



"""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": 1_6_0_0, "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_6_0_0, "eval_accuracy": 0.3, "eval_loss": 1.2},
        },
    ]    )
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Tuple						)					->		str:






				"""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=lowercase_			,)
				assert hasattr(self			,'''env'''						)







		def 					UpperCAmelCase    (	self						:  List[Any]			,_snake_case						:  str						)					->		Any:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			{
				    '''enabled''': True,
				    '''processes_per_host''': 8,
				}
				lowercase__   :    List[str]				=			{
				    '''enabled''': True,
				    '''parameters''': {
				        '''microbatches''': 4,
				        '''placement_strategy''': '''spread''',
				        '''pipeline''': '''interleaved''',
				        '''optimize''': '''speed''',
				        '''partitions''': 4,
				        '''ddp''': True,
				    },
				}

				lowercase__   :    Optional[Any]				=			{'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

				lowercase__   :    Dict				=			'''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=lowercase_			,instance_type=self.instance_type			,debugger_hook_config=lowercase_			,hyperparameters={
				        **self.env.hyperparameters,
				        '''model_name_or_path''': self.model_name_or_path,
				        '''max_steps''': 500,
				    }			,metric_definitions=self.env.metric_definitions			,distribution=lowercase_			,py_version='''py36'''			,)







		def 					UpperCAmelCase    (	self						:  Optional[int]			,_snake_case						:  Dict						)					->		Union[str, Any]:






				"""simple docstring"""



				TrainingJobAnalytics(lowercase_						).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv"""						)







		@parameterized.expand([(1,)]						)
		def 					UpperCAmelCase    (	self						:  Union[str, Any]			,_snake_case						:  int						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			self.create_estimator(lowercase_						)

				# run training
				estimator.fit()

				# result dataframe
				lowercase__   :    List[Any]				=			TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

				# extract kpis
				lowercase__   :    Union[str, Any]				=			list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']						)
				lowercase__   :    Any				=			list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']						)
				# get train time from SageMaker job, this includes starting, preprocessing, stopping
				lowercase__   :    Union[str, Any]				=			(
				    Session().describe_training_job(estimator.latest_training_job.name						).get('''TrainingTimeInSeconds'''			,999_999						)
				)

				# 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}			,lowercase_						)







'''simple docstring'''

import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)



