

'''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': 6_50, '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': 6_00, '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': 6_00, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
        },
    ]  )
class 							A__   (	unittest.TestCase  ):





  def 						A  (		self				:     Tuple			)     ->   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=_a							, )
       assert hasattr(self							, 'env'			)





  def 						A  (		self				:     List[str]							, _a				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
       # distributed data settings
       _SCREAMING_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=_a							, instance_count=_a							, instance_type=self.instance_type							, debugger_hook_config=_a							, hyperparameters={**self.env.distributed_hyperparameters, 'model_name_or_path': self.model_name_or_path}							, metric_definitions=self.env.metric_definitions							, distribution=_a							, py_version='py36'							, )





  def 						A  (		self				:     Optional[Any]							, _a				:     int			)     ->   Dict:





       '''simple docstring'''






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





  @parameterized.expand([(2,)]			)
  def 						A  (		self				:     List[Any]							, _a				:     Any			)     ->   Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.create_estimator(_a			)

       # run training
       estimator.fit()

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

       # extract kpis
       _SCREAMING_SNAKE_CASE							   =list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']			)
       _SCREAMING_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
       _SCREAMING_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}							, _a			)



'''simple docstring'''
from ..utils import DummyObject, requires_backends







class 							A__   (	metaclass=A__  ):
  A__					=    ['note_seq']





  def __init__(		self				:     List[str]							, *_a				:     Any							, **_a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       requires_backends(self							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     Any							, *_a				:     str							, **_a				:     List[Any]			)     ->   List[str]:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)





  @classmethod
  def 						A  (		cls				:     int							, *_a				:     Optional[Any]							, **_a				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       requires_backends(cls							, ['note_seq']			)
