



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
	import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase      :							Optional[Any]							       =logging.get_logger(__name__)

UpperCAmelCase      :							Optional[Any]							       ={'''vocab_file''': '''vocab.txt'''}

UpperCAmelCase      :							Optional[int]							       ={
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

UpperCAmelCase      :							Dict							       ={
    '''openbmb/cpm-ant-10b''': 1024,
}



def       _lowerCAmelCase			(_lowerCAmelCase):
					UpperCamelCase_						     =      collections.OrderedDict()
					with open(__UpperCamelCase      ,		"r"      ,		encoding="utf-8") as reader:
										UpperCamelCase_						     =      reader.readlines()
					for index, token in enumerate(__UpperCamelCase):
										UpperCamelCase_						     =      token.rstrip("\n")
										UpperCamelCase_						     =      index
					return vocab


class 					_lowercase      (lowerCamelCase_					):
				'''simple docstring'''







				def __init__(       self   ,       snake_case__   ,       snake_case__="<unk>"   ,       snake_case__=200			):

									'''simple docstring'''


									UpperCamelCase_						     =      vocab
									UpperCamelCase_						     =      unk_token
									UpperCamelCase_						     =      max_input_chars_per_word

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      list(_lowercase			)
									if len(_lowercase			) > self.max_input_chars_per_word:
														return [self.unk_token]

									UpperCamelCase_						     =      0
									UpperCamelCase_						     =      []
									while start < len(_lowercase			):
														UpperCamelCase_						     =      len(_lowercase			)
														UpperCamelCase_						     =      None
														while start < end:
																			UpperCamelCase_						     =      "".join(chars[start:end]			)
																			if substr in self.vocab:
																								UpperCamelCase_						     =      substr
																								break
																			end -= 1
														if cur_substr is None:
																			sub_tokens.append(self.unk_token			)
																			start += 1
														else:
																			sub_tokens.append(_lowercase			)
																			UpperCamelCase_						     =      end

									return sub_tokens




class 					_lowercase      (lowerCamelCase_					):
				'''simple docstring'''







				lowercase__        =  VOCAB_FILES_NAMES
				lowercase__        =  PRETRAINED_VOCAB_FILES_MAP
				lowercase__        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__        =  ["""input_ids""", """attention_mask"""]
				lowercase__        =  False

				def __init__(       self   ,       snake_case__   ,       snake_case__="<d>"   ,       snake_case__="</d>"   ,       snake_case__="<s>"   ,       snake_case__="</s>"   ,       snake_case__="<pad>"   ,       snake_case__="<unk>"   ,       snake_case__="</n>"   ,       snake_case__="</_>"   ,       snake_case__="left"   ,       **snake_case__   ,       ):

									'''simple docstring'''


									requires_backends(self   ,       ["jieba"]			)
									super().__init__(
									    bod_token=_lowercase   ,       eod_token=_lowercase   ,       bos_token=_lowercase   ,       eos_token=_lowercase   ,       pad_token=_lowercase   ,       unk_token=_lowercase   ,       line_token=_lowercase   ,       space_token=_lowercase   ,       padding_side=_lowercase   ,       **_lowercase   ,       )
									UpperCamelCase_						     =      bod_token
									UpperCamelCase_						     =      eod_token
									UpperCamelCase_						     =      load_vocab(_lowercase			)
									UpperCamelCase_						     =      self.encoder[space_token]
									UpperCamelCase_						     =      self.encoder[line_token]

									del self.encoder[space_token]
									del self.encoder[line_token]

									UpperCamelCase_						     =      collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda snake_case__			: x[1]			)			)
									UpperCamelCase_						     =      {v: k for k, v in self.encoder.items()}

									UpperCamelCase_						     =      WordpieceTokenizer(vocab=self.encoder   ,       unk_token=self.unk_token			)

				@property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return self.encoder[self.bod_token]

				@property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return self.encoder[self.eod_token]

				@property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return self.encoder["\n"]

				@property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return len(self.encoder			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									return dict(self.encoder   ,       **self.added_tokens_encoder			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      []
									for x in jieba.cut(_lowercase   ,       cut_all=_lowercase			):
														output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase			)			)
									return output_tokens

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       **snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      [i for i in token_ids if i >= 0]
									UpperCamelCase_						     =      [
									    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
									]
									return super()._decode(_lowercase   ,       **_lowercase			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									return token in self.encoder

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									return "".join(_lowercase			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									return self.encoder.get(_lowercase   ,       self.encoder.get(self.unk_token			)			)

				def 					_lowerCamelCase    (       self   ,       snake_case__			):

									'''simple docstring'''


									return self.decoder.get(_lowercase   ,       self.unk_token			)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__ = None			):

									'''simple docstring'''


									if os.path.isdir(_lowercase			):
														UpperCamelCase_						     =      os.path.join(
														    _lowercase   ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)
									else:
														UpperCamelCase_						     =      (filename_prefix + "-" if filename_prefix else "") + save_directory
									UpperCamelCase_						     =      0
									if " " in self.encoder:
														UpperCamelCase_						     =      self.encoder[" "]
														del self.encoder[" "]
									if "\n" in self.encoder:
														UpperCamelCase_						     =      self.encoder["\n"]
														del self.encoder["\n"]
									UpperCamelCase_						     =      collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda snake_case__			: x[1]			)			)
									with open(_lowercase   ,       "w"   ,       encoding="utf-8"			) as writer:
														for token, token_index in self.encoder.items():
																			if index != token_index:
																								logger.warning(
																								    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																								    " Please check that the vocabulary is not corrupted!"			)
																								UpperCamelCase_						     =      token_index
																			writer.write(token + "\n"			)
																			index += 1
									return (vocab_file,)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__ = None			):

