

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": 650, "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": 600, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]					)
class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Dict							)      ->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=_UpperCamelCase							,   )
									assert hasattr(self							,   '''env'''							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Tuple=1							)      ->List[str]:
									# 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}-single'''							,   instance_count=_UpperCamelCase							,   instance_type=self.instance_type							,   debugger_hook_config=_UpperCamelCase							,   hyperparameters={**self.env.hyperparameters, '''model_name_or_path''': self.model_name_or_path}							,   metric_definitions=self.env.metric_definitions							,   py_version='''py36'''							,   )







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Optional[int]							)      ->int:
									TrainingJobAnalytics(_UpperCamelCase							).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''							)







			def 				snake_case__(     self :				int							)      ->List[str]:
									# create estimator
									snake_case_   =	self.create_estimator()

									# run training
									estimator.fit()

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

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


from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()