Pegasus example not working
@patrickvonplaten 

Hi, 

I am trying to run on the pegasus example on Colab. 
"!pip install git+https://github.com/huggingface/transformers.git
!pip install sentencepiece
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import torch

src_text = [
    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
]

model_name = 'google/pegasus-xsum'
torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PegasusTokenizer.from_pretrained(model_name)
model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
translated = model.generate(**batch)
tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
assert tgt_text[0] == "California's largest electricity provider has turned off power to hundreds of thousands of customers.

Collecting git+https://github.com/huggingface/transformers.git
  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-gvb7jrr9
  Running command git clone -q https://github.com/huggingface/transformers.git /tmp/pip-req-build-gvb7jrr9
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
    Preparing wheel metadata ... done
Requirement already satisfied (use --upgrade to upgrade): transformers==4.0.0rc1 from git+https://github.com/huggingface/transformers.git in /usr/local/lib/python3.6/dist-packages
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Building wheels for collected packages: transformers
  Building wheel for transformers (PEP 517) ... done
  Created wheel for transformers: filename=transformers-4.0.0rc1-cp36-none-any.whl size=1349475 sha256=8f08b76fc03d4cd0c1532e37462b5f1682fc58ad7f92ed533533b276fc4ecaf5
  Stored in directory: /tmp/pip-ephem-wheel-cache-8gbsru65/wheels/33/eb/3b/4bf5dd835e865e472d4fc0754f35ac0edb08fe852e8f21655f
Successfully built transformers
Requirement already satisfied: sentencepiece in /usr/local/lib/python3.6/dist-packages (0.1.94)
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-1-ad40feda49b0> in <module>()
     12 tokenizer = PegasusTokenizer.from_pretrained(model_name)
     13 model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
---> 14 batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)
     15 translated = model.generate(**batch)
     16 tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)

2 frames
/usr/local/lib/python3.6/dist-packages/transformers/file_utils.py in wrapper(*args, **kwargs)
   1236     def wrapper(*args, **kwargs):
   1237         if is_torch_available():
-> 1238             return func(*args, **kwargs)
   1239         else:
   1240             raise ImportError(f"Method `{func.__name__}` requires PyTorch.")

/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in to(self, device)
    777             modification.
    778         """
--> 779         self.data = {k: v.to(device) for k, v in self.data.items()}
    780         return self
    781 

/usr/local/lib/python3.6/dist-packages/transformers/tokenization_utils_base.py in <dictcomp>(.0)
    777             modification.
    778         """
--> 779         self.data = {k: v.to(device) for k, v in self.data.items()}
    780         return self
    781 

AttributeError: 'list' object has no attribute 'to' "

Please help.

Thanks,
Akila
 
@greenstars having the same issue - How did you resolve this?<|||||>> @greenstars having the same issue - How did you resolve this?

@EliaKunz I changed "!pip install git+https://github.com/huggingface/transformers.git" to "!pip install transformers". <|||||>Thx! Was on datalore with the latest transformers 4 - downgraded to 3.5 and everything is working now.<|||||>I had the same issue with the latest transformers 4.1 (pip installed). It's fixed after adding return_tensors point. 

From

`batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)`

to

`batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors='pt').to(torch_device)`

did the job for me.
<|||||>On running 
`batch = tokenizer(src_text, truncation=True, padding='longest', return_tensors="pt").to(device)`
I am getting the error
```
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-49-e6e55e18a32c> in <module>()
----> 1 batch = tokenizer(src_text, truncation=True, padding='longest', return_tensors="pt").to(device)
      2 translated = model.generate(**batch)
      3 tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)

TypeError: 'NoneType' object is not callable
```
and on running `batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors='pt').to(torch_device)`
I am getting the error
```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-50-b7183fa2a37c> in <module>()
----> 1 batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors='pt').to(torch_device)
      2 translated = model.generate(**batch)
      3 tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)

AttributeError: 'NoneType' object has no attribute 'prepare_seq2seq_batch'
```

Any help would be greatly appreciated. <|||||>@YatinKapoor your tokenizer seems to be `None`<|||||>Need to replace PegasusTokenizer with AutoTokenizer
```
from transformers import PegasusForConditionalGeneration, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
````
<|||||>@maifeng thanks! AutoTokenizer did the job for me! <|||||>> I had the same issue with the latest transformers 4.1 (pip installed). It's fixed after adding return_tensors point.
> 
> From
> 
> `batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest').to(torch_device)`
> 
> to
> 
> `batch = tokenizer.prepare_seq2seq_batch(src_text, truncation=True, padding='longest', return_tensors='pt').to(torch_device)`
> 
> did the job for me.

Worked for me