

from dataclasses import asdict, dataclass
from typing import Optional

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


A_	:			Tuple  	=      logging.get_logger(__name__)

# TODO Update this
A_	:			List[str]  	=      {
    "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    _a						(__magic_name__    ):


     '''simple docstring'''






     UpperCAmelCase__:       List[str]  							=	"""esm"""






     def __init__(			self   ,	A__=None   ,	A__=None   ,	A__=None   ,	A__=768   ,	A__=12   ,	A__=12   ,	A__=3072   ,	A__=0.1   ,	A__=0.1   ,	A__=1026   ,	A__=0.0_2   ,	A__=1e-12   ,	A__="absolute"   ,	A__=True   ,	A__=None   ,	A__=False   ,	A__=False   ,	A__=None   ,	A__=None   ,	**A__   ,	):
      super().__init__(pad_token_id=_A   ,	mask_token_id=_A   ,	**_A      )

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






     def   __A      (			self      ):
      A__							:   List[Any]					       =		super().to_dict()
      if isinstance(self.esmfold_config   ,	_A      ):
       A__							:   Tuple					       =		self.esmfold_config.to_dict()
      return output



@dataclass
class    _a						:


     '''simple docstring'''






     UpperCAmelCase__:       str  							=	None
     UpperCAmelCase__:       bool  							=	True
     UpperCAmelCase__:       bool  							=	False
     UpperCAmelCase__:       bool  							=	False
     UpperCAmelCase__:       bool  							=	False
     UpperCAmelCase__:       float  							=	0

     UpperCAmelCase__:       bool  							=	True
     UpperCAmelCase__:       bool  							=	False

     UpperCAmelCase__:       int  							=	1_28
     UpperCAmelCase__:       "TrunkConfig"  							=	None






     def   __A      (			self      ):
      if self.trunk is None:
       A__							:   List[str]					       =		TrunkConfig()
      elif isinstance(self.trunk   ,	_A      ):
       A__							:   Optional[int]					       =		TrunkConfig(**self.trunk      )






     def   __A      (			self      ):
      A__							:   str					       =		asdict(self      )
      A__							:   Tuple					       =		self.trunk.to_dict()
      return output



@dataclass
class    _a						:


     '''simple docstring'''






     UpperCAmelCase__:       int  							=	48
     UpperCAmelCase__:       int  							=	10_24
     UpperCAmelCase__:       int  							=	1_28
     UpperCAmelCase__:       int  							=	32
     UpperCAmelCase__:       int  							=	32
     UpperCAmelCase__:       int  							=	32
     UpperCAmelCase__:       float  							=	0
     UpperCAmelCase__:       float  							=	0
     UpperCAmelCase__:       bool  							=	False
     UpperCAmelCase__:       int  							=	4
     UpperCAmelCase__:       Optional[int]  							=	1_28
     UpperCAmelCase__:       "StructureModuleConfig"  							=	None






     def   __A      (			self      ):
      if self.structure_module is None:
       A__							:   str					       =		StructureModuleConfig()
      elif isinstance(self.structure_module   ,	_A      ):
       A__							:   Union[str, Any]					       =		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__							:   List[Any]					       =		self.sequence_state_dim // self.sequence_head_width
      A__							:   Optional[Any]					       =		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   __A      (			self      ):
      A__							:   List[Any]					       =		asdict(self      )
      A__							:   Dict					       =		self.structure_module.to_dict()
      return output



@dataclass
class    _a						:


     '''simple docstring'''






     UpperCAmelCase__:       int  							=	3_84
     UpperCAmelCase__:       int  							=	1_28
     UpperCAmelCase__:       int  							=	16
     UpperCAmelCase__:       int  							=	1_28
     UpperCAmelCase__:       int  							=	12
     UpperCAmelCase__:       int  							=	4
     UpperCAmelCase__:       int  							=	8
     UpperCAmelCase__:       float  							=	0.1
     UpperCAmelCase__:       int  							=	8
     UpperCAmelCase__:       int  							=	1
     UpperCAmelCase__:       int  							=	2
     UpperCAmelCase__:       int  							=	7
     UpperCAmelCase__:       int  							=	10
     UpperCAmelCase__:       float  							=	1E-8
     UpperCAmelCase__:       float  							=	1E5






     def   __A      (			self      ):
      return asdict(self      )



def 						UpperCamelCase ()					->				Tuple:
 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>",
 )






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



