



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)
__snake_case						:Union[str, Any]      		=     logging.getLogger()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				'''\n'''.join(_UpperCAmelCase				)
    Path(_UpperCAmelCase				).open('''w'''				).writelines(_UpperCAmelCase				)


__snake_case						:Tuple      		=     '''patrickvonplaten/t5-tiny-random'''
__snake_case						:List[Any]      		=     '''sshleifer/bart-tiny-random'''
__snake_case						:int      		=     '''sshleifer/tiny-mbart'''

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

class     _A       (	__UpperCAmelCase    ):



       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

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

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



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           self.run_eval_tester(__SCREAMING_SNAKE_CASE)



       @parameterized.expand([BART_TINY, MBART_TINY])
       @slow
       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           self.run_eval_tester(__SCREAMING_SNAKE_CASE)





       @parameterized.expand([T5_TINY, MBART_TINY])
       @slow
       def 		_lowerCamelCase			(		self							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

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

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










from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     NestedDataStructureLike[PathLike]     ,		__SCREAMING_SNAKE_CASE							:     Optional[NamedSplit] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[Features] = None     ,		__SCREAMING_SNAKE_CASE							:     str = None     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		**__SCREAMING_SNAKE_CASE							:     List[str]     ,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE     ,		split=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		cache_dir=__SCREAMING_SNAKE_CASE     ,		keep_in_memory=__SCREAMING_SNAKE_CASE     ,		streaming=__SCREAMING_SNAKE_CASE     ,		num_proc=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)
           __a						       =				path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a						       =				Text(
               cache_dir=__SCREAMING_SNAKE_CASE     ,		data_files=__SCREAMING_SNAKE_CASE     ,		features=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           if self.streaming:
               __a						       =				self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a						       =				None
               __a						       =				None
               __a						       =				None
               __a						       =				None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE     ,		download_mode=__SCREAMING_SNAKE_CASE     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		base_path=__SCREAMING_SNAKE_CASE     ,		num_proc=self.num_proc     ,		)
               __a						       =				self.builder.as_dataset(
                   split=self.split     ,		verification_mode=__SCREAMING_SNAKE_CASE     ,		in_memory=self.keep_in_memory)
           return dataset





