





"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation




def 			__snake_case						(      __A ,__A ,__A = False ,__A = 1 ,__A = 1 ,__A = 10000 ,)		->				Dict:
			assert len(timesteps.shape    ) == 1, "Timesteps should be a 1d-array"

			lowercase    :			int  =       embedding_dim // 2
			lowercase    :			Tuple  =       -math.log(__A    ) * torch.arange(
			    start=0 ,end=__A ,dtype=torch.floataa ,device=timesteps.device    )
			lowercase    :			Dict  =       exponent / (half_dim - downscale_freq_shift)

			lowercase    :			Tuple  =       torch.exp(__A    )
			lowercase    :			List[Any]  =       timesteps[:, None].float() * emb[None, :]

			# scale embeddings
			lowercase    :			Optional[int]  =       scale * emb

			# concat sine and cosine embeddings
			lowercase    :			Any  =       torch.cat([torch.sin(__A    ), torch.cos(__A    )] ,dim=-1    )

			# flip sine and cosine embeddings
			if flip_sin_to_cos:
						lowercase    :			Optional[int]  =       torch.cat([emb[:, half_dim:], emb[:, :half_dim]] ,dim=-1    )

			# zero pad
			if embedding_dim % 2 == 1:
						lowercase    :			int  =       torch.nn.functional.pad(__A ,(0, 1, 0, 0)    )
			return emb




def 			__snake_case						(      __A ,__A ,__A=False ,__A=0    )		->				Union[str, Any]:
			lowercase    :			Tuple  =       np.arange(__A ,dtype=np.floataa    )
			lowercase    :			str  =       np.arange(__A ,dtype=np.floataa    )
			lowercase    :			int  =       np.meshgrid(__A ,__A    )  # here w goes first
			lowercase    :			int  =       np.stack(__A ,axis=0    )

			lowercase    :			Any  =       grid.reshape([2, 1, grid_size, grid_size]    )
			lowercase    :			List[Any]  =       get_ad_sincos_pos_embed_from_grid(__A ,__A    )
			if cls_token and extra_tokens > 0:
						lowercase    :			Tuple  =       np.concatenate([np.zeros([extra_tokens, embed_dim]    ), pos_embed] ,axis=0    )
			return pos_embed




def 			__snake_case						(      __A ,__A    )		->				Optional[int]:


			if embed_dim % 2 != 0:
						raise ValueError("""embed_dim must be divisible by 2"""    )

			# use half of dimensions to encode grid_h
			lowercase    :			Dict  =       get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,grid[0]    )  # (H*W, D/2)
			lowercase    :			List[str]  =       get_ad_sincos_pos_embed_from_grid(embed_dim // 2 ,grid[1]    )  # (H*W, D/2)

			lowercase    :			Any  =       np.concatenate([emb_h, emb_w] ,axis=1    )  # (H*W, D)
			return emb




def 			__snake_case						(      __A ,__A    )		->				List[Any]:


			if embed_dim % 2 != 0:
						raise ValueError("""embed_dim must be divisible by 2"""    )

			lowercase    :			Any  =       np.arange(embed_dim // 2 ,dtype=np.floataa    )
			omega /= embed_dim / 2.0
			lowercase    :			List[Any]  =       1.0 / 10000**omega  # (D/2,)

			lowercase    :			int  =       pos.reshape(-1    )  # (M,)
			lowercase    :			int  =       np.einsum("""m,d->md""" ,__A ,__A    )  # (M, D/2), outer product

			lowercase    :			Tuple  =       np.sin(__A    )  # (M, D/2)
			lowercase    :			int  =       np.cos(__A    )  # (M, D/2)

			lowercase    :			str  =       np.concatenate([emb_sin, emb_cos] ,axis=1    )  # (M, D)
			return emb
class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case=2_2_4      ,   snake_case=2_2_4      ,   snake_case=1_6      ,   snake_case=3      ,   snake_case=7_6_8      ,   snake_case=False      ,   snake_case=True      ,   snake_case=True      ,   ) ->    int:







