eladn

import math
import random
import itertools
import multiprocessing
from typing import Optional, Callable, List, Tuple, Any, Dict, TypeVar


__all__ = ['parallel_chunks_map']

eladn

import torch
import hashlib
import functools
import dataclasses
import numpy as np
from typing import List, Union, Optional, Tuple, Dict, Set, Any, final
from typing_extensions import Protocol


def seq_lengths_to_mask(seq_lengths: torch.LongTensor, max_seq_len: int, batch_first: bool = True):

eladn

import torch
import torch.nn as nn
from typing import Optional, Union


def apply_batched_embeddings(
        batched_embeddings: torch.Tensor, indices: torch.Tensor,
        mask: Optional[torch.Tensor] = None, padding_embedding_vector: Optional[torch.Tensor] = None,
        common_embeddings: Optional[Union[torch.Tensor, nn.Embedding]] = None) -> torch.Tensor:
    indices_device = indices.device

eladn

import networkx as nx
import numpy as np


def calc_number_of_simple