


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							: int      =   logging.getLogger()






def 							__lowercase     (				__lowerCAmelCase							:       Path      ,					__lowerCAmelCase							:       list     ):
							a__    =  "\n".join(SCREAMING_SNAKE_CASE_     )
							Path(SCREAMING_SNAKE_CASE_     ).open('w'     ).writelines(SCREAMING_SNAKE_CASE_     )


snake_case							: int      =   '''patrickvonplaten/t5-tiny-random'''
snake_case							: str      =   '''sshleifer/bart-tiny-random'''
snake_case							: Optional[int]      =   '''sshleifer/tiny-mbart'''

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







class  snake_case_     (_UpperCAmelCase   ):
					def 				lowerCamelCase__(   self     :Tuple    ,__snake_case     :int   )      ->					Dict:
												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(lowercase__    ,lowercase__   )

												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(lowercase__    ,'argv'    ,lowercase__   ):
																			run_generate()
																			assert Path(lowercase__   ).exists()
																			# os.remove(Path(output_file_name))
					def 				lowerCamelCase__(   self     :Optional[int]   )      ->					List[Any]:
												self.run_eval_tester(lowercase__   )
					@parameterized.expand([BART_TINY, MBART_TINY]   )
					@slow
					def 				lowerCamelCase__(   self     :Optional[int]    ,__snake_case     :List[Any]   )      ->					Dict:
												self.run_eval_tester(lowercase__   )



					@parameterized.expand([T5_TINY, MBART_TINY]   )
					@slow
					def 				lowerCamelCase__(   self     :str    ,__snake_case     :Union[str, Any]   )      ->					int:
												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(lowercase__    ,text['en']   )
												_dump_articles(lowercase__    ,text['de']   )
												a__    =  "translation_en_to_de" if model == T5_TINY else "summarization"
												a__    =  F'\n            run_eval_search.py\n            {model}\n            {str(lowercase__   )}\n            {str(lowercase__   )}\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(lowercase__    ,'argv'    ,lowercase__   ):
																			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(lowercase__   )
																			for w in expected_strings:
																										assert w in cs.out
																			for w in un_expected_strings:
																										assert w not in cs.out
																			assert Path(lowercase__   ).exists()
																			os.remove(Path(lowercase__   )   )




from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case							: Optional[Any]      =   {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[int]      =   [
						    '''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FocalNetForImageClassification''',
						    '''FocalNetForMaskedImageModeling''',
						    '''FocalNetBackbone''',
						    '''FocalNetModel''',
						    '''FocalNetPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_focalnet import (
												    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FocalNetBackbone,
												    FocalNetForImageClassification,
												    FocalNetForMaskedImageModeling,
												    FocalNetModel,
												    FocalNetPreTrainedModel,
												)

else:
						import sys

						snake_case							: Union[str, Any]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




