






"""simple docstring"""



import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class 							__lowercase(							unittest.TestCase	):
						lowerCAmelCase_		:				Union[str, Any]=							"""hf-internal-testing/tiny-stable-diffusion-pipe"""
						lowerCAmelCase_		:				Optional[int]=							1
						lowerCAmelCase_		:				Optional[int]=							77
						lowerCAmelCase_		:				Optional[int]=							32
						lowerCAmelCase_		:				Dict=							4
						lowerCAmelCase_		:				List[Any]=							64
						lowerCAmelCase_		:				List[str]=							torch.manual_seed(0	)






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE=0.9999)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					=UNetaDConditionModel.from_pretrained(self.model_id     ,    subfolder='unet')
											lowerCamelCase__					=unet.to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=EMAModel(unet.parameters()     ,    decay=_SCREAMING_SNAKE_CASE     ,    model_cls=_SCREAMING_SNAKE_CASE     ,    model_config=unet.config)
											return unet, ema_unet






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											lowerCamelCase__					=torch.randn(
											    self.batch_size     ,    self.num_in_channels     ,    self.latent_height     ,    self.latent_width     ,    generator=self.generator).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.randint(0     ,    10_00     ,    size=(self.batch_size,)     ,    generator=self.generator).to(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=torch.randn(
											    self.batch_size     ,    self.prompt_length     ,    self.text_encoder_hidden_dim     ,    generator=self.generator).to(_SCREAMING_SNAKE_CASE)
											return noisy_latents, timesteps, encoder_hidden_states






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




											"""simple docstring"""





											lowerCamelCase__					={}
											for k, param in unet.state_dict().items():
																lowerCamelCase__					=torch.randn_like(_SCREAMING_SNAKE_CASE) + (param * torch.randn_like(_SCREAMING_SNAKE_CASE))
																updated_state_dict.update({k: updated_param})
											unet.load_state_dict(_SCREAMING_SNAKE_CASE)
											return unet






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




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models()
											# Take the first (hypothetical) EMA step.
											ema_unet.step(unet.parameters())
											assert ema_unet.optimization_step == 1

											# Take two more.
											for _ in range(2):
																ema_unet.step(unet.parameters())
											assert ema_unet.optimization_step == 3






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




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models()
											# Since the `unet` is not being updated (i.e., backprop'd)
											# there won't be any difference between the `params` of `unet`
											# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
											ema_unet.step(unet.parameters())
											lowerCamelCase__					=list(unet.parameters())
											for s_param, param in zip(ema_unet.shadow_params     ,    _SCREAMING_SNAKE_CASE):
																assert torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

											# The above holds true even if we call `ema.step()` multiple times since
											# `unet` params are still not being updated.
											for _ in range(4):
																ema_unet.step(unet.parameters())
											for s_param, param in zip(ema_unet.shadow_params     ,    _SCREAMING_SNAKE_CASE):
																assert torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models()
											# Here we simulate the parameter updates for `unet`. Since there might
											# be some parameters which are initialized to zero we take extra care to
											# initialize their values to something non-zero before the multiplication.
											lowerCamelCase__					=self.simulate_backprop(_SCREAMING_SNAKE_CASE)

											# Take the EMA step.
											ema_unet.step(unet_pseudo_updated_step_one.parameters())

											# Now the EMA'd parameters won't be equal to the original model parameters.
											lowerCamelCase__					=list(unet_pseudo_updated_step_one.parameters())
											for s_param, param in zip(ema_unet.shadow_params     ,    _SCREAMING_SNAKE_CASE):
																assert ~torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)

											# Ensure this is the case when we take multiple EMA steps.
											for _ in range(4):
																ema_unet.step(unet.parameters())
											for s_param, param in zip(ema_unet.shadow_params     ,    _SCREAMING_SNAKE_CASE):
																assert ~torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






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




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models()

											# First backprop + EMA
											lowerCamelCase__					=self.simulate_backprop(_SCREAMING_SNAKE_CASE)
											ema_unet.step(unet_step_one.parameters())
											lowerCamelCase__					=ema_unet.shadow_params

											# Second backprop + EMA
											lowerCamelCase__					=self.simulate_backprop(_SCREAMING_SNAKE_CASE)
											ema_unet.step(unet_step_two.parameters())
											lowerCamelCase__					=ema_unet.shadow_params

											for step_one, step_two in zip(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																assert ~torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						def 		lowerCAmelCase (       self)      ->	Union[str, Any]:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models(decay=0.0)
											lowerCamelCase__					=self.simulate_backprop(_SCREAMING_SNAKE_CASE)
											ema_unet.step(unet_step_one.parameters())
											lowerCamelCase__					=ema_unet.shadow_params

											lowerCamelCase__					=self.simulate_backprop(_SCREAMING_SNAKE_CASE)
											ema_unet.step(unet_step_two.parameters())
											lowerCamelCase__					=ema_unet.shadow_params

											for step_one, step_two in zip(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																assert torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)






						@skip_mps
						def 		lowerCAmelCase (       self)      ->	Tuple:




											"""simple docstring"""





											lowerCamelCase__					,  lowerCamelCase__					=self.get_models()
											lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdir:
																ema_unet.save_pretrained(_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=UNetaDConditionModel.from_pretrained(_SCREAMING_SNAKE_CASE     ,    model_cls=_SCREAMING_SNAKE_CASE)
																lowerCamelCase__					=loaded_unet.to(unet.device)

											# Since no EMA step has been performed the outputs should match.
											lowerCamelCase__					=unet(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE).sample
											lowerCamelCase__					=loaded_unet(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE).sample

											assert torch.allclose(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    atol=1E-4)







"""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_					(	__A	:		List[str]      ,						__A	:		int = 512      ):
					lowerCamelCase__					=x.shape[1] if len(x.shape      ) > 1 else 1
					lowerCamelCase__					=1 + np.arange(0      ,						feature_num * offset      ,						__A      ,						dtype=np.intaa      )
					lowerCamelCase__					=x + feature_offset
					return x




def     lowerCamelCase_					(	__A	:		str      ,						__A	:		Optional[Any]=True      ):
					requires_backends(__A      ,						['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(__A      ) + 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(__A      ) + 1

					# node adj matrix [num_nodes, num_nodes] bool
					lowerCamelCase__					=np.zeros([num_nodes, num_nodes]      ,						dtype=__A      )
					lowerCamelCase__					=True

					lowerCamelCase__					,  lowerCamelCase__					=algos_graphormer.floyd_warshall(__A      )
					lowerCamelCase__					=np.amax(__A      )

					lowerCamelCase__					=algos_graphormer.gen_edge_input(__A      ,						__A      ,						__A      )
					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(__A      ,						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)      ->	Any:




											"""simple docstring"""





											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[str, Any]:




											"""simple docstring"""





											if self.on_the_fly_processing:
																lowerCamelCase__					=[preprocess_item(_SCREAMING_SNAKE_CASE) for i in features]

											if not isinstance(features[0]     ,    _SCREAMING_SNAKE_CASE):
																lowerCamelCase__					=[vars(_SCREAMING_SNAKE_CASE) 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(_SCREAMING_SNAKE_CASE)

											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    max_node_num + 1     ,    max_node_num + 1     ,    dtype=torch.float)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=torch.zeros(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    dtype=torch.long)
											lowerCamelCase__					=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"]:
																					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(_SCREAMING_SNAKE_CASE) == 1:  # one task
																if isinstance(sample[0]     ,    _SCREAMING_SNAKE_CASE):  # 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