






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)
SCREAMING_SNAKE_CASE__				     =			logging.getLogger()

def        UpperCAmelCase__		(						lowerCamelCase_       :       Path   ,  lowerCamelCase_       :       list				):
			__a						:  Tuple									=     '\n'.join(lowerCamelCase_				)
			Path(lowerCamelCase_				).open('w'				).writelines(lowerCamelCase_				)


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

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






class 			_UpperCamelCase(       __lowerCamelCase		):

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ):



					'''simple docstring'''



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

					__a						:  List[str]									=     str(Path(self.get_auto_remove_tmp_dir()    ) / 'scores.json'    )
					__a						:  Any									=     'translation_en_to_de' if model == T5_TINY else 'summarization'
					__a						:  Union[str, Any]									=     f'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

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

		def    __lowerCAmelCase  (					self		:    str    ):



					'''simple docstring'''



					self.run_eval_tester(SCREAMING_SNAKE_CASE__    )

		@parameterized.expand([BART_TINY, MBART_TINY]    )
		@slow
		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ):



					'''simple docstring'''



					self.run_eval_tester(SCREAMING_SNAKE_CASE__    )



		@parameterized.expand([T5_TINY, MBART_TINY]    )
		@slow
		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  str									=     Path(self.get_auto_remove_tmp_dir()    ) / 'utest_input.source'
					__a						:  Any									=     input_file_name.parent / 'utest_output.txt'
					assert not output_file_name.exists()

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

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












def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

		while queue:
				__lowercase											=     queue.pop(0							)
				for ind in range(len(graph[u]							)							):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   graph[parent[s]][s]							)
						__lowercase											=     parent[s]

				max_flow += path_flow
				__lowercase											=     sink

				while v != source:
						__lowercase											=     parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase											=     parent[v]
		return max_flow


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




