


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Tuple											=      [
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "_float_tensor",
      "decoder.output_projection.weight",
  ]
  for k in ignore_keys:
    state_dict.pop(A_  ,       A_	)


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a							,		a						:		Union[str, Any]											=      emb.weight.shape
  a						:		Tuple											=      nn.Linear(A_  ,       A_  ,       bias=A_	)
  a						:		Optional[Any]											=      emb.weight.data
  return lin_layer


def 					lowercase      (    A_  ,       A_="facebook/mbart-large-en-ro"  ,       A_=False  ,       A_=False	)->				Dict:
  '''simple docstring'''







  a						:		Any											=      torch.load(A_  ,       map_location="cpu"	)["model"]
  remove_ignore_keys_(A_	)
  a						:		Tuple											=      state_dict["encoder.embed_tokens.weight"].shape[0]

  a						:		Optional[Any]											=      MBartConfig.from_pretrained(A_  ,       vocab_size=A_	)
  if mbart_aa and finetuned:
    a						:		Union[str, Any]											=      "relu"

  a						:		Dict											=      state_dict["decoder.embed_tokens.weight"]
  a						:		Tuple											=      MBartForConditionalGeneration(A_	)
  model.model.load_state_dict(A_	)

  if finetuned:
    a						:		Tuple											=      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            "The hidden size is not divisble by the number of attention heads! Make sure to update them!")






    def      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) or len(self.rope_scaling) != 2:
        raise ValueError(
            "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
            f'''got {self.rope_scaling}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






