



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 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Union[str, Any]   )						->					Optional[Any]:
									"""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 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       Union[str, Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				F"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
									# distributed data settings
									__lowercase					=				{"""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=lowercase			,     instance_count=lowercase			,     instance_type=self.instance_type			,     debugger_hook_config=lowercase			,     hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path}			,     metric_definitions=self.env.metric_definitions			,     distribution=lowercase			,     py_version="""py36"""			,     )





			def 		snake_case__			(				self	:       Any			,     lowercase	:       Optional[Any]   )						->					Optional[int]:
									"""simple docstring"""







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







			@parameterized.expand([(2,)]   )
			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.create_estimator(lowercase   )

									# run training
									estimator.fit()

									# result dataframe
									__lowercase					=				TrainingJobAnalytics(estimator.latest_training_job.name   ).dataframe()

									# extract kpis
									__lowercase					=				list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""]   )
									__lowercase					=				list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""]   )
									# get train time from SageMaker job, this includes starting, preprocessing, stopping
									__lowercase					=				(
									    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   )










import unittest

import numpy as np






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__ = None						,       ) ->      np.ndarray:
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )

						if shape_a[0] != shape_b[0]:
												__lowercase					=				(
												    """Expected the same number of rows for A and B. """
												    F"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(lowercase__   )

						if shape_b[1] != shape_c[1]:
												__lowercase					=				(
												    """Expected the same number of columns for B and C. """
												    F"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(lowercase__   )

						__lowercase					=				pseudo_inv
						if a_inv is None:
												try:
																		__lowercase					=				np.linalg.inv(lowercase__   )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

						return mat_c - mat_b.T @ a_inv @ mat_b
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Dict   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1], [6, 3]]   )

									__lowercase					=				schur_complement(lowercase			,     lowercase			,     lowercase   )

									__lowercase					=				np.block([[a, b], [b.T, c]]   )

									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )

									self.assertAlmostEqual(lowercase			,     det_a * det_s   )





			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1], [6, 3]]   )

									with self.assertRaises(lowercase   ):
															schur_complement(lowercase			,     lowercase			,     lowercase   )







			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1, 3], [6, 3, 5]]   )

									with self.assertRaises(lowercase   ):
															schur_complement(lowercase			,     lowercase			,     lowercase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()





