



'''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__ (		_A       ):
  a		:      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    lowerCamelCase__ (		_A       ):
  a		:      Optional[int]       =  emb.weight.shape
  a		:      str       =  nn.Linear(_A				,    _A				,    bias=_A       )
  a		:      int       =  emb.weight.data
  return lin_layer
def    lowerCamelCase__ (		_A				,    _A=None       ):
  a		:      Optional[Any]       =  {}
  for old_key in state_dict.keys():
    a		:      Optional[int]       =  old_key
    if "moe_layer.experts." in key:
      if expert_idx is not None:
        a		:      Any       =  key.replace('moe_layer.experts.0'				,    f"""ffn.experts.expert_{expert_idx}"""       )
      else:
        a		:      Union[str, Any]       =  key.replace('moe_layer.experts.'				,    'ffn.experts.expert_'       )
    if "gate" in key:
      a		:      List[Any]       =  key.replace('.moe_layer.gate.wg'				,    '.ffn.router.classifier'       )
    if "fc2" and "experts" not in key:
      a		:      List[Any]       =  key.replace('.fc2.'				,    '.ffn.fc2.'       )
    if "fc1" and "experts" not in key:
      a		:      Dict       =  key.replace('.fc1.'				,    '.ffn.fc1.'       )
    if ".encoder_attn." in key:
      a		:      Union[str, Any]       =  key.replace('.encoder_attn.'				,    '.cross_attention.'       )
    if "encoder_attn_layer_norm" in key:
      a		:      Tuple       =  key.replace('encoder_attn_layer_norm'				,    'cross_attention_layer_norm'       )
    if "final_layer_norm" in key:
      a		:      Tuple       =  key.replace('final_layer_norm'				,    'ff_layer_norm'       )
    a		:      Tuple       =  state_dict[old_key]
  return new_dict
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A = WEIGHTS_NAME       ):
  a		:      Any       =  []
  a		:      Optional[int]       =  0
  os.makedirs(_A				,    exist_ok=_A       )

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

    # Add the metadata
  a		:      Tuple       =  {'total_size': total_size}
  a		:      Dict       =  {'metadata': metadata, 'weight_map': weight_map}

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

  return metadata, index


if __name__ == "__main__":
     lowerCAmelCase:      Tuple		 =		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.',
     )
     lowerCAmelCase:      Tuple		 =		parser.parse_args()
     lowerCAmelCase ,	lowerCAmelCase:      List[Any]		 =		shard_on_the_fly(
         args.nllb_moe_checkpoint_path,
         args.pytorch_dump_folder_path,
         1_2_8,
         args.dtype,
     )

     lowerCAmelCase:      str		 =		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)
     lowerCAmelCase:      List[str]		 =		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print('Done')
     model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")