



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)
UpperCamelCase__ =						logging.getLogger()






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Any:
						__lowercase					=				"""\n""".join(lowercase__   )
						Path(lowercase__   ).open("""w"""   ).writelines(lowercase__   )


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

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







class 			_lowerCAmelCase   ( _UpperCAmelCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       int			,     lowercase	:       int   )						->					List[Any]:
									"""simple docstring"""







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

									__lowercase					=				str(Path(self.get_auto_remove_tmp_dir()   ) / """scores.json"""   )
									__lowercase					=				"""translation_en_to_de""" if model == T5_TINY else """summarization"""
									__lowercase					=				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 		snake_case__			(				self	:       Optional[int]   )						->					int:
									"""simple docstring"""







									self.run_eval_tester(lowercase   )





			@parameterized.expand([BART_TINY, MBART_TINY]   )
			@slow
			def 		snake_case__			(				self	:       str			,     lowercase	:       Union[str, Any]   )						->					Optional[Any]:
									"""simple docstring"""







									self.run_eval_tester(lowercase   )







			@parameterized.expand([T5_TINY, MBART_TINY]   )
			@slow
			def 		snake_case__			(				self	:       Union[str, Any]			,     lowercase	:       List[Any]   )						->					Optional[int]:
									"""simple docstring"""







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

									__lowercase					=				{
									    """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!""",
									    ],
									}

									__lowercase					=				Path(self.get_auto_remove_tmp_dir()   )
									__lowercase					=				str(tmp_dir / """scores.json"""   )
									__lowercase					=				str(tmp_dir / """val.target"""   )
									_dump_articles(lowercase			,     text["""en"""]   )
									_dump_articles(lowercase			,     text["""de"""]   )
									__lowercase					=				"""translation_en_to_de""" if model == T5_TINY else """summarization"""
									__lowercase					=				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()
															__lowercase					=				[""" num_beams | length_penalty""", model, """Best score args"""]
															__lowercase					=				["""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   )   )










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





