


'''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					(      lowerCamelCase  :      Any):
      A_						:  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      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  str          =				emb.weight.shape
      A_						:  Optional[Any]          =				nn.Linear(_lowerCamelCase							,						_lowerCamelCase							,						bias=_lowerCamelCase)
      A_						:  List[str]          =				emb.weight.data
      return lin_layer




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




def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str = WEIGHTS_NAME):
      A_						:  Any          =				[]
      A_						:  str          =				0
      os.makedirs(_lowerCamelCase							,						exist_ok=_lowerCamelCase)

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

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

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

      return metadata, index


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

     __magic_name__			=      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)
     __magic_name__			=      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
     print('Done')
     model.save_pretrained(args.pytorch_dump_folder_path)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__			=      {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
