
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase      =1_6
lowerCamelCase      =3_2


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ = 1_6 ,		UpperCamelCase__ = "bert-base-cased"		):
      UpperCamelCase__    :   Any					=				AutoTokenizer.from_pretrained(UpperCamelCase__		)
      UpperCamelCase__    :   Tuple					=				load_dataset('''glue''' ,		'''mrpc'''		)

      def tokenize_function(UpperCamelCase__		):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase__    :   Union[str, Any]					=				tokenizer(examples['''sentence1'''] ,		examples['''sentence2'''] ,		truncation=UpperCamelCase__ ,		max_length=UpperCamelCase__		)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCamelCase__    :   Tuple					=				datasets.map(
          UpperCamelCase__ ,		batched=UpperCamelCase__ ,		remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,		load_from_cache_file=UpperCamelCase__		)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      UpperCamelCase__    :   List[Any]					=				tokenized_datasets.rename_column('''label''' ,		'''labels'''		)

      def collate_fn(UpperCamelCase__		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(UpperCamelCase__ ,		padding='''max_length''' ,		max_length=1_2_8 ,		return_tensors='''pt'''		)
            return tokenizer.pad(UpperCamelCase__ ,		padding='''longest''' ,		return_tensors='''pt'''		)

      # Instantiate dataloaders.
      UpperCamelCase__    :   List[Any]					=				DataLoader(
          tokenized_datasets['''train'''] ,		shuffle=UpperCamelCase__ ,		collate_fn=UpperCamelCase__ ,		batch_size=UpperCamelCase__		)
      UpperCamelCase__    :   Tuple					=				DataLoader(
          tokenized_datasets['''validation'''] ,		shuffle=UpperCamelCase__ ,		collate_fn=UpperCamelCase__ ,		batch_size=UpperCamelCase__		)

      return train_dataloader, eval_dataloader


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      model.eval()
      UpperCamelCase__    :   Union[str, Any]					=				0
      for step, batch in enumerate(UpperCamelCase__		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            with torch.no_grad():
                  UpperCamelCase__    :   str					=				model(**UpperCamelCase__		)
            UpperCamelCase__    :   int					=				outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				accelerator.gather(
                (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(UpperCamelCase__		) - 1:
                        UpperCamelCase__    :   Union[str, Any]					=				predictions[: len(eval_dataloader.dataset		) - samples_seen]
                        UpperCamelCase__    :   Any					=				references[: len(eval_dataloader.dataset		) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=UpperCamelCase__ ,		references=UpperCamelCase__ ,		)

      UpperCamelCase__    :   int					=				metric.compute()
      return eval_metric["accuracy"]


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      # Initialize accelerator
      UpperCamelCase__    :   int					=				Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase__    :   Any					=				config['''lr''']
      UpperCamelCase__    :   Tuple					=				int(config['''num_epochs''']		)
      UpperCamelCase__    :   Optional[int]					=				int(config['''seed''']		)
      UpperCamelCase__    :   List[Any]					=				int(config['''batch_size''']		)
      UpperCamelCase__    :   Any					=				args.model_name_or_path

      set_seed(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Dict					=				get_dataloaders(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase__    :   Any					=				AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__ ,		return_dict=UpperCamelCase__		)

      # Instantiate optimizer
      UpperCamelCase__    :   Any					=				(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCamelCase__    :   Dict					=				optimizer_cls(params=model.parameters() ,		lr=UpperCamelCase__		)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCamelCase__    :   Dict					=				accelerator.state.deepspeed_plugin.deepspeed_config[
                '''gradient_accumulation_steps'''
            ]
      else:
            UpperCamelCase__    :   str					=				1
      UpperCamelCase__    :   int					=				(len(UpperCamelCase__		) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCamelCase__    :   Tuple					=				get_linear_schedule_with_warmup(
                optimizer=UpperCamelCase__ ,		num_warmup_steps=0 ,		num_training_steps=UpperCamelCase__ ,		)
      else:
            UpperCamelCase__    :   str					=				DummyScheduler(UpperCamelCase__ ,		total_num_steps=UpperCamelCase__ ,		warmup_num_steps=0		)

      # 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__    :   Dict					=				accelerator.prepare(
          UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      # We need to keep track of how many total steps we have iterated over
      UpperCamelCase__    :   Tuple					=				0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCamelCase__    :   int					=				0
      UpperCamelCase__    :   int					=				evaluate.load('''glue''' ,		'''mrpc'''		)
      UpperCamelCase__    :   Dict					=				num_epochs

      if args.partial_train_epoch is not None:
            UpperCamelCase__    :   Any					=				args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint		)
            UpperCamelCase__    :   Optional[Any]					=				args.resume_from_checkpoint.split('''epoch_'''		)[1]
            UpperCamelCase__    :   Optional[int]					=				''''''
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            UpperCamelCase__    :   Union[str, Any]					=				int(UpperCamelCase__		) + 1
            UpperCamelCase__    :   List[Any]					=				evaluation_loop(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
            accelerator.print('''resumed checkpoint performance:''' ,		UpperCamelCase__		)
            accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' ,		lr_scheduler.get_lr()[0]		)
            accelerator.print('''resumed optimizers\'s lr:''' ,		optimizer.param_groups[0]['''lr''']		)
            with open(os.path.join(args.output_dir ,		f'''state_{starting_epoch-1}.json'''		) ,		'''r'''		) as f:
                  UpperCamelCase__    :   Union[str, Any]					=				json.load(UpperCamelCase__		)
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      UpperCamelCase__    :   Optional[Any]					=				{}
      for epoch in range(UpperCamelCase__ ,		UpperCamelCase__		):
            model.train()
            for step, batch in enumerate(UpperCamelCase__		):
                  UpperCamelCase__    :   Dict					=				model(**UpperCamelCase__		)
                  UpperCamelCase__    :   List[Any]					=				outputs.loss
                  UpperCamelCase__    :   Optional[Any]					=				loss / gradient_accumulation_steps
                  accelerator.backward(UpperCamelCase__		)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            UpperCamelCase__    :   List[str]					=				f'''epoch_{epoch}'''
            UpperCamelCase__    :   Optional[Any]					=				os.path.join(args.output_dir ,		UpperCamelCase__		)
            accelerator.save_state(UpperCamelCase__		)
            UpperCamelCase__    :   Any					=				evaluation_loop(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
            UpperCamelCase__    :   Any					=				accuracy
            UpperCamelCase__    :   Optional[Any]					=				lr_scheduler.get_lr()[0]
            UpperCamelCase__    :   Any					=				optimizer.param_groups[0]['''lr''']
            UpperCamelCase__    :   Any					=				epoch
            UpperCamelCase__    :   str					=				overall_step
            accelerator.print(f'''epoch {epoch}:''' ,		UpperCamelCase__		)

