



'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__  :			List[str]          =					{
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase__  :			Any          =					[
      '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BigBirdPegasusForCausalLM''',
      '''BigBirdPegasusForConditionalGeneration''',
      '''BigBirdPegasusForQuestionAnswering''',
      '''BigBirdPegasusForSequenceClassification''',
      '''BigBirdPegasusModel''',
      '''BigBirdPegasusPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  UpperCamelCase__  :			str          =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           '_float_tensor',
           'decoder.output_projection.weight',
       ]
       for k in ignore_keys:
              state_dict.pop(__lowercase				,  __lowercase     )
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  torch.load(__lowercase				,  map_location='cpu'     )['model']
       remove_ignore_keys_(__lowercase     )
       __UpperCamelCase       =  state_dict['encoder.embed_tokens.weight'].shape[0]

       __UpperCamelCase       =  MBartConfig.from_pretrained(__lowercase				,  vocab_size=__lowercase     )
       if mbart_aa and finetuned:
              __UpperCamelCase       =  'relu'

       __UpperCamelCase       =  state_dict['decoder.embed_tokens.weight']
       __UpperCamelCase       =  MBartForConditionalGeneration(__lowercase     )
       model.model.load_state_dict(__lowercase     )

       if finetuned:
              __UpperCamelCase       =  make_linear_from_emb(model.model.shared     )

       return model


if __name__ == "__main__":
    a__							:							Dict =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
    )
    parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--hf_config''',
        default='''facebook/mbart-large-cc25''',
        type=str,
        help='''Which huggingface architecture to use: mbart-large''',
    )
    parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
    parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							str =convert_fairseq_mbart_checkpoint_from_disk(
        args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
    )
    model.save_pretrained(args.pytorch_dump_folder_path)
