



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_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		="\n".join(SCREAMING_SNAKE_CASE_       )
     Path(SCREAMING_SNAKE_CASE_       ).open("w"       ).writelines(SCREAMING_SNAKE_CASE_       )


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 	UpperCAmelCase	(		_UpperCAmelCase):


 '''simple docstring'''




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      Optional[Any]:



      """simple docstring"""







      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(lowercase__							,  lowercase__)

      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(lowercase__							,  "argv"							,  lowercase__):
           run_generate()
           assert Path(lowercase__).exists()
           # os.remove(Path(output_file_name))




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



      """simple docstring"""







      self.run_eval_tester(lowercase__)




 @parameterized.expand([BART_TINY, MBART_TINY])
 @slow
 def       lowercase_  (   self							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







      self.run_eval_tester(lowercase__)




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



      """simple docstring"""







      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(lowercase__							,  text["en"])
      _dump_articles(lowercase__							,  text["de"])
      a_       		="translation_en_to_de" if model == T5_TINY else "summarization"
      a_       		=f"""\n            run_eval_search.py\n            {model}\n            {str(lowercase__)}\n            {str(lowercase__)}\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(lowercase__							,  "argv"							,  lowercase__):
           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(lowercase__)
           for w in expected_strings:
                assert w in cs.out
           for w in un_expected_strings:
                assert w not in cs.out
           assert Path(lowercase__).exists()
           os.remove(Path(lowercase__))







'''simple docstring'''


from __future__ import annotations






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     print(F"""Vertex\tShortest Distance from vertex {src}"""       )
     for i, d in enumerate(lowercase__       ):
          print(F"""{i}\t\t{d}"""       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     for j in range(lowercase__       ):
          a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])
          if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
               return True
     return False






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=[float("inf"       )] * vertex_count
     a_       		=0.0

     for _ in range(vertex_count - 1       ):
          for j in range(lowercase__       ):
               a_		,						a_		,						a_       		=(graph[j][k] for k in ["src", "dst", "weight"])

               if distance[u] != float("inf"       ) and distance[u] + w < distance[v]:
                    a_       		=distance[u] + w

     a_       		=check_negative_cycle(lowercase__       ,	lowercase__       ,	lowercase__       )
     if negative_cycle_exists:
          raise Exception("Negative cycle found"       )

     return distance


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowercase							       =			int(input('''Enter number of vertices: ''').strip())
  lowercase							       =			int(input('''Enter number of edges: ''').strip())

  lowercase							       =			[{} for _ in range(E)]

  for i in range(E):
    print('''Edge ''', i + 1)
    lowercase       ,      lowercase       ,      lowercase							       =			(
        int(x)
        for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
    )
    lowercase							       =			{'''src''': src, '''dst''': dest, '''weight''': weight}

  lowercase							       =			int(input('''\nEnter shortest path source:''').strip())
  lowercase							       =			bellman_ford(graph, V, E, source)
  print_distance(shortest_distance, 0)


