





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_model_parallelism.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''roberta-large''',
            '''instance_type''': '''ml.p3dn.24xlarge''',
            '''results''': {'''train_runtime''': 1600, '''eval_accuracy''': 0.3, '''eval_loss''': 1.2},
        },
    ]			)
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""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=_lowerCAmelCase			, )
										assert hasattr(self			, '''env'''					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   {
										    '''enabled''': True,
										    '''processes_per_host''': 8,
										}
										__SCREAMING_SNAKE_CASE:				Tuple           =   {
										    '''enabled''': True,
										    '''parameters''': {
										        '''microbatches''': 4,
										        '''placement_strategy''': '''spread''',
										        '''pipeline''': '''interleaved''',
										        '''optimize''': '''speed''',
										        '''partitions''': 4,
										        '''ddp''': True,
										    },
										}

										__SCREAMING_SNAKE_CASE:				List[Any]           =   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

										__SCREAMING_SNAKE_CASE:				List[Any]           =   '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
										# 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=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""			, instance_count=_lowerCAmelCase			, instance_type=self.instance_type			, debugger_hook_config=_lowerCAmelCase			, hyperparameters={
										        **self.env.hyperparameters,
										        '''model_name_or_path''': self.model_name_or_path,
										        '''max_steps''': 500,
										    }			, metric_definitions=self.env.metric_definitions			, distribution=_lowerCAmelCase			, py_version='''py36'''			, )






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




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






				@parameterized.expand([(1,)]					)
				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.create_estimator(_lowerCAmelCase					)

										# run training
										estimator.fit()

										# result dataframe
										__SCREAMING_SNAKE_CASE:				Dict           =   TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

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










def 		lowerCAmelCase  (	UpperCamelCase__   :   int       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float(moles / volume					) * nfactor					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (volume)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float       ,			UpperCamelCase__   :   float					)      ->    float:

						"""simple docstring"""





						return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()



