






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        _SCREAMING_SNAKE_CASE  (				A  :       str  )    -> Tuple:







   """simple docstring"""
   __snake_case				:      Dict     							= [
       '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        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   __snake_case				:      Optional[int]     							= emb.weight.shape
   __snake_case				:      str     							= nn.Linear(A  ,		A  ,		bias=A  )
   __snake_case				:      int     							= emb.weight.data
   return lin_layer




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       Dict=None  )    -> List[str]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Optional[Any]  ,		A  :       Any  ,		A  :       List[str]  ,		A  :       str  ,		A  :       str = WEIGHTS_NAME  )    -> Dict:







   """simple docstring"""
   __snake_case				:      Any     							= []
   __snake_case				:      Optional[int]     							= 0
   os.makedirs(A  ,		exist_ok=A  )

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

    # Add the metadata
   __snake_case				:      Tuple     							= {'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				:      int     							= json.dumps(A  ,		indent=2  ,		sort_keys=A  ) + '\n'
      f.write(A  )

   return metadata, index


if __name__ == "__main__":
      __A    	=   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    	=   parser.parse_args()
      __A	,   __A    	=   shard_on_the_fly(
          args.nllb_moe_checkpoint_path,
          args.pytorch_dump_folder_path,
          1_2_8,
          args.dtype,
      )

      __A    	=   NllbMoeConfig.from_pretrained(
          '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
      )
      config.save_pretrained(args.pytorch_dump_folder_path)
      __A    	=   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
      print('''Done''')
      model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''






from functools import lru_cache




@lru_cache
def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   if num < 0:
      raise ValueError('Number should not be negative.'  )

   return 1 if num in (0, 1) else num * factorial(num - 1  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()