'''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       (      _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      UpperCAmelCase_            =  [
          '''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  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  emb.weight.shape
      UpperCAmelCase_            =  nn.Linear(_UpperCamelCase							,    _UpperCamelCase							,    bias=_UpperCamelCase					)
      UpperCAmelCase_            =  emb.weight.data
      return lin_layer



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Dict=None					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  str = WEIGHTS_NAME					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  0
      os.makedirs(_UpperCamelCase							,    exist_ok=_UpperCamelCase					)

      for expert in range(_UpperCamelCase					):
            UpperCAmelCase_            =  switch_checkpoint_path + F"""-rank-{expert}.pt"""
            if os.path.isfile(_UpperCamelCase					):
                  UpperCAmelCase_            =  torch.load(_UpperCamelCase					)['''model''']
                  remove_ignore_keys_(_UpperCamelCase					)
                  UpperCAmelCase_            =  rename_fairseq_keys(_UpperCamelCase							,    _UpperCamelCase					)
                  UpperCAmelCase_            =  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
      UpperCAmelCase_            =  os.path.join(_UpperCamelCase							,    weights_name.replace('''.bin'''							,    F"""-{len(_UpperCamelCase					)+1:05d}-of-???.bin"""					)					)
      UpperCAmelCase_            =  torch.load(switch_checkpoint_path + '''-shared.pt'''					)['''model''']
      remove_ignore_keys_(_UpperCamelCase					)
      UpperCAmelCase_            =  rename_fairseq_keys(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  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:
            UpperCAmelCase_            =  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
      UpperCAmelCase_            =  {}
      for idx, shard in enumerate(_UpperCamelCase					):
            UpperCAmelCase_            =  weights_name.replace('''.bin'''							,    F"""-{idx+1:05d}-of-{len(_UpperCamelCase					):05d}.bin"""					)
            UpperCAmelCase_            =  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:
                  UpperCAmelCase_            =  shard_file

    # Add the metadata
      UpperCAmelCase_            =  {'''total_size''': total_size}
      UpperCAmelCase_            =  {'''metadata''': metadata, '''weight_map''': weight_map}

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

      return metadata, index


if __name__ == "__main__":
      lowercase__  :      Optional[Any]        =					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.",
      )
      lowercase__  :      Dict        =					parser.parse_args()
      lowercase__ ,			lowercase__  :      Union[str, Any]        =					shard_on_the_fly(
          args.nllb_moe_checkpoint_path,
          args.pytorch_dump_folder_path,
          128,
          args.dtype,
      )

      lowercase__  :      Tuple        =					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)
      lowercase__  :      List[Any]        =					NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
      print("Done")
      model.save_pretrained(args.pytorch_dump_folder_path)






'''simple docstring'''
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  list[str]					):


      '''simple docstring'''


      UpperCAmelCase_            =  ''''''
      for word_or_phrase in separated:
            if not isinstance(_UpperCamelCase							,    _UpperCamelCase					):
                  raise Exception('''join() accepts only strings to be joined'''					)
            joined += word_or_phrase + separator
      return joined.strip(_UpperCamelCase					)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





