




"""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 						snake_case__       (					_lowerCamelCase				)			->List[Any]:






  """simple docstring"""


  __lowercase				:		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(_lowerCamelCase,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






  """simple docstring"""


  __lowercase				:		Optional[int]												=				emb.weight.shape
  __lowercase				:		Optional[Any]												=				nn.Linear(_lowerCamelCase,  _lowerCamelCase,  bias=_lowerCamelCase				)
  __lowercase				:		List[Any]												=				emb.weight.data
  return lin_layer

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->Optional[int]:






  """simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase = WEIGHTS_NAME				)			->List[str]:






  """simple docstring"""


  __lowercase				:		Any												=				[]
  __lowercase				:		Any												=				0
  os.makedirs(_lowerCamelCase,  exist_ok=_lowerCamelCase				)

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

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

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

  return metadata, index


if __name__ == "__main__":
      __A   :  Optional[int]					=      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   :  Optional[Any]					=      parser.parse_args()
      __A   :  List[str]					=      shard_on_the_fly(
          args.nllb_moe_checkpoint_path,
          args.pytorch_dump_folder_path,
          1_2_8,
          args.dtype,
      )

      __A   :  Dict					=      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)
      __A   :  Union[str, Any]					=      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
      print('Done')
      model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		return abs(_lowerCamelCase				) if a == 0 else greatest_common_divisor(b % a,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				__lowercase						,__lowercase				:		Any												=				y, x % y
		return abs(_lowerCamelCase				)

def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		try:
				__lowercase				:		Optional[int]												=				input("Enter two integers separated by comma (,): "				).split(","				)
				__lowercase				:		Optional[Any]												=				int(nums[0]				)
				__lowercase				:		str												=				int(nums[1]				)
				print(
				    F'greatest_common_divisor({num_a}, {num_a}) = '
				    F'{greatest_common_divisor(_lowerCamelCase,  _lowerCamelCase				)}'				)
				print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase,  _lowerCamelCase				)}'				)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"				)


if __name__ == "__main__":
						main()