						"""simple docstring"""
						super().__init__()

						lowercase    :			List[Any]  =       (height // patch_size) * (width // patch_size)
						lowercase    :			Union[str, Any]  =       flatten
						lowercase    :			Union[str, Any]  =       layer_norm

						lowercase    :			Optional[Any]  =       nn.Convad(
						    snake_case      ,   snake_case      ,   kernel_size=(patch_size, patch_size)      ,   stride=snake_case      ,   bias=snake_case	)
						if layer_norm:
									lowercase    :			List[str]  =       nn.LayerNorm(snake_case      ,   elementwise_affine=snake_case      ,   eps=1E-6	)
						else:
									lowercase    :			int  =       None

						lowercase    :			str  =       get_ad_sincos_pos_embed(snake_case      ,   int(num_patches**0.5	)	)
						self.register_buffer("""pos_embed"""      ,   torch.from_numpy(snake_case	).float().unsqueeze(0	)      ,   persistent=snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Union[str, Any]:







						"""simple docstring"""
						lowercase    :			int  =       self.proj(snake_case	)
						if self.flatten:
									lowercase    :			List[Any]  =       latent.flatten(2	).transpose(1      ,   2	)  # BCHW -> BNC
						if self.layer_norm:
									lowercase    :			Tuple  =       self.norm(snake_case	)
						return latent + self.pos_embed




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case = "silu"      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   ) ->    Optional[Any]:







						"""simple docstring"""
						super().__init__()

						lowercase    :			Optional[Any]  =       nn.Linear(snake_case      ,   snake_case	)

						if cond_proj_dim is not None:
									lowercase    :			Tuple  =       nn.Linear(snake_case      ,   snake_case      ,   bias=snake_case	)
						else:
									lowercase    :			int  =       None

						lowercase    :			List[Any]  =       get_activation(snake_case	)

						if out_dim is not None:
									lowercase    :			Tuple  =       out_dim
						else:
									lowercase    :			int  =       time_embed_dim
						lowercase    :			Optional[int]  =       nn.Linear(snake_case      ,   snake_case	)

						if post_act_fn is None:
									lowercase    :			int  =       None
						else:
									lowercase    :			Optional[int]  =       get_activation(snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case=None	) ->    Tuple:







						"""simple docstring"""






						if condition is not None:
									lowercase    :			Any  =       sample + self.cond_proj(snake_case	)
						lowercase    :			str  =       self.linear_a(snake_case	)

						if self.act is not None:
									lowercase    :			Optional[Any]  =       self.act(snake_case	)

						lowercase    :			Optional[int]  =       self.linear_a(snake_case	)

						if self.post_act is not None:
									lowercase    :			Dict  =       self.post_act(snake_case	)
						return sample




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    int:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Tuple  =       num_channels
						lowercase    :			Optional[int]  =       flip_sin_to_cos
						lowercase    :			Any  =       downscale_freq_shift





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    List[str]:







						"""simple docstring"""
						lowercase    :			Tuple  =       get_timestep_embedding(
						    snake_case      ,   self.num_channels      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   downscale_freq_shift=self.downscale_freq_shift      ,   )
						return t_emb




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 2_5_6      ,   snake_case = 1.0      ,   snake_case=True      ,   snake_case=True      ,   snake_case=False	) ->    Optional[int]:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Dict  =       nn.Parameter(torch.randn(snake_case	) * scale      ,   requires_grad=snake_case	)
						lowercase    :			Union[str, Any]  =       log
						lowercase    :			Tuple  =       flip_sin_to_cos

						if set_W_to_weight:
									# to delete later
									lowercase    :			Optional[Any]  =       nn.Parameter(torch.randn(snake_case	) * scale      ,   requires_grad=snake_case	)

									lowercase    :			List[str]  =       self.W





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Optional[Any]:







						"""simple docstring"""






						if self.log:
									lowercase    :			Dict  =       torch.log(snake_case	)

						lowercase    :			Dict  =       x[:, None] * self.weight[None, :] * 2 * np.pi

						if self.flip_sin_to_cos:
									lowercase    :			str  =       torch.cat([torch.cos(snake_case	), torch.sin(snake_case	)]      ,   dim=-1	)
						else:
									lowercase    :			Optional[Any]  =       torch.cat([torch.sin(snake_case	), torch.cos(snake_case	)]      ,   dim=-1	)
						return out




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   ) ->    List[str]:







