





'''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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				List[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=snake_case_      ,  )
     assert hasattr(self      ,  "env"		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     _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=snake_case_      ,  instance_count=snake_case_      ,  instance_type=self.instance_type      ,  debugger_hook_config=snake_case_      ,  hyperparameters={**self.env.distributed_hyperparameters, "model_name_or_path": self.model_name_or_path}      ,  metric_definitions=self.env.metric_definitions      ,  distribution=snake_case_      ,  py_version="py36"      ,  )




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[Any]:
     TrainingJobAnalytics(snake_case_		).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''		)





 @parameterized.expand([(2,)]		)
 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				List[Any]:
     # create estimator
     _lowerCAmelCase						      =   self.create_estimator(snake_case_		)

     # 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"      ,  999999		)
     )

     # 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}      ,  snake_case_		)









import numpy as np





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> np.array:




    """simple docstring"""




    return (2 / (1 + np.exp(-2 * vector				))) - 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()





