


"""simple docstring"""



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 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		Dict:


    """simple docstring"""





    snake_case					:   str         =					[
        "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(__snake_case		,							__snake_case				)







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		str:


    """simple docstring"""





    snake_case					:   Optional[int]         =					emb.weight.shape
    snake_case					:   Any         =					nn.Linear(__snake_case		,							__snake_case		,							bias=__snake_case				)
    snake_case					:   Dict         =					emb.weight.data
    return lin_layer







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						Any=None				)			->		List[Any]:


    """simple docstring"""





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







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						Optional[Any]		,							lowercase						:						Tuple		,							lowercase						:						List[Any]		,							lowercase						:						str = WEIGHTS_NAME				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					[]
    snake_case					:   Union[str, Any]         =					0
    os.makedirs(__snake_case		,							exist_ok=__snake_case				)

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

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

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

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

    return metadata, index


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

 __snake_case         =					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)
 __snake_case         =					NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("""Done""")
 model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""



import baseaa







def 						__lowerCAmelCase		(		lowercase						:						str				)			->		bytes:


    """simple docstring"""





    return baseaa.aaaencode(string.encode("utf-8"				)				)







def 						__lowerCAmelCase		(		lowercase						:						bytes				)			->		str:


    """simple docstring"""





    return baseaa.aaadecode(lowercase				).decode("utf-8"				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()