						"""simple docstring"""
						super().__init__()

						lowercase    :			str  =       height
						lowercase    :			Tuple  =       width
						lowercase    :			int  =       num_embed
						lowercase    :			Optional[int]  =       embed_dim

						lowercase    :			int  =       nn.Embedding(self.num_embed      ,   snake_case	)
						lowercase    :			Union[str, Any]  =       nn.Embedding(self.height      ,   snake_case	)
						lowercase    :			List[str]  =       nn.Embedding(self.width      ,   snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    str:







						"""simple docstring"""
						lowercase    :			List[str]  =       self.emb(snake_case	)

						lowercase    :			Dict  =       self.height_emb(torch.arange(self.height      ,   device=index.device	).view(1      ,   self.height	)	)

						# 1 x H x D -> 1 x H x 1 x D
						lowercase    :			Tuple  =       height_emb.unsqueeze(2	)

						lowercase    :			Union[str, Any]  =       self.width_emb(torch.arange(self.width      ,   device=index.device	).view(1      ,   self.width	)	)

						# 1 x W x D -> 1 x 1 x W x D
						lowercase    :			Tuple  =       width_emb.unsqueeze(1	)

						lowercase    :			List[str]  =       height_emb + width_emb

						# 1 x H x W x D -> 1 x L xD
						lowercase    :			Dict  =       pos_emb.view(1      ,   self.height * self.width      ,   -1	)

						lowercase    :			Optional[int]  =       emb + pos_emb[:, : emb.shape[1], :]

						return emb




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case	) ->    Union[str, Any]:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Optional[int]  =       dropout_prob > 0
						lowercase    :			Optional[Any]  =       nn.Embedding(num_classes + use_cfg_embedding      ,   snake_case	)
						lowercase    :			Union[str, Any]  =       num_classes
						lowercase    :			Union[str, Any]  =       dropout_prob



			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case=None	) ->    Dict:







						"""simple docstring"""






						if force_drop_ids is None:
									lowercase    :			Dict  =       torch.rand(labels.shape[0]      ,   device=labels.device	) < self.dropout_prob
						else:
									lowercase    :			List[Any]  =       torch.tensor(force_drop_ids == 1	)
						lowercase    :			List[Any]  =       torch.where(snake_case      ,   self.num_classes      ,   snake_case	)
						return labels





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case=None	) ->    Dict:







						"""simple docstring"""
						lowercase    :			str  =       self.dropout_prob > 0
						if (self.training and use_dropout) or (force_drop_ids is not None):
									lowercase    :			Any  =       self.token_drop(snake_case      ,   snake_case	)
						lowercase    :			Dict  =       self.embedding_table(snake_case	)
						return embeddings




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 1_0_2_4      ,   snake_case = 7_6_8      ,   snake_case = 7_6_8      ,   snake_case = 1_0      ,   ) ->    Optional[int]:







						"""simple docstring"""
						super().__init__()

						lowercase    :			List[Any]  =       num_image_text_embeds
						lowercase    :			Dict  =       nn.Linear(snake_case      ,   self.num_image_text_embeds * cross_attention_dim	)
						lowercase    :			Union[str, Any]  =       nn.Linear(snake_case      ,   snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    Optional[int]:







						"""simple docstring"""
						lowercase    :			Union[str, Any]  =       text_embeds.shape[0]

						# image
						lowercase    :			Union[str, Any]  =       self.image_embeds(snake_case	)
						lowercase    :			Dict  =       image_text_embeds.reshape(snake_case      ,   self.num_image_text_embeds      ,   -1	)

						# text
						lowercase    :			Dict  =       self.text_proj(snake_case	)

						return torch.cat([image_text_embeds, text_embeds]      ,   dim=1	)




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 7_6_8      ,   snake_case = 7_6_8      ,   snake_case = 3_2      ,   ) ->    Dict:







						"""simple docstring"""
						super().__init__()

						lowercase    :			int  =       num_image_text_embeds
						lowercase    :			List[str]  =       nn.Linear(snake_case      ,   self.num_image_text_embeds * cross_attention_dim	)
						lowercase    :			Union[str, Any]  =       nn.LayerNorm(snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Tuple:







						"""simple docstring"""
						lowercase    :			List[Any]  =       image_embeds.shape[0]

