





'''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 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		List[Any]:
		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   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
		# distributed data settings
		snake_case__	       =   {'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   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Tuple:
		TrainingJobAnalytics(UpperCamelCase_		).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''		)





	@parameterized.expand([(2,)]		)
	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Optional[Any]:
		# create estimator
		snake_case__	       =   self.create_estimator(UpperCamelCase_		)

		# run training
		estimator.fit()

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

		# extract kpis
		snake_case__	       =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']		)
		snake_case__	       =   list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']		)
		# get train time from SageMaker job, this includes starting, preprocessing, stopping
		snake_case__	       =   (
		    Session().describe_training_job(estimator.latest_training_job.name		).get('TrainingTimeInSeconds'      ,       99_9999		)
		)

		# 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 requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



