






"""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_   : Dict   			= logging.getLogger()



def 					_A			(__a    ,				__a							) ->       str:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_      :  str    						=		"""\n""".join(__a							)
    Path(__a							).open('''w'''							).writelines(__a							)


UpperCAmelCase_   : int   			= "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_   : Dict   			= "sshleifer/bart-tiny-random"
UpperCAmelCase_   : Tuple   			= "sshleifer/tiny-mbart"

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





class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  Dict):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		str(Path(self.get_auto_remove_tmp_dir()) / '''scores.json''')
          SCREAMING_SNAKE_CASE_      :  str    						=		"""translation_en_to_de""" if model == T5_TINY else """summarization"""
          SCREAMING_SNAKE_CASE_      :  Dict    						=		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 _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          self.run_eval_tester(_SCREAMING_SNAKE_CASE)
      @parameterized.expand([BART_TINY, MBART_TINY])
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  Any):




          '''simple docstring'''


          self.run_eval_tester(_SCREAMING_SNAKE_CASE)



      @parameterized.expand([T5_TINY, MBART_TINY])
      @slow
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		Path(self.get_auto_remove_tmp_dir()) / """utest_input.source"""
          SCREAMING_SNAKE_CASE_      :  int    						=		input_file_name.parent / """utest_output.txt"""
          assert not output_file_name.exists()

          SCREAMING_SNAKE_CASE_      :  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!""",
              ],
          }

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		Path(self.get_auto_remove_tmp_dir())
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		str(tmp_dir / '''scores.json''')
          SCREAMING_SNAKE_CASE_      :  Dict    						=		str(tmp_dir / '''val.target''')
          _dump_articles(_SCREAMING_SNAKE_CASE						,	text['''en'''])
          _dump_articles(_SCREAMING_SNAKE_CASE						,	text['''de'''])
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		"""translation_en_to_de""" if model == T5_TINY else """summarization"""
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		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()
              SCREAMING_SNAKE_CASE_      :  str    						=		[""" num_beams | length_penalty""", model, """Best score args"""]
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		["""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))







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
