"""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  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Dict		,			_SCREAMING_SNAKE_CASE	:int = 512  )				-> Dict:
						a_							:			int     		=					x.shape[1] if len(x.shape  ) > 1 else 1
						a_							:			List[str]     		=					1 + np.arange(0		,			feature_num * offset		,			_SCREAMING_SNAKE_CASE		,			dtype=np.intaa  )
						a_							:			Any     		=					x + feature_offset
						return x

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[Any]		,			_SCREAMING_SNAKE_CASE	:List[str]=True  )				-> List[Any]:
						requires_backends(_SCREAMING_SNAKE_CASE		,			["cython"]  )

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

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

						a_							:			List[str]     		=					convert_to_single_emb(_SCREAMING_SNAKE_CASE  ) + 1
						a_							:			str     		=					item["num_nodes"]

						if len(edge_attr.shape  ) == 1:
												a_							:			Union[str, Any]     		=					edge_attr[:, None]
						a_							:			List[str]     		=					np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]		,			dtype=np.intaa  )
						a_							:			int     		=					convert_to_single_emb(_SCREAMING_SNAKE_CASE  ) + 1

						# node adj matrix [num_nodes, num_nodes] bool
						a_							:			int     		=					np.zeros([num_nodes, num_nodes]		,			dtype=_SCREAMING_SNAKE_CASE  )
						a_							:			Tuple     		=					True

						a_							:			Optional[Any]     		=					algos_graphormer.floyd_warshall(_SCREAMING_SNAKE_CASE  )
						a_							:			Tuple     		=					np.amax(_SCREAMING_SNAKE_CASE  )

						a_							:			int     		=					algos_graphormer.gen_edge_input(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						a_							:			Any     		=					np.zeros([num_nodes + 1, num_nodes + 1]		,			dtype=np.single  )  # with graph token

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

						return item



class   UpperCAmelCase__				:


	"""simple docstring"""







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=2_0       ,     _SCREAMING_SNAKE_CASE=False )    ->	List[Any]:
							if not is_cython_available():
													raise ImportError("Graphormer preprocessing needs Cython (pyximport)" )

							a_							:			Tuple     		=					spatial_pos_max
							a_							:			List[Any]     		=					on_the_fly_processing







	def __call__(			self       ,     _SCREAMING_SNAKE_CASE )    ->	Dict[str, Any]:
							if self.on_the_fly_processing:
													a_							:			int     		=					[preprocess_item(_SCREAMING_SNAKE_CASE ) for i in features]

							if not isinstance(features[0]       ,     _SCREAMING_SNAKE_CASE ):
													a_							:			Union[str, Any]     		=					[vars(_SCREAMING_SNAKE_CASE ) for f in features]
							a_							:			Optional[int]     		=					{}

							a_							:			Union[str, Any]     		=					max(len(i["input_nodes"] ) for i in features )
							a_							:			Optional[int]     		=					len(features[0]["input_nodes"][0] )
							a_							:			List[str]     		=					len(features[0]["attn_edge_type"][0][0] )
							a_							:			int     		=					max(len(i["input_edges"][0][0] ) for i in features )
							a_							:			Optional[int]     		=					len(features[0]["input_edges"][0][0][0] )
							a_							:			Tuple     		=					len(_SCREAMING_SNAKE_CASE )

							a_							:			Optional[int]     		=					torch.zeros(_SCREAMING_SNAKE_CASE       ,     max_node_num + 1       ,     max_node_num + 1       ,     dtype=torch.float )
							a_							:			Dict     		=					torch.zeros(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=torch.long )
							a_							:			List[Any]     		=					torch.zeros(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=torch.long )
							a_							:			int     		=					torch.zeros(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=torch.long )
							a_							:			Optional[Any]     		=					torch.zeros(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=torch.long )
							a_							:			int     		=					torch.zeros(
							    _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     dtype=torch.long )

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

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

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

							a_							:			Any     		=					batch["in_degree"]

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

							return batch





"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference
@slow
@require_torch_gpu
class   UpperCAmelCase__				(    unittest.TestCase			):


	"""simple docstring"""







	def       A    (			self )    ->	List[str]:
							a_							:			List[str]     		=					"runwayml/stable-diffusion-v1-5"
							a_							:			Optional[int]     		=					TextToVideoZeroPipeline.from_pretrained(_SCREAMING_SNAKE_CASE       ,     torch_dtype=torch.floataa ).to("cuda" )
							a_							:			Dict     		=					DDIMScheduler.from_config(pipe.scheduler.config )
							a_							:			Optional[Any]     		=					torch.Generator(device="cuda" ).manual_seed(0 )

							a_							:			List[Any]     		=					"A bear is playing a guitar on Times Square"
							a_							:			List[Any]     		=					pipe(prompt=_SCREAMING_SNAKE_CASE       ,     generator=_SCREAMING_SNAKE_CASE ).images

							a_							:			Optional[int]     		=					load_pt(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt" )

							assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE )




