
'''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)
lowercase    :							List[Any]      =					logging.getLogger()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		'\n'.join(__A	)
	Path(__A	).open('w'	).writelines(__A	)


lowercase    :							List[Any]      =					"patrickvonplaten/t5-tiny-random"
lowercase    :							Optional[int]      =					"sshleifer/bart-tiny-random"
lowercase    :							Dict      =					"sshleifer/tiny-mbart"

lowercase    :							List[Any]      =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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

								_snake_case   						=		str(Path(self.get_auto_remove_tmp_dir()  ) / 'scores.json'  )
								_snake_case   						=		'translation_en_to_de' if model == T5_TINY else 'summarization'
								_snake_case   						=		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(lowerCAmelCase_    ,   'argv'    ,   lowerCAmelCase_  ):
									run_generate()
									assert Path(lowerCAmelCase_  ).exists()
									# os.remove(Path(output_file_name))


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self.run_eval_tester(lowerCAmelCase_  )


							@parameterized.expand([BART_TINY, MBART_TINY]  )
							@slow
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								self.run_eval_tester(lowerCAmelCase_  )




							@parameterized.expand([T5_TINY, MBART_TINY]  )
							@slow
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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

								_snake_case   						=		{
								    '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!',
								    ],
								}

								_snake_case   						=		Path(self.get_auto_remove_tmp_dir()  )
								_snake_case   						=		str(tmp_dir / 'scores.json'  )
								_snake_case   						=		str(tmp_dir / 'val.target'  )
								_dump_articles(lowerCAmelCase_    ,   text['en']  )
								_dump_articles(lowerCAmelCase_    ,   text['de']  )
								_snake_case   						=		'translation_en_to_de' if model == T5_TINY else 'summarization'
								_snake_case   						=		F'\n            run_eval_search.py\n            {model}\n            {str(lowerCAmelCase_  )}\n            {str(lowerCAmelCase_  )}\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(lowerCAmelCase_    ,   'argv'    ,   lowerCAmelCase_  ):
									with CaptureStdout() as cs:
										run_search()
									_snake_case   						=		[' num_beams | length_penalty', model, 'Best score args']
									_snake_case   						=		['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"""







import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCAmelCase_   						=   logging.get_logger(__name__)
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Dict			,*_snake_case						:  Any			,**_snake_case						:  str						)					->		None:






				"""simple docstring"""



				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''			,_snake_case			,)
				super().__init__(*_snake_case			,**_snake_case						)
