





'''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						(       lowerCamelCase_	,	lowerCamelCase_ = 5_12					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Any						=	x.shape[1] if len(x.shape					) > 1 else 1
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	1 + np.arange(0	,	feature_num * offset	,	lowerCamelCase_	,	dtype=np.intaa					)
		SCREAMING_SNAKE_CASE				:							str						=	x + feature_offset
		return x



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_=True					):


		"""simple docstring"""



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

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

		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.asarray(item["""edge_index"""]	,	dtype=np.intaa					)

		SCREAMING_SNAKE_CASE				:							str						=	convert_to_single_emb(lowerCamelCase_					) + 1
		SCREAMING_SNAKE_CASE				:							List[Any]						=	item["""num_nodes"""]

		if len(edge_attr.shape					) == 1:
				SCREAMING_SNAKE_CASE				:							str						=	edge_attr[:, None]
		SCREAMING_SNAKE_CASE				:							List[str]						=	np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]	,	dtype=np.intaa					)
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	convert_to_single_emb(lowerCamelCase_					) + 1

		# node adj matrix [num_nodes, num_nodes] bool
		SCREAMING_SNAKE_CASE				:							Any						=	np.zeros([num_nodes, num_nodes]	,	dtype=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	True

		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	algos_graphormer.floyd_warshall(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							int						=	np.amax(lowerCamelCase_					)

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	algos_graphormer.gen_edge_input(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[Any]						=	np.zeros([num_nodes + 1, num_nodes + 1]	,	dtype=np.single					)  # with graph token

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

		return item




class       UpperCamelCase__			:




					"""simple docstring"""


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

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE				:							Tuple						=	spatial_pos_max
							SCREAMING_SNAKE_CASE				:							int						=	on_the_fly_processing



					def __call__(		self							:	List[Any]     ,		lowerCamelCase_							:	Union[str, Any]		):

							'''simple docstring'''



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

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

							SCREAMING_SNAKE_CASE				:							Any						=	max(len(i["""input_nodes"""]		) for i in features		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	len(features[0]["""input_nodes"""][0]		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	len(features[0]["""attn_edge_type"""][0][0]		)
							SCREAMING_SNAKE_CASE				:							List[Any]						=	max(len(i["""input_edges"""][0][0]		) for i in features		)
							SCREAMING_SNAKE_CASE				:							int						=	len(features[0]["""input_edges"""][0][0][0]		)
							SCREAMING_SNAKE_CASE				:							Dict						=	len(SCREAMING_SNAKE_CASE_		)

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

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

									SCREAMING_SNAKE_CASE				:							List[str]						=	f["""attn_bias"""]
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	f[
									    """attn_edge_type"""
									]
									SCREAMING_SNAKE_CASE				:							Tuple						=	f["""spatial_pos"""]
									SCREAMING_SNAKE_CASE				:							Dict						=	f["""in_degree"""]
									SCREAMING_SNAKE_CASE				:							str						=	f["""input_nodes"""]
									SCREAMING_SNAKE_CASE				:							List[Any]						=	f["""input_edges"""]

							SCREAMING_SNAKE_CASE				:							Dict						=	batch["""in_degree"""]

							SCREAMING_SNAKE_CASE				:							Dict						=	features[0]["""labels"""]
							if len(SCREAMING_SNAKE_CASE_		) == 1:  # one task
									if isinstance(sample[0]     ,		SCREAMING_SNAKE_CASE_		):  # regression
											SCREAMING_SNAKE_CASE				:							Tuple						=	torch.from_numpy(np.concatenate([i["""labels"""] for i in features]		)		)
									else:  # binary classification
											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	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				:							List[Any]						=	torch.from_numpy(np.stack([i["""labels"""] for i in features]     ,		axis=0		)		)

							return batch










'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
							from PIL import Image




class       UpperCamelCase__			(			unittest.TestCase		,			lowercase_	):




					"""simple docstring"""


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	load_tool("""image-captioning"""		)
							self.tool.setup()
							SCREAMING_SNAKE_CASE				:							str						=	load_tool("""image-captioning"""     ,		remote=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.remote_tool(lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							str						=	self.tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""		)		) / """000000039769.png"""		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.remote_tool(image=lowerCamelCase_		)
							self.assertEqual(lowerCamelCase_     ,		"""two cats sleeping on a couch"""		)



