





"""simple docstring"""
import numpy as np




def 			__snake_case						(      __A ,__A="utf-8" ,__A="surrogatepass"    )		->				Dict:
			lowercase    :			Dict  =       bin(int.from_bytes(text.encode(UpperCamelCase__ ,UpperCamelCase__    ) ,"""big"""    )    )[2:]
			return bits.zfill(8 * ((len(UpperCamelCase__    ) + 7) // 8)    )




def 			__snake_case						(      __A ,__A="utf-8" ,__A="surrogatepass"    )		->				Tuple:
			lowercase    :			int  =       int(UpperCamelCase__ ,2    )
			return n.to_bytes((n.bit_length() + 7) // 8 ,"""big"""    ).decode(UpperCamelCase__ ,UpperCamelCase__    ) or "\0"




def 			__snake_case						(      __A ,__A    )		->				Optional[Any]:
			if size_par + len(UpperCamelCase__    ) <= 2**size_par - (len(UpperCamelCase__    ) - 1):
						raise ValueError("""size of parity don't match with size of data"""    )

			lowercase    :			int  =       []
			lowercase    :			str  =       []
			lowercase    :			int  =       [bin(UpperCamelCase__    )[2:] for x in range(1 ,size_par + len(UpperCamelCase__    ) + 1    )]

			# sorted information data for the size of the output data
			lowercase    :			Tuple  =       []
			# data position template + parity
			lowercase    :			Any  =       []
			# parity bit counter
			lowercase    :			Optional[Any]  =       0
			# counter position of data bits
			lowercase    :			Dict  =       0

			for x in range(1 ,size_par + len(UpperCamelCase__    ) + 1    ):
						# Performs a template of bit positions - who should be given,
						# and who should be parity
						if qtd_bp < size_par:
									if (np.log(UpperCamelCase__    ) / np.log(2    )).is_integer():
												data_out_gab.append("""P"""    )
												lowercase    :			int  =       qtd_bp + 1
									else:
												data_out_gab.append("""D"""    )
						else:
									data_out_gab.append("""D"""    )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data[cont_data]    )
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__    )

    # Calculates parity
			lowercase    :			str  =       0  # parity bit counter
			for bp in range(1 ,size_par + 1    ):
						# Bit counter one for a given parity
						lowercase    :			Any  =       0
						# counter to control the loop reading
						lowercase    :			Optional[Any]  =       0
						for x in data_ord:
									if x is not None:
												try:
															lowercase    :			Optional[int]  =       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															lowercase    :			Optional[Any]  =       "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(cont_bo % 2    )

						qtd_bp += 1

			# Mount the message
			lowercase    :			Any  =       0  # parity bit counter
			for x in range(0 ,size_par + len(UpperCamelCase__    )    ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]    )    )
									cont_bp += 1
						else:
									data_out.append(data_ord[x]    )

			return data_out







def 			__snake_case						(      __A ,__A    )		->				List[str]:
			lowercase    :			Optional[Any]  =       []
			# Parity bit counter
			lowercase    :			List[Any]  =       0
			# Counter p data bit reading
			lowercase    :			int  =       0
			# list of parity received
			lowercase    :			List[str]  =       []
			lowercase    :			Optional[Any]  =       []

			for x in range(1 ,len(UpperCamelCase__    ) + 1    ):
						# Performs a template of bit positions - who should be given,
						#  and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__    ) / np.log(2    )).is_integer():
									data_out_gab.append("""P"""    )
									lowercase    :			Optional[Any]  =       qtd_bp + 1
						else:
									data_out_gab.append("""D"""    )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_output.append(data[cont_data]    )
						else:
									parity_received.append(data[cont_data]    )
						cont_data += 1

			# -----------calculates the parity with the data
			lowercase    :			Union[str, Any]  =       []
			lowercase    :			List[Any]  =       []
			lowercase    :			Dict  =       [bin(UpperCamelCase__    )[2:] for x in range(1 ,size_par + len(UpperCamelCase__    ) + 1    )]

			#  sorted information data for the size of the output data
			lowercase    :			Union[str, Any]  =       []
			# Data position feedback + parity
			lowercase    :			Union[str, Any]  =       []
			# Parity bit counter
			lowercase    :			Dict  =       0
			# Counter p data bit reading
			lowercase    :			Union[str, Any]  =       0

			for x in range(1 ,size_par + len(UpperCamelCase__    ) + 1    ):
						# Performs a template position of bits - who should be given,
						# and who should be parity
						if qtd_bp < size_par and (np.log(UpperCamelCase__    ) / np.log(2    )).is_integer():
									data_out_gab.append("""P"""    )
									lowercase    :			Union[str, Any]  =       qtd_bp + 1
						else:
									data_out_gab.append("""D"""    )

