





"""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   (_SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			: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(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->List[str]:





  """simple docstring"""


  lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							emb.weight.shape
  lowerCAmelCase__			:Optional[Any]											=							nn.Linear(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   bias=_SCREAMING_SNAKE_CASE			)
  lowerCAmelCase__			:int											=							emb.weight.data
  return lin_layer
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE=None			)					->Any:





  """simple docstring"""


  lowerCAmelCase__			:Tuple											=							{}
  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[Any]											=							key.replace('moe_layer.experts.0'     ,   F"ffn.experts.expert_{expert_idx}"			)
      else:
        lowerCAmelCase__			:Optional[Any]											=							key.replace('moe_layer.experts.'     ,   'ffn.experts.expert_'			)
    if "gate" in key:
      lowerCAmelCase__			:Any											=							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__			:List[str]											=							key.replace('.fc1.'     ,   '.ffn.fc1.'			)
    if ".encoder_attn." in key:
      lowerCAmelCase__			:Dict											=							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[Any]											=							key.replace('final_layer_norm'     ,   'ff_layer_norm'			)
    lowerCAmelCase__			:Tuple											=							state_dict[old_key]
  return new_dict
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE = WEIGHTS_NAME			)					->int:





  """simple docstring"""


  lowerCAmelCase__			:int											=							[]
  lowerCAmelCase__			:List[str]											=							0
  os.makedirs(_SCREAMING_SNAKE_CASE     ,   exist_ok=_SCREAMING_SNAKE_CASE			)

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

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

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

  with open(os.path.join(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)     ,   'w'     ,   encoding='utf-8'			) as f:
    lowerCAmelCase__			:Union[str, Any]											=							json.dumps(_SCREAMING_SNAKE_CASE     ,   indent=2     ,   sort_keys=_SCREAMING_SNAKE_CASE			) + '\n'
    f.write(_SCREAMING_SNAKE_CASE			)

  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,
      128,
      args.dtype,
  )

  __A											=		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)
  __A											=		NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
  print("""Done""")
  model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""





from __future__ import annotations

__A											=		1.6_021e-19  # units = C
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   )					->tuple[str, float]:





  """simple docstring"""


  if (conductivity, electron_conc, mobility).count(0			) != 1:
    raise ValueError('You cannot supply more or less than 2 values'			)
  elif conductivity < 0:
    raise ValueError('Conductivity cannot be negative'			)
  elif electron_conc < 0:
    raise ValueError('Electron concentration cannot be negative'			)
  elif mobility < 0:
    raise ValueError('mobility cannot be negative'			)
  elif conductivity == 0:
    return (
        "conductivity",
        mobility * electron_conc * ELECTRON_CHARGE,
    )
  elif electron_conc == 0:
    return (
        "electron_conc",
        conductivity / (mobility * ELECTRON_CHARGE),
    )
  else:
    return (
        "mobility",
        conductivity / (electron_conc * ELECTRON_CHARGE),
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
