


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    (    lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Any = 512	):





	"""simple docstring"""







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





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			Tuple=True	):





	"""simple docstring"""







	requires_backends(lowerCAmelCase	,	['cython']	)

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

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

	__magic_name__ :					Optional[int]  			=  convert_to_single_emb(lowerCAmelCase	) + 1
	__magic_name__ :					List[Any]  			=  item['num_nodes']

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

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

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

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

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

	return item





class _lowerCamelCase :



		'''simple docstring'''







		def __init__(      self	:		List[Any]						,  _A	:		Optional[Any]=20						,  _A	:		str=False    )						->				Optional[Any]:






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

			__magic_name__ :					Any  			=  spatial_pos_max
			__magic_name__ :					List[str]  			=  on_the_fly_processing







		def __call__(      self	:		List[Any]						,  _A	:		List[dict]    )						->				Dict[str, Any]:






			if self.on_the_fly_processing:
				__magic_name__ :					Tuple  			=  [preprocess_item(lowerCAmelCase_    ) for i in features]

			if not isinstance(features[0]						,  lowerCAmelCase_    ):
				__magic_name__ :					Dict  			=  [vars(lowerCAmelCase_    ) for f in features]
			__magic_name__ :					List[str]  			=  {}

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

			__magic_name__ :					Union[str, Any]  			=  torch.zeros(lowerCAmelCase_						,  max_node_num + 1						,  max_node_num + 1						,  dtype=torch.float    )
			__magic_name__ :					Tuple  			=  torch.zeros(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  dtype=torch.long    )
			__magic_name__ :					Tuple  			=  torch.zeros(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  dtype=torch.long    )
			__magic_name__ :					Optional[int]  			=  torch.zeros(lowerCAmelCase_						,  lowerCAmelCase_						,  dtype=torch.long    )
			__magic_name__ :					Dict  			=  torch.zeros(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  dtype=torch.long    )
			__magic_name__ :					Optional[int]  			=  torch.zeros(
			    lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  dtype=torch.long    )

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

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

				__magic_name__ :					Optional[int]  			=  f['attn_bias']
				__magic_name__ :					int  			=  f[
				    'attn_edge_type'
				]
				__magic_name__ :					Optional[int]  			=  f['spatial_pos']
				__magic_name__ :					Optional[Any]  			=  f['in_degree']
				__magic_name__ :					Any  			=  f['input_nodes']
				__magic_name__ :					Optional[int]  			=  f['input_edges']

			__magic_name__ :					List[Any]  			=  batch['in_degree']

			__magic_name__ :					List[str]  			=  features[0]['labels']
			if len(lowerCAmelCase_    ) == 1:  # one task
				if isinstance(sample[0]						,  lowerCAmelCase_    ):  # regression
					__magic_name__ :					List[str]  			=  torch.from_numpy(np.concatenate([i['labels'] for i in features]    )    )
				else:  # binary classification
					__magic_name__ :					Union[str, 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 random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		@property
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  1
			__magic_name__ :					str  			=  3
			__magic_name__ :					Optional[Any]  			=  (32, 32)

			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
			return image







		@property
		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
			torch.manual_seed(0    )
			__magic_name__ :					Dict  			=  UNetaDModel(
			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
			return model







		@property
		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			torch.manual_seed(0    )
			__magic_name__ :					Union[str, Any]  			=  VQModel(
			    block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,  latent_channels=3						,  )
			return model







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
			__magic_name__ :					Dict  			=  'cpu'
			__magic_name__ :					str  			=  self.dummy_uncond_unet
			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model

			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )

			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )

			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2







		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
			__magic_name__ :					Tuple  			=  DDIMScheduler()
			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model

			# put models in fp16
			__magic_name__ :					Optional[int]  			=  unet.half()
			__magic_name__ :					int  			=  vqvae.half()

			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					str  			=  self.dummy_image.to(_A    )

			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			assert image.shape == (1, 64, 64, 3)







@slow
@require_torch
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
			__magic_name__ :					Tuple  			=  load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool.png'    )
			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )

			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					int  			=  torch.manual_seed(0    )
			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images

			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )

			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2