						# Sorts the data to the new output size
						if data_out_gab[-1] == "D":
									data_ord.append(data_output[cont_data]    )
									cont_data += 1
						else:
									data_ord.append(UpperCamelCase__    )

    # Calculates parity
			lowercase    :			int  =       0  # parity bit counter
			for bp in range(1 ,size_par + 1    ):
						# Bit counter one for a certain parity
						lowercase    :			Dict  =       0
						# Counter to control loop reading
						lowercase    :			Dict  =       0
						for x in data_ord:
									if x is not None:
												try:
															lowercase    :			Optional[int]  =       (bin_pos[cont_loop])[-1 * (bp)]
												except IndexError:
															lowercase    :			Union[str, Any]  =       "0"
												if aux == "1" and x == "1":
															cont_bo += 1
									cont_loop += 1
						parity.append(str(cont_bo % 2    )    )

						qtd_bp += 1

			# Mount the message
			lowercase    :			List[str]  =       0  # Parity bit counter
			for x in range(0 ,size_par + len(UpperCamelCase__    )    ):
						if data_ord[x] is None:
									data_out.append(str(parity[cont_bp]    )    )
									cont_bp += 1
						else:
									data_out.append(data_ord[x]    )

			lowercase    :			Tuple  =       parity_received == parity
			return data_output, ack









"""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 ,__A = 512    )		->				Optional[Any]:
   lowercase    :			Optional[Any]  =       x.shape[1] if len(x.shape    ) > 1 else 1
   lowercase    :			Tuple  =       1 + np.arange(0 ,feature_num * offset ,__A ,dtype=np.intaa    )
   lowercase    :			Any  =       x + feature_offset
   return x




def 			__snake_case						(      __A ,__A=True    )		->				List[Any]:
   requires_backends(__A ,["""cython"""]    )

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

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

   lowercase    :			Tuple  =       convert_to_single_emb(__A    ) + 1
   lowercase    :			Dict  =       item["""num_nodes"""]

   if len(edge_attr.shape    ) == 1:
      lowercase    :			Dict  =       edge_attr[:, None]
   lowercase    :			Tuple  =       np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]] ,dtype=np.intaa    )
   lowercase    :			List[Any]  =       convert_to_single_emb(__A    ) + 1

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

   lowercase  ,					lowercase    :			List[str]  =       algos_graphormer.floyd_warshall(__A    )
   lowercase    :			str  =       np.amax(__A    )

   lowercase    :			int  =       algos_graphormer.gen_edge_input(__A ,__A ,__A    )
   lowercase    :			Optional[int]  =       np.zeros([num_nodes + 1, num_nodes + 1] ,dtype=np.single    )  # with graph token

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

   return item







class lowerCamelCase__  :



   def __init__(       self      ,   snake_case=2_0      ,   snake_case=False	) ->    List[Any]:







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

      lowercase    :			Any  =       spatial_pos_max
      lowercase    :			Dict  =       on_the_fly_processing





   def __call__(       self      ,   snake_case	) ->    Dict[str, Any]:







      """simple docstring"""
      if self.on_the_fly_processing:
         lowercase    :			Dict  =       [preprocess_item(snake_case	) for i in features]

      if not isinstance(features[0]      ,   snake_case	):
         lowercase    :			Optional[int]  =       [vars(snake_case	) for f in features]
      lowercase    :			Any  =       {}

      lowercase    :			Any  =       max(len(i["""input_nodes"""]	) for i in features	)
      lowercase    :			List[str]  =       len(features[0]["""input_nodes"""][0]	)
      lowercase    :			Tuple  =       len(features[0]["""attn_edge_type"""][0][0]	)
      lowercase    :			str  =       max(len(i["""input_edges"""][0][0]	) for i in features	)
      lowercase    :			Dict  =       len(features[0]["""input_edges"""][0][0][0]	)
      lowercase    :			Union[str, Any]  =       len(snake_case	)

      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   max_node_num + 1      ,   max_node_num + 1      ,   dtype=torch.float	)
      lowercase    :			List[Any]  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			int  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			List[str]  =       torch.zeros(snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Tuple  =       torch.zeros(snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)
      lowercase    :			Dict  =       torch.zeros(
          snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   dtype=torch.long	)

      for ix, f in enumerate(snake_case	):
         for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
            lowercase    :			Optional[int]  =       torch.tensor(f[k]	)

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

         lowercase    :			str  =       f["""attn_bias"""]
         lowercase    :			Union[str, Any]  =       f[
             """attn_edge_type"""
         ]
         lowercase    :			Union[str, Any]  =       f["""spatial_pos"""]
         lowercase    :			int  =       f["""in_degree"""]
         lowercase    :			int  =       f["""input_nodes"""]
         lowercase    :			Any  =       f["""input_edges"""]

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

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

      return batch


