

"""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": 6_5_0, "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_0_0, "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_0_0, "eval_accuracy": 0.6, "eval_loss": 0.7},
        },
    ]				)
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Tuple       ):






         """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=UpperCamelCase    ,						)
         assert hasattr(self    ,						"""env"""       )

    def 				UpperCamelCase	(     self: Optional[Any]    ,						UpperCamelCase: int       ):






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

    def 				UpperCamelCase	(     self: Union[str, Any]    ,						UpperCamelCase: int       ):






         """simple docstring"""


         TrainingJobAnalytics(UpperCamelCase       ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""       )





    @parameterized.expand([(2,)]       )
    def 				UpperCamelCase	(     self: List[str]    ,						UpperCamelCase: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.create_estimator(UpperCamelCase       )

         # 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"""    ,						99_99_99       )
         )

         # 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}    ,						UpperCamelCase       )









"""simple docstring"""



import baseaa





def  _snake_case     (    UpperCAmelCase_  :					str			):
     return baseaa.aaaencode(string.encode("""utf-8"""			)			)





def  _snake_case     (    UpperCAmelCase_  :					bytes			):
     return baseaa.aaadecode(UpperCAmelCase_			).decode("""utf-8"""			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()






