
"""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_model_parallelism.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
        {
            "framework": "pytorch",
            "script": "run_glue.py",
            "model_name_or_path": "roberta-large",
            "instance_type": "ml.p3dn.24xlarge",
            "results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]							)
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			def  __lowerCAmelCase (					self       )			->     Dict:
						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  __lowerCAmelCase (					self       ,   __A       )			->     Any:
						# configuration for running training on smdistributed Model Parallel
						lowerCAmelCase_    :Union[str, Any]			     =  {
						    """enabled""": True,
						    """processes_per_host""": 8,
						}
						lowerCAmelCase_    :Tuple			     =  {
						    """enabled""": True,
						    """parameters""": {
						        """microbatches""": 4,
						        """placement_strategy""": """spread""",
						        """pipeline""": """interleaved""",
						        """optimize""": """speed""",
						        """partitions""": 4,
						        """ddp""": True,
						    },
						}

						lowerCAmelCase_    :Any			     =  {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}

						lowerCAmelCase_    :Any			     =  """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
						# 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=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""       ,   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,
						        """max_steps""": 500,
						    }       ,   metric_definitions=self.env.metric_definitions       ,   distribution=__A       ,   py_version="""py36"""       ,   )






			def  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:
						TrainingJobAnalytics(__A       ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""       )






			@parameterized.expand([(1,)]       )
			def  __lowerCAmelCase (					self       ,   __A       )			->     List[str]:
						# create estimator
						lowerCAmelCase_    :Any			     =  self.create_estimator(__A       )

						# run training
						estimator.fit()

						# result dataframe
						lowerCAmelCase_    :Optional[Any]			     =  TrainingJobAnalytics(estimator.latest_training_job.name       ).dataframe()

						# extract kpis
						lowerCAmelCase_    :List[str]			     =  list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]       )
						lowerCAmelCase_    :Optional[int]			     =  list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]       )
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						lowerCAmelCase_    :Optional[int]			     =  (
						    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       )













def  _SCREAMING_SNAKE_CASE				(   a  )      ->      str:
					if number > 0:
										raise ValueError('input must be a negative integer'  )
					__A	:						Optional[int]       =				len(bin(a  )[3:]  )
					__A	:						Dict       =				bin(abs(a  ) - (1 << binary_number_length)  )[3:]
					__A	:						int       =				(
					    (
					        '1'
					        + '0' * (binary_number_length - len(a  ))
					        + twos_complement_number
					    )
					    if number < 0
					    else '0'
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()




