'''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_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 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def lowerCAmelCase     (		self    ):
         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 lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # configuration for running training on smdistributed Model Parallel
         __UpperCamelCase	:	List[Any]		    =  {
             'enabled': True,
             'processes_per_host': 8,
         }
         __UpperCamelCase	:	Optional[int]		    =  {
             'enabled': True,
             'parameters': {
                 'microbatches': 4,
                 'placement_strategy': 'spread',
                 'pipeline': 'interleaved',
                 'optimize': 'speed',
                 'partitions': 4,
                 'ddp': True,
             },
         }

         __UpperCamelCase	:	Any		    =  {'smdistributed': {'modelparallel': smp_options}, 'mpi': mpi_options}

         __UpperCamelCase	:	Optional[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': 5_0_0,
             }     ,			metric_definitions=self.env.metric_definitions     ,			distribution=_lowerCamelCase     ,			py_version='py36'     ,			)







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         TrainingJobAnalytics(_lowerCamelCase    ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""    )







     @parameterized.expand([(1,)]    )
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # create estimator
         __UpperCamelCase	:	Tuple		    =  self.create_estimator(_lowerCamelCase    )

         # run training
         estimator.fit()

         # result dataframe
         __UpperCamelCase	:	Any		    =  TrainingJobAnalytics(estimator.latest_training_job.name    ).dataframe()

         # extract kpis
         __UpperCamelCase	:	Tuple		    =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']    )
         __UpperCamelCase	:	Any		    =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']    )
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         __UpperCamelCase	:	Dict		    =  (
             Session().describe_training_job(estimator.latest_training_job.name    ).get('TrainingTimeInSeconds'     ,			9_9_9_9_9_9    )
         )

         # 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    )




'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



