




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  (		lowerCAmelCase_	:   Any     ):
     """simple docstring"""

     lowerCAmelCase__            =					[
         "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(lowerCAmelCase_  ,							lowerCAmelCase_     )



def 				_A  (		lowerCAmelCase_	:   str     ):
     """simple docstring"""

     lowerCAmelCase__      ,						lowerCAmelCase__            =					emb.weight.shape
     lowerCAmelCase__            =					nn.Linear(lowerCAmelCase_  ,							lowerCAmelCase_  ,							bias=lowerCAmelCase_     )
     lowerCAmelCase__            =					emb.weight.data
     return lin_layer



def 				_A  (		lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Dict=None     ):
     """simple docstring"""

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



def 				_A  (		lowerCAmelCase_	:   int  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Tuple  ,							lowerCAmelCase_	:   List[str]  ,							lowerCAmelCase_	:   str = WEIGHTS_NAME     ):
     """simple docstring"""

     lowerCAmelCase__            =					[]
     lowerCAmelCase__            =					0
     os.makedirs(lowerCAmelCase_  ,							exist_ok=lowerCAmelCase_     )

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

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

    # Add the metadata
     lowerCAmelCase__            =					{"total_size": total_size}
     lowerCAmelCase__            =					{"metadata": metadata, "weight_map": weight_map}

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

     return metadata, index


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

     UpperCamelCase								=	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								=	NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print('Done')
     model.save_pretrained(args.pytorch_dump_folder_path)






def    UpperCAmelCase_				( __UpperCAmelCase		:      list     ,     __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      int = 0     ,     __UpperCAmelCase		:      int = 0				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     right or len(__UpperCAmelCase				) - 1
      if left > right:
            return -1
      elif list_data[left] == key:
            return left
      elif list_data[right] == key:
            return right
      else:
            return search(__UpperCAmelCase     ,     __UpperCAmelCase     ,     left + 1     ,     right - 1				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()