

'''simple docstring'''





import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
UpperCamelCase_    						=       object()

# For specifying empty leaf dict `{}`
UpperCamelCase_    						=       object()






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     int						)				->  Optional[int]:
						lowercase :							Optional[Any]    		=tuple((re.compile(x + '''$'''						) for x in qs)						)
						for i in range(len(__magic_name__						) - len(__magic_name__						) + 1						):
												lowercase :							Union[str, Any]    		=[x.match(__magic_name__						) for x, y in zip(__magic_name__   , ks[i:]						)]
												if matches and all(__magic_name__						):
																		return True
						return False






def 				_lowerCAmelCase			(   __magic_name__						:     Dict						)				->  List[str]:
						def replace(__magic_name__						:     Optional[int]   , __magic_name__						:     Optional[Any]						):
												for rule, replacement in rules:
																		if _match(__magic_name__   , __magic_name__						):
																								return replacement
												return val

						return replace






def 				_lowerCAmelCase			(   )				->  int:
						return [
						    # embeddings
						    (("transformer", "wpe", "embedding"), P('''mp'''   , __magic_name__						)),
						    (("transformer", "wte", "embedding"), P('''mp'''   , __magic_name__						)),
						    # atention
						    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("attention", "out_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("attention", "out_proj", "bias"), None),
						    # mlp
						    (("mlp", "c_fc", "kernel"), P(__magic_name__   , '''mp'''						)),
						    (("mlp", "c_fc", "bias"), P('''mp'''						)),
						    (("mlp", "c_proj", "kernel"), P('''mp'''   , __magic_name__						)),
						    (("mlp", "c_proj", "bias"), None),
						    # layer norms
						    ((r"ln_\d+", "bias"), None),
						    ((r"\d+", r"ln_\d+", "scale"), None),
						    (("ln_f", "bias"), None),
						    (("ln_f", "scale"), None),
						]







def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  int:
						lowercase :							int    		=_get_partition_rules()
						lowercase :							Tuple    		=_replacement_rules(__magic_name__						)
						lowercase :							Any    		={k: _unmatched for k in flatten_dict(__magic_name__						)}
						lowercase :							Any    		={k: replace(__magic_name__   , __magic_name__						) for k, v in initd.items()}
						assert _unmatched not in result.values(), "Incomplete partition spec."
						return freeze(unflatten_dict(__magic_name__						)						)












"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						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(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= 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")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= 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)
