



'''simple docstring'''



import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


_lowercase       :						Dict						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="MMBTConfig"






class 			_SCREAMING_SNAKE_CASE				(nn.Module      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Tuple			)      ->      Union[str, Any]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   Optional[int]      =    config
							snake_case__      :   str      =    encoder
							snake_case__      :   Optional[int]      =    nn.Linear(config.modal_hidden_size    ,			config.hidden_size			)
							snake_case__      :   List[Any]      =    embeddings.position_embeddings
							snake_case__      :   List[str]      =    embeddings.token_type_embeddings
							snake_case__      :   List[str]      =    embeddings.word_embeddings
							snake_case__      :   Any      =    embeddings.LayerNorm
							snake_case__      :   str      =    nn.Dropout(p=config.hidden_dropout_prob			)







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Tuple=None			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    self.proj_embeddings(self.encoder(__UpperCamelCase			)			)
							snake_case__      :   Dict      =    token_embeddings.size(1			)

							if start_token is not None:
											snake_case__      :   List[str]      =    self.word_embeddings(__UpperCamelCase			)
											seq_length += 1
											snake_case__      :   Dict      =    torch.cat([start_token_embeds.unsqueeze(1			), token_embeddings]    ,			dim=1			)

							if end_token is not None:
											snake_case__      :   Optional[int]      =    self.word_embeddings(__UpperCamelCase			)
											seq_length += 1
											snake_case__      :   str      =    torch.cat([token_embeddings, end_token_embeds.unsqueeze(1			)]    ,			dim=1			)

							if position_ids is None:
											snake_case__      :   int      =    torch.arange(__UpperCamelCase    ,			dtype=torch.long    ,			device=input_modal.device			)
											snake_case__      :   Optional[Any]      =    position_ids.unsqueeze(0			).expand(input_modal.size(0			)    ,			__UpperCamelCase			)

							if token_type_ids is None:
											snake_case__      :   List[Any]      =    torch.zeros(
											    (input_modal.size(0			), seq_length)    ,			dtype=torch.long    ,			device=input_modal.device			)

							snake_case__      :   Any      =    self.position_embeddings(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    self.token_type_embeddings(__UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    token_embeddings + position_embeddings + token_type_embeddings
							snake_case__      :   List[Any]      =    self.LayerNorm(__UpperCamelCase			)
							snake_case__      :   str      =    self.dropout(__UpperCamelCase			)
							return embeddings



_lowercase       :						Union[str, Any]						=R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"

_lowercase       :						List[Any]						=R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"






@add_start_docstrings(
    'The bare MMBT Model outputting raw hidden-states without any specific head on top.',	lowercase__,	)
class 			_SCREAMING_SNAKE_CASE				(nn.Module,	lowercase__      ):



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						Dict			)      ->      Tuple:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   Optional[int]      =    config
							snake_case__      :   str      =    transformer
							snake_case__      :   List[str]      =    ModalEmbeddings(__UpperCamelCase    ,			__UpperCamelCase    ,			transformer.embeddings			)



			@add_start_docstrings_to_model_forward(__UpperCamelCase			)
			@replace_return_docstrings(output_type=__UpperCamelCase    ,			config_class=_CONFIG_FOR_DOC			)
			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						int=None    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						Any=None    ,			__UpperCamelCase		:						List[Any]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Optional[int]=None    ,			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   Any      =    output_attentions if output_attentions is not None else self.config.output_attentions
							snake_case__      :   Dict      =    (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							snake_case__      :   Any      =    return_dict if return_dict is not None else self.config.use_return_dict

							if input_ids is not None and inputs_embeds is not None:
											raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''			)
							elif input_ids is not None:
											snake_case__      :   int      =    input_ids.size()
							elif inputs_embeds is not None:
											snake_case__      :   int      =    inputs_embeds.size()[:-1]
							else:
											raise ValueError('''You have to specify either input_ids or inputs_embeds'''			)

							snake_case__      :   Any      =    input_ids.device if input_ids is not None else inputs_embeds.device

							snake_case__      :   List[str]      =    self.modal_encoder(
							    __UpperCamelCase    ,			start_token=__UpperCamelCase    ,			end_token=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			token_type_ids=__UpperCamelCase    ,			)

							snake_case__      :   Union[str, Any]      =    modal_embeddings.size()[:-1]

							if token_type_ids is None:
											snake_case__      :   int      =    torch.ones(__UpperCamelCase    ,			dtype=torch.long    ,			device=__UpperCamelCase			)

							snake_case__      :   Union[str, Any]      =    self.transformer.embeddings(
							    input_ids=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			token_type_ids=__UpperCamelCase    ,			inputs_embeds=__UpperCamelCase			)

							snake_case__      :   str      =    torch.cat([modal_embeddings, txt_embeddings]    ,			1			)

							snake_case__      :   str      =    embedding_output.size()[:-1]

