code: 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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__ = 5_1_2							)  ->			Optional[Any]:
  							lowerCAmelCase				=	x.shape[1] if len(x.shape							) > 1 else 1
  							lowerCAmelCase				=	1 + np.arange(0     ,						feature_num * offset     ,						snake_case__     ,						dtype=np.intaa							)
  							lowerCAmelCase				=	x + feature_offset
  							return x
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__=True							)  ->			Optional[Any]:
  							requires_backends(snake_case__     ,						['''cython''']							)
  
  							if keep_features and "edge_attr" in item.keys():  # edge_attr
  														lowerCAmelCase				=	np.asarray(item['''edge_attr''']     ,						dtype=np.intaa							)
  							else:
  														lowerCAmelCase				=	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
  														lowerCAmelCase				=	np.asarray(item['''node_feat''']     ,						dtype=np.intaa							)
  							else:
  														lowerCAmelCase				=	np.ones((item['''num_nodes'''], 1)     ,						dtype=np.intaa							)  # same embedding for all
  
  							lowerCAmelCase				=	np.asarray(item['''edge_index''']     ,						dtype=np.intaa							)
  
  							lowerCAmelCase				=	convert_to_single_emb(snake_case__							) + 1
  							lowerCAmelCase				=	item['''num_nodes''']
  
  							if len(edge_attr.shape							) == 1:
  														lowerCAmelCase				=	edge_attr[:, None]
  							lowerCAmelCase				=	np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]     ,						dtype=np.intaa							)
  							lowerCAmelCase				=	convert_to_single_emb(snake_case__							) + 1
  
  							# node adj matrix [num_nodes, num_nodes] bool
  							lowerCAmelCase				=	np.zeros([num_nodes, num_nodes]     ,						dtype=snake_case__							)
  							lowerCAmelCase				=	True
  
  							lowerCAmelCase	,   lowerCAmelCase				=	algos_graphormer.floyd_warshall(snake_case__							)
  							lowerCAmelCase				=	np.amax(snake_case__							)
  
  							lowerCAmelCase				=	algos_graphormer.gen_edge_input(snake_case__     ,						snake_case__     ,						snake_case__							)
  							lowerCAmelCase				=	np.zeros([num_nodes + 1, num_nodes + 1]     ,						dtype=np.single							)  # with graph token
  
  							# combine
  							lowerCAmelCase				=	input_nodes + 1  # we shift all indices by one for padding
  							lowerCAmelCase				=	attn_bias
  							lowerCAmelCase				=	attn_edge_type
  							lowerCAmelCase				=	shortest_path_result.astype(np.intaa							) + 1  # we shift all indices by one for padding
  							lowerCAmelCase				=	np.sum(snake_case__     ,						axis=1							).reshape(-1							) + 1  # we shift all indices by one for padding
  							lowerCAmelCase				=	item['''in_degree''']  # for undirected graph
  							lowerCAmelCase				=	input_edges + 1  # we shift all indices by one for padding
  							if "labels" not in item:
  														lowerCAmelCase				=	item['''y''']
  
  							return item
  
  
  
  
  
  
  class      lowercase_						:
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE=20	,    __SCREAMING_SNAKE_CASE=False       )		->Optional[int]:
  								if not is_cython_available():
  															raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''       )
  
  								lowerCAmelCase				=	spatial_pos_max
  								lowerCAmelCase				=	on_the_fly_processing
  
  
  
  
  	def __call__(							self	,    __SCREAMING_SNAKE_CASE       )		->Dict:
  								if self.on_the_fly_processing:
  															lowerCAmelCase				=	[preprocess_item(UpperCamelCase__       ) for i in features]
  
  								if not isinstance(features[0]	,    UpperCamelCase__       ):
  															lowerCAmelCase				=	[vars(UpperCamelCase__       ) for f in features]
  								lowerCAmelCase				=	{}
  
  								lowerCAmelCase				=	max(len(i['''input_nodes''']       ) for i in features       )
  								lowerCAmelCase				=	len(features[0]['''input_nodes'''][0]       )
  								lowerCAmelCase				=	len(features[0]['''attn_edge_type'''][0][0]       )
  								lowerCAmelCase				=	max(len(i['''input_edges'''][0][0]       ) for i in features       )
  								lowerCAmelCase				=	len(features[0]['''input_edges'''][0][0][0]       )
  								lowerCAmelCase				=	len(UpperCamelCase__       )
  
  								lowerCAmelCase				=	torch.zeros(UpperCamelCase__	,    max_node_num + 1	,    max_node_num + 1	,    dtype=torch.float       )
  								lowerCAmelCase				=	torch.zeros(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.long       )
  								lowerCAmelCase				=	torch.zeros(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.long       )
  								lowerCAmelCase				=	torch.zeros(UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.long       )
  								lowerCAmelCase				=	torch.zeros(UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.long       )
  								lowerCAmelCase				=	torch.zeros(
  								    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    UpperCamelCase__	,    dtype=torch.long       )
  
  								for ix, f in enumerate(UpperCamelCase__       ):
  															for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
  																						lowerCAmelCase				=	torch.tensor(f[k]       )
  
  															if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]       ) > 0:
  																						lowerCAmelCase				=	float('''-inf'''       )
  
  															lowerCAmelCase				=	f['''attn_bias''']
  															lowerCAmelCase				=	f[
  															    '''attn_edge_type'''
  															]
  															lowerCAmelCase				=	f['''spatial_pos''']
  															lowerCAmelCase				=	f['''in_degree''']
  															lowerCAmelCase				=	f['''input_nodes''']
  															lowerCAmelCase				=	f['''input_edges''']
  
  								lowerCAmelCase				=	batch['''in_degree''']
  
  								lowerCAmelCase				=	features[0]['''labels''']
  								if len(UpperCamelCase__       ) == 1:  # one task
  															if isinstance(sample[0]	,    UpperCamelCase__       ):  # regression
  																						lowerCAmelCase				=	torch.from_numpy(np.concatenate([i['''labels'''] for i in features]       )       )
  															else:  # binary classification
  																						lowerCAmelCase				=	torch.from_numpy(np.concatenate([i['''labels'''] for i in features]       )       )
  								else:  # multi task classification, left to float to keep the NaNs
  															lowerCAmelCase				=	torch.from_numpy(np.stack([i['''labels'''] for i in features]	,    axis=0       )       )
  
  								return batch
  
  
  
  
style_context: from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  
  from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available
  
  
  
  
  @dataclass
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Union[List[PIL.Image.Image], np.ndarray]
  
  
  if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
  	from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
  	from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
  
  
  
  