





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Tuple			 =	'fnet'


   def __init__(   self      :    Optional[int]      ,					a      :    Any=32_000      ,					a      :    Optional[Any]=768      ,					a      :    Optional[Any]=12      ,					a      :    Tuple=3_072      ,					a      :    Any="gelu_new"      ,					a      :    Tuple=0.1      ,					a      :    int=512      ,					a      :    Dict=4      ,					a      :    int=0.02      ,					a      :    Optional[Any]=1E-1_2      ,					a      :    str=False      ,					a      :    str=512      ,					a      :    Dict=3      ,					a      :    int=1      ,					a      :    List[Any]=2      ,					**a      :    List[Any]      ,					)->       str:





     """simple docstring"""







     super().__init__(pad_token_id=a      ,					bos_token_id=a      ,					eos_token_id=a      ,					**a	)

     lowercase__           =  vocab_size
     lowercase__           =  max_position_embeddings
     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  initializer_range
     lowercase__           =  type_vocab_size
     lowercase__           =  layer_norm_eps
     lowercase__           =  use_tpu_fourier_optimizations
     lowercase__           =  tpu_short_seq_length









import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Union[str, Any]:
  lowercase__           =  [
      'encoder.version',
      'decoder.version',
      'model.encoder.version',
      'model.decoder.version',
      '_float_tensor',
      'decoder.output_projection.weight',
  ]
  for k in ignore_keys:
    state_dict.pop(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__      ,   lowercase__           =  emb.weight.shape
  lowercase__           =  nn.Linear(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						bias=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  emb.weight.data
  return lin_layer




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"		,						_SCREAMING_SNAKE_CASE=False		,						_SCREAMING_SNAKE_CASE=False		)    ->       str:
  lowercase__           =  torch.load(_SCREAMING_SNAKE_CASE		,						map_location='cpu'		)['model']
  remove_ignore_keys_(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  state_dict['encoder.embed_tokens.weight'].shape[0]

  lowercase__           =  MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE		,						vocab_size=_SCREAMING_SNAKE_CASE		)
  if mbart_aa and finetuned:
    lowercase__           =  'relu'

  lowercase__           =  state_dict['decoder.embed_tokens.weight']
  lowercase__           =  MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE		)
  model.model.load_state_dict(_SCREAMING_SNAKE_CASE		)

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

  return model


if __name__ == "__main__":
      lowercase_          =		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_          =		parser.parse_args()
      lowercase_          =		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)