							if attention_mask is None:
											snake_case__      :   Optional[int]      =    torch.ones(__UpperCamelCase    ,			device=__UpperCamelCase			)
							else:
											snake_case__      :   int      =    torch.cat(
											    [torch.ones(__UpperCamelCase    ,			device=__UpperCamelCase    ,			dtype=torch.long			), attention_mask]    ,			dim=1			)
							if encoder_attention_mask is None:
											snake_case__      :   Optional[int]      =    torch.ones(__UpperCamelCase    ,			device=__UpperCamelCase			)
							else:
											snake_case__      :   int      =    torch.cat(
											    [torch.ones(__UpperCamelCase    ,			device=__UpperCamelCase			), encoder_attention_mask]    ,			dim=1			)

							snake_case__      :   str      =    self.get_extended_attention_mask(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   int      =    self.invert_attention_mask(__UpperCamelCase			)
							snake_case__      :   Optional[int]      =    self.get_head_mask(__UpperCamelCase    ,			self.config.num_hidden_layers			)

							snake_case__      :   Union[str, Any]      =    self.transformer.encoder(
							    __UpperCamelCase    ,			attention_mask=__UpperCamelCase    ,			head_mask=__UpperCamelCase    ,			encoder_hidden_states=__UpperCamelCase    ,			encoder_attention_mask=__UpperCamelCase    ,			output_attentions=__UpperCamelCase    ,			output_hidden_states=__UpperCamelCase    ,			return_dict=__UpperCamelCase    ,			)

							snake_case__      :   Any      =    encoder_outputs[0]
							snake_case__      :   Union[str, Any]      =    self.transformer.pooler(__UpperCamelCase			)

							if not return_dict:
											return (sequence_output, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPooling(
							    last_hidden_state=__UpperCamelCase    ,			pooler_output=__UpperCamelCase    ,			hidden_states=encoder_outputs.hidden_states    ,			attentions=encoder_outputs.attentions    ,			)



			def 				lowerCAmelCase (     self		:						Any			)      ->      Tuple:
							"""simple docstring"""





							return self.embeddings.word_embeddings







			def 				lowerCAmelCase (     self		:						int    ,			__UpperCamelCase		:						List[str]			)      ->      Dict:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    value






@add_start_docstrings(
    '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    ',	lowercase__,	lowercase__,	)
class 			_SCREAMING_SNAKE_CASE				(nn.Module      ):



			def __init__(     self		:						List[str]    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						str			)      ->      List[str]:
							"""simple docstring"""





							super().__init__()
							snake_case__      :   Any      =    config.num_labels

							snake_case__      :   Dict      =    MMBTModel(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   List[Any]      =    nn.Dropout(config.hidden_dropout_prob			)
							snake_case__      :   Optional[Any]      =    nn.Linear(config.hidden_size    ,			config.num_labels			)







			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						Optional[int]=None    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						Dict=None    ,			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    return_dict if return_dict is not None else self.config.use_return_dict

							snake_case__      :   Optional[int]      =    self.mmbt(
							    input_modal=__UpperCamelCase    ,			input_ids=__UpperCamelCase    ,			modal_start_tokens=__UpperCamelCase    ,			modal_end_tokens=__UpperCamelCase    ,			attention_mask=__UpperCamelCase    ,			token_type_ids=__UpperCamelCase    ,			modal_token_type_ids=__UpperCamelCase    ,			position_ids=__UpperCamelCase    ,			modal_position_ids=__UpperCamelCase    ,			head_mask=__UpperCamelCase    ,			inputs_embeds=__UpperCamelCase    ,			return_dict=__UpperCamelCase    ,			)

							snake_case__      :   List[str]      =    outputs[1]

							snake_case__      :   int      =    self.dropout(__UpperCamelCase			)
							snake_case__      :   int      =    self.classifier(__UpperCamelCase			)

							snake_case__      :   List[str]      =    None
							if labels is not None:
											if self.num_labels == 1:
															#  We are doing regression
															snake_case__      :   Tuple      =    MSELoss()
															snake_case__      :   int      =    loss_fct(logits.view(-1			)    ,			labels.view(-1			)			)
											else:
															snake_case__      :   Any      =    CrossEntropyLoss()
															snake_case__      :   List[str]      =    loss_fct(logits.view(-1    ,			self.num_labels			)    ,			labels.view(-1			)			)

							if not return_dict:
											snake_case__      :   Optional[int]      =    (logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

							return SequenceClassifierOutput(
							    loss=__UpperCamelCase    ,			logits=__UpperCamelCase    ,			hidden_states=outputs.hidden_states    ,			attentions=outputs.attentions    ,			)











from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
				import pyximport

				pyximport.install(setup_args={"""include_dirs""": np.get_include()})
				from . import algos_graphormer  # noqa E402




def  A_(      A					,   A = 512 ):
	UpperCAmelCase_		    =  x.shape[1] if len(x.shape ) > 1 else 1
	UpperCAmelCase_		    =  1 + np.arange(0					,   feature_num * offset					,   A					,   dtype=np.intaa )
	UpperCAmelCase_		    =  x + feature_offset
	return x




def  A_(      A					,   A=True ):
	requires_backends(A					,   ["""cython"""] )

