

"""simple docstring"""






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
 from sagemaker import Session, TrainingJobAnalytics
 from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv("TEST_SAGEMAKER"							,							"False"						)						) is not True							,							reason="Skipping test because should only be run when releasing minor transformers version"							,							)
@pytest.mark.usefixtures("sm_env"						)
@parameterized_class(
    [
        {
            "framework": "pytorch",
            "script": "run_glue_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 16_00, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]						)
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      if self.framework == "pytorch":
          subprocess.run(
              F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()    ,encoding='utf-8'    ,check=_A    ,)
      assert hasattr(self    ,'env'      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    {
          'enabled': True,
          'processes_per_host': 8,
      }
      _lowerCAmelCase       :   Dict  =    {
          'enabled': True,
          'parameters': {
              'microbatches': 4,
              'placement_strategy': 'spread',
              'pipeline': 'interleaved',
              'optimize': 'speed',
              'partitions': 4,
              'ddp': True,
          },
      }

      _lowerCAmelCase       :   List[str]  =    {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

      _lowerCAmelCase       :   Dict  =    'trainer' if self.script == 'run_glue.py' else 'smtrainer'
      # creates estimator
      return HuggingFace(
          entry_point=self.script    ,source_dir=self.env.test_path    ,role=self.env.role    ,image_uri=self.env.image_uri    ,base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""    ,instance_count=_A    ,instance_type=self.instance_type    ,debugger_hook_config=_A    ,hyperparameters={
              **self.env.hyperparameters,
              'model_name_or_path': self.model_name_or_path,
              'max_steps': 500,
          }    ,metric_definitions=self.env.metric_definitions    ,distribution=_A    ,py_version='py36'    ,)

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      TrainingJobAnalytics(_A      ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""      )







  @parameterized.expand([(1,)]      )
  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.create_estimator(_A      )

      # run training
      estimator.fit()

      # result dataframe
      _lowerCAmelCase       :   Union[str, Any]  =    TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

      # extract kpis
      _lowerCAmelCase       :   str  =    list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
      _lowerCAmelCase       :   Optional[int]  =    list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _lowerCAmelCase       :   List[str]  =    (
          Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'    ,99_9999      )
      )

      # assert kpis
      assert train_runtime <= self.results["train_runtime"]
      assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy      )
      assert all(t <= self.results['eval_loss'] for t in eval_loss      )

      # dump tests result into json file to share in PR
      with open(F"""{estimator.latest_training_job.name}.json"""    ,'w'      ) as outfile:
          json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}    ,_A      )








"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if (
        (cp >= 0X4E00 and cp <= 0X9FFF)
        or (cp >= 0X3400 and cp <= 0X4DBF)  #
        or (cp >= 0X20000 and cp <= 0X2A6DF)  #
        or (cp >= 0X2A700 and cp <= 0X2B73F)  #
        or (cp >= 0X2B740 and cp <= 0X2B81F)  #
        or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
        or (cp >= 0XF900 and cp <= 0XFAFF)
        or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
    ):  #
        return True

    return False

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for char in word:
        _lowerCAmelCase       :   Dict  =    ord(_lowerCamelCase   )
        if not _is_chinese_char(_lowerCamelCase   ):
            return 0
    return 1

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    set()

    for token in tokens:
        _lowerCAmelCase       :   Optional[int]  =    len(_lowerCamelCase   ) > 1 and is_chinese(_lowerCamelCase   )
        if chinese_word:
            word_set.add(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    list(_lowerCamelCase   )
    return word_list

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    if not chinese_word_set:
        return bert_tokens
    _lowerCAmelCase       :   Optional[Any]  =    max([len(_lowerCamelCase   ) for w in chinese_word_set]   )

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

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Tuple  =    ltp_tokenizer.seg(lines[i : i + 100]   )[0]
        _lowerCAmelCase       :   List[Any]  =    [get_chinese_word(_lowerCamelCase   ) for r in res]
        ltp_res.extend(_lowerCamelCase   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    for i in range(0 ,   len(_lowerCamelCase   ) ,   100   ):
        _lowerCAmelCase       :   Dict  =    bert_tokenizer(lines[i : i + 100] ,   add_special_tokens=_lowerCamelCase ,   truncation=_lowerCamelCase ,   max_length=512   )
        bert_res.extend(res['input_ids']   )
    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

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

    assert len(_lowerCamelCase   ) == len(_lowerCamelCase   )

    return ref_ids

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



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

    _lowerCAmelCase       :   Optional[Any]  =    prepare_ref(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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""")

 _lowerCAmelCase						=		parser.parse_args()
 main(args)





