






"""simple docstring"""
def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			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      ,A__      ,A__      ,A__		)
						else:
									UpperCAmelCase_	:					str     	=   max(
									    mf_knapsack(i - 1      ,A__      ,A__      ,A__		)      ,mf_knapsack(i - 1      ,A__      ,A__      ,j - wt[i - 1]		) + val[i - 1]      ,)
						UpperCAmelCase_	:					int     	=   val
			return f[i][j]

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Optional[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_	:					Optional[int]     	=   dp[i - 1][w_]

			return dp[n][w_], dp

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

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

			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   knapsack(A__      ,A__      ,A__      ,A__		)
			UpperCAmelCase_	:					set     	=   set()
			_construct_solution(A__      ,A__      ,A__      ,A__      ,A__		)

			return optimal_val, example_optional_set

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__		):
			# 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(A__      ,A__      ,i - 1      ,A__      ,A__		)
						else:
									optimal_set.add(A__		)
									_construct_solution(A__      ,A__      ,i - 1      ,j - wt[i - 1]      ,A__		)


if __name__ == "__main__":
			lowerCamelCase_							      = [3, 2, 4, 4]
			lowerCamelCase_							      = [4, 3, 2, 3]
			lowerCamelCase_							      = 4
			lowerCamelCase_							      = 6
			lowerCamelCase_							      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			lowerCamelCase_      ,						lowerCamelCase_							      = 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
			lowerCamelCase_      ,						lowerCamelCase_							      = 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)











"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase_							      = logging.getLogger()

def 							snake_case (  ):
			UpperCAmelCase_	:					str     	=   argparse.ArgumentParser()
			parser.add_argument("-f"		)
			UpperCAmelCase_	:					Optional[Any]     	=   parser.parse_args()
			return args.f







class      UpperCamelCase_			(__A	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     None:
				UpperCAmelCase_	:					List[str]     	=   logging.StreamHandler(sys.stdout		)
				logger.addHandler(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					List[str]		)		->     Tuple:
				UpperCAmelCase_	:					Optional[int]     	=   get_gpu_count()

				if n_gpu > 1:
							pass
							# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
							# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
							# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
							# cmd = [sys.executable] + distributed_args + args
							# execute_subprocess_async(cmd, env=self.get_env())
							# XXX: test the results - need to save them first into .json file
				else:
							args.insert(0      ,      "run_glue_deebert.py"		)
							with patch.object(lowerCAmelCase_      ,      "argv"      ,      lowerCAmelCase_		):
										UpperCAmelCase_	:					Dict     	=   run_glue_deebert.main()
										for value in result.values():
													self.assertGreaterEqual(lowerCAmelCase_      ,      0.6_6_6		)





	@slow
	@require_torch_non_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     int:
				UpperCAmelCase_	:					Tuple     	=   "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
				self.run_and_check(lowerCAmelCase_		)



