old_contents: # coding=utf-8
  # Copyright 2020 Marian Team Authors and The HuggingFace Inc. team.
  #
  # 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.
  """PyTorch MarianMTModel model, ported from the Marian C++ repo."""
  
  
  from transformers.modeling_bart import BartForConditionalGeneration
  
  
  class MarianMTModel(BartForConditionalGeneration):
      r"""
      Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
      Model API is identical to BartForConditionalGeneration.
      Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
  
      Examples::
  
          from transformers import MarianTokenizer, MarianMTModel
          from typing import List
          src = 'fr'  # source language
          trg = 'en'  # target language
          sample_text = "où est l'arrêt de bus ?"
          mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'   # `Model List`__
  
          model = MarianMTModel.from_pretrained(mname)
          tok = MarianTokenizer.from_pretrained(mname)
          batch = tok.prepare_translation_batch(src_texts=[sample_text])  # don't need tgt_text for inference
          gen = model.generate(**batch)  # for forward pass: model(**batch)
          words: List[str] = tok.decode_batch(gen, skip_special_tokens=True)  # returns "Where is the the bus stop ?"
  
      """
  
      pretrained_model_archive_map = {}  # see https://huggingface.co/models?search=Helsinki-NLP
  
      def prepare_scores_for_generation(self, scores, cur_len, max_length):
          if cur_len == max_length - 1 and self.config.eos_token_id is not None:
              self._force_token_ids_generation(scores, self.config.eos_token_id)
          return scores
  
new_contents: # coding=utf-8
  # Copyright 2020 Marian Team Authors and The HuggingFace Inc. team.
  #
  # 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.
  """PyTorch MarianMTModel model, ported from the Marian C++ repo."""
  
  
  from transformers.modeling_bart import BartForConditionalGeneration
  
  
  class MarianMTModel(BartForConditionalGeneration):
      r"""
      Pytorch version of marian-nmt's transformer.h (c++). Designed for the OPUS-NMT translation checkpoints.
      Model API is identical to BartForConditionalGeneration.
      Available models are listed at `Model List <https://huggingface.co/models?search=Helsinki-NLP>`__
  
      Examples::
  
          from transformers import MarianTokenizer, MarianMTModel
          from typing import List
          src = 'fr'  # source language
          trg = 'en'  # target language
          sample_text = "où est l'arrêt de bus ?"
          mname = f'Helsinki-NLP/opus-mt-{src}-{trg}'   # `Model List`__
  
          model = MarianMTModel.from_pretrained(mname)
          tok = MarianTokenizer.from_pretrained(mname)
          batch = tok.prepare_translation_batch(src_texts=[sample_text])  # don't need tgt_text for inference
          gen = model.generate(**batch)  # for forward pass: model(**batch)
          words: List[str] = tok.batch_decode(gen, skip_special_tokens=True)  # returns "Where is the the bus stop ?"
  
      """
  
      pretrained_model_archive_map = {}  # see https://huggingface.co/models?search=Helsinki-NLP
  
      def prepare_scores_for_generation(self, scores, cur_len, max_length):
          if cur_len == max_length - 1 and self.config.eos_token_id is not None:
              self._force_token_ids_generation(scores, self.config.eos_token_id)
          return scores
  
subject: Fix typo in docstring (#4284)