
"""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)
UpperCamelCase_					:						str				= logging.getLogger()
def 							A_     (__a				,   __a				):

					'''simple docstring'''





					A_				 =						"\n".join(__a				)
					Path(__a				).open("w"				).writelines(__a				)


UpperCamelCase_					:						Dict				= '''patrickvonplaten/t5-tiny-random'''
UpperCamelCase_					:						Any				= '''sshleifer/bart-tiny-random'''
UpperCamelCase_					:						List[str]				= '''sshleifer/tiny-mbart'''

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




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					int	)      -> List[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_				 =						[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
								_dump_articles(_snake_case       ,   _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(_snake_case       ,   "argv"       ,   _snake_case	):
													run_generate()
													assert Path(_snake_case	).exists()
													# os.remove(Path(output_file_name))




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Tuple:







								"""simple docstring"""
								self.run_eval_tester(_snake_case	)




			@parameterized.expand([BART_TINY, MBART_TINY]	)
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								self.run_eval_tester(_snake_case	)






			@parameterized.expand([T5_TINY, MBART_TINY]	)
			@slow
			def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					Union[str, Any]	)      -> List[str]:







								"""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(_snake_case       ,   text["en"]	)
								_dump_articles(_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(_snake_case	)}\n            {str(_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(_snake_case       ,   "argv"       ,   _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(_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(_snake_case	).exists()
													os.remove(Path(_snake_case	)	)






"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_					:						str				= {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_					:						Union[str, Any]				= ['''TimmBackbone''']


if TYPE_CHECKING:
		from .configuration_timm_backbone import TimmBackboneConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_timm_backbone import TimmBackbone

else:
		import sys

		UpperCamelCase_					:						List[Any]				= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




