code: 
  
  
  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 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase = 512      )->		List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       x.shape[1] if len(x.shape      ) > 1 else 1
  					SCREAMING_SNAKE_CASE_	      =       1 + np.arange(0				,feature_num * offset				,UpperCAmelCase				,dtype=np.intaa      )
  					SCREAMING_SNAKE_CASE_	      =       x + feature_offset
  					return x
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase=True      )->		Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					requires_backends(UpperCAmelCase				,['''cython''']      )
  
  					if keep_features and "edge_attr" in item.keys():  # edge_attr
  										SCREAMING_SNAKE_CASE_	      =       np.asarray(item['''edge_attr''']				,dtype=np.intaa      )
  					else:
  										SCREAMING_SNAKE_CASE_	      =       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
  										SCREAMING_SNAKE_CASE_	      =       np.asarray(item['''node_feat''']				,dtype=np.intaa      )
  					else:
  										SCREAMING_SNAKE_CASE_	      =       np.ones((item['''num_nodes'''], 1)				,dtype=np.intaa      )  # same embedding for all
  
  					SCREAMING_SNAKE_CASE_	      =       np.asarray(item['''edge_index''']				,dtype=np.intaa      )
  
  					SCREAMING_SNAKE_CASE_	      =       convert_to_single_emb(UpperCAmelCase      ) + 1
  					SCREAMING_SNAKE_CASE_	      =       item['''num_nodes''']
  
  					if len(edge_attr.shape      ) == 1:
  										SCREAMING_SNAKE_CASE_	      =       edge_attr[:, None]
  					SCREAMING_SNAKE_CASE_	      =       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]				,dtype=np.intaa      )
  					SCREAMING_SNAKE_CASE_	      =       convert_to_single_emb(UpperCAmelCase      ) + 1
  
  					# node adj matrix [num_nodes, num_nodes] bool
  					SCREAMING_SNAKE_CASE_	      =       np.zeros([num_nodes, num_nodes]				,dtype=UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       True
  
  					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       algos_graphormer.floyd_warshall(UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       np.amax(UpperCAmelCase      )
  
  					SCREAMING_SNAKE_CASE_	      =       algos_graphormer.gen_edge_input(UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       np.zeros([num_nodes + 1, num_nodes + 1]				,dtype=np.single      )  # with graph token
  
  					# combine
  					SCREAMING_SNAKE_CASE_	      =       input_nodes + 1  # we shift all indices by one for padding
  					SCREAMING_SNAKE_CASE_	      =       attn_bias
  					SCREAMING_SNAKE_CASE_	      =       attn_edge_type
  					SCREAMING_SNAKE_CASE_	      =       shortest_path_result.astype(np.intaa      ) + 1  # we shift all indices by one for padding
  					SCREAMING_SNAKE_CASE_	      =       np.sum(UpperCAmelCase				,axis=1      ).reshape(-1      ) + 1  # we shift all indices by one for padding
  					SCREAMING_SNAKE_CASE_	      =       item['''in_degree''']  # for undirected graph
  					SCREAMING_SNAKE_CASE_	      =       input_edges + 1  # we shift all indices by one for padding
  					if "labels" not in item:
  										SCREAMING_SNAKE_CASE_	      =       item['''y''']
  
  					return item
  
  
  
  
  
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     List[Any]							,							lowerCAmelCase_     :     Optional[Any]=20							,							lowerCAmelCase_     :     str=False					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if not is_cython_available():
  													raise ImportError('''Graphormer preprocessing needs Cython (pyximport)'''					)
  
  								SCREAMING_SNAKE_CASE_	      =       spatial_pos_max
  								SCREAMING_SNAKE_CASE_	      =       on_the_fly_processing
  
  
  
  
  			def __call__(	self     :     List[Any]							,							lowerCAmelCase_     :     List[dict]					)					->					Dict[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.on_the_fly_processing:
  													SCREAMING_SNAKE_CASE_	      =       [preprocess_item(lowerCAmelCase_					) for i in features]
  
  								if not isinstance(features[0]							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [vars(lowerCAmelCase_					) for f in features]
  								SCREAMING_SNAKE_CASE_	      =       {}
  
  								SCREAMING_SNAKE_CASE_	      =       max(len(i['''input_nodes''']					) for i in features					)
  								SCREAMING_SNAKE_CASE_	      =       len(features[0]['''input_nodes'''][0]					)
  								SCREAMING_SNAKE_CASE_	      =       len(features[0]['''attn_edge_type'''][0][0]					)
  								SCREAMING_SNAKE_CASE_	      =       max(len(i['''input_edges'''][0][0]					) for i in features					)
  								SCREAMING_SNAKE_CASE_	      =       len(features[0]['''input_edges'''][0][0][0]					)
  								SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       torch.zeros(lowerCAmelCase_							,							max_node_num + 1							,							max_node_num + 1							,							dtype=torch.float					)
  								SCREAMING_SNAKE_CASE_	      =       torch.zeros(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							dtype=torch.long					)
  								SCREAMING_SNAKE_CASE_	      =       torch.zeros(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							dtype=torch.long					)
  								SCREAMING_SNAKE_CASE_	      =       torch.zeros(lowerCAmelCase_							,							lowerCAmelCase_							,							dtype=torch.long					)
  								SCREAMING_SNAKE_CASE_	      =       torch.zeros(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							dtype=torch.long					)
  								SCREAMING_SNAKE_CASE_	      =       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"]:
  																		SCREAMING_SNAKE_CASE_	      =       torch.tensor(f[k]					)
  
  													if len(f['''attn_bias'''][1:, 1:][f['''spatial_pos'''] >= self.spatial_pos_max]					) > 0:
  																		SCREAMING_SNAKE_CASE_	      =       float('''-inf'''					)
  
