



'''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_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 _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  int):







											'''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 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Dict=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=_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 				__lowerCamelCase     (							self  :  Optional[int]  ,       _lowerCAmelCase  :  Dict):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  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'  ,       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'''



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							if number > 0:
														raise ValueError('input must be a negative integer'					)
							__lowercase       	=len(bin(_lowerCAmelCase					)[3:]					)
							__lowercase       	=bin(abs(_lowerCAmelCase					) - (1 << binary_number_length)					)[3:]
							__lowercase       	=(
							    (
							        '1'
							        + '0' * (binary_number_length - len(_lowerCAmelCase					))
							        + twos_complement_number
							    )
							    if number < 0
							    else '0'
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
		import doctest

		doctest.testmod()



