





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge',
            'results': {'train_runtime': 6_50, 'eval_accuracy': 0.6, 'eval_loss': 0.9},
        },
        {
            'framework': 'tensorflow',
            'script': 'run_tf.py',
            'model_name_or_path': 'distilbert-base-cased',
            'instance_type': 'ml.g4dn.xlarge',
            'results': {'train_runtime': 6_00, 'eval_accuracy': 0.3, 'eval_loss': 0.9},
        },
    ]				)
class     SCREAMING_SNAKE_CASE       (unittest.TestCase				):


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       List[Any]:





     """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 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    str=1	)->       int:





     """simple docstring"""







     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}-single"""      ,					instance_count=a      ,					instance_type=self.instance_type      ,					debugger_hook_config=a      ,					hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}      ,					metric_definitions=self.env.metric_definitions      ,					py_version='py36'      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    Union[str, Any]	)->       Optional[int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Dict:





     """simple docstring"""







     lowercase__           =  self.create_estimator()

     # run training
     estimator.fit()

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

     # extract kpis
     lowercase__           =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']	)
     lowercase__           =  list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']	)
     # get train time from SageMaker job, this includes starting, preprocessing, stopping
     lowercase__           =  (
         Session().describe_training_job(estimator.latest_training_job.name	).get('TrainingTimeInSeconds'      ,					999_999	)
     )

     # 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	)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


