
"""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__          =	logging.getLogger()
def 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    UpperCAmelCase__	:      str     							=					'\n'.join(_snake_case )
    Path(_snake_case ).open('w' ).writelines(_snake_case )


UpperCamelCase__          =	'patrickvonplaten/t5-tiny-random'
UpperCamelCase__          =	'sshleifer/bart-tiny-random'
UpperCamelCase__          =	'sshleifer/tiny-mbart'

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





class 				a			(					lowercase      ):



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[Any]     							=					Path(self.get_auto_remove_tmp_dir()      ) / 'utest_input.source'
          UpperCAmelCase__	:      Optional[Any]     							=					input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()
          UpperCAmelCase__	:      Tuple     							=					[' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
          _dump_articles(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					str(Path(self.get_auto_remove_tmp_dir()      ) / 'scores.json'      )
          UpperCAmelCase__	:      int     							=					'translation_en_to_de' if model == T5_TINY else 'summarization'
          UpperCAmelCase__	:      List[str]     							=					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(UpperCamelCase_     ,	'argv'     ,	UpperCamelCase_      ):
              run_generate()
              assert Path(UpperCamelCase_      ).exists()
              # os.remove(Path(output_file_name))



      def  __snake_case							(   self      ):
          self.run_eval_tester(UpperCamelCase_      )



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



      @parameterized.expand([T5_TINY, MBART_TINY]      )
      @slow
      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					Path(self.get_auto_remove_tmp_dir()      ) / 'utest_input.source'
          UpperCAmelCase__	:      Any     							=					input_file_name.parent / 'utest_output.txt'
          assert not output_file_name.exists()

          UpperCAmelCase__	:      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!',
              ],
          }

          UpperCAmelCase__	:      Any     							=					Path(self.get_auto_remove_tmp_dir()      )
          UpperCAmelCase__	:      Optional[Any]     							=					str(tmp_dir / 'scores.json'      )
          UpperCAmelCase__	:      Tuple     							=					str(tmp_dir / 'val.target'      )
          _dump_articles(UpperCamelCase_     ,	text['en']      )
          _dump_articles(UpperCamelCase_     ,	text['de']      )
          UpperCAmelCase__	:      Tuple     							=					'translation_en_to_de' if model == T5_TINY else 'summarization'
          UpperCAmelCase__	:      Optional[int]     							=					F'''
            run_eval_search.py
            {model}
            {str(UpperCamelCase_      )}
            {str(UpperCamelCase_      )}
            --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(UpperCamelCase_     ,	'argv'     ,	UpperCamelCase_      ):
              with CaptureStdout() as cs:
                  run_search()
              UpperCAmelCase__	:      Union[str, Any]     							=					[' num_beams | length_penalty', model, 'Best score args']
              UpperCAmelCase__	:      Dict     							=					['Info']
              if "translation" in task:
                  expected_strings.append('bleu'      )
              else:
                  expected_strings.extend(UpperCamelCase_      )
              for w in expected_strings:
                  assert w in cs.out
              for w in un_expected_strings:
                  assert w not in cs.out
              assert Path(UpperCamelCase_      ).exists()
              os.remove(Path(UpperCamelCase_      )      )



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__          =	{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__          =	[
        'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'MegaForCausalLM',
        'MegaForMaskedLM',
        'MegaForMultipleChoice',
        'MegaForQuestionAnswering',
        'MegaForSequenceClassification',
        'MegaForTokenClassification',
        'MegaModel',
        'MegaPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mega import (
            MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
            MegaForCausalLM,
            MegaForMaskedLM,
            MegaForMultipleChoice,
            MegaForQuestionAnswering,
            MegaForSequenceClassification,
            MegaForTokenClassification,
            MegaModel,
            MegaPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase__          =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