  													SCREAMING_SNAKE_CASE_	      =       f['''attn_bias''']
  													SCREAMING_SNAKE_CASE_	      =       f[
  													    '''attn_edge_type'''
  													]
  													SCREAMING_SNAKE_CASE_	      =       f['''spatial_pos''']
  													SCREAMING_SNAKE_CASE_	      =       f['''in_degree''']
  													SCREAMING_SNAKE_CASE_	      =       f['''input_nodes''']
  													SCREAMING_SNAKE_CASE_	      =       f['''input_edges''']
  
  								SCREAMING_SNAKE_CASE_	      =       batch['''in_degree''']
  
  								SCREAMING_SNAKE_CASE_	      =       features[0]['''labels''']
  								if len(lowerCAmelCase_					) == 1:  # one task
  													if isinstance(sample[0]							,							lowerCAmelCase_					):  # regression
  																		SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(np.concatenate([i['''labels'''] for i in features]					)					)
  													else:  # binary classification
  																		SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(np.concatenate([i['''labels'''] for i in features]					)					)
  								else:  # multi task classification, left to float to keep the NaNs
  													SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(np.stack([i['''labels'''] for i in features]							,							axis=0					)					)
  
  								return batch
  
  
  
style_context: 
  
  
  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 						snake_case			(       unittest.TestCase	):
  			'''simple docstring'''
  
  			UpperCAmelCase       :       Union[str, Any]       = """hf-internal-testing/tiny-stable-diffusion-pipe"""
  			UpperCAmelCase       :       int       = 1
  			UpperCAmelCase       :       Optional[Any]       = 77
  			UpperCAmelCase       :       Any       = 32
  			UpperCAmelCase       :       str       = 4
  			UpperCAmelCase       :       Optional[int]       = 64
  			UpperCAmelCase       :       Tuple       = torch.manual_seed(0	)
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     str=0.9_999					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(self.model_id							,							subfolder='''unet'''					)
  								SCREAMING_SNAKE_CASE_	      =       unet.to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       EMAModel(unet.parameters()							,							decay=lowerCAmelCase_							,							model_cls=lowerCAmelCase_							,							model_config=unet.config					)
  								return unet, ema_unet
  
  			def _lowercase (	self     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.num_in_channels							,							self.latent_height							,							self.latent_width							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randint(0							,							1_000							,							size=(self.batch_size,)							,							generator=self.generator					).to(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(
  								    self.batch_size							,							self.prompt_length							,							self.text_encoder_hidden_dim							,							generator=self.generator					).to(lowerCAmelCase_					)
  								return noisy_latents, timesteps, encoder_hidden_states
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       {}
  								for k, param in unet.state_dict().items():
  													SCREAMING_SNAKE_CASE_	      =       torch.randn_like(lowerCAmelCase_					) + (param * torch.randn_like(lowerCAmelCase_					))
  													updated_state_dict.update({k: updated_param}					)
  								unet.load_state_dict(lowerCAmelCase_					)
  								return unet
  
  			def _lowercase (	self     :     Dict					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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 _lowercase (	self     :     Union[str, Any]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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()					)
  								SCREAMING_SNAKE_CASE_	      =       list(unet.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# 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							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Union[str, Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       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.
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  
  								# 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.
  								SCREAMING_SNAKE_CASE_	      =       list(unet_pseudo_updated_step_one.parameters()					)
  								for s_param, param in zip(ema_unet.shadow_params							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# 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							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Any					)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  
  								# First backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								# Second backprop + EMA
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert ~torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  			def _lowercase (	self     :     str					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models(decay=0.0					)
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_one.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								SCREAMING_SNAKE_CASE_	      =       self.simulate_backprop(lowerCAmelCase_					)
  								ema_unet.step(unet_step_two.parameters()					)
  								SCREAMING_SNAKE_CASE_	      =       ema_unet.shadow_params
  
  								for step_one, step_two in zip(lowerCAmelCase_							,							lowerCAmelCase_					):
  													assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  
  
  
  			@skip_mps
  			def _lowercase (	self     :     str					)					->					List[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_models()
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self.get_dummy_inputs()
  
  								with tempfile.TemporaryDirectory() as tmpdir:
  													ema_unet.save_pretrained(lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       UNetaDConditionModel.from_pretrained(lowerCAmelCase_							,							model_cls=lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       loaded_unet.to(unet.device					)
  
  								# Since no EMA step has been performed the outputs should match.
  								SCREAMING_SNAKE_CASE_	      =       unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  								SCREAMING_SNAKE_CASE_	      =       loaded_unet(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					).sample
  
  								assert torch.allclose(lowerCAmelCase_							,							lowerCAmelCase_							,							atol=1e-4					)
  
  
  