



"""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)
SCREAMING_SNAKE_CASE_      						=			logging.getLogger()



def 							lowerCAmelCase_    (				SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__						)      ->   List[Any]:
		a_  :   Any			    =  "\n".join(snake_case__						)
		Path(snake_case__						).open("w"						).writelines(snake_case__						)


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       snake_case_    (    a_					):






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

									a_  :   List[Any]			    =  str(Path(self.get_auto_remove_tmp_dir()   ) / "scores.json"   )
									a_  :   Optional[int]			    =  "translation_en_to_de" if model == T5_TINY else "summarization"
									a_  :   Optional[Any]			    =  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 snake_case_	( self   ):
									self.run_eval_tester(a_   )






							@parameterized.expand([BART_TINY, MBART_TINY]   )
							@slow
							def snake_case_	( self      ,  a_   ):
									self.run_eval_tester(a_   )






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

									a_  :   int			    =  {
									    "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_  :   int			    =  Path(self.get_auto_remove_tmp_dir()   )
									a_  :   int			    =  str(tmp_dir / "scores.json"   )
									a_  :   str			    =  str(tmp_dir / "val.target"   )
									_dump_articles(a_      ,  text["en"]   )
									_dump_articles(a_      ,  text["de"]   )
									a_  :   int			    =  "translation_en_to_de" if model == T5_TINY else "summarization"
									a_  :   Dict			    =  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()
											a_  :   Union[str, Any]			    =  [" num_beams | length_penalty", model, "Best score args"]
											a_  :   Optional[Any]			    =  ["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_   )   )




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()
