



'''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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  List[str]	)    ->	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=__snake_case	,    )
								assert hasattr(self	,    '''env'''	)







	def   A			(		self    :  int	,    __snake_case    :  Dict	)    ->	str:
								UpperCAmelCase				: List[Any]											=	F"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
								# distributed data settings
								UpperCAmelCase				: Optional[int]											=	{'''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=__snake_case	,    instance_count=__snake_case	,    instance_type=self.instance_type	,    debugger_hook_config=__snake_case	,    hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path}	,    metric_definitions=self.env.metric_definitions	,    distribution=__snake_case	,    py_version='''py36'''	,    )







	def   A			(		self    :  List[Any]	,    __snake_case    :  Optional[Any]	)    ->	Optional[int]:
								TrainingJobAnalytics(__snake_case	).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""	)







	@parameterized.expand([(2,)]	)
	def   A			(		self    :  Optional[int]	,    __snake_case    :  int	)    ->	int:
								# create estimator
								UpperCAmelCase				: Tuple											=	self.create_estimator(__snake_case	)

								# run training
								estimator.fit()

								# result dataframe
								UpperCAmelCase				: Optional[int]											=	TrainingJobAnalytics(estimator.latest_training_job.name	).dataframe()

								# extract kpis
								UpperCAmelCase				: List[str]											=	list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']	)
								UpperCAmelCase				: Union[str, 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				: Tuple											=	(
								    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}	,    __snake_case	)










'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    *__snake_case    :  List[str]	,    **__snake_case    :  str	)    ->	None:
								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.'''	,    __snake_case	,    )
								super().__init__(*__snake_case	,    **__snake_case	)