						# image
						lowercase    :			Union[str, Any]  =       self.image_embeds(snake_case	)
						lowercase    :			str  =       image_embeds.reshape(snake_case      ,   self.num_image_text_embeds      ,   -1	)
						lowercase    :			Any  =       self.norm(snake_case	)
						return image_embeds




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case=0.1	) ->    int:







						"""simple docstring"""
						super().__init__()

						lowercase    :			str  =       Timesteps(num_channels=2_5_6      ,   flip_sin_to_cos=snake_case      ,   downscale_freq_shift=1	)
						lowercase    :			Dict  =       TimestepEmbedding(in_channels=2_5_6      ,   time_embed_dim=snake_case	)
						lowercase    :			Union[str, Any]  =       LabelEmbedding(snake_case      ,   snake_case      ,   snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case      ,   snake_case=None	) ->    List[str]:







						"""simple docstring"""
						lowercase    :			List[Any]  =       self.time_proj(snake_case	)
						lowercase    :			int  =       self.timestep_embedder(timesteps_proj.to(dtype=snake_case	)	)  # (N, D)

						lowercase    :			Tuple  =       self.class_embedder(snake_case	)  # (N, D)

						lowercase    :			int  =       timesteps_emb + class_labels  # (N, D)

						return conditioning




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case = 6_4	) ->    str:







						"""simple docstring"""
						super().__init__()
						lowercase    :			List[str]  =       nn.LayerNorm(snake_case	)
						lowercase    :			Any  =       AttentionPooling(snake_case      ,   snake_case	)
						lowercase    :			Tuple  =       nn.Linear(snake_case      ,   snake_case	)
						lowercase    :			Union[str, Any]  =       nn.LayerNorm(snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Dict:







						"""simple docstring"""
						lowercase    :			Any  =       self.norma(snake_case	)
						lowercase    :			Optional[int]  =       self.pool(snake_case	)
						lowercase    :			List[Any]  =       self.proj(snake_case	)
						lowercase    :			List[str]  =       self.norma(snake_case	)
						return hidden_states




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 7_6_8      ,   snake_case = 7_6_8      ,   snake_case = 1_5_3_6	) ->    Union[str, Any]:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Union[str, Any]  =       nn.Linear(snake_case      ,   snake_case	)
						lowercase    :			Union[str, Any]  =       nn.LayerNorm(snake_case	)
						lowercase    :			Tuple  =       nn.Linear(snake_case      ,   snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    List[Any]:







						"""simple docstring"""
						lowercase    :			List[str]  =       self.text_proj(snake_case	)
						lowercase    :			Dict  =       self.text_norm(snake_case	)

						# image
						lowercase    :			Union[str, Any]  =       self.image_proj(snake_case	)

						return time_image_embeds + time_text_embeds




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 7_6_8      ,   snake_case = 1_5_3_6	) ->    List[str]:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Union[str, Any]  =       nn.Linear(snake_case      ,   snake_case	)
						lowercase    :			Optional[Any]  =       nn.LayerNorm(snake_case	)





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Any:







						"""simple docstring"""
						lowercase    :			Tuple  =       self.image_proj(snake_case	)
						lowercase    :			int  =       self.image_norm(snake_case	)
						return time_image_embeds




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case = 7_6_8      ,   snake_case = 1_5_3_6	) ->    str:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Optional[int]  =       nn.Linear(snake_case      ,   snake_case	)
						lowercase    :			List[str]  =       nn.LayerNorm(snake_case	)
						lowercase    :			List[str]  =       nn.Sequential(
						    nn.Convad(3      ,   1_6      ,   3      ,   padding=1	)      ,   nn.SiLU()      ,   nn.Convad(1_6      ,   1_6      ,   3      ,   padding=1	)      ,   nn.SiLU()      ,   nn.Convad(1_6      ,   3_2      ,   3      ,   padding=1      ,   stride=2	)      ,   nn.SiLU()      ,   nn.Convad(3_2      ,   3_2      ,   3      ,   padding=1	)      ,   nn.SiLU()      ,   nn.Convad(3_2      ,   9_6      ,   3      ,   padding=1      ,   stride=2	)      ,   nn.SiLU()      ,   nn.Convad(9_6      ,   9_6      ,   3      ,   padding=1	)      ,   nn.SiLU()      ,   nn.Convad(9_6      ,   2_5_6      ,   3      ,   padding=1      ,   stride=2	)      ,   nn.SiLU()      ,   nn.Convad(2_5_6      ,   4      ,   3      ,   padding=1	)      ,   )





			def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case	) ->    Any:







