





"""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 = 5_1_2		)			->     Tuple:

 '''simple docstring'''






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





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=True		)			->     Optional[int]:

 '''simple docstring'''






 requires_backends(_UpperCAmelCase		,   ["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(_UpperCAmelCase		) + 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(_UpperCAmelCase		) + 1

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

 snake_case__							,      snake_case__										= algos_graphormer.floyd_warshall(_UpperCAmelCase		)
 snake_case__										= np.amax(_UpperCAmelCase		)

 snake_case__										= algos_graphormer.gen_edge_input(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
 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(_UpperCAmelCase		,   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    lowercase_ :

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE=20      ,    __SCREAMING_SNAKE_CASE=False   ):






        '''simple docstring'''

        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      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if self.on_the_fly_processing:
         snake_case__										= [preprocess_item(__SCREAMING_SNAKE_CASE   ) for i in features]

        if not isinstance(features[0]      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= [vars(__SCREAMING_SNAKE_CASE   ) 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(__SCREAMING_SNAKE_CASE   )

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







import torch
from torch import autograd





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.numel()
					__lowerCAmelCase											=				int(0.005 * nb_elems			) + 1
					if sigmoid:
								__lowerCAmelCase											=				(torch.sigmoid(__SCREAMING_SNAKE_CASE			) > threshold).type(inputs.type()			)
					else:
								__lowerCAmelCase											=				(inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__lowerCAmelCase											=				inputs.flatten().kthvalue(max(nb_elems - nb_min,1			)			).values
								__lowerCAmelCase											=				(inputs > k_threshold).type(inputs.type()			)
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None, None





class     _UpperCAmelCase							(						autograd.Function   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask







		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					return gradOutput, None





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		@staticmethod
		def   lowerCamelCase__			(  __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				inputs.clone()
					__lowerCAmelCase						,       __lowerCAmelCase											=				inputs.abs().flatten().sort(descending=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					__lowerCAmelCase											=				mask.flatten()
					__lowerCAmelCase											=				0
					__lowerCAmelCase											=				1
					return mask

