




"""simple docstring"""





import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import 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.g4dn.xlarge",
            "results": {"train_runtime": 6_5_0, "eval_accuracy": 0.6, "eval_loss": 0.9},
        },
        {
            "framework": "tensorflow",
            "script": "run_tf.py",
            "model_name_or_path": "distilbert-base-cased",
            "instance_type": "ml.g4dn.xlarge",
            "results": {"train_runtime": 6_0_0, "eval_accuracy": 0.3, "eval_loss": 0.9},
        },
    ]   )
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    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=UpperCamelCase_      ,  )
						assert hasattr(self      ,  "env")


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    int=1):


						"""simple docstring"""




						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=F"{self.env.base_job_name}-single"      ,  instance_count=UpperCamelCase_      ,  instance_type=self.instance_type      ,  debugger_hook_config=UpperCamelCase_      ,  hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path}      ,  metric_definitions=self.env.metric_definitions      ,  py_version="py36"      ,  )


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str):


						"""simple docstring"""




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


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self.create_estimator()

						# run training
						estimator.fit()

						# result dataframe
						__UpperCAmelCase	: Any		=					TrainingJobAnalytics(estimator.latest_training_job.name).dataframe()

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








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


