





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  a__     : Tuple  =[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "encoder.embed_positions._float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Any  =emb.weight.shape
  a__     : Optional[Any]  =nn.Linear(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	bias=SCREAMING_SNAKE_CASE						)
  a__     : Any  =emb.weight.data
  return lin_layer
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]=None						):





  """simple docstring"""



  a__     : Tuple  ={}
  for old_key in state_dict.keys():
    a__     : int  =old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        a__     : int  =key.replace("moe_layer.experts.0"	,	f'''ffn.experts.expert_{expert_idx}'''						)
      else:
        a__     : int  =key.replace("moe_layer.experts."	,	"ffn.experts.expert_"						)
    if "gate" in key:
      a__     : List[Any]  =key.replace(".moe_layer.gate.wg"	,	".ffn.router.classifier"						)
    if "fc2" and "experts" not in key:
      a__     : Union[str, Any]  =key.replace(".fc2."	,	".ffn.fc2."						)
    if "fc1" and "experts" not in key:
      a__     : Optional[Any]  =key.replace(".fc1."	,	".ffn.fc1."						)
    if ".encoder_attn." in key:
      a__     : int  =key.replace(".encoder_attn."	,	".cross_attention."						)
    if "encoder_attn_layer_norm" in key:
      a__     : Union[str, Any]  =key.replace("encoder_attn_layer_norm"	,	"cross_attention_layer_norm"						)
    if "final_layer_norm" in key:
      a__     : Tuple  =key.replace("final_layer_norm"	,	"ff_layer_norm"						)
    a__     : Optional[int]  =state_dict[old_key]
  return new_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       str = WEIGHTS_NAME						):





  """simple docstring"""



  a__     : Any  =[]
  a__     : List[str]  =0
  os.makedirs(SCREAMING_SNAKE_CASE	,	exist_ok=SCREAMING_SNAKE_CASE						)

  for expert in range(SCREAMING_SNAKE_CASE						):
    a__     : Optional[Any]  =switch_checkpoint_path + f'''-rank-{expert}.pt'''
    if os.path.isfile(SCREAMING_SNAKE_CASE						):
      a__     : Union[str, Any]  =torch.load(SCREAMING_SNAKE_CASE						)["model"]
      remove_ignore_keys_(SCREAMING_SNAKE_CASE						)
      a__     : Tuple  =rename_fairseq_keys(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      a__     : str  =os.path.join(
          SCREAMING_SNAKE_CASE	,	weights_name.replace(".bin"	,	f'''-{len(SCREAMING_SNAKE_CASE						)+1:05d}-of-???.bin'''						)						)
      torch.save(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      sharded_state_dicts.append(expert_state.keys()						)
      total_size += sum([value.numel() for key, value in expert_state.items()]						) * dtype_byte_size(
          expert_state[list(SCREAMING_SNAKE_CASE						)[0]].dtype						)

    # Add the last block
  a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	weights_name.replace(".bin"	,	f'''-{len(SCREAMING_SNAKE_CASE						)+1:05d}-of-???.bin'''						)						)
  a__     : List[str]  =torch.load(switch_checkpoint_path + "-shared.pt"						)["model"]
  remove_ignore_keys_(SCREAMING_SNAKE_CASE						)
  a__     : Optional[int]  =rename_fairseq_keys(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  a__     : List[Any]  =shared_weights["decoder.embed_tokens.weight"]
  sharded_state_dicts.append(shared_weights.keys()						)

  # If we only have the shared weights (dummy model/experts saved on the same file)
  if len(SCREAMING_SNAKE_CASE						) == 1:
    a__     : Dict  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    torch.save(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    return {weights_name: sharded_state_dicts[0]}, None
  else:
    torch.save(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  # Otherwise, let's build the index
  a__     : List[Any]  ={}
  for idx, shard in enumerate(SCREAMING_SNAKE_CASE						):
    a__     : Union[str, Any]  =weights_name.replace(".bin"	,	f'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE						):05d}.bin'''						)
    a__     : Dict  =os.path.join(SCREAMING_SNAKE_CASE	,	weights_name.replace(".bin"	,	f'''-{idx+1:05d}-of-???.bin'''						)						)
    os.rename(SCREAMING_SNAKE_CASE	,	os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)
    for key in shard:
      a__     : List[Any]  =shard_file

