




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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = 5_12   )							->						Dict:

    """simple docstring"""


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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=True   )							->						Optional[int]:

    """simple docstring"""


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

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

    __UpperCamelCase			=    np.asarray(item['''edge_index''']       ,  dtype=np.intaa   )

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

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

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

    __UpperCamelCase		,   __UpperCamelCase			=    algos_graphormer.floyd_warshall(lowercase_   )
    __UpperCamelCase			=    np.amax(lowercase_   )

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

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

    return item


class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			str			,							snake_case       :			int=20			,							snake_case       :			Union[str, Any]=False					):







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

          __UpperCamelCase			=    spatial_pos_max
          __UpperCamelCase			=    on_the_fly_processing







      def __call__(  self       :			List[str]			,							snake_case       :			List[dict]					):







          if self.on_the_fly_processing:
              __UpperCamelCase			=    [preprocess_item(__lowercase					) for i in features]

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

          __UpperCamelCase			=    max(len(i['''input_nodes''']					) for i in features					)
          __UpperCamelCase			=    len(features[0]['''input_nodes'''][0]					)
          __UpperCamelCase			=    len(features[0]['''attn_edge_type'''][0][0]					)
          __UpperCamelCase			=    max(len(i['''input_edges'''][0][0]					) for i in features					)
          __UpperCamelCase			=    len(features[0]['''input_edges'''][0][0][0]					)
          __UpperCamelCase			=    len(__lowercase					)

          __UpperCamelCase			=    torch.zeros(__lowercase			,							max_node_num + 1			,							max_node_num + 1			,							dtype=torch.float					)
          __UpperCamelCase			=    torch.zeros(__lowercase			,							__lowercase			,							__lowercase			,							__lowercase			,							dtype=torch.long					)
          __UpperCamelCase			=    torch.zeros(__lowercase			,							__lowercase			,							__lowercase			,							dtype=torch.long					)
          __UpperCamelCase			=    torch.zeros(__lowercase			,							__lowercase			,							dtype=torch.long					)
          __UpperCamelCase			=    torch.zeros(__lowercase			,							__lowercase			,							__lowercase			,							dtype=torch.long					)
          __UpperCamelCase			=    torch.zeros(
              __lowercase			,							__lowercase			,							__lowercase			,							__lowercase			,							__lowercase			,							dtype=torch.long					)

          for ix, f in enumerate(__lowercase					):
              for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
                  __UpperCamelCase			=    torch.tensor(f[k]					)

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

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

          __UpperCamelCase			=    batch['''in_degree''']

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

          return batch













import doctest

import projectq
from projectq.ops import H, Measure




def  A_(      A ):
	UpperCAmelCase_		    =  quantum_engine.allocate_qubit()
	H | qubit
	Measure | qubit
	return int(A )


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				UpperCamelCase__ :			Dict       	=			projectq.MainEngine()

				# Generate a list of 10 random numbers
				UpperCamelCase__ :			Dict       	=			[get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print("""Random numbers""", random_numbers_list)


