Hello, I am trying to set up an EncoderDecoderModel using PEGASUS encoder and ProphetNet decoder.
First, I initialize a PegasusModel and access its encoder:
from transformers import PegasusModel, PegasusConfig, EncoderDecoderModel

pegasus = PegasusModel(PegasusConfig()).encoder

Then I try to pass that to the EncoderDecoderModel together with decoder from ProphetNet:
pegasus2prophet = EncoderDecoderModel.from_encoder_decoder_pretrained(pegasus, "microsoft/prophetnet-large-uncased")

However running that piece of code results in following errors:
HTTPError                                 Traceback (most recent call last)
~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    416             # Load from URL or cache if already cached
--> 417             resolved_config_file = cached_path(
    418                 config_file,

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/file_utils.py in cached_path(url_or_filename, cache_dir, force_download, proxies, resume_download, user_agent, extract_compressed_file, force_extract, use_auth_token, local_files_only)
   1077         # URL, so get it from the cache (downloading if necessary)
-> 1078         output_path = get_from_cache(
   1079             url_or_filename,

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/file_utils.py in get_from_cache(url, cache_dir, force_download, proxies, etag_timeout, resume_download, user_agent, use_auth_token, local_files_only)
   1215             r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=etag_timeout)
-> 1216             r.raise_for_status()
   1217             etag = r.headers.get("X-Linked-Etag") or r.headers.get("ETag")

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/requests/models.py in raise_for_status(self)
    942         if http_error_msg:
--> 943             raise HTTPError(http_error_msg, response=self)
    944

HTTPError: 414 Client Error: Request-URI Too Large for url: https://huggingface.co/PegasusEncoder(%0A%20%20(embed_tokens):%20Embedding(50265,%201024,%20padding_idx=0)%0A%20%20(embed_positions):%20PegasusSinusoidalPositionalEmbed

(several rows of the URI follow)
During handling of the above exception, another exception occurred:

OSError                                   Traceback (most recent call last)
<ipython-input-3-2cdd15f1864e> in <module>
----> 1 pegasus2prophet = EncoderDecoderModel.from_encoder_decoder_pretrained(pegasus, "microsoft/prophetnet-large-uncased")

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py in from_encoder_decoder_pretrained(cls, encoder_pretrained_model_name_or_path, decoder_pretrained_model_name_or_path, *model_args, **kwargs)
    304                 from ..auto.configuration_auto import AutoConfig
    305 
--> 306                 encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
    307                 if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
    308 

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/models/auto/configuration_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
    366             {'foo': False}
    367         """
--> 368         config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
    369 
    370         if "model_type" in config_dict:

~/stanford/xcs224u-project/project_venv/lib/python3.8/site-packages/transformers/configuration_utils.py in get_config_dict(cls, pretrained_model_name_or_path, **kwargs)
    434                 f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
    435             )
--> 436             raise EnvironmentError(msg)
    437 
    438         except json.JSONDecodeError:


OSError: Can't load config for 'PegasusEncoder(
  (embed_tokens): Embedding(50265, 1024, padding_idx=0)
  (embed_positions): PegasusSinusoidalPositionalEmbedding(1024, 1024)
  (layers): ModuleList(
    (0): PegasusEncoderLayer(
      (self_attn): PegasusAttention(
      ...

Would anyone know if there is any way to bypass this problem?