





'''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    __a (     UpperCAmelCase       ,			UpperCAmelCase = 512    )      ->Union[str, Any]:



	"""simple docstring"""







	A							  =      x.shape[1] if len(x.shape    ) > 1 else 1
	A							  =      1 + np.arange(0       ,			feature_num * offset       ,			lowercase_       ,			dtype=np.intaa    )
	A							  =      x + feature_offset
	return x




def    __a (     UpperCAmelCase       ,			UpperCAmelCase=True    )      ->Tuple:



	"""simple docstring"""







	requires_backends(lowercase_       ,			["""cython"""]    )

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

	A							  =      np.asarray(item["""edge_index"""]       ,			dtype=np.intaa    )

	A							  =      convert_to_single_emb(lowercase_    ) + 1
	A							  =      item['num_nodes']

	if len(edge_attr.shape    ) == 1:
		A							  =      edge_attr[:, None]
	A							  =      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]       ,			dtype=np.intaa    )
	A							  =      convert_to_single_emb(lowercase_    ) + 1

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

	A							  =      algos_graphormer.floyd_warshall(lowercase_    )
	A							  =      np.amax(lowercase_    )

	A							  =      algos_graphormer.gen_edge_input(lowercase_       ,			lowercase_       ,			lowercase_    )
	A							  =      np.zeros([num_nodes + 1, num_nodes + 1]       ,			dtype=np.single    )  # with graph token

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

	return item



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      str      ,      _lowerCAmelCase						:      Optional[int]=20      ,      _lowerCAmelCase						:      Tuple=False      ):
			if not is_cython_available():
				raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""      )

			A							  =      spatial_pos_max
			A							  =      on_the_fly_processing







		def __call__(self						:      Optional[int]      ,      _lowerCAmelCase						:      List[dict]      ):
			if self.on_the_fly_processing:
				A							  =      [preprocess_item(_lowerCAmelCase      ) for i in features]

			if not isinstance(features[0]      ,      _lowerCAmelCase      ):
				A							  =      [vars(_lowerCAmelCase      ) for f in features]
			A							  =      {}

			A							  =      max(len(i["""input_nodes"""]      ) for i in features      )
			A							  =      len(features[0]["""input_nodes"""][0]      )
			A							  =      len(features[0]["""attn_edge_type"""][0][0]      )
			A							  =      max(len(i["""input_edges"""][0][0]      ) for i in features      )
			A							  =      len(features[0]["""input_edges"""][0][0][0]      )
			A							  =      len(_lowerCAmelCase      )

			A							  =      torch.zeros(_lowerCAmelCase      ,      max_node_num + 1      ,      max_node_num + 1      ,      dtype=torch.float      )
			A							  =      torch.zeros(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      dtype=torch.long      )
			A							  =      torch.zeros(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      dtype=torch.long      )
			A							  =      torch.zeros(_lowerCAmelCase      ,      _lowerCAmelCase      ,      dtype=torch.long      )
			A							  =      torch.zeros(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      dtype=torch.long      )
			A							  =      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"]:
					A							  =      torch.tensor(f[k]      )

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

				A							  =      f['attn_bias']
				A							  =      f[
				    'attn_edge_type'
				]
				A							  =      f['spatial_pos']
				A							  =      f['in_degree']
				A							  =      f['input_nodes']
				A							  =      f['input_edges']

			A							  =      batch['in_degree']

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

			return batch








'''simple docstring'''


import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor



class 							__UpperCAmelCase  (       unittest.TestCase						):







		'''simple docstring'''







		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      bool = False      ):
			A							  =      10

			if only_cross_attention:
				A							  =      12
			else:
				# when only cross attention is not set, the cross attention dim must be the same as the query dim
				A							  =      query_dim

			return {
			    "query_dim": query_dim,
			    "cross_attention_dim": cross_attention_dim,
			    "heads": 2,
			    "dim_head": 4,
			    "added_kv_proj_dim": 6,
			    "norm_num_groups": 1,
			    "only_cross_attention": only_cross_attention,
			    "processor": AttnAddedKVProcessor(),
			}







		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Any      ):
			A							  =      2

			A							  =      torch.rand(_lowerCAmelCase      ,      _lowerCAmelCase      ,      3      ,      2      )
			A							  =      torch.rand(_lowerCAmelCase      ,      4      ,      _lowerCAmelCase      )
			A							  =      None

			return {
			    "hidden_states": hidden_states,
			    "encoder_hidden_states": encoder_hidden_states,
			    "attention_mask": attention_mask,
			}







		def        A     (self						:      Optional[Any]      ):
			# self and cross attention

			torch.manual_seed(0      )

			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
			A							  =      Attention(**_lowerCAmelCase      )

			self.assertTrue(attn.to_k is not None      )
			self.assertTrue(attn.to_v is not None      )

			A							  =      self.get_forward_arguments(
			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

			A							  =      attn(**_lowerCAmelCase      )

			# only self attention

			torch.manual_seed(0      )

			A							  =      self.get_constructor_arguments(only_cross_attention=_lowerCAmelCase      )
			A							  =      Attention(**_lowerCAmelCase      )

			self.assertTrue(attn.to_k is None      )
			self.assertTrue(attn.to_v is None      )

			A							  =      self.get_forward_arguments(
			    query_dim=constructor_args["""query_dim"""]      ,      added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

			A							  =      attn(**_lowerCAmelCase      )

			self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )






class 							__UpperCAmelCase  (       unittest.TestCase						):







		'''simple docstring'''







		def        A     (self						:      Any      ):
			A							  =      DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      safety_checker=_lowerCAmelCase      )

			A							  =      pipe(
			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images

			# the initial conversion succeeds
			A							  =      DiffusionPipeline.from_pretrained(
			    """hf-internal-testing/tiny-stable-diffusion-pipe"""      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )

			A							  =      pipe(
			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images

			with tempfile.TemporaryDirectory() as tmpdir:
				# save the converted model
				pipe.save_pretrained(_lowerCAmelCase      )

				# can also load the converted weights
				A							  =      DiffusionPipeline.from_pretrained(_lowerCAmelCase      ,      device_map="""sequential"""      ,      safety_checker=_lowerCAmelCase      )

			A							  =      pipe(
			    """foo"""      ,      num_inference_steps=2      ,      generator=torch.Generator("""cpu"""      ).manual_seed(0      )      ,      output_type="""np"""      ,      ).images

			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )
			self.assertTrue(np.allclose(_lowerCAmelCase      ,      _lowerCAmelCase      ,      atol=1e-5      )      )

