

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)
a_						     =    logging.getLogger()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "\n".join(_a)
	Path(_a).open("w").writelines(_a)


a_						     =    'patrickvonplaten/t5-tiny-random'
a_						     =    'sshleifer/bart-tiny-random'
a_						     =    'sshleifer/tiny-mbart'

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






class _UpperCamelCase							(							__A   ):
		'''simple docstring'''







		def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: int	)							->       Optional[int]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Dict				     =       Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
			SCREAMING_SNAKE_CASE   :      str				     =       input_file_name.parent / "utest_output.txt"
			assert not output_file_name.exists()
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
			_dump_articles(a      ,      a	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       "translation_en_to_de" if model == T5_TINY else "summarization"
			SCREAMING_SNAKE_CASE   :      Tuple				     =       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 							__UpperCamelCase (			self							: str	)							->       Tuple:

			"""simple docstring"""

			self.run_eval_tester(a	)
		@parameterized.expand([BART_TINY, MBART_TINY]	)
		@slow
		def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: List[str]	)							->       Optional[Any]:

			"""simple docstring"""

			self.run_eval_tester(a	)






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

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       input_file_name.parent / "utest_output.txt"
			assert not output_file_name.exists()

			SCREAMING_SNAKE_CASE   :      Any				     =       {
			    "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   :      Optional[int]				     =       Path(self.get_auto_remove_tmp_dir()	)
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       str(tmp_dir / "scores.json"	)
			SCREAMING_SNAKE_CASE   :      int				     =       str(tmp_dir / "val.target"	)
			_dump_articles(a      ,      text["en"]	)
			_dump_articles(a      ,      text["de"]	)
			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       "translation_en_to_de" if model == T5_TINY else "summarization"
			SCREAMING_SNAKE_CASE   :      List[str]				     =       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   :      Any				     =       [" num_beams | length_penalty", model, "Best score args"]
				SCREAMING_SNAKE_CASE   :      str				     =       ["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  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, 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_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_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")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "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):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_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__":
			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)