





'''simple docstring'''

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


__lowerCAmelCase							:	Dict			=logging.get_logger(__name__)

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






class    UpperCAmelCase	(       lowercase_				):
 __lowercase   		=				"""nllb-moe"""
 __lowercase   		=				["""past_key_values"""]
 __lowercase   		=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(				self      :Optional[Any]						,       lowercase_      :Union[str, Any]=12_81_12						,       lowercase_      :str=10_24						,       lowercase_      :Union[str, Any]=12						,       lowercase_      :Union[str, Any]=40_96						,       lowercase_      :Union[str, Any]=16						,       lowercase_      :Tuple=12						,       lowercase_      :Union[str, Any]=40_96						,       lowercase_      :List[str]=16						,       lowercase_      :int=0.0_5						,       lowercase_      :Any=0.0_5						,       lowercase_      :Optional[Any]=True						,       lowercase_      :Tuple=True						,       lowercase_      :Optional[int]="relu"						,       lowercase_      :Optional[int]=10_24						,       lowercase_      :str=0.1						,       lowercase_      :Optional[Any]=0.1						,       lowercase_      :List[Any]=0.0						,       lowercase_      :Dict=0.0_2						,       lowercase_      :Union[str, Any]=2						,       lowercase_      :Optional[Any]=True						,       lowercase_      :int=False						,       lowercase_      :List[Any]="float32"						,       lowercase_      :List[Any]=False						,       lowercase_      :Optional[Any]=1_28						,       lowercase_      :List[Any]=64						,       lowercase_      :Dict=4						,       lowercase_      :int=4						,       lowercase_      :Dict=0.0_0_1						,       lowercase_      :List[Any]=0.0_0_1						,       lowercase_      :List[Any]="all"						,       lowercase_      :Optional[int]=False						,       lowercase_      :List[str]=False						,       lowercase_      :List[str]=1.0						,       lowercase_      :List[Any]=0.2						,       lowercase_      :List[Any]=1						,       lowercase_      :List[str]=0						,       lowercase_      :str=2						,       lowercase_      :Tuple=False						,       **lowercase_      :Optional[int]						,       )->							int:
     A__				=			vocab_size
     A__				=			max_position_embeddings
     A__				=			d_model
     A__				=			encoder_ffn_dim
     A__				=			encoder_layers
     A__				=			encoder_attention_heads
     A__				=			decoder_ffn_dim
     A__				=			decoder_layers
     A__				=			decoder_attention_heads
     A__				=			dropout
     A__				=			attention_dropout
     A__				=			activation_dropout
     A__				=			activation_function
     A__				=			init_std
     A__				=			encoder_layerdrop
     A__				=			decoder_layerdrop
     A__				=			use_cache
     A__				=			encoder_layers
     A__				=			scale_embedding  # scale factor will be sqrt(d_model) if True
     A__				=			router_z_loss_coef
     A__				=			router_aux_loss_coef
     A__				=			decoder_sparse_step
     A__				=			encoder_sparse_step
     A__				=			num_experts
     A__				=			expert_capacity
     A__				=			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}"							)
     A__				=			router_dtype

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








"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





