




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 		lowercase__ (		__A:						List[str]				,__A:						int = 5_1_2					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				x.shape[1] if len(x.shape					) > 1 else 1
					__magic_name__				:						int     				=				1 + np.arange(0				,feature_num * offset				,__A				,dtype=np.intaa					)
					__magic_name__				:						Tuple     				=				x + feature_offset
					return x







def 		lowercase__ (		__A:						str				,__A:						Optional[Any]=True					):





					'''simple docstring'''






					requires_backends(__A				,['''cython''']					)

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

					__magic_name__				:						List[str]     				=				np.asarray(item['''edge_index''']				,dtype=np.intaa					)

					__magic_name__				:						List[str]     				=				convert_to_single_emb(__A					) + 1
					__magic_name__				:						int     				=				item['''num_nodes''']

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

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

					__magic_name__				,     __magic_name__				:						List[str]     				=				algos_graphormer.floyd_warshall(__A					)
					__magic_name__				:						Optional[Any]     				=				np.amax(__A					)

					__magic_name__				:						str     				=				algos_graphormer.gen_edge_input(__A				,__A				,__A					)
					__magic_name__				:						List[str]     				=				np.zeros([num_nodes + 1, num_nodes + 1]				,dtype=np.single					)  # with graph token

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

					return item


class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : Tuple							,    lowerCamelCase_      : Any=20							,    lowerCamelCase_      : str=False	)    -> Any:





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

								__magic_name__				:						Union[str, Any]     				=				spatial_pos_max
								__magic_name__				:						str     				=				on_the_fly_processing


			def __call__(			self      : Dict							,    lowerCamelCase_      : Optional[int]	)    -> Dict[str, Any]:





								if self.on_the_fly_processing:
													__magic_name__				:						Union[str, Any]     				=				[preprocess_item(_SCREAMING_SNAKE_CASE	) for i in features]

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

								__magic_name__				:						List[str]     				=				max(len(i['''input_nodes''']	) for i in features	)
								__magic_name__				:						List[Any]     				=				len(features[0]['''input_nodes'''][0]	)
								__magic_name__				:						Any     				=				len(features[0]['''attn_edge_type'''][0][0]	)
								__magic_name__				:						Tuple     				=				max(len(i['''input_edges'''][0][0]	) for i in features	)
								__magic_name__				:						Dict     				=				len(features[0]['''input_edges'''][0][0][0]	)
								__magic_name__				:						List[str]     				=				len(_SCREAMING_SNAKE_CASE	)

								__magic_name__				:						str     				=				torch.zeros(_SCREAMING_SNAKE_CASE							,    max_node_num + 1							,    max_node_num + 1							,    dtype=torch.float	)
								__magic_name__				:						Union[str, Any]     				=				torch.zeros(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    dtype=torch.long	)
								__magic_name__				:						List[Any]     				=				torch.zeros(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    dtype=torch.long	)
								__magic_name__				:						Optional[Any]     				=				torch.zeros(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    dtype=torch.long	)
								__magic_name__				:						List[str]     				=				torch.zeros(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    dtype=torch.long	)
								__magic_name__				:						List[str]     				=				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"]:
																		__magic_name__				:						Any     				=				torch.tensor(f[k]	)

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

													__magic_name__				:						str     				=				f['''attn_bias''']
													__magic_name__				:						Tuple     				=				f[
													    '''attn_edge_type'''
													]
													__magic_name__				:						int     				=				f['''spatial_pos''']
													__magic_name__				:						Union[str, Any]     				=				f['''in_degree''']
													__magic_name__				:						Dict     				=				f['''input_nodes''']
													__magic_name__				:						str     				=				f['''input_edges''']

								__magic_name__				:						Union[str, Any]     				=				batch['''in_degree''']

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

								return batch











"""simple docstring"""



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				Dict=							(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_		:				int=							10






						def 		lowerCAmelCase (       self     ,    **_SCREAMING_SNAKE_CASE)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					={
											    'num_train_timesteps': 11_00,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											}

											config.update(**_SCREAMING_SNAKE_CASE)
											return config






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for timesteps in [10, 50, 1_00, 10_00]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,    [0.0002, 0.002, 0.02]):
																self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE     ,    beta_end=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[Any]:




											"""simple docstring"""





											for schedule in ["linear", "scaled_linear"]:
																self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3






						def 		lowerCAmelCase (       self)      ->	str:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config(prediction_type='v_prediction')
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps)

											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for i, t in enumerate(scheduler.timesteps):
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 108.4439) < 1E-2
											assert abs(result_mean.item() - 0.1412) < 1E-3






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=self.scheduler_classes[0]
											lowerCamelCase__					=self.get_scheduler_config()
											lowerCamelCase__					=scheduler_class(**_SCREAMING_SNAKE_CASE)

											scheduler.set_timesteps(self.num_inference_steps     ,    device=_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.manual_seed(0)

											lowerCamelCase__					=self.dummy_model()
											lowerCamelCase__					=self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											lowerCamelCase__					=sample.to(_SCREAMING_SNAKE_CASE)

											for t in scheduler.timesteps:
																lowerCamelCase__					=scheduler.scale_model_input(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=model(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

																lowerCamelCase__					=scheduler.step(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    generator=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=output.prev_sample

											lowerCamelCase__					=torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
											lowerCamelCase__					=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

											assert abs(result_sum.item() - 152.3192) < 1E-2
											assert abs(result_mean.item() - 0.1983) < 1E-3