






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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Any:
  lowercase__			    =						[
      '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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__    ,					lowercase__			    =						emb.weight.shape
  lowercase__			    =						nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		bias=_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						emb.weight.data
  return lin_layer




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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = WEIGHTS_NAME	)       ->     List[Any]:
  lowercase__			    =						[]
  lowercase__			    =						0
  os.makedirs(_SCREAMING_SNAKE_CASE	,		exist_ok=_SCREAMING_SNAKE_CASE	)

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

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

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

  return metadata, index


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

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









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     bool:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  if len(_SCREAMING_SNAKE_CASE	) == 1:
    return True
  lowercase__			    =						series[1] - series[0]
  for index in range(len(_SCREAMING_SNAKE_CASE	) - 1	):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     float:
  if not isinstance(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
    raise ValueError('Input series is not valid, valid series - [2, 4, 6]'	)
  if len(_SCREAMING_SNAKE_CASE	) == 0:
    raise ValueError('Input list must be a non empty list'	)
  lowercase__			    =						0
  for val in series:
    answer += val
  return answer / len(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


