



'''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.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     UpperCamelCase_				(							unittest.TestCase    ):





	def 				_lowercase(						self					)    ->    int:
								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=A	,	)
								assert hasattr(self	,	"""env"""					)





	def 				_lowercase(						self	,	A					)    ->    str:
								UpperCAmelCase  :	Any							  =	f'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
								# distributed data settings
								UpperCAmelCase  :	Tuple							  =	{"""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=A	,	instance_count=A	,	instance_type=self.instance_type	,	debugger_hook_config=A	,	hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}	,	metric_definitions=self.env.metric_definitions	,	distribution=A	,	py_version="""py36"""	,	)





	def 				_lowercase(						self	,	A					)    ->    Any:
								TrainingJobAnalytics(A					).export_csv(f'''{self.env.test_path}/{job_name}_metrics.csv'''					)






	@parameterized.expand([(2,)]					)
	def 				_lowercase(						self	,	A					)    ->    str:
								# create estimator
								UpperCAmelCase  :	Union[str, Any]							  =	self.create_estimator(A					)

								# run training
								estimator.fit()

								# result dataframe
								UpperCAmelCase  :	str							  =	TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

								# extract kpis
								UpperCAmelCase  :	Optional[Any]							  =	list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
								UpperCAmelCase  :	Any							  =	list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]					)
								# get train time from SageMaker job, this includes starting, preprocessing, stopping
								UpperCAmelCase  :	List[Any]							  =	(
								    Session().describe_training_job(estimator.latest_training_job.name					).get("""TrainingTimeInSeconds"""	,	999999					)
								)

								# 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}	,	A					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

