


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->Any:
   UpperCAmelCase							=     OmegaConf.load(lowerCAmelCase_   )
   if display:
      print(yaml.dump(OmegaConf.to_container(lowerCAmelCase_   )   )   )
   return config
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None   )						->str:
   if conf_path is None:
      UpperCAmelCase							=     """./model_checkpoints/vqgan_only.yaml"""
   UpperCAmelCase							=     load_config(lowerCAmelCase_  ,						display=lowerCAmelCase_   )
   UpperCAmelCase							=     VQModel(**config.model.params   )
   if ckpt_path is None:
      UpperCAmelCase							=     """./model_checkpoints/vqgan_only.pt"""
   UpperCAmelCase							=     torch.load(lowerCAmelCase_  ,						map_location=lowerCAmelCase_   )
   if ".ckpt" in ckpt_path:
      UpperCAmelCase							=     sd["""state_dict"""]
   model.load_state_dict(lowerCAmelCase_  ,						strict=lowerCAmelCase_   )
   model.to(lowerCAmelCase_   )
   del sd
   return model
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     model.encode(lowerCAmelCase_   )
   print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
   UpperCAmelCase							=     model.decode(lowerCAmelCase_   )
   return xrec
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=False   )						->Tuple:
   UpperCAmelCase				,  UpperCAmelCase							=     string.rsplit("""."""  ,						1   )
   if reload:
      UpperCAmelCase							=     importlib.import_module(lowerCAmelCase_   )
      importlib.reload(lowerCAmelCase_   )
   return getattr(importlib.import_module(lowerCAmelCase_  ,						package=lowerCAmelCase_   )  ,						cls   )
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   if "target" not in config:
      raise KeyError("""Expected key `target` to instantiate."""   )
   return get_obj_from_str(config["""target"""]   )(**config.get("""params"""  ,						{}   )   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=True  ,						lowerCAmelCase_=True   )						->Optional[int]:
   UpperCAmelCase							=     instantiate_from_config(lowerCAmelCase_   )
   if sd is not None:
      model.load_state_dict(lowerCAmelCase_   )
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   # load the specified checkpoint
   if ckpt:
      UpperCAmelCase							=     torch.load(lowerCAmelCase_  ,						map_location="""cpu"""   )
      UpperCAmelCase							=     pl_sd["""global_step"""]
      print(F"""loaded model from global step {global_step}."""   )
   else:
      UpperCAmelCase							=     {"""state_dict""": None}
      UpperCAmelCase							=     None
   UpperCAmelCase							=     load_model_from_config(config.model  ,						pl_sd["""state_dict"""]  ,						gpu=lowerCAmelCase_  ,						eval_mode=lowerCAmelCase_   )["""model"""]
   return model, global_step







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


__a     =			logging.get_logger(__name__)

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






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''nllb-moe'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]=1_2_8_1_1_2					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					Optional[int]=1_2					,       __lowerCamelCase						:					Union[str, Any]=4_0_9_6					,       __lowerCamelCase						:					List[str]=1_6					,       __lowerCamelCase						:					List[str]=1_2					,       __lowerCamelCase						:					int=4_0_9_6					,       __lowerCamelCase						:					Tuple=1_6					,       __lowerCamelCase						:					str=0.05					,       __lowerCamelCase						:					List[str]=0.05					,       __lowerCamelCase						:					List[Any]=True					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					str="relu"					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					List[str]=0.1					,       __lowerCamelCase						:					Optional[int]=0.1					,       __lowerCamelCase						:					List[Any]=0.0					,       __lowerCamelCase						:					Optional[Any]=0.02					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Tuple="float32"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=1_2_8					,       __lowerCamelCase						:					List[str]=6_4					,       __lowerCamelCase						:					List[Any]=4					,       __lowerCamelCase						:					Tuple=4					,       __lowerCamelCase						:					str=0.001					,       __lowerCamelCase						:					Optional[int]=0.001					,       __lowerCamelCase						:					Tuple="all"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					List[str]=1.0					,       __lowerCamelCase						:					Dict=0.2					,       __lowerCamelCase						:					Union[str, Any]=1					,       __lowerCamelCase						:					int=0					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					int=False					,       **__lowerCamelCase						:					str					,       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     max_position_embeddings
         UpperCAmelCase							=     d_model
         UpperCAmelCase							=     encoder_ffn_dim
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     encoder_attention_heads
         UpperCAmelCase							=     decoder_ffn_dim
         UpperCAmelCase							=     decoder_layers
         UpperCAmelCase							=     decoder_attention_heads
         UpperCAmelCase							=     dropout
         UpperCAmelCase							=     attention_dropout
         UpperCAmelCase							=     activation_dropout
         UpperCAmelCase							=     activation_function
         UpperCAmelCase							=     init_std
         UpperCAmelCase							=     encoder_layerdrop
         UpperCAmelCase							=     decoder_layerdrop
         UpperCAmelCase							=     use_cache
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase							=     router_z_loss_coef
         UpperCAmelCase							=     router_aux_loss_coef
         UpperCAmelCase							=     decoder_sparse_step
         UpperCAmelCase							=     encoder_sparse_step
         UpperCAmelCase							=     num_experts
         UpperCAmelCase							=     expert_capacity
         UpperCAmelCase							=     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}"""       )
         UpperCAmelCase							=     router_dtype

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



