



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 						__a (     unittest.TestCase				):



					def 		UpperCamelCase							(						self							:    Dict)->							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=snake_case_				,	)
										assert hasattr(self				,	"""env""")



					def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    str)->							Dict:
										__lowerCAmelCase	   =F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
										# distributed data settings
										__lowerCAmelCase	   ={"""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=snake_case_				,	instance_count=snake_case_				,	instance_type=self.instance_type				,	debugger_hook_config=snake_case_				,	hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}				,	metric_definitions=self.env.metric_definitions				,	distribution=snake_case_				,	py_version="""py36"""				,	)



					def 		UpperCamelCase							(						self							:    int				,	snake_case_							:    List[Any])->							int:
										TrainingJobAnalytics(snake_case_).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""")



					@parameterized.expand([(2,)])
					def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Dict)->							Optional[Any]:
										# create estimator
										__lowerCAmelCase	   =self.create_estimator(snake_case_)

										# 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}				,	snake_case_)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list	)  ->						list:
					if len(__lowerCamelCase	) <= 1:
										return lst

					__lowerCAmelCase	   =1

					while i < len(__lowerCamelCase	):
										if lst[i - 1] <= lst[i]:
															i += 1
										else:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =lst[i], lst[i - 1]
															i -= 1
															if i == 0:
																				__lowerCAmelCase	   =1

					return lst


if __name__ == "__main__":
				lowercase_	       =			input('''Enter numbers separated by a comma:\n''').strip()
				lowercase_	       =			[int(item) for item in user_input.split(''',''')]
				print(gnome_sort(unsorted))




