

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	)	)


class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return index | (index + 1)
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return (index & (index + 1)) - 1
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       value
				while index < self.size:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_prev(a	) + 1
					if current_left_border == index:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       max(a      ,      a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_next(a	)






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

				"""simple docstring"""

				right -= 1  # Because of right is exclusive
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0
				while left <= right:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_prev(a	)
					if left <= current_left:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      self.tree[right]	)
						SCREAMING_SNAKE_CASE   :      str				     =       current_left
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       max(a      ,      self.arr[right]	)
						right -= 1
				return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()