



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": 650, "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": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]						)
class    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      str)						->					Dict:




         """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       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Tuple=1)						->					str:




         """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=lowerCAmelCase      ,					instance_type=self.instance_type      ,					debugger_hook_config=lowerCAmelCase      ,					hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}      ,					metric_definitions=self.env.metric_definitions      ,					py_version='py36'      ,					)


   def       UpperCAmelCase		(    self	:      Tuple      ,					lowerCAmelCase	:      List[Any])						->					List[Any]:




         """simple docstring"""

         TrainingJobAnalytics(lowerCAmelCase).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv''')



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




         """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'      ,					99_99_99)
         )

         # 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)





a__   :					Tuple 						=      "Tobias Carryer"

from time import time



class    UpperCAmelCase__:

   '''simple docstring'''




   def __init__(    self	:      str      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      str      ,					lowerCAmelCase	:      str=int(time()))						->					List[Any]:  # noqa: B008




         """simple docstring"""

         lowercase__  =	multiplier
         lowercase__  =	increment
         lowercase__  =	modulo
         lowercase__  =	seed



   def       UpperCAmelCase		(    self	:      int)						->					Tuple:




         """simple docstring"""

         lowercase__  =	(self.multiplier * self.seed + self.increment) % self.modulo
         return self.seed


if __name__ == "__main__":
       # Show the LCG in action.
       a__   :					str 						=      LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
       while True:
              print(lcg.next_number())
