
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.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 650, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """pytorch""",
            """script""": """run_ddp.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
        },
        {
            """framework""": """tensorflow""",
            """script""": """run_tf_dist.py""",
            """model_name_or_path""": """distilbert-base-cased""",
            """instance_type""": """ml.p3.16xlarge""",
            """results""": {"""train_runtime""": 600, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
        },
    ]  )
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							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=_SCREAMING_SNAKE_CASE						,	)
							assert hasattr(self						,	"""env"""      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							List[str]	=						f'''{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'''
							# distributed data settings
							UpperCamelCase_:							List[str]	=						{"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None

							# 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=_SCREAMING_SNAKE_CASE						,	instance_count=_SCREAMING_SNAKE_CASE						,	instance_type=self.instance_type						,	debugger_hook_config=_SCREAMING_SNAKE_CASE						,	hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}						,	metric_definitions=self.env.metric_definitions						,	distribution=_SCREAMING_SNAKE_CASE						,	py_version="""py36"""						,	)

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Optional[int]      ):
							TrainingJobAnalytics(_SCREAMING_SNAKE_CASE      ).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''      )




			@parameterized.expand([(2,)]      )
			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int      ):
							# create estimator
							UpperCamelCase_:							Any	=						self.create_estimator(_SCREAMING_SNAKE_CASE      )

							# run training
							estimator.fit()

							# result dataframe
							UpperCamelCase_:							Dict	=						TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

							# extract kpis
							UpperCamelCase_:							List[str]	=						list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]      )
							UpperCamelCase_:							Union[str, Any]	=						list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]      )
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							UpperCamelCase_:							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}						,	_SCREAMING_SNAKE_CASE      )








def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				while second != 0:
								UpperCamelCase_:							Optional[Any]	=						first & second
								first ^= second
								UpperCamelCase_:							Any	=						c << 1
				return first


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCamelCase_						: List[Any]         =  int(input("""Enter the first number: """).strip())
							lowerCamelCase_						: Tuple         =  int(input("""Enter the second number: """).strip())
							print(F"""{add(first, second) = }""")






