
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       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						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       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  int ):
								"""simple docstring"""







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

								__lowerCAmelCase         =							{"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options}

								__lowerCAmelCase         =							"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": 500,
								    }      ,				metric_definitions=self.env.metric_definitions      ,				distribution=snake_case__      ,				py_version="py36"      ,				)







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								TrainingJobAnalytics(snake_case__ ).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv" )







							@parameterized.expand([(1,)] )
							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  str ):
								"""simple docstring"""







								__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"      ,				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}      ,				snake_case__ )








import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCamelCase_   	=	pytest.mark.integration






@pytest.mark.parametrize("path"			,							["paws", "csv"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	inspect_dataset(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.filterwarnings("ignore:inspect_metric is deprecated:FutureWarning"  )
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"  )
@pytest.mark.parametrize("path"			,							["accuracy"]  )
def 		_UpperCAmelCase   (	UpperCamelCase:     List[Any]			,							UpperCamelCase:     Any  ):






	"""simple docstring"""


	inspect_metric(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							path + ".py"
	assert script_name in os.listdir(UpperCamelCase  )
	assert "__pycache__" not in os.listdir(UpperCamelCase  )






@pytest.mark.parametrize(
    "path, config_name, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int			,							UpperCamelCase:     Tuple  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )
	assert info.config_name == config_name
	assert list(info.splits.keys()  ) == expected_splits






@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Any			,							UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_config_info(UpperCamelCase			,							config_name=UpperCamelCase  )






@pytest.mark.parametrize(
    "path, expected"			,							[
        ("squad", "plain_text"),
        ("acronym_identification", "default"),
        ("lhoestq/squad", "plain_text"),
        ("lhoestq/test", "default"),
        ("lhoestq/demo1", "lhoestq--demo1"),
        ("dalle-mini/wit", "dalle-mini--wit"),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[int]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_config_names(UpperCamelCase  )
	assert expected in config_names






@pytest.mark.parametrize(
    "path, expected_configs, expected_splits_in_first_config"			,							[
        ("squad", ["plain_text"], ["train", "validation"]),
        ("dalle-mini/wit", ["dalle-mini--wit"], ["train"]),
        ("paws", ["labeled_final", "labeled_swap", "unlabeled_final"], ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert list(infos.keys()  ) == expected_configs
	__lowerCAmelCase         =							expected_configs[0]
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits_in_first_config






@pytest.mark.parametrize(
    "path, expected_config, expected_splits"			,							[
        ("squad", "plain_text", ["train", "validation"]),
        ("dalle-mini/wit", "dalle-mini--wit", ["train"]),
        ("paws", "labeled_final", ["train", "test", "validation"]),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     int			,							UpperCamelCase:     Optional[Any]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							get_dataset_infos(UpperCamelCase  )
	assert expected_config in infos
	__lowerCAmelCase         =							infos[expected_config]
	assert info.config_name == expected_config
	assert list(info.splits.keys()  ) == expected_splits







@pytest.mark.parametrize(
    "path, config_name, expected_exception"			,							[
        ("paws", None, ValueError),
    ]			,							)
def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


	with pytest.raises(UpperCamelCase  ):
		get_dataset_split_names(UpperCamelCase			,							config_name=UpperCamelCase  )






