





'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Dict											=       '''philschmid/bart-large-cnn-samsum'''
      _lowercase:      str											=       (
          '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
          '''and returns a summary of the text.'''
      )
      _lowercase:      Tuple											=       '''summarizer'''
      _lowercase:      int											=       AutoTokenizer
      _lowercase:      Optional[int]											=       AutoModelForSeqaSeqLM

      _lowercase:      Optional[Any]											=       ['''text''']
      _lowercase:      str											=       ['''text''']




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			int  )    ->				Optional[Any]:
            return self.pre_processor(__snake_case      ,      return_tensors="""pt"""      ,      truncation=__snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			Any  )    ->				Optional[Any]:
            return self.model.generate(**__snake_case  )[0]




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			Any  )    ->				int:
            return self.pre_processor.decode(__snake_case      ,      skip_special_tokens=__snake_case      ,      clean_up_tokenization_spaces=__snake_case  )













'''simple docstring'''






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(snake_case_       )							,		'''Tatoeba directory does not exist.'''       )
class 			UpperCAmelCase					(      unittest.TestCase       ):




      @cached_property
      def     lowercase__   (      self      :			int  )    ->				Any:
            _lowerCAmelCase												=							tempfile.mkdtemp()
            return TatoebaConverter(save_dir=__snake_case  )




      @slow
      def     lowercase__   (      self      :			Dict  )    ->				int:
            self.resolver.convert_models(["""heb-eng"""]  )




      @slow
      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            _lowerCAmelCase					,							_lowerCAmelCase												=							self.resolver.write_model_card("""opus-mt-he-en"""      ,      dry_run=__snake_case  )
            assert mmeta["long_pair"] == "heb-eng"






