





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






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
			a__	:						Union[str, Any]      		=  "\n".join(lowerCamelCase   )
			Path(lowerCamelCase   ).open("w"   ).writelines(lowerCamelCase   )


SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						"""patrickvonplaten/t5-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						"""sshleifer/tiny-mbart"""

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







class   __lowerCAmelCase			(					_UpperCamelCase  ):





			def 	_snake_case ( self					,			snake_case      )				->  str:
						"""simple docstring"""





						a__	:						Optional[Any]      		=  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__	:						int      		=  [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
						_dump_articles(snake_case					,			snake_case      )

						a__	:						List[Any]      		=  str(Path(self.get_auto_remove_tmp_dir()      ) / "scores.json"      )
						a__	:						int      		=  "translation_en_to_de" if model == T5_TINY else "summarization"
						a__	:						Optional[Any]      		=  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 	_snake_case ( self      )				->  str:
						"""simple docstring"""





						self.run_eval_tester(snake_case      )





			@parameterized.expand([BART_TINY, MBART_TINY]      )
			@slow
			def 	_snake_case ( self					,			snake_case      )				->  List[Any]:
						"""simple docstring"""





						self.run_eval_tester(snake_case      )





			@parameterized.expand([T5_TINY, MBART_TINY]      )
			@slow
			def 	_snake_case ( self					,			snake_case      )				->  Dict:
						"""simple docstring"""





						a__	:						Union[str, Any]      		=  Path(self.get_auto_remove_tmp_dir()      ) / "utest_input.source"
						a__	:						Dict      		=  input_file_name.parent / "utest_output.txt"
						assert not output_file_name.exists()

						a__	:						Optional[int]      		=  {
						    "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__	:						Optional[Any]      		=  Path(self.get_auto_remove_tmp_dir()      )
						a__	:						str      		=  str(tmp_dir / "scores.json"      )
						a__	:						Optional[int]      		=  str(tmp_dir / "val.target"      )
						_dump_articles(snake_case					,			text["en"]      )
						_dump_articles(snake_case					,			text["de"]      )
						a__	:						int      		=  "translation_en_to_de" if model == T5_TINY else "summarization"
						a__	:						int      		=  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__	:						List[str]      		=  [" num_beams | length_penalty", model, "Best score args"]
									a__	:						List[str]      		=  ["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      )      )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__       :			Any			      =						{"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						[
      """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """SEWForCTC""",
      """SEWForSequenceClassification""",
      """SEWModel""",
      """SEWPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