						"""simple docstring"""
						lowercase    :			Optional[Any]  =       self.image_proj(snake_case	)
						lowercase    :			Any  =       self.image_norm(snake_case	)
						lowercase    :			Dict  =       self.input_hint_block(snake_case	)
						return time_image_embeds, hint




class lowerCamelCase__  (    nn.Module				):



			def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case=None	) ->    Optional[Any]:







						"""simple docstring"""
						super().__init__()
						lowercase    :			Dict  =       dtype
						lowercase    :			str  =       nn.Parameter(torch.randn(1      ,   snake_case	) / embed_dim**0.5	)
						lowercase    :			Any  =       nn.Linear(snake_case      ,   snake_case      ,   dtype=self.dtype	)
						lowercase    :			Optional[int]  =       nn.Linear(snake_case      ,   snake_case      ,   dtype=self.dtype	)
						lowercase    :			Optional[Any]  =       nn.Linear(snake_case      ,   snake_case      ,   dtype=self.dtype	)
						lowercase    :			str  =       num_heads
						lowercase    :			Optional[int]  =       embed_dim // self.num_heads





			def    _UpperCAmelCase	(       self      ,   snake_case	) ->    List[str]:







						"""simple docstring"""
						lowercase  ,					lowercase  ,					lowercase    :			Dict  =       x.size()

						def shape(snake_case	):
									# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
									lowercase    :			Optional[int]  =       x.view(snake_case      ,   -1      ,   self.num_heads      ,   self.dim_per_head	)
									# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
									lowercase    :			str  =       x.transpose(1      ,   2	)
									# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
									lowercase    :			int  =       x.reshape(bs * self.num_heads      ,   -1      ,   self.dim_per_head	)
									# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
									lowercase    :			List[Any]  =       x.transpose(1      ,   2	)
									return x

						lowercase    :			Any  =       x.mean(dim=1      ,   keepdim=snake_case	) + self.positional_embedding.to(x.dtype	)
						lowercase    :			List[Any]  =       torch.cat([class_token, x]      ,   dim=1	)  # (bs, length+1, width)

						# (bs*n_heads, class_token_length, dim_per_head)
						lowercase    :			Optional[Any]  =       shape(self.q_proj(snake_case	)	)
						# (bs*n_heads, length+class_token_length, dim_per_head)
						lowercase    :			Any  =       shape(self.k_proj(snake_case	)	)
						lowercase    :			int  =       shape(self.v_proj(snake_case	)	)

						# (bs*n_heads, class_token_length, length+class_token_length):
						lowercase    :			Dict  =       1 / math.sqrt(math.sqrt(self.dim_per_head	)	)
						lowercase    :			List[str]  =       torch.einsum("""bct,bcs->bts"""      ,   q * scale      ,   k * scale	)  # More stable with f16 than dividing afterwards
						lowercase    :			Union[str, Any]  =       torch.softmax(weight.float()      ,   dim=-1	).type(weight.dtype	)

						# (bs*n_heads, dim_per_head, class_token_length)
						lowercase    :			Optional[int]  =       torch.einsum("""bts,bcs->bct"""      ,   snake_case      ,   snake_case	)

						# (bs, length+1, width)
						lowercase    :			Optional[int]  =       a.reshape(snake_case      ,   -1      ,   1	).transpose(1      ,   2	)

						return a[:, 0, :]  # cls_token









"""simple docstring"""
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 			__snake_case						(      __A ,__A = 512    )		->				Optional[Any]:
   lowercase    :			Optional[Any]  =       x.shape[1] if len(x.shape    ) > 1 else 1
   lowercase    :			Tuple  =       1 + np.arange(0 ,feature_num * offset ,__A ,dtype=np.intaa    )
   lowercase    :			Any  =       x + feature_offset
   return x




def 			__snake_case						(      __A ,__A=True    )		->				List[Any]:
   requires_backends(__A ,["""cython"""]    )

