






"""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)
_lowerCAmelCase         =							logging.getLogger()






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						str	    =   """\n""".join(snake_case__					)
	Path(snake_case__					).open("""w"""					).writelines(snake_case__					)


_lowerCAmelCase         =							"patrickvonplaten/t5-tiny-random"
_lowerCAmelCase         =							"sshleifer/bart-tiny-random"
_lowerCAmelCase         =							"sshleifer/tiny-mbart"

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




class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




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

		A_		:						int	    =   str(Path(self.get_auto_remove_tmp_dir()							) / """scores.json"""							)
		A_		:						List[Any]	    =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
		A_		:						int	    =   f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

		with patch.object(lowerCAmelCase_     ,     """argv"""     ,     lowerCAmelCase_							):
			run_generate()
			assert Path(lowerCAmelCase_							).exists()
			# os.remove(Path(output_file_name))





	def    lowerCamelCase(self							):
		self.run_eval_tester(lowerCAmelCase_							)





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





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

		A_		:						List[Any]	    =   {
		    """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_		:						str	    =   Path(self.get_auto_remove_tmp_dir()							)
		A_		:						int	    =   str(tmp_dir / """scores.json"""							)
		A_		:						List[str]	    =   str(tmp_dir / """val.target"""							)
		_dump_articles(lowerCAmelCase_     ,     text["""en"""]							)
		_dump_articles(lowerCAmelCase_     ,     text["""de"""]							)
		A_		:						List[Any]	    =   """translation_en_to_de""" if model == T5_TINY else """summarization"""
		A_		:						Optional[Any]	    =   f"""
            run_eval_search.py
            {model}
            {str(lowerCAmelCase_							)}
            {str(lowerCAmelCase_							)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.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()
			A_		:						List[str]	    =   [""" num_beams | length_penalty""", model, """Best score args"""]
			A_		:						Tuple	    =   ["""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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =							{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase         =							[
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase         =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

