'''simple docstring'''




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_5_0, "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_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ] )
class     a__      (   unittest.TestCase ):






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """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		:		int  ,				a		:		Optional[int]=1    ):





         """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		:		Union[str, Any]  ,				a		:		str    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Tuple    ):





         """simple docstring"""



         __lowerCamelCase		=  self.create_estimator()

         # run training
         estimator.fit()

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

         # extract kpis
         __lowerCamelCase		=  list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']    )
         __lowerCamelCase		=  list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']    )
         # get train time from SageMaker job, this includes starting, preprocessing, stopping
         __lowerCamelCase		=  (
             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}  ,				a    )


"""simple docstring"""





__magic_name__       =			"Tobias Carryer"

from time import time
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=int(time())):  # noqa: B008
				__SCREAMING_SNAKE_CASE            = multiplier
				__SCREAMING_SNAKE_CASE            = increment
				__SCREAMING_SNAKE_CASE            = modulo
				__SCREAMING_SNAKE_CASE            = seed




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = (self.multiplier * self.seed + self.increment) % self.modulo
				return self.seed


if __name__ == "__main__":
							# Show the LCG in action.
							__magic_name__       =			LinearCongruentialGenerator(1664525, 1013904223, 2 << 31)
							while True:
														print(lcg.next_number())




