



'''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.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'pytorch',
            'script': 'run_ddp.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf_dist.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.p3.16xlarge',
            'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]		)
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def    a__    (self			)       ->	List[str]:


      """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    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
      # distributed data settings
      _a           =    {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None

      # 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=A					,  instance_count=A					,  instance_type=self.instance_type					,  debugger_hook_config=A					,  hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}					,  metric_definitions=self.env.metric_definitions					,  distribution=A					,  py_version='''py36'''					,  )



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      TrainingJobAnalytics(A			).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''			)



 @parameterized.expand([(2,)]			)
 def    a__    (self					,  A			)       ->	List[str]:


      """simple docstring"""



      _a           =    self.create_estimator(A			)

      # run training
      estimator.fit()

      # result dataframe
      _a           =    TrainingJobAnalytics(estimator.latest_training_job.name			).dataframe()

      # extract kpis
      _a           =    list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']			)
      _a           =    list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']			)
      # get train time from SageMaker job, this includes starting, preprocessing, stopping
      _a           =    (
          Session().describe_training_job(estimator.latest_training_job.name			).get('''TrainingTimeInSeconds'''					,  999_999			)
      )

      # 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 json
import os
from typing import Optional, Tuple

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Tuple:
    return dict(self.vocab     ,      **self.added_tokens_encoder  )




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






