'''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 _lowercase						(   lowerCamelCase__    )       ->							Dict:





       """simple docstring"""


       __UpperCAmelCase	:					List[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(__snake_case				,     __snake_case    )

def _lowercase						(   lowerCamelCase__    )       ->							Union[str, Any]:





       """simple docstring"""


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

def _lowercase						(   lowerCamelCase__				,     lowerCamelCase__=None    )       ->							Optional[Any]:





       """simple docstring"""


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

def _lowercase						(   lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__ = WEIGHTS_NAME    )       ->							int:





       """simple docstring"""


       __UpperCAmelCase	:					str												=		[]
       __UpperCAmelCase	:					Dict												=		0
       os.makedirs(__snake_case				,     exist_ok=__snake_case    )

       for expert in range(__snake_case    ):
              __UpperCAmelCase	:					Tuple												=		switch_checkpoint_path + f"""-rank-{expert}.pt"""
              if os.path.isfile(__snake_case    ):
                     __UpperCAmelCase	:					int												=		torch.load(__snake_case    )["""model"""]
                     remove_ignore_keys_(__snake_case    )
                     __UpperCAmelCase	:					int												=		rename_fairseq_keys(__snake_case				,     __snake_case    )
                     __UpperCAmelCase	:					Union[str, Any]												=		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
       __UpperCAmelCase	:					List[Any]												=		os.path.join(__snake_case				,     weights_name.replace(".bin"				,     f"""-{len(__snake_case    )+1:05d}-of-???.bin"""    )    )
       __UpperCAmelCase	:					Dict												=		torch.load(switch_checkpoint_path + "-shared.pt"    )["""model"""]
       remove_ignore_keys_(__snake_case    )
       __UpperCAmelCase	:					Optional[int]												=		rename_fairseq_keys(__snake_case				,     __snake_case    )
       __UpperCAmelCase	:					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(__snake_case    ) == 1:
              __UpperCAmelCase	:					Dict												=		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
       __UpperCAmelCase	:					str												=		{}
       for idx, shard in enumerate(__snake_case    ):
              __UpperCAmelCase	:					Optional[int]												=		weights_name.replace(".bin"				,     f"""-{idx+1:05d}-of-{len(__snake_case    ):05d}.bin"""    )
              __UpperCAmelCase	:					List[str]												=		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:
                     __UpperCAmelCase	:					List[str]												=		shard_file

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

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

       return metadata, index


if __name__ == "__main__":
   _a :						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.",
   )
   _a :						Tuple							= 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 :						Optional[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)
   _a :						Optional[Any]							= NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print("Done")
   model.save_pretrained(args.pytorch_dump_folder_path)










"""simple docstring"""
def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int						, __snake_case							:      int						, __snake_case							:      list[int]   ):




   """simple docstring"""







   if graph[path[curr_ind - 1]][next_ver] == 0:
      return False

   # 2. Validate that next vertex is not already in path
   return not any(vertex == next_ver for vertex in path   )

def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      list[int]						, __snake_case							:      int   ):




   """simple docstring"""







   if curr_ind == len(__snake_case   ):
      # return whether path exists between current and starting vertices
      return graph[path[curr_ind - 1]][path[0]] == 1

   # Recursive Step
   for next_ver in range(0						, len(__snake_case   )   ):
      if valid_connection(__snake_case						, __snake_case						, __snake_case						, __snake_case   ):
         # Insert current vertex  into path as next transition
         _lowerCamelCase	:					List[str]   =		next_ver
         # Validate created path
         if util_hamilton_cycle(__snake_case						, __snake_case						, curr_ind + 1   ):
            return True
         # Backtrack
         _lowerCamelCase	:					Tuple   =		-1
   return False





def 				_snake_case      (   __snake_case							:      list[list[int]]						, __snake_case							:      int = 0   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		[-1] * (len(__snake_case   ) + 1)
   # initialize start and end of path with starting index
   _lowerCamelCase	:					Optional[int]   =		start_index
   # evaluate and if we find answer return path either return empty array
   return path if util_hamilton_cycle(__snake_case						, __snake_case						, 1   ) else []

