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




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