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir ,		f'''state_{epoch}.json'''		) ,		'''w'''		) as f:
                        json.dump(UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Optional[int]					=				argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
      parser.add_argument(
          '''--model_name_or_path''' ,		type=UpperCamelCase__ ,		default='''bert-base-cased''' ,		help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,		required=UpperCamelCase__ ,		)
      parser.add_argument(
          '''--output_dir''' ,		type=UpperCamelCase__ ,		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=UpperCamelCase__ ,		default=UpperCamelCase__ ,		help='''If the training should continue from a checkpoint folder.''' ,		)
      parser.add_argument(
          '''--partial_train_epoch''' ,		type=UpperCamelCase__ ,		default=UpperCamelCase__ ,		help='''If passed, the training will stop after this number of epochs.''' ,		)
      parser.add_argument(
          '''--num_epochs''' ,		type=UpperCamelCase__ ,		default=2 ,		help='''Number of train epochs.''' ,		)
      UpperCamelCase__    :   Optional[int]					=				parser.parse_args()
      UpperCamelCase__    :   Optional[int]					=				{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

      training_function(UpperCamelCase__ ,		UpperCamelCase__		)


if __name__ == "__main__":
    main()




import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # This defines a "chinese character" as anything in the CJK Unicode block:
      #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
      #
      # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
      # despite its name. The modern Korean Hangul alphabet is a different block,
      # as is Japanese Hiragana and Katakana. Those alphabets are used to write
      # space-separated words, so they are not treated specially and handled
      # like the all of the other languages.
      if (
          (cp >= 0x4e_00 and cp <= 0x9f_ff)
          or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
          or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
          or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
          or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
          or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
          or (cp >= 0xf9_00 and cp <= 0xfa_ff)
          or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
      ):  #
            return True

      return False


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # word like '180' or '身高' or '神'
      for char in word:
            UpperCamelCase__    :   Optional[Any]					=				ord(UpperCamelCase__		)
            if not _is_chinese_char(UpperCamelCase__		):
                  return 0
      return 1


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Dict					=				set()

      for token in tokens:
            UpperCamelCase__    :   Optional[int]					=				len(UpperCamelCase__		) > 1 and is_chinese(UpperCamelCase__		)
            if chinese_word:
                  word_set.add(UpperCamelCase__		)
      UpperCamelCase__    :   List[Any]					=				list(UpperCamelCase__		)
      return word_list


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if not chinese_word_set:
            return bert_tokens
      UpperCamelCase__    :   str					=				max([len(UpperCamelCase__		) for w in chinese_word_set]		)

      UpperCamelCase__    :   Optional[int]					=				bert_tokens
      UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				0, len(UpperCamelCase__		)
      while start < end:
            UpperCamelCase__    :   Any					=				True
            if is_chinese(bert_word[start]		):
                  UpperCamelCase__    :   Any					=				min(end - start ,		UpperCamelCase__		)
                  for i in range(UpperCamelCase__ ,		1 ,		-1		):
                        UpperCamelCase__    :   List[Any]					=				''''''.join(bert_word[start : start + i]		)
                        if whole_word in chinese_word_set:
                              for j in range(start + 1 ,		start + i		):
                                    UpperCamelCase__    :   Optional[Any]					=				'''##''' + bert_word[j]
                              UpperCamelCase__    :   Dict					=				start + i
                              UpperCamelCase__    :   List[str]					=				False
                              break
            if single_word:
                  start += 1
      return bert_word


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				[]

      for i in range(0 ,		len(UpperCamelCase__		) ,		1_0_0		):
            UpperCamelCase__    :   Optional[Any]					=				ltp_tokenizer.pipeline(lines[i : i + 1_0_0] ,		tasks=['''cws''']		).cws
            UpperCamelCase__    :   List[str]					=				[get_chinese_word(UpperCamelCase__		) for r in res]
            ltp_res.extend(UpperCamelCase__		)
      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

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

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

      assert len(UpperCamelCase__		) == len(UpperCamelCase__		)

      return ref_ids


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
      # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
      with open(args.file_name ,		'''r''' ,		encoding='''utf-8'''		) as f:
            UpperCamelCase__    :   List[Any]					=				f.readlines()
      UpperCamelCase__    :   Tuple					=				[line.strip() for line in data if len(UpperCamelCase__		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      UpperCamelCase__    :   int					=				LTP(args.ltp		)  # faster in GPU device
      UpperCamelCase__    :   int					=				BertTokenizer.from_pretrained(args.bert		)

      UpperCamelCase__    :   Dict					=				prepare_ref(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

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


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

    lowerCamelCase      =parser.parse_args()
    main(args)


