



"""simple docstring"""

from dataclasses import asdict, dataclass
from typing import Optional

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


lowercase__			   =  logging.get_logger(__name__)

# TODO Update this
lowercase__			   =  {
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}




class SCREAMING_SNAKE_CASE__					(		UpperCAmelCase_      ):
      _lowerCAmelCase							=					'esm'



      def __init__(self						,     _lowercase=None						,     _lowercase=None						,     _lowercase=None						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=1026						,     _lowercase=0.02						,     _lowercase=1e-12						,     _lowercase="absolute"						,     _lowercase=True						,     _lowercase=None						,     _lowercase=False						,     _lowercase=False						,     _lowercase=None						,     _lowercase=None						,     **_lowercase						,     ):





             '''simple docstring'''

             super().__init__(pad_token_id=_lowercase						,     mask_token_id=_lowercase						,     **_lowercase							)

             __a		:		Tuple									=       vocab_size
             __a		:		List[str]									=       hidden_size
             __a		:		Optional[int]									=       num_hidden_layers
             __a		:		Dict									=       num_attention_heads
             __a		:		str									=       intermediate_size
             __a		:		Union[str, Any]									=       hidden_dropout_prob
             __a		:		Any									=       attention_probs_dropout_prob
             __a		:		List[str]									=       max_position_embeddings
             __a		:		Optional[int]									=       initializer_range
             __a		:		Any									=       layer_norm_eps
             __a		:		Tuple									=       position_embedding_type
             __a		:		Dict									=       use_cache
             __a		:		str									=       emb_layer_norm_before
             __a		:		Union[str, Any]									=       token_dropout
             __a		:		int									=       is_folding_model
             if is_folding_model:
                    if esmfold_config is None:
                           logger.info("""No esmfold_config supplied for folding model, using default values."""							)
                           __a		:		List[str]									=       EsmFoldConfig()
                    elif isinstance(_lowercase						,     _lowercase							):
                           __a		:		int									=       EsmFoldConfig(**_lowercase							)
                    __a		:		int									=       esmfold_config
                    if vocab_list is None:
                           logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"""							)
                           __a		:		List[str]									=       get_default_vocab_list()
                    else:
                           __a		:		Any									=       vocab_list
             else:
                    __a		:		str									=       None
                    __a		:		int									=       None
             if self.esmfold_config is not None and getattr(self.esmfold_config						,     """use_esm_attn_map"""						,     _lowercase							):
                    raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"""							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[int]									=       super().to_dict()
             if isinstance(self.esmfold_config						,     _lowercase							):
                    __a		:		List[Any]									=       self.esmfold_config.to_dict()
             return output







@dataclass
class SCREAMING_SNAKE_CASE__					:
      _lowerCAmelCase							=					None
      _lowerCAmelCase							=					True
      _lowerCAmelCase							=					False
      _lowerCAmelCase							=					False
      _lowerCAmelCase							=					False
      _lowerCAmelCase							=					0

      _lowerCAmelCase							=					True
      _lowerCAmelCase							=					False

      _lowerCAmelCase							=					1_2_8
      _lowerCAmelCase							=					None



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             if self.trunk is None:
                    __a		:		List[Any]									=       TrunkConfig()
             elif isinstance(self.trunk						,     _lowercase							):
                    __a		:		Any									=       TrunkConfig(**self.trunk							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[Any]									=       asdict(self							)
             __a		:		Union[str, Any]									=       self.trunk.to_dict()
             return output







@dataclass
class SCREAMING_SNAKE_CASE__					:
      _lowerCAmelCase							=					4_8
      _lowerCAmelCase							=					1_0_2_4
      _lowerCAmelCase							=					1_2_8
      _lowerCAmelCase							=					3_2
      _lowerCAmelCase							=					3_2
      _lowerCAmelCase							=					3_2
      _lowerCAmelCase							=					0
      _lowerCAmelCase							=					0
      _lowerCAmelCase							=					False
      _lowerCAmelCase							=					4
      _lowerCAmelCase							=					1_2_8
      _lowerCAmelCase							=					None



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             if self.structure_module is None:
                    __a		:		str									=       StructureModuleConfig()
             elif isinstance(self.structure_module						,     _lowercase							):
                    __a		:		Dict									=       StructureModuleConfig(**self.structure_module							)

             if self.max_recycles <= 0:
                    raise ValueError(F'''`max_recycles` should be positive, got {self.max_recycles}.'''							)
             if self.sequence_state_dim % self.sequence_state_dim != 0:
                    raise ValueError(
                        """`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
                        F''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''							)
             if self.pairwise_state_dim % self.pairwise_state_dim != 0:
                    raise ValueError(
                        """`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
                        F''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''							)

             __a		:		Dict									=       self.sequence_state_dim // self.sequence_head_width
             __a		:		Optional[int]									=       self.pairwise_state_dim // self.pairwise_head_width

             if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
                    raise ValueError(
                        """`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
                        F''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''							)
             if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
                    raise ValueError(
                        """`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
                        F''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''							)
             if self.pairwise_state_dim % 2 != 0:
                    raise ValueError(F'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''							)

             if self.dropout >= 0.4:
                    raise ValueError(F'''`dropout` should not be greater than 0.4, got {self.dropout}.'''							)





      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Dict									=       asdict(self							)
             __a		:		Union[str, Any]									=       self.structure_module.to_dict()
             return output







@dataclass
class SCREAMING_SNAKE_CASE__					:
      _lowerCAmelCase							=					3_8_4
      _lowerCAmelCase							=					1_2_8
      _lowerCAmelCase							=					1_6
      _lowerCAmelCase							=					1_2_8
      _lowerCAmelCase							=					1_2
      _lowerCAmelCase							=					4
      _lowerCAmelCase							=					8
      _lowerCAmelCase							=					0.1
      _lowerCAmelCase							=					8
      _lowerCAmelCase							=					1
      _lowerCAmelCase							=					2
      _lowerCAmelCase							=					7
      _lowerCAmelCase							=					1_0
      _lowerCAmelCase							=					1E-8
      _lowerCAmelCase							=					1E5



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             return asdict(self							)







def       __magic_name__    (   ):
       return (
           "<cls>",
           "<pad>",
           "<eos>",
           "<unk>",
           "L",
           "A",
           "G",
           "V",
           "S",
           "E",
           "R",
           "T",
           "I",
           "D",
           "P",
           "K",
           "Q",
           "N",
           "F",
           "Y",
           "M",
           "H",
           "W",
           "C",
           "X",
           "B",
           "U",
           "Z",
           "O",
           ".",
           "-",
           "<null_1>",
           "<mask>",
       )






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       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       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

