


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": 1_600, "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": 1_600, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]				)
class 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







					def A      (				self						)   ->			Dict:







								'''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=snake_case_ ,       )
								assert hasattr(self ,       '''env'''						)






					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   {
								    '''enabled''': True,
								    '''processes_per_host''': 8,
								}
								__lowercase 					=   {
								    '''enabled''': True,
								    '''parameters''': {
								        '''microbatches''': 4,
								        '''placement_strategy''': '''spread''',
								        '''pipeline''': '''interleaved''',
								        '''optimize''': '''speed''',
								        '''partitions''': 4,
								        '''ddp''': True,
								    },
								}

								__lowercase 					=   {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

								__lowercase 					=   '''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=snake_case_ ,       instance_type=self.instance_type ,       debugger_hook_config=snake_case_ ,       hyperparameters={
								        **self.env.hyperparameters,
								        '''model_name_or_path''': self.model_name_or_path,
								        '''max_steps''': 5_0_0,
								    } ,       metric_definitions=self.env.metric_definitions ,       distribution=snake_case_ ,       py_version='''py36''' ,       )






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								TrainingJobAnalytics(snake_case_						).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'						)






					@parameterized.expand([(1,)]						)
					def A      (				self ,       snake_case_						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self.create_estimator(snake_case_						)

								# 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} ,       snake_case_						)






from ..utils import DummyObject, requires_backends
class 	lowerCamelCase_       (				metaclass=lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["onnx"]






					def __init__(				self ,       *snake_case_ ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								requires_backends(self ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)






					@classmethod
					def A      (				cls ,       *snake_case_ ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								requires_backends(cls ,       ['''onnx''']						)


