

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_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [
						    "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(_UpperCAmelCase				,	_UpperCAmelCase    )







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       emb.weight.shape
						SCREAMING_SNAKE_CASE_: Dict  		=       nn.Linear(_UpperCAmelCase				,	_UpperCAmelCase				,	bias=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       emb.weight.data
						return lin_layer







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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase = WEIGHTS_NAME    ):
						SCREAMING_SNAKE_CASE_: Tuple  		=       []
						SCREAMING_SNAKE_CASE_: Any  		=       0
						os.makedirs(_UpperCAmelCase				,	exist_ok=_UpperCAmelCase    )

						for expert in range(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       switch_checkpoint_path + f"-rank-{expert}.pt"
												if os.path.isfile(_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       torch.load(_UpperCAmelCase    )["model"]
																		remove_ignore_keys_(_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_fairseq_keys(_UpperCAmelCase				,	_UpperCAmelCase    )
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       os.path.join(
																		    _UpperCAmelCase				,	weights_name.replace(".bin"				,	f"-{len(_UpperCAmelCase    )+1:05d}-of-???.bin"    )    )
																		torch.save(_UpperCAmelCase				,	_UpperCAmelCase    )
																		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(_UpperCAmelCase    )[0]].dtype    )

    # Add the last block
						SCREAMING_SNAKE_CASE_: List[Any]  		=       os.path.join(_UpperCAmelCase				,	weights_name.replace(".bin"				,	f"-{len(_UpperCAmelCase    )+1:05d}-of-???.bin"    )    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       torch.load(switch_checkpoint_path + "-shared.pt"    )["model"]
						remove_ignore_keys_(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_fairseq_keys(_UpperCAmelCase				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: 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(_UpperCAmelCase    ) == 1:
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )
												torch.save(_UpperCAmelCase				,	_UpperCAmelCase    )
												return {weights_name: sharded_state_dicts[0]}, None
						else:
												torch.save(_UpperCAmelCase				,	_UpperCAmelCase    )
						# Otherwise, let's build the index
						SCREAMING_SNAKE_CASE_: Tuple  		=       {}
						for idx, shard in enumerate(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: str  		=       weights_name.replace(".bin"				,	f"-{idx+1:05d}-of-{len(_UpperCAmelCase    ):05d}.bin"    )
												SCREAMING_SNAKE_CASE_: List[Any]  		=       os.path.join(_UpperCAmelCase				,	weights_name.replace(".bin"				,	f"-{idx+1:05d}-of-???.bin"    )    )
												os.rename(_UpperCAmelCase				,	os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )    )
												for key in shard:
																		SCREAMING_SNAKE_CASE_: List[str]  		=       shard_file

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

						with open(os.path.join(_UpperCAmelCase				,	_UpperCAmelCase    )				,	"w"				,	encoding="utf-8"    ) as f:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       json.dumps(_UpperCAmelCase				,	indent=2				,	sort_keys=_UpperCAmelCase    ) + "\n"
												f.write(_UpperCAmelCase    )

						return metadata, index


if __name__ == "__main__":
						lowerCAmelCase							:  Dict   	=						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.""",
						)
						lowerCAmelCase							:  Dict   	=						parser.parse_args()
						lowerCAmelCase	,			lowerCAmelCase							:  Dict   	=						shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    128,
						    args.dtype,
						)

						lowerCAmelCase							:  Union[str, Any]   	=						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)
						lowerCAmelCase							:  int   	=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print("""Done""")
						model.save_pretrained(args.pytorch_dump_folder_path)











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        lowercase_							(A : List[str]      ):
       snake_case__							:	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(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

       for expert in range(A      ):
              snake_case__							:	Tuple   	=	switch_checkpoint_path + F'''-rank-{expert}.pt'''
              if os.path.isfile(A      ):
                     snake_case__							:	Optional[Any]   	=	torch.load(A      )['model']
                     remove_ignore_keys_(A      )
                     snake_case__							:	Optional[Any]   	=	rename_fairseq_keys(A		,					A      )
                     snake_case__							:	Dict   	=	os.path.join(
                         A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
                     torch.save(A		,					A      )
                     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(A      )[0]].dtype      )

    # Add the last block
       snake_case__							:	Tuple   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
       snake_case__							:	Union[str, Any]   	=	torch.load(switch_checkpoint_path + '-shared.pt'      )['model']
       remove_ignore_keys_(A      )
       snake_case__							:	str   	=	rename_fairseq_keys(A		,					A      )
       snake_case__							:	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(A      ) == 1:
              snake_case__							:	Any   	=	os.path.join(A		,					A      )
              torch.save(A		,					A      )
              return {weights_name: sharded_state_dicts[0]}, None
       else:
              torch.save(A		,					A      )
       # Otherwise, let's build the index
       snake_case__							:	Tuple   	=	{}
       for idx, shard in enumerate(A      ):
              snake_case__							:	Optional[int]   	=	weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-{len(A      ):05d}.bin'''      )
              snake_case__							:	List[Any]   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-???.bin'''      )      )
              os.rename(A		,					os.path.join(A		,					A      )      )
              for key in shard:
                     snake_case__							:	Any   	=	shard_file

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

       with open(os.path.join(A		,					A      )		,					'w'		,					encoding='utf-8'      ) as f:
              snake_case__							:	Any   	=	json.dumps(A		,					indent=2		,					sort_keys=A      ) + '\n'
              f.write(A      )

       return metadata, index


if __name__ == "__main__":
 a_   :int        =      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.",
 )
 a_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 a_   :List[str]        =      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)
 a_   :int        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)