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		UpperCAmelCase_		    =  np.asarray(item["""edge_attr"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((len(item["""edge_index"""][0] ), 1)					,   dtype=np.intaa )  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		UpperCAmelCase_		    =  np.asarray(item["""node_feat"""]					,   dtype=np.intaa )
	else:
		UpperCAmelCase_		    =  np.ones((item["""num_nodes"""], 1)					,   dtype=np.intaa )  # same embedding for all

	UpperCAmelCase_		    =  np.asarray(item["""edge_index"""]					,   dtype=np.intaa )

	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1
	UpperCAmelCase_		    =  item["""num_nodes"""]

	if len(edge_attr.shape ) == 1:
		UpperCAmelCase_		    =  edge_attr[:, None]
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]					,   dtype=np.intaa )
	UpperCAmelCase_		    =  convert_to_single_emb(A ) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	UpperCAmelCase_		    =  np.zeros([num_nodes, num_nodes]					,   dtype=A )
	UpperCAmelCase_		    =  True

	UpperCAmelCase_    ,						UpperCAmelCase_		    =  algos_graphormer.floyd_warshall(A )
	UpperCAmelCase_		    =  np.amax(A )

	UpperCAmelCase_		    =  algos_graphormer.gen_edge_input(A					,   A					,   A )
	UpperCAmelCase_		    =  np.zeros([num_nodes + 1, num_nodes + 1]					,   dtype=np.single )  # with graph token

	# combine
	UpperCAmelCase_		    =  input_nodes + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  attn_bias
	UpperCAmelCase_		    =  attn_edge_type
	UpperCAmelCase_		    =  shortest_path_result.astype(np.intaa ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  np.sum(A					,   axis=1 ).reshape(-1 ) + 1  # we shift all indices by one for padding
	UpperCAmelCase_		    =  item["""in_degree"""]  # for undirected graph
	UpperCAmelCase_		    =  input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		UpperCAmelCase_		    =  item["""y"""]

	return item







class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	str			,       __lowercase     :	int=20			,       __lowercase     :	Union[str, Any]=False		):






						'''simple docstring'''







						if not is_cython_available():
							raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""		)

						UpperCAmelCase_		    =  spatial_pos_max
						UpperCAmelCase_		    =  on_the_fly_processing





					def __call__(    self     :	List[str]			,       __lowercase     :	List[dict]		):






						'''simple docstring'''







						if self.on_the_fly_processing:
							UpperCAmelCase_		    =  [preprocess_item(__lowercase		) for i in features]

						if not isinstance(features[0]			,       __lowercase		):
							UpperCAmelCase_		    =  [vars(__lowercase		) for f in features]
						UpperCAmelCase_		    =  {}

						UpperCAmelCase_		    =  max(len(i["""input_nodes"""]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_nodes"""][0]		)
						UpperCAmelCase_		    =  len(features[0]["""attn_edge_type"""][0][0]		)
						UpperCAmelCase_		    =  max(len(i["""input_edges"""][0][0]		) for i in features		)
						UpperCAmelCase_		    =  len(features[0]["""input_edges"""][0][0][0]		)
						UpperCAmelCase_		    =  len(__lowercase		)

						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       max_node_num + 1			,       max_node_num + 1			,       dtype=torch.float		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(__lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)
						UpperCAmelCase_		    =  torch.zeros(
						    __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       __lowercase			,       dtype=torch.long		)

						for ix, f in enumerate(__lowercase		):
							for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
								UpperCAmelCase_		    =  torch.tensor(f[k]		)

							if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]		) > 0:
								UpperCAmelCase_		    =  float("""-inf"""		)

							UpperCAmelCase_		    =  f["""attn_bias"""]
							UpperCAmelCase_		    =  f[
							    """attn_edge_type"""
							]
							UpperCAmelCase_		    =  f["""spatial_pos"""]
							UpperCAmelCase_		    =  f["""in_degree"""]
							UpperCAmelCase_		    =  f["""input_nodes"""]
							UpperCAmelCase_		    =  f["""input_edges"""]

						UpperCAmelCase_		    =  batch["""in_degree"""]

						UpperCAmelCase_		    =  features[0]["""labels"""]
						if len(__lowercase		) == 1:  # one task
							if isinstance(sample[0]			,       __lowercase		):  # regression
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
							else:  # binary classification
								UpperCAmelCase_		    =  torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
						else:  # multi task classification, left to float to keep the NaNs
							UpperCAmelCase_		    =  torch.from_numpy(np.stack([i["""labels"""] for i in features]			,       axis=0		)		)

						return batch


