






'''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 _lowercase     (    unittest.TestCase		):




							def 		lowerCamelCase_    (							self:    List[str]					):
														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=UpperCamelCase__      ,							)
														assert hasattr(self      ,							"""env"""					)




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Dict	  =	F'''{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}'''
														# distributed data settings
														lowerCamelCase__ :     Union[str, Any]	  =	{"""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=UpperCamelCase__      ,							instance_count=UpperCamelCase__      ,							instance_type=self.instance_type      ,							debugger_hook_config=UpperCamelCase__      ,							hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}      ,							metric_definitions=self.env.metric_definitions      ,							distribution=UpperCamelCase__      ,							py_version="""py36"""      ,							)




							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    List[Any]					):
														TrainingJobAnalytics(UpperCamelCase__					).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv'''					)




							@parameterized.expand([(2,)]					)
							def 		lowerCamelCase_    (							self:    Dict      ,							UpperCamelCase__:    str					):
														# create estimator
														lowerCamelCase__ :     Tuple	  =	self.create_estimator(UpperCamelCase__					)

														# run training
														estimator.fit()

														# result dataframe
														lowerCamelCase__ :     Tuple	  =	TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

														# extract kpis
														lowerCamelCase__ :     Union[str, Any]	  =	list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]					)
														lowerCamelCase__ :     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
														lowerCamelCase__ :     Optional[int]	  =	(
														    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}      ,							UpperCamelCase__					)









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

