

'''simple docstring'''
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase :      List[Any]					 =     logging.getLogger()


def        _lowerCAmelCase					(						_UpperCamelCase						:     Path							, _UpperCamelCase						:     list				)      ->			Any:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    ='\n'.join(_UpperCamelCase				)
     Path(_UpperCamelCase				).open('w'				).writelines(_UpperCamelCase				)


lowerCamelCase :      Tuple					 =     "patrickvonplaten/t5-tiny-random"
lowerCamelCase :      Tuple					 =     "sshleifer/bart-tiny-random"
lowerCamelCase :      List[Any]					 =     "sshleifer/tiny-mbart"

lowerCamelCase :      str					 =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks







class 							A__    (       A__  ):





  def 						A  (		self      :	Any							,					_a      :	Dict )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
       _SCREAMING_SNAKE_CASE							    =input_file_name.parent / 'utest_output.txt'
       assert not output_file_name.exists()
       _SCREAMING_SNAKE_CASE							    =[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
       _dump_articles(_a							,					_a )

       _SCREAMING_SNAKE_CASE							    =str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
       _SCREAMING_SNAKE_CASE							    ='translation_en_to_de' if model == T5_TINY else 'summarization'
       _SCREAMING_SNAKE_CASE							    =f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

       with patch.object(_a							,					'argv'							,					_a ):
            run_generate()
            assert Path(_a ).exists()
            # os.remove(Path(output_file_name))





  def 						A  (		self      :	List[str] )	->    str:





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([BART_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Optional[Any]							,					_a      :	Tuple )	->    List[str]:





       '''simple docstring'''






       self.run_eval_tester(_a )





  @parameterized.expand([T5_TINY, MBART_TINY] )
  @slow
  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
       _SCREAMING_SNAKE_CASE							    =input_file_name.parent / 'utest_output.txt'
       assert not output_file_name.exists()

       _SCREAMING_SNAKE_CASE							    ={
           'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
           'de': [
               'Maschinelles Lernen ist großartig, oder?',
               'Ich esse gerne Bananen',
               'Morgen ist wieder ein toller Tag!',
           ],
       }

       _SCREAMING_SNAKE_CASE							    =Path(self.get_auto_remove_tmp_dir() )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'scores.json' )
       _SCREAMING_SNAKE_CASE							    =str(tmp_dir / 'val.target' )
       _dump_articles(_a							,					text['en'] )
       _dump_articles(_a							,					text['de'] )
       _SCREAMING_SNAKE_CASE							    ='translation_en_to_de' if model == T5_TINY else 'summarization'
       _SCREAMING_SNAKE_CASE							    =f"\n            run_eval_search.py\n            {model}\n            {str(_a )}\n            {str(_a )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
       testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )

       with patch.object(_a							,					'argv'							,					_a ):
            with CaptureStdout() as cs:
                 run_search()
            _SCREAMING_SNAKE_CASE							    =[' num_beams | length_penalty', model, 'Best score args']
            _SCREAMING_SNAKE_CASE							    =['Info']
            if "translation" in task:
                 expected_strings.append('bleu' )
            else:
                 expected_strings.extend(_a )
            for w in expected_strings:
                 assert w in cs.out
            for w in un_expected_strings:
                 assert w not in cs.out
            assert Path(_a ).exists()
            os.remove(Path(_a ) )


def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[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_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






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

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

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






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


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

