



"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]						)						->       Optional[int]:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[int]								=			[
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       int:

  """simple docstring"""




  lowerCAmelCase_						,lowerCAmelCase_       :    Tuple								=			emb.weight.shape
  lowerCAmelCase_       :    Dict								=			nn.Linear(lowerCAmelCase__   ,       lowerCAmelCase__   ,       bias=lowerCAmelCase__						)
  lowerCAmelCase_       :    List[str]								=			emb.weight.data
  return lin_layer

def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    Tuple="facebook/mbart-large-en-ro"   ,       lowerCAmelCase__				:    List[Any]=False   ,       lowerCAmelCase__				:    Optional[Any]=False						)						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			torch.load(lowerCAmelCase__   ,       map_location='cpu'						)['model']
  remove_ignore_keys_(lowerCAmelCase__						)
  lowerCAmelCase_       :    Dict								=			state_dict['encoder.embed_tokens.weight'].shape[0]

  lowerCAmelCase_       :    List[str]								=			MBartConfig.from_pretrained(lowerCAmelCase__   ,       vocab_size=lowerCAmelCase__						)
  if mbart_aa and finetuned:
    lowerCAmelCase_       :    Optional[int]								=			'relu'

  lowerCAmelCase_       :    List[str]								=			state_dict['decoder.embed_tokens.weight']
  lowerCAmelCase_       :    Optional[int]								=			MBartForConditionalGeneration(lowerCAmelCase__						)
  model.model.load_state_dict(lowerCAmelCase__						)

  if finetuned:
    lowerCAmelCase_       :    Optional[int]								=			make_linear_from_emb(model.model.shared						)

  return model


if __name__ == "__main__":
     lowercase__		:    int				   =     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""")
     lowercase__		:    List[str]				   =     parser.parse_args()
     lowercase__		:    List[Any]				   =     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)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase__		:    Dict				   =     {
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase__		:    str				   =     [
         """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """FalconForCausalLM""",
         """FalconModel""",
         """FalconPreTrainedModel""",
         """FalconForSequenceClassification""",
         """FalconForTokenClassification""",
         """FalconForQuestionAnswering""",
     ]


if TYPE_CHECKING:
     from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_falcon import (
              FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
              FalconForCausalLM,
              FalconForQuestionAnswering,
              FalconForSequenceClassification,
              FalconForTokenClassification,
              FalconModel,
              FalconPreTrainedModel,
          )


else:
     import sys

     lowercase__		:    Dict				   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