   if keep_features and "edge_attr" in item.keys():  # edge_attr
      lowercase    :			Any  =       np.asarray(item["""edge_attr"""] ,dtype=np.intaa    )
   else:
      lowercase    :			Optional[int]  =       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
      lowercase    :			Dict  =       np.asarray(item["""node_feat"""] ,dtype=np.intaa    )
   else:
      lowercase    :			Optional[Any]  =       np.ones((item["""num_nodes"""], 1) ,dtype=np.intaa    )  # same embedding for all

   lowercase    :			str  =       np.asarray(item["""edge_index"""] ,dtype=np.intaa    )

   lowercase    :			Tuple  =       convert_to_single_emb(__A    ) + 1
   lowercase    :			Dict  =       item["""num_nodes"""]

   if len(edge_attr.shape    ) == 1:
      lowercase    :			Dict  =       edge_attr[:, None]
   lowercase    :			Tuple  =       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,dtype=np.intaa    )
   lowercase    :			List[Any]  =       convert_to_single_emb(__A    ) + 1

   # node adj matrix [num_nodes, num_nodes] bool
   lowercase    :			List[Any]  =       np.zeros([num_nodes, num_nodes] ,dtype=__A    )
   lowercase    :			str  =       True

   lowercase  ,					lowercase    :			List[str]  =       algos_graphormer.floyd_warshall(__A    )
   lowercase    :			str  =       np.amax(__A    )

   lowercase    :			int  =       algos_graphormer.gen_edge_input(__A ,__A ,__A    )
   lowercase    :			Optional[int]  =       np.zeros([num_nodes + 1, num_nodes + 1] ,dtype=np.single    )  # with graph token

   # combine
   lowercase    :			List[Any]  =       input_nodes + 1  # we shift all indices by one for padding
   lowercase    :			Optional[int]  =       attn_bias
   lowercase    :			str  =       attn_edge_type
   lowercase    :			int  =       shortest_path_result.astype(np.intaa    ) + 1  # we shift all indices by one for padding
   lowercase    :			Union[str, Any]  =       np.sum(__A ,axis=1    ).reshape(-1    ) + 1  # we shift all indices by one for padding
   lowercase    :			int  =       item["""in_degree"""]  # for undirected graph
   lowercase    :			Dict  =       input_edges + 1  # we shift all indices by one for padding
   if "labels" not in item:
      lowercase    :			str  =       item["""y"""]

   return item







class lowerCamelCase__  :



   def __init__(       self      ,   snake_case=2_0      ,   snake_case=False	) ->    List[Any]:







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

      lowercase    :			Any  =       spatial_pos_max
      lowercase    :			Dict  =       on_the_fly_processing





   def __call__(       self      ,   snake_case	) ->    Dict[str, Any]:







      """simple docstring"""
      if self.on_the_fly_processing:
         lowercase    :			Dict  =       [preprocess_item(snake_case	) for i in features]

      if not isinstance(features[0]      ,   snake_case	):
         lowercase    :			Optional[int]  =       [vars(snake_case	) for f in features]
      lowercase    :			Any  =       {}

      lowercase    :			Any  =       max(len(i["""input_nodes"""]	) for i in features	)
      lowercase    :			List[str]  =       len(features[0]["""input_nodes"""][0]	)
      lowercase    :			Tuple  =       len(features[0]["""attn_edge_type"""][0][0]	)
      lowercase    :			str  =       max(len(i["""input_edges"""][0][0]	) for i in features	)
      lowercase    :			Dict  =       len(features[0]["""input_edges"""][0][0][0]	)
      lowercase    :			Union[str, Any]  =       len(snake_case	)

      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   max_node_num + 1      ,   max_node_num + 1      ,   dtype=torch.float	)
      lowercase    :			List[Any]  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			int  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Tuple  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Dict  =       torch.zeros(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)

      for ix, f in enumerate(snake_case	):
         for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
            lowercase    :			Optional[int]  =       torch.tensor(f[k]	)

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

         lowercase    :			str  =       f["""attn_bias"""]
         lowercase    :			Union[str, Any]  =       f[
             """attn_edge_type"""
         ]
         lowercase    :			Union[str, Any]  =       f["""spatial_pos"""]
         lowercase    :			int  =       f["""in_degree"""]
         lowercase    :			int  =       f["""input_nodes"""]
         lowercase    :			Any  =       f["""input_edges"""]

      lowercase    :			Dict  =       batch["""in_degree"""]

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

      return batch


