


'''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)
lowercase__				     =		logging.getLogger()






def __UpperCamelCase   (			__lowerCamelCase		: Path      ,  __lowerCamelCase		: list				)      ->     str:







     '''simple docstring'''






     _a						=		"\n".join(__lowerCamelCase				)
     Path(__lowerCamelCase				).open("w"				).writelines(__lowerCamelCase				)


lowercase__				     =		"patrickvonplaten/t5-tiny-random"
lowercase__				     =		"sshleifer/bart-tiny-random"
lowercase__				     =		"sshleifer/tiny-mbart"

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






class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






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

          _a						=		str(Path(self.get_auto_remove_tmp_dir()		) / "scores.json"		)
          _a						=		"translation_en_to_de" if model == T5_TINY else "summarization"
          _a						=		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(__UpperCamelCase     ,							"argv"     ,							__UpperCamelCase		):
               run_generate()
               assert Path(__UpperCamelCase		).exists()
               # os.remove(Path(output_file_name))






     def  a_					(			self		)       ->    int:
          self.run_eval_tester(__UpperCamelCase		)






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






     @parameterized.expand([T5_TINY, MBART_TINY]		)
     @slow
     def  a_					(			self     ,							__UpperCamelCase		)       ->    Any:
          _a						=		Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
          _a						=		input_file_name.parent / "utest_output.txt"
          assert not output_file_name.exists()

          _a						=		{
              "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!",
              ],
          }

          _a						=		Path(self.get_auto_remove_tmp_dir()		)
          _a						=		str(tmp_dir / "scores.json"		)
          _a						=		str(tmp_dir / "val.target"		)
          _dump_articles(__UpperCamelCase     ,							text["en"]		)
          _dump_articles(__UpperCamelCase     ,							text["de"]		)
          _a						=		"translation_en_to_de" if model == T5_TINY else "summarization"
          _a						=		f"\n            run_eval_search.py\n            {model}\n            {str(__UpperCamelCase		)}\n            {str(__UpperCamelCase		)}\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(__UpperCamelCase     ,							"argv"     ,							__UpperCamelCase		):
               with CaptureStdout() as cs:
                    run_search()
               _a						=		[" num_beams | length_penalty", model, "Best score args"]
               _a						=		["Info"]
               if "translation" in task:
                    expected_strings.append("bleu"		)
               else:
                    expected_strings.extend(__UpperCamelCase		)
               for w in expected_strings:
                    assert w in cs.out
               for w in un_expected_strings:
                    assert w not in cs.out
               assert Path(__UpperCamelCase		).exists()
               os.remove(Path(__UpperCamelCase		)		)





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

