__A			)
              UpperCAmelCase__										=     []
              # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
              for i, token in enumerate(__A			):
                     if token[:2] == "##":
                            UpperCAmelCase__										=     token[2:]
                            # save chinese tokens' pos
                            if len(__A			) == 1 and _is_chinese_char(ord(__A			)			):
                                   ref_id.append(__A			)
              ref_ids.append(__A			)

       assert len(__A			) == len(__A			)

       return ref_ids





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



       '''simple docstring'''





       with open(args.file_name ,						"r" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     f.readlines()
       UpperCAmelCase__										=     [line.strip() for line in data if len(__A			) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
       UpperCAmelCase__										=     LTP(args.ltp			)  # faster in GPU device
       UpperCAmelCase__										=     BertTokenizer.from_pretrained(args.bert			)

       UpperCAmelCase__										=     prepare_ref(__A ,						__A ,						__A			)

       with open(args.save_path ,						"w" ,						encoding="utf-8"			) as f:
              UpperCAmelCase__										=     [json.dumps(__A			) + "\n" for ref in ref_ids]
              f.writelines(__A			)


if __name__ == "__main__":
    A      = argparse.ArgumentParser(description="prepare_chinese_ref")
    parser.add_argument(
        "--file_name",
        type=str,
        default="./resources/chinese-demo.txt",
        help="file need process, same as training data in lm",
    )
    parser.add_argument(
        "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
    )
    parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
    parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

    A      = parser.parse_args()
    main(args)





