
'''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		):





     def 							__lowerCAmelCase				(				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=SCREAMING_SNAKE_CASE		,     )
         assert hasattr(self		,     '''env'''      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  Tuple   =		F'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
         # distributed data settings
         A			:  List[Any]   =		{'''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=SCREAMING_SNAKE_CASE		,     instance_count=SCREAMING_SNAKE_CASE		,     instance_type=self.instance_type		,     debugger_hook_config=SCREAMING_SNAKE_CASE		,     hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}		,     metric_definitions=self.env.metric_definitions		,     distribution=SCREAMING_SNAKE_CASE		,     py_version='''py36'''		,     )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         TrainingJobAnalytics(SCREAMING_SNAKE_CASE      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )





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






         """simple docstring"""






         A			:  List[Any]   =		self.create_estimator(SCREAMING_SNAKE_CASE      )

         # run training
         estimator.fit()

         # result dataframe
         A			:  List[str]   =		TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

         # extract kpis
         A			:  List[str]   =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']      )
         A			:  Dict   =		list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']      )
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         A			:  Optional[int]   =		(
             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}		,     SCREAMING_SNAKE_CASE      )






'''simple docstring'''




import fire

from utils import calculate_rouge, save_json







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__=None						,							**snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		[x.strip() for x in open(snake_case__		).readlines()]
    A			:  Tuple   =		[x.strip() for x in open(snake_case__		).readlines()][: len(snake_case__		)]
    A			:  Union[str, Any]   =		calculate_rouge(snake_case__						,							snake_case__						,							**snake_case__		)
    if save_path is not None:
        save_json(snake_case__						,							snake_case__						,							indent=snake_case__		)
    return metrics  # these print nicely


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




