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	# coding=utf-8
	# Copyright 2022 Meta and The HuggingFace Inc. team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" ESM model configuration"""

	from dataclasses import asdict, dataclass
	from typing import Optional

	from transformers import PretrainedConfig, logging

	logger = logging.get_logger(__name__)

	# TODO Update this
	ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"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 EsmConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
	according to the specified arguments, defining the model architecture. Instantiating a configuration with the
	defaults will yield a similar configuration to that of the ESM
	[facebook/esm-1b](https://huggingface.co/facebook/esm-1b) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.


	Args:
	vocab_size (`int`, optional):
	Vocabulary size of the ESM model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`ESMModel`].
	mask_token_id (`int`, optional):
	The index of the mask token in the vocabulary. This must be included in the config because of the
	"mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.
	pad_token_id (`int`, optional):
	The index of the padding token in the vocabulary. This must be included in the config because certain parts
	of the ESM code use this instead of the attention mask.
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the encoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	intermediate_size (`int`, optional, defaults to 3072):
	Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
	hidden_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention probabilities.
	max_position_embeddings (`int`, optional, defaults to 1026):
	The maximum sequence length that this model might ever be used with. Typically set this to something large
	just in case (e.g., 512 or 1024 or 2048).
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-12):
	The epsilon used by the layer normalization layers.
	position_embedding_type (`str`, optional, defaults to `"absolute"`):
	Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.
	For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
	[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
	For more information on `"relative_key_query"`, please refer to Method 4 in [Improve Transformer Models
	with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
	is_decoder (`bool`, optional, defaults to `False`):
	Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	emb_layer_norm_before (`bool`, optional):
	Whether to apply layer normalization after embeddings but before the main stem of the network.
	token_dropout (`bool`, defaults to `False`):
	When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.

	Examples:

	```python
	>>> from transformers import EsmModel, EsmConfig

	>>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()

	>>> # Initializing a model from the configuration >>> model = ESMModel(configuration)

	>>> # Accessing the model configuration >>> configuration = model.config
	```"""
	model_type = "esm"

	def __init__(
	self,
	vocab_size=None,
	mask_token_id=None,
	pad_token_id=None,
	hidden_size=768,
	num_hidden_layers=12,
	num_attention_heads=12,
	intermediate_size=3072,
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=1026,
	initializer_range=0.02,
	layer_norm_eps=1e-12,
	position_embedding_type="absolute",
	use_cache=True,
	emb_layer_norm_before=None,
	token_dropout=False,
	is_folding_model=False,
	esmfold_config=None,
	vocab_list=None,
	add_bias_fnn=True,
	**kwargs,
	):
	super().__init__(
	pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
	)

	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.intermediate_size = intermediate_size
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.max_position_embeddings = max_position_embeddings
	self.initializer_range = initializer_range
	self.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.use_cache = use_cache
	self.emb_layer_norm_before = emb_layer_norm_before
	self.token_dropout = token_dropout
	self.is_folding_model = is_folding_model
	# Arguments needed for Dalmatian
	self.add_bias_fnn = add_bias_fnn
	if is_folding_model:
	if esmfold_config is None:
	logger.info(
	"No esmfold_config supplied for folding model, using default values."
	)
	esmfold_config = EsmFoldConfig()
	elif isinstance(esmfold_config, dict):
	esmfold_config = EsmFoldConfig(**esmfold_config)
	self.esmfold_config = esmfold_config
	if vocab_list is None:
	logger.warning(
	"No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
	)
	self.vocab_list = get_default_vocab_list()
	else:
	self.vocab_list = vocab_list
	else:
	self.esmfold_config = None
	self.vocab_list = None
	if self.esmfold_config is not None and getattr(
	self.esmfold_config, "use_esm_attn_map", False
	):
	raise ValueError(
	"The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"
	)

	def to_dict(self):
	"""
	Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

	Returns:
	`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
	"""
	output = super().to_dict()
	if isinstance(self.esmfold_config, EsmFoldConfig):
	output["esmfold_config"] = self.esmfold_config.to_dict()
	return output


	@dataclass
	class EsmFoldConfig:
	esm_type: str = None
	fp16_esm: bool = True
	use_esm_attn_map: bool = False
	esm_ablate_pairwise: bool = False
	esm_ablate_sequence: bool = False
	esm_input_dropout: float = 0

	embed_aa: bool = True
	bypass_lm: bool = False

	lddt_head_hid_dim: int = 128
	trunk: "TrunkConfig" = None

	def __post_init__(self):
	if self.trunk is None:
	self.trunk = TrunkConfig()
	elif isinstance(self.trunk, dict):
	self.trunk = TrunkConfig(**self.trunk)

	def to_dict(self):
	"""
	Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

	Returns:
	`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
	"""
	output = asdict(self)
	output["trunk"] = self.trunk.to_dict()
	return output


	@dataclass
	class TrunkConfig:
	num_blocks: int = 48
	sequence_state_dim: int = 1024
	pairwise_state_dim: int = 128
	sequence_head_width: int = 32
	pairwise_head_width: int = 32
	position_bins: int = 32
	dropout: float = 0
	layer_drop: float = 0
	cpu_grad_checkpoint: bool = False
	max_recycles: int = 4
	chunk_size: Optional[int] = 128
	structure_module: "StructureModuleConfig" = None

	def __post_init__(self):
	if self.structure_module is None:
	self.structure_module = StructureModuleConfig()
	elif isinstance(self.structure_module, dict):
	self.structure_module = 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}."
	)

	sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
	pairwise_num_heads = 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 to_dict(self):
	"""
	Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

	Returns:
	`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
	"""
	output = asdict(self)
	output["structure_module"] = self.structure_module.to_dict()
	return output


	@dataclass
	class StructureModuleConfig:
	"""
	Args:
	sequence_dim:
	Single representation channel dimension
	pairwise_dim:
	Pair representation channel dimension
	ipa_dim:
	IPA hidden channel dimension
	resnet_dim:
	Angle resnet (Alg. 23 lines 11-14) hidden channel dimension
	num_heads_ipa:
	Number of IPA heads
	num_qk_points:
	Number of query/key points to generate during IPA
	num_v_points:
	Number of value points to generate during IPA
	dropout_rate:
	Dropout rate used throughout the layer
	num_blocks:
	Number of structure module blocks
	num_transition_layers:
	Number of layers in the single representation transition (Alg. 23 lines 8-9)
	num_resnet_blocks:
	Number of blocks in the angle resnet
	num_angles:
	Number of angles to generate in the angle resnet
	trans_scale_factor:
	Scale of single representation transition hidden dimension
	epsilon:
	Small number used in angle resnet normalization
	inf:
	Large number used for attention masking
	"""

	sequence_dim: int = 384
	pairwise_dim: int = 128
	ipa_dim: int = 16
	resnet_dim: int = 128
	num_heads_ipa: int = 12
	num_qk_points: int = 4
	num_v_points: int = 8
	dropout_rate: float = 0.1
	num_blocks: int = 8
	num_transition_layers: int = 1
	num_resnet_blocks: int = 2
	num_angles: int = 7
	trans_scale_factor: int = 10
	epsilon: float = 1e-8
	inf: float = 1e5

	def to_dict(self):
	return asdict(self)


	def get_default_vocab_list():
	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>",
	)