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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   [
      """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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]			)							->      Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   emb.weight.shape
  SCREAMING_SNAKE_CASE__    							=   nn.Linear(__UpperCamelCase				,  __UpperCamelCase				,  bias=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   emb.weight.data
  return lin_layer



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]=None			)							->      Dict:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: str = WEIGHTS_NAME			)							->      Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   []
  SCREAMING_SNAKE_CASE__    							=   0
  os.makedirs(__UpperCamelCase				,  exist_ok=__UpperCamelCase			)

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

    # Add the metadata
  SCREAMING_SNAKE_CASE__    							=   {"""total_size""": total_size}
  SCREAMING_SNAKE_CASE__    							=   {"""metadata""": metadata, """weight_map""": weight_map}

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

  return metadata, index


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

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				Optional[int]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"nllb-moe"
   lowerCAmelCase_														=							["past_key_values"]
   lowerCAmelCase_														=							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(					self    :       int ,			_lowercase    :       Tuple=12_81_12 ,			_lowercase    :       List[Any]=10_24 ,			_lowercase    :       Any=12 ,			_lowercase    :       List[Any]=40_96 ,			_lowercase    :       str=16 ,			_lowercase    :       str=12 ,			_lowercase    :       Optional[int]=40_96 ,			_lowercase    :       List[Any]=16 ,			_lowercase    :       str=0.05 ,			_lowercase    :       Tuple=0.05 ,			_lowercase    :       str=True ,			_lowercase    :       List[str]=True ,			_lowercase    :       Optional[Any]="relu" ,			_lowercase    :       str=10_24 ,			_lowercase    :       Tuple=0.1 ,			_lowercase    :       int=0.1 ,			_lowercase    :       Dict=0.0 ,			_lowercase    :       List[str]=0.02 ,			_lowercase    :       int=2 ,			_lowercase    :       Optional[Any]=True ,			_lowercase    :       List[Any]=False ,			_lowercase    :       List[str]="float32" ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       str=1_28 ,			_lowercase    :       int=64 ,			_lowercase    :       Optional[int]=4 ,			_lowercase    :       List[str]=4 ,			_lowercase    :       Union[str, Any]=0.0_01 ,			_lowercase    :       List[Any]=0.0_01 ,			_lowercase    :       List[str]="all" ,			_lowercase    :       Optional[Any]=False ,			_lowercase    :       int=False ,			_lowercase    :       Tuple=1.0 ,			_lowercase    :       Optional[int]=0.2 ,			_lowercase    :       Optional[int]=1 ,			_lowercase    :       List[Any]=0 ,			_lowercase    :       List[Any]=2 ,			_lowercase    :       int=False ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   d_model
     SCREAMING_SNAKE_CASE__    							=   encoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   encoder_layers
     SCREAMING_SNAKE_CASE__    							=   encoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   decoder_ffn_dim
     SCREAMING_SNAKE_CASE__    							=   decoder_layers
     SCREAMING_SNAKE_CASE__    							=   decoder_attention_heads
     SCREAMING_SNAKE_CASE__    							=   dropout
     SCREAMING_SNAKE_CASE__    							=   attention_dropout
     SCREAMING_SNAKE_CASE__    							=   activation_dropout
     SCREAMING_SNAKE_CASE__    							=   activation_function
     SCREAMING_SNAKE_CASE__    							=   init_std
     SCREAMING_SNAKE_CASE__    							=   encoder_layerdrop
     SCREAMING_SNAKE_CASE__    							=   decoder_layerdrop
     SCREAMING_SNAKE_CASE__    							=   use_cache
     SCREAMING_SNAKE_CASE__    							=   encoder_layers
     SCREAMING_SNAKE_CASE__    							=   scale_embedding  # scale factor will be sqrt(d_model) if True
     SCREAMING_SNAKE_CASE__    							=   router_z_loss_coef
     SCREAMING_SNAKE_CASE__    							=   router_aux_loss_coef
     SCREAMING_SNAKE_CASE__    							=   decoder_sparse_step
     SCREAMING_SNAKE_CASE__    							=   encoder_sparse_step
     SCREAMING_SNAKE_CASE__    							=   num_experts
     SCREAMING_SNAKE_CASE__    							=   expert_capacity
     SCREAMING_SNAKE_CASE__    							=   router_bias
     if router_dtype not in ["float32", "float16", "bfloat16"]:
       raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""   )
     SCREAMING_SNAKE_CASE__    							=   router_dtype

     SCREAMING_SNAKE_CASE__    							=   router_ignore_padding_tokens
     SCREAMING_SNAKE_CASE__    							=   batch_prioritized_routing
     SCREAMING_SNAKE_CASE__    							=   second_expert_policy
     SCREAMING_SNAKE_CASE__    							=   normalize_router_prob_before_dropping
     SCREAMING_SNAKE_CASE__    							=   moe_eval_capacity_token_fraction
     SCREAMING_SNAKE_CASE__    							=   moe_token_dropout
     SCREAMING_SNAKE_CASE__    							=   output_router_logits
     super().__init__(
         pad_token_id=_lowercase ,			bos_token_id=_lowercase ,			eos_token_id=_lowercase ,			is_encoder_decoder=_lowercase ,			decoder_start_token_id=_lowercase ,			**_lowercase ,			)