									'''simple docstring'''


									if token_ids_a is None:
														return [self.bos_token_id] + token_ids_a
									return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__ = None   ,       snake_case__ = False			):

									'''simple docstring'''


									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowercase   ,       token_ids_a=_lowercase   ,       already_has_special_tokens=_lowercase			)

									if token_ids_a is not None:
														return [1] + ([0] * len(_lowercase			)) + [1] + ([0] * len(_lowercase			))
									return [1] + ([0] * len(_lowercase			))

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			: Any							   =		16
__lowerCamelCase			: List[Any]							   =		32



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Accelerator ,     __UpperCamelCase    :       int = 16 ,     __UpperCamelCase    :       str = "bert-base-cased"	)							->					str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      load_dataset("""glue""" ,     """mrpc"""	)

  def tokenize_function(__UpperCamelCase    :       Optional[Any]	):
    # max_length=None => use the model max length (it's actually the default)
    SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      tokenized_datasets.rename_column("""label""" ,     """labels"""	)

  def collate_fn(__UpperCamelCase    :       List[Any]	):
    # 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_28 ,     return_tensors="""pt"""	)
    return tokenizer.pad(__UpperCamelCase ,     padding="""longest""" ,     return_tensors="""pt"""	)

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""train"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      DataLoader(
      tokenized_datasets["""validation"""] ,     shuffle=__UpperCamelCase ,     collate_fn=__UpperCamelCase ,     batch_size=__UpperCamelCase	)

  return train_dataloader, eval_dataloader



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Optional[int]	)							->					List[str]:







  """simple docstring"""





  model.eval()
  SCREAMING_SNAKE_CASE__										=      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():
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      outputs.logits.argmax(dim=-1	)
    # It is slightly faster to call this once, than multiple times
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      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:
        SCREAMING_SNAKE_CASE__										=      predictions[: len(eval_dataloader.dataset	) - samples_seen]
        SCREAMING_SNAKE_CASE__										=      references[: len(eval_dataloader.dataset	) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=__UpperCamelCase ,     references=__UpperCamelCase ,     )

  SCREAMING_SNAKE_CASE__										=      metric.compute()
  return eval_metric["accuracy"]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Union[str, Any] ,     __UpperCamelCase    :       Any	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  SCREAMING_SNAKE_CASE__										=      config["""lr"""]
  SCREAMING_SNAKE_CASE__										=      int(config["""num_epochs"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""seed"""]	)
  SCREAMING_SNAKE_CASE__										=      int(config["""batch_size"""]	)
  SCREAMING_SNAKE_CASE__										=      args.model_name_or_path

  set_seed(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_dataloaders(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

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

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__										=      (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  SCREAMING_SNAKE_CASE__										=      optimizer_cls(params=model.parameters() ,     lr=__UpperCamelCase	)

  if accelerator.state.deepspeed_plugin is not None:
    SCREAMING_SNAKE_CASE__										=      accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    SCREAMING_SNAKE_CASE__										=      1
  SCREAMING_SNAKE_CASE__										=      (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
  ):
    SCREAMING_SNAKE_CASE__										=      get_linear_schedule_with_warmup(
        optimizer=__UpperCamelCase ,     num_warmup_steps=0 ,     num_training_steps=__UpperCamelCase ,     )
  else:
    SCREAMING_SNAKE_CASE__										=      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.
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      accelerator.prepare(
      __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)

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

  if args.partial_train_epoch is not None:
    SCREAMING_SNAKE_CASE__										=      args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint	)
    SCREAMING_SNAKE_CASE__										=      args.resume_from_checkpoint.split("""epoch_"""	)[1]
    SCREAMING_SNAKE_CASE__										=      """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    SCREAMING_SNAKE_CASE__										=      int(__UpperCamelCase	) + 1
    SCREAMING_SNAKE_CASE__										=      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:
      SCREAMING_SNAKE_CASE__										=      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
  SCREAMING_SNAKE_CASE__										=      {}
  for epoch in range(__UpperCamelCase ,     __UpperCamelCase	):
    model.train()
    for step, batch in enumerate(__UpperCamelCase	):
      SCREAMING_SNAKE_CASE__										=      model(**__UpperCamelCase	)
      SCREAMING_SNAKE_CASE__										=      outputs.loss
      SCREAMING_SNAKE_CASE__										=      loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase	)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    SCREAMING_SNAKE_CASE__										=      f"""epoch_{epoch}"""
    SCREAMING_SNAKE_CASE__										=      os.path.join(args.output_dir ,     __UpperCamelCase	)
    accelerator.save_state(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      evaluation_loop(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      accuracy
    SCREAMING_SNAKE_CASE__										=      lr_scheduler.get_lr()[0]
    SCREAMING_SNAKE_CASE__										=      optimizer.param_groups[0]["""lr"""]
    SCREAMING_SNAKE_CASE__										=      epoch
    SCREAMING_SNAKE_CASE__										=      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  (					)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      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.""" ,     )
  SCREAMING_SNAKE_CASE__										=      parser.parse_args()
  SCREAMING_SNAKE_CASE__										=      {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(__UpperCamelCase ,     __UpperCamelCase	)


if __name__ == "__main__":
   main()
