



'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_       		=[
         "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  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     a_		,						a_       		=emb.weight.shape
     a_       		=nn.Linear(lowercase__       ,	lowercase__       ,	bias=lowercase__       )
     a_       		=emb.weight.data
     return lin_layer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__="facebook/mbart-large-en-ro"       ,	lowercase__=False       ,	lowercase__=False       ):


     '''simple docstring'''







     a_       		=torch.load(lowercase__       ,	map_location="cpu"       )["model"]
     remove_ignore_keys_(lowercase__       )
     a_       		=state_dict["encoder.embed_tokens.weight"].shape[0]

     a_       		=MBartConfig.from_pretrained(lowercase__       ,	vocab_size=lowercase__       )
     if mbart_aa and finetuned:
          a_       		="relu"

     a_       		=state_dict["decoder.embed_tokens.weight"]
     a_       		=MBartForConditionalGeneration(lowercase__       )
     model.model.load_state_dict(lowercase__       )

     if finetuned:
          a_       		=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)







'''simple docstring'''


from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip


