[MarianMTModel] 'list' object has no attribute 'size'
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version: 4.1.1
- Platform: Google Colab
- Python version: 3.7
- PyTorch version (GPU?): 1.8.1
- Tensorflow version (GPU?):
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?:

### Who can help
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Models:

- albert, bert, xlm: @LysandreJik
- blenderbot, bart, marian, pegasus, encoderdecoder,  t5: @patrickvonplaten, @patil-suraj
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
- fsmt: @stas00
- funnel: @sgugger
- gpt2: @patrickvonplaten, @LysandreJik
- rag: @patrickvonplaten, @lhoestq
- tensorflow: @LysandreJik

Library:

- benchmarks: @patrickvonplaten
- deepspeed: @stas00
- ray/raytune: @richardliaw, @amogkam
- text generation: @patrickvonplaten
- tokenizers: @LysandreJik
- trainer: @sgugger
- pipelines: @LysandreJik

Documentation: @sgugger

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- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.

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Examples:

- maintained examples (not research project or legacy): @sgugger, @patil-suraj
- research_projects/bert-loses-patience: @JetRunner
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## Information

Model I am using (Bert, XLNet ...): 

```
!pip install transformers==4.1.1 sentencepiece==0.1.94
!pip install mosestokenizer==1.1.0

from transformers import MarianMTModel, MarianTokenizer

target_model_name = 'Helsinki-NLP/opus-mt-en-ROMANCE'
target_tokenizer = MarianTokenizer.from_pretrained(target_model_name)
target_model = MarianMTModel.from_pretrained(target_model_name)

en_model_name = 'Helsinki-NLP/opus-mt-ROMANCE-en'
en_tokenizer = MarianTokenizer.from_pretrained(en_model_name)
en_model = MarianMTModel.from_pretrained(en_model_name)

def translate(texts, model, tokenizer, language="fr"):
    # Prepare the text data into appropriate format for the model
    template = lambda text: f"{text}" if language == "en" else f">>{language}<< {text}"
    src_texts = [template(text) for text in texts]

    # Tokenize the texts
    encoded = tokenizer.prepare_seq2seq_batch(src_texts)
    
    # Generate translation using model
    translated = model.generate(**encoded)

    # Convert the generated tokens indices back into text
    translated_texts = tokenizer.batch_decode(translated, skip_special_tokens=True)
    
    return translated_texts

def back_translate(texts, source_lang="en", target_lang="vi"):
    # Translate from source to target language
    fr_texts = translate(texts, target_model, target_tokenizer, 
                         language=target_lang)

    # Translate from target language back to source language
    back_translated_texts = translate(fr_texts, en_model, en_tokenizer, 
                                      language=source_lang)
    
    return back_translated_texts


en_texts = ['This is so cool', 'I hated the food', 'They were very helpful']

aug_texts = back_translate(en_texts, source_lang="en", target_lang="es")
print(aug_texts)

```
The problem arises when using:
* [x] my own modified scripts: 
```
<ipython-input-1-83d3425f13db> in back_translate(texts, source_lang, target_lang)
     36     # Translate from source to target language
     37     fr_texts = translate(texts, target_model, target_tokenizer, 
---> 38                          language=target_lang)
     39 
     40     # Translate from target language back to source language

<ipython-input-1-83d3425f13db> in translate(texts, model, tokenizer, language)
     26 
     27     # Generate translation using model
---> 28     translated = model.generate(**encoded)
     29 
     30     # Convert the generated tokens indices back into text

/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
     25         def decorate_context(*args, **kwargs):
     26             with self.__class__():
---> 27                 return func(*args, **kwargs)
     28         return cast(F, decorate_context)
     29 

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in generate(self, input_ids, max_length, min_length, do_sample, early_stopping, num_beams, temperature, top_k, top_p, repetition_penalty, bad_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, max_time, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, **model_kwargs)
    914         if self.config.is_encoder_decoder:
    915             # add encoder_outputs to model_kwargs
--> 916             model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(input_ids, model_kwargs)
    917 
    918             # set input_ids as decoder_input_ids

/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py in _prepare_encoder_decoder_kwargs_for_generation(self, input_ids, model_kwargs)
    409                 argument: value for argument, value in model_kwargs.items() if not argument.startswith("decoder_")
    410             }
--> 411             model_kwargs["encoder_outputs"]: ModelOutput = encoder(input_ids, return_dict=True, **encoder_kwargs)
    412         return model_kwargs
    413 

/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

/usr/local/lib/python3.7/dist-packages/transformers/models/marian/modeling_marian.py in forward(self, input_ids, attention_mask, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict)
    712             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
    713         elif input_ids is not None:
--> 714             input_shape = input_ids.size()
    715             input_ids = input_ids.view(-1, input_shape[-1])
    716         elif inputs_embeds is not None:

AttributeError: 'list' object has no attribute 'size'

```

Thanks for your support




This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>I confirm the error:
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path="Helsinki-NLP/opus-mt-en-de"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
batch = tokenizer.prepare_seq2seq_batch(src_texts=["Alice has a cat."])
gen = model.generate(**batch)
```<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.