






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   (UpperCAmelCase__    )			->							Any:
  UpperCamelCase_:   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(UpperCAmelCase__      ,       UpperCAmelCase__    )

def     snake_case   (UpperCAmelCase__    )			->							int:
  UpperCamelCase_ ,UpperCamelCase_:   str        =							emb.weight.shape
  UpperCamelCase_:   int        =							nn.Linear(UpperCAmelCase__      ,       UpperCAmelCase__      ,       bias=UpperCAmelCase__    )
  UpperCamelCase_:   Any        =							emb.weight.data
  return lin_layer

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

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__ = WEIGHTS_NAME    )			->							Any:
  UpperCamelCase_:   Tuple        =							[]
  UpperCamelCase_:   Any        =							0
  os.makedirs(UpperCAmelCase__      ,       exist_ok=UpperCAmelCase__    )

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

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

    # Add the metadata
  UpperCamelCase_:   Dict        =							{'total_size': total_size}
  UpperCamelCase_:   str        =							{'metadata': metadata, 'weight_map': weight_map}

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

  return metadata, index


if __name__ == "__main__":
   A_						:						str						      =					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[int]						      =					parser.parse_args()
   A_      ,					A_						:						str						      =					shard_on_the_fly(
       args.nllb_moe_checkpoint_path,
       args.pytorch_dump_folder_path,
       128,
       args.dtype,
   )

   A_						:						str						      =					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_						:						Dict						      =					NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
   print('Done')
   model.save_pretrained(args.pytorch_dump_folder_path)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
