
'''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 	snake_case_							(				_a  :				Any			,       _a  :				int = 512      ):
      snake_case_    							=				x.shape[1] if len(x.shape      ) > 1 else 1
      snake_case_    							=				1 + np.arange(0			,       feature_num * offset			,       _a			,       dtype=np.intaa      )
      snake_case_    							=				x + feature_offset
      return x
def 	snake_case_							(				_a  :				Any			,       _a  :				List[str]=True      ):
      requires_backends(_a			,       ["cython"]      )

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

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

      snake_case_    							=				convert_to_single_emb(_a      ) + 1
      snake_case_    							=				item["num_nodes"]

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

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

      snake_case_							,						snake_case_    							=				algos_graphormer.floyd_warshall(_a      )
      snake_case_    							=				np.amax(_a      )

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

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

      return item




class 			lowerCAmelCase__ :



  '''simple docstring'''






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

        snake_case_    							=				spatial_pos_max
        snake_case_    							=				on_the_fly_processing


  def __call__(self	:			Union[str, Any]						,			lowerCAmelCase_	:			List[dict]     ) ->							Dict[str, Any]:
        if self.on_the_fly_processing:
              snake_case_    							=				[preprocess_item(lowerCAmelCase_     ) for i in features]

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

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

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

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

              snake_case_    							=				f["attn_bias"]
              snake_case_    							=				f[
                  "attn_edge_type"
              ]
              snake_case_    							=				f["spatial_pos"]
              snake_case_    							=				f["in_degree"]
              snake_case_    							=				f["input_nodes"]
              snake_case_    							=				f["input_edges"]

        snake_case_    							=				batch["in_degree"]

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

        return batch





"""simple docstring"""
from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize





class   __A  (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict				   =     1
							__lowercase:		List[str]				   =     2
							__lowercase:		int				   =     3



							def __repr__(					self				:   Optional[Any]							)       ->Optional[int]:



														"""simple docstring"""



														return F"""{self.__class__.__name__}.{self.name}"""







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					float:
							snake_case_							,    snake_case_     							=		point
							return degrees(atana(y - miny					,			x - minx					)					)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Direction:
							snake_case_							,    snake_case_     							=		starting
							snake_case_							,    snake_case_     							=		via
							snake_case_							,    snake_case_     							=		target
							snake_case_     							=		degrees(atana(ya - ya					,			xa - xa					)					)
							via_angle %= 360
							snake_case_     							=		degrees(atana(ya - ya					,			xa - xa					)					)
							target_angle %= 360
							# t-
							#  \ \
							#   \ v
							#    \|
							#     s
							# via_angle is always lower than target_angle, if direction is left.
							# If they are same, it means they are on a same line of convex hull.
							if target_angle > via_angle:
														return Direction.left
							elif target_angle == via_angle:
														return Direction.straight
							else:
														return Direction.right







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					list[tuple[int, int]]:

							if len(_SCREAMING_SNAKE_CASE					) <= 2:
														# There is no convex hull
														raise ValueError("""graham_scan: argument must contain more than 3 points."""					)
							if len(_SCREAMING_SNAKE_CASE					) == 3:
														return points
							# find the lowest and the most left point
							snake_case_     							=		0
							snake_case_							,    snake_case_     							=		maxsize, maxsize
							for i, point in enumerate(_SCREAMING_SNAKE_CASE					):
														snake_case_     							=		point[0]
														snake_case_     							=		point[1]
														if y < miny:
																					snake_case_     							=		y
																					snake_case_     							=		x
																					snake_case_     							=		i
														if y == miny and x < minx:
																					snake_case_     							=		x
																					snake_case_     							=		i

    # remove the lowest and the most left point from points for preparing for sort
							points.pop(_SCREAMING_SNAKE_CASE					)

							snake_case_     							=		sorted(_SCREAMING_SNAKE_CASE					,			key=lambda _SCREAMING_SNAKE_CASE					: angle_comparer(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)					)
							# This insert actually costs complexity,
							# and you should instead add (minx, miny) into stack later.
							# I'm using insert just for easy understanding.
							sorted_points.insert(0					,			(minx, miny)					)

							snake_case_     							=		deque()
							stack.append(sorted_points[0]					)
							stack.append(sorted_points[1]					)
							stack.append(sorted_points[2]					)
							# In any ways, the first 3 points line are towards left.
							# Because we sort them the angle from minx, miny.
							snake_case_     							=		Direction.left

							for i in range(3					,			len(_SCREAMING_SNAKE_CASE					)					):
														while True:
																					snake_case_     							=		stack[-2]
																					snake_case_     							=		stack[-1]
																					snake_case_     							=		sorted_points[i]
																					snake_case_     							=		check_direction(_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)

																					if next_direction == Direction.left:
																												snake_case_     							=		Direction.left
																												break
																					if next_direction == Direction.straight:
																												if current_direction == Direction.left:
																																			# We keep current_direction as left.
																																			# Because if the straight line keeps as straight,
																																			# we want to know if this straight line is towards left.
																																			break
																												elif current_direction == Direction.right:
																																			# If the straight line is towards right,
																																			# every previous points on those straigh line is not convex hull.
																																			stack.pop()
																					if next_direction == Direction.right:
																												stack.pop()
														stack.append(sorted_points[i]					)
							return list(_SCREAMING_SNAKE_CASE					)