    # Add the metadata
  a__     : List[Any]  ={"total_size": total_size}
  a__     : int  ={"metadata": metadata, "weight_map": weight_map}

  with open(os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)	,	"w"	,	encoding="utf-8"						) as f:
    a__     : Tuple  =json.dumps(SCREAMING_SNAKE_CASE	,	indent=2	,	sort_keys=SCREAMING_SNAKE_CASE						) + "\n"
    f.write(SCREAMING_SNAKE_CASE						)

  return metadata, index


if __name__ == "__main__":
     UpperCAmelCase     :     str							=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--nllb_moe_checkpoint_path""",
         default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
         type=str,
         required=False,
         help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
     )
     parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
         type=str,
         required=False,
         help="""Path to the output pytorch model.""",
     )
     UpperCAmelCase     :     Tuple							=	parser.parse_args()
     UpperCAmelCase     :     Tuple							=	shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         128,
         args.dtype,
     )

     UpperCAmelCase     :     Optional[int]							=	NllbMoeConfig.from_pretrained(
         """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
     )
     config.save_pretrained(args.pytorch_dump_folder_path)
     UpperCAmelCase     :     Dict							=	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print("""Done""")
     model.save_pretrained(args.pytorch_dump_folder_path)













import warnings

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


UpperCAmelCase     :     Dict							=	logging.get_logger(__name__)

UpperCAmelCase     :     Dict							=	{
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[Any]            =      """mvp"""
      _lowercase    : Tuple            =      ["""past_key_values"""]
      _lowercase    : Dict            =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







      def __init__(   self		,				lowerCAmelCase__=5_0_2_6_7		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=False		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1		,				lowerCAmelCase__=0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=True		,				lowerCAmelCase__=2		,				lowerCAmelCase__=2		,				lowerCAmelCase__=False		,				lowerCAmelCase__=1_0_0		,				lowerCAmelCase__=8_0_0		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        a__     : Dict  =vocab_size
        a__     : List[str]  =max_position_embeddings
        a__     : List[str]  =d_model
        a__     : Optional[Any]  =encoder_ffn_dim
        a__     : Dict  =encoder_layers
        a__     : List[str]  =encoder_attention_heads
        a__     : List[str]  =decoder_ffn_dim
        a__     : Tuple  =decoder_layers
        a__     : Tuple  =decoder_attention_heads
        a__     : Any  =dropout
        a__     : str  =attention_dropout
        a__     : str  =activation_dropout
        a__     : Optional[int]  =activation_function
        a__     : Union[str, Any]  =init_std
        a__     : Dict  =encoder_layerdrop
        a__     : List[str]  =decoder_layerdrop
        a__     : Union[str, Any]  =classifier_dropout
        a__     : Union[str, Any]  =use_cache
        a__     : Optional[Any]  =encoder_layers
        a__     : List[str]  =scale_embedding  # scale factor will be sqrt(d_model) if True
        a__     : int  =use_prompt
        a__     : Union[str, Any]  =prompt_length
        a__     : Dict  =prompt_mid_dim

        super().__init__(
            pad_token_id=lowerCAmelCase__		,				bos_token_id=lowerCAmelCase__		,				eos_token_id=lowerCAmelCase__		,				is_encoder_decoder=lowerCAmelCase__		,				decoder_start_token_id=lowerCAmelCase__		,				forced_eos_token_id=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"		,				lowerCAmelCase__							):
          a__     : Tuple  =self.bos_token_id
          warnings.warn(
              F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
              "The config can simply be saved and uploaded again to be fixed."							)






