





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""": 6_50, """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""": 6_00, """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""": 6_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]    )
class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Optional[int]     )							->						str:
     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=lowerCAmelCase_					,  )
     assert hasattr(self					,  'env'     )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		int     )							->						Dict:
     __lowerCAmelCase								=	f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
     # distributed data settings
     __lowerCAmelCase								=	{'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=lowerCAmelCase_					,  instance_count=lowerCAmelCase_					,  instance_type=self.instance_type					,  debugger_hook_config=lowerCAmelCase_					,  hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}					,  metric_definitions=self.env.metric_definitions					,  distribution=lowerCAmelCase_					,  py_version='py36'					,  )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Any     )							->						List[str]:
     TrainingJobAnalytics(lowerCAmelCase_     ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""     )






  @parameterized.expand([(2,)]     )
  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		List[str]     )							->						Tuple:
     # create estimator
     __lowerCAmelCase								=	self.create_estimator(lowerCAmelCase_     )

     # run training
     estimator.fit()

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

     # extract kpis
     __lowerCAmelCase								=	list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']     )
     __lowerCAmelCase								=	list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']     )
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     __lowerCAmelCase								=	(
         Session().describe_training_job(estimator.latest_training_job.name     ).get('TrainingTimeInSeconds'					,  9_9_9_9_9_9     )
     )

     # 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}					,  lowerCAmelCase_     )








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

