




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     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	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				:	Dict			,      __lowercase				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	f"{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"
							# distributed data settings
							snake_case_						       =	{"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				:	Union[str, Any]			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							TrainingJobAnalytics(__lowercase		).export_csv(f"{self.env.test_path}/{job_name}_metrics.csv"		)






						@parameterized.expand([(2,)]		)
						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






							snake_case_						       =	self.create_estimator(__lowercase		)

							# run training
							estimator.fit()

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

							# extract kpis
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"]		)
							snake_case_						       =	list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"]		)
							# get train time from SageMaker job, this includes starting, preprocessing, stopping
							snake_case_						       =	(
							    Session().describe_training_job(estimator.latest_training_job.name		).get("TrainingTimeInSeconds"			,      99_99_99		)
							)

							# 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		)






from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')
