





"""simple docstring"""






from dataclasses import asdict, dataclass
from typing import Optional

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


A 			=  logging.get_logger(__name__)

# TODO Update this
A 			=  {
    '''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 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''esm'''







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

									__a :						Union[str, Any]			       =  vocab_size
									__a :						Dict			       =  hidden_size
									__a :						List[str]			       =  num_hidden_layers
									__a :						Optional[Any]			       =  num_attention_heads
									__a :						int			       =  intermediate_size
									__a :						List[Any]			       =  hidden_dropout_prob
									__a :						Dict			       =  attention_probs_dropout_prob
									__a :						Optional[Any]			       =  max_position_embeddings
									__a :						int			       =  initializer_range
									__a :						List[str]			       =  layer_norm_eps
									__a :						Any			       =  position_embedding_type
									__a :						List[Any]			       =  use_cache
									__a :						List[str]			       =  emb_layer_norm_before
									__a :						Union[str, Any]			       =  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 :						str			       =  EsmFoldConfig()
													elif isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						Tuple			       =  EsmFoldConfig(**_UpperCAmelCase     )
													__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 :						Optional[Any]			       =  get_default_vocab_list()
													else:
																	__a :						int			       =  vocab_list
									else:
													__a :						Any			       =  None
													__a :						int			       =  None
									if self.esmfold_config is not None and getattr(self.esmfold_config	,			'''use_esm_attn_map'''	,			_UpperCAmelCase     ):
													raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''     )







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  super().to_dict()
									if isinstance(self.esmfold_config	,			_UpperCAmelCase     ):
													__a :						Tuple			       =  self.esmfold_config.to_dict()
									return output






@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  None
					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  0

					__lowerCAmelCase  =  True
					__lowerCAmelCase  =  False

					__lowerCAmelCase  =  128
					__lowerCAmelCase  =  None







					def  _lowerCamelCase		(						self     ):
									if self.trunk is None:
													__a :						Optional[int]			       =  TrunkConfig()
									elif isinstance(self.trunk	,			_UpperCAmelCase     ):
													__a :						int			       =  TrunkConfig(**self.trunk     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  asdict(self     )
									__a :						Tuple			       =  self.trunk.to_dict()
									return output






@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  48
					__lowerCAmelCase  =  1024
					__lowerCAmelCase  =  128
					__lowerCAmelCase  =  32
					__lowerCAmelCase  =  32
					__lowerCAmelCase  =  32
					__lowerCAmelCase  =  0
					__lowerCAmelCase  =  0
					__lowerCAmelCase  =  False
					__lowerCAmelCase  =  4
					__lowerCAmelCase  =  128
					__lowerCAmelCase  =  None







					def  _lowerCamelCase		(						self     ):
									if self.structure_module is None:
													__a :						List[str]			       =  StructureModuleConfig()
									elif isinstance(self.structure_module	,			_UpperCAmelCase     ):
													__a :						List[str]			       =  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 :						str			       =  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     ):
									__a :						Optional[Any]			       =  asdict(self     )
									__a :						Union[str, Any]			       =  self.structure_module.to_dict()
									return output






@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  384
					__lowerCAmelCase  =  128
					__lowerCAmelCase  =  16
					__lowerCAmelCase  =  128
					__lowerCAmelCase  =  12
					__lowerCAmelCase  =  4
					__lowerCAmelCase  =  8
					__lowerCAmelCase  =  0.1
					__lowerCAmelCase  =  8
					__lowerCAmelCase  =  1
					__lowerCAmelCase  =  2
					__lowerCAmelCase  =  7
					__lowerCAmelCase  =  10
					__lowerCAmelCase  =  1E-8
					__lowerCAmelCase  =  1E5







					def  _lowerCamelCase		(						self     ):
									return asdict(self     )






def     __A      (			)		->		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>",
				)






"""simple docstring"""






from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass




@dataclass
class 				__lowercase			:







					'''simple docstring'''




					__lowerCAmelCase  =  42
					__lowerCAmelCase  =  42






class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									__a :						list[list[Edge]]			       =  [[] for _ in range(_UpperCAmelCase     )]
									__a :						Dict			       =  size







					def __getitem__(						self	,			_UpperCAmelCase     ):
									return iter(self._graph[vertex]     )







					@property
					def  _lowerCamelCase		(						self     ):
									return self._size







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									if weight not in (0, 1):
													raise ValueError('''Edge weight must be either 0 or 1.'''     )

									if to_vertex < 0 or to_vertex >= self.size:
													raise ValueError('''Vertex indexes must be in [0; size).'''     )

									self._graph[from_vertex].append(Edge(_UpperCAmelCase	,			_UpperCAmelCase     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  deque([start_vertex]     )
									__a :						list[int | None]			       =  [None] * self.size
									__a :						int			       =  0

									while queue:
													__a :						Any			       =  queue.popleft()
													__a :						Tuple			       =  distances[current_vertex]
													if current_distance is None:
																	continue

													for edge in self[current_vertex]:
																	__a :						List[str]			       =  current_distance + edge.weight
																	__a :						str			       =  distances[edge.destination_vertex]
																	if (
																	    isinstance(_UpperCAmelCase	,			_UpperCAmelCase     )
																	    and new_distance >= dest_vertex_distance
																	):
																					continue
																	__a :						str			       =  new_distance
																	if edge.weight == 0:
																					queue.appendleft(edge.destination_vertex     )
																	else:
																					queue.append(edge.destination_vertex     )

									if distances[finish_vertex] is None:
													raise ValueError('''No path from start_vertex to finish_vertex.'''     )

									return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()