




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					:	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=lowercase__    ,   )
						assert hasattr(self    ,   "env"						)



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	Optional[Any]						):
						__lowercase				:		Optional[Any]												=				f'{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}'
						# distributed data settings
						__lowercase				:		str												=				{"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					:	Dict    ,   lowercase__					:	Optional[Any]						):
						TrainingJobAnalytics(lowercase__						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)



				@parameterized.expand([(2,)]						)
				def       snake_case    (     self					:	Optional[Any]    ,   lowercase__					:	Optional[int]						):
						# create estimator
						__lowercase				:		Any												=				self.create_estimator(lowercase__						)

						# run training
						estimator.fit()

						# result dataframe
						__lowercase				:		Union[str, Any]												=				TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

						# extract kpis
						__lowercase				:		Optional[Any]												=				list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]						)
						__lowercase				:		Tuple												=				list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]						)
						# get train time from SageMaker job, this includes starting, preprocessing, stopping
						__lowercase				:		int												=				(
						    Session().describe_training_job(estimator.latest_training_job.name						).get("TrainingTimeInSeconds"    ,   9_9_9_9_9_9						)
						)

						# 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__						)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
