



'''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        a_     (							_lowerCAmelCase       )      ->      Union[str, Any]:
  __lowerCamelCase       :       Union[str, 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(_lowerCamelCase       ,_lowerCamelCase       )

def        a_     (							_lowerCAmelCase       )      ->      int:
  __lowerCamelCase       :       str           = emb.weight.shape
  __lowerCamelCase       :       List[str]           = nn.Linear(_lowerCamelCase       ,_lowerCamelCase       ,bias=_lowerCamelCase       )
  __lowerCamelCase       :       Tuple           = emb.weight.data
  return lin_layer

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

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = WEIGHTS_NAME       )      ->      int:
  __lowerCamelCase       :       Optional[int]           = []
  __lowerCamelCase       :       Optional[Any]           = 0
  os.makedirs(_lowerCamelCase       ,exist_ok=_lowerCamelCase       )

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

    # Add the metadata
  __lowerCamelCase       :       Any           = {"""total_size""": total_size}
  __lowerCamelCase       :       List[str]           = {"""metadata""": metadata, """weight_map""": weight_map}

  with open(os.path.join(_lowerCamelCase       ,_lowerCamelCase       )       ,'w'       ,encoding='utf-8'       ) as f:
    __lowerCamelCase       :       List[str]           = 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											=						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)







"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any	)       ->      List[Any]:  # noqa: E741
  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     0
  _lowerCAmelCase						:       Any  			=     [0] * n
  _lowerCAmelCase						:       str  			=     [False] * n
  _lowerCAmelCase						:       str  			=     [False] * n

  def dfs(_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str	):
    if parent == root:
      out_edge_count += 1
    _lowerCAmelCase						:       Any  			=     True
    _lowerCAmelCase						:       int  			=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _lowerCAmelCase						:       Union[str, Any]  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
        _lowerCAmelCase						:       Optional[int]  			=     min(low[at]			,low[to]	)

        # AP found via bridge
        if at < low[to]:
          _lowerCAmelCase						:       int  			=     True
        # AP found via cycle
        if at == low[to]:
          _lowerCAmelCase						:       Tuple  			=     True
      else:
        _lowerCAmelCase						:       Union[str, Any]  			=     min(low[at]			,_lowerCamelCase	)
    return out_edge_count

  for i in range(_lowerCamelCase	):
    if not visited[i]:
      _lowerCAmelCase						:       int  			=     0
      _lowerCAmelCase						:       Dict  			=     dfs(_lowerCamelCase			,_lowerCamelCase			,-1			,_lowerCamelCase	)
      _lowerCAmelCase						:       List[str]  			=     out_edge_count > 1

  for x in range(len(_lowerCamelCase	)	):
    if is_art[x] is True:
      print(_lowerCamelCase	)


# Adjacency list of graph
_a      :		Optional[Any]           =   {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}






compute_ap(data)




