






"""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)
lowerCamelCase_       			=   logging.getLogger()

def        snake_case   (     A__			,A__		):
			UpperCAmelCase_  :				Optional[int]      =      "\n".join(A__		)
			Path(A__		).open("w"		).writelines(A__		)


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

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







class     UpperCamelCase_ (__A  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Dict		)		->    Tuple:
				UpperCAmelCase_  :				Dict      =      Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
				UpperCAmelCase_  :				int      =      input_file_name.parent / "utest_output.txt"
				assert not output_file_name.exists()
				UpperCAmelCase_  :				int      =      [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
				_dump_articles(lowerCAmelCase_			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      str(Path(self.get_auto_remove_tmp_dir()		) / "scores.json"		)
				UpperCAmelCase_  :				Optional[int]      =      "translation_en_to_de" if model == T5_TINY else "summarization"
				UpperCAmelCase_  :				Optional[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(lowerCAmelCase_			,			"argv"			,			lowerCAmelCase_		):
							run_generate()
							assert Path(lowerCAmelCase_		).exists()
							# os.remove(Path(output_file_name))





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    str:
				self.run_eval_tester(lowerCAmelCase_		)





	@parameterized.expand([BART_TINY, MBART_TINY]		)
	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Any		)		->    Optional[Any]:
				self.run_eval_tester(lowerCAmelCase_		)





	@parameterized.expand([T5_TINY, MBART_TINY]		)
	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Tuple		)		->    Tuple:
				UpperCAmelCase_  :				str      =      Path(self.get_auto_remove_tmp_dir()		) / "utest_input.source"
				UpperCAmelCase_  :				List[Any]      =      input_file_name.parent / "utest_output.txt"
				assert not output_file_name.exists()

				UpperCAmelCase_  :				Union[str, 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!",
				    ],
				}

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











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


if __name__ == "__main__":
			print(f'{solution() = }')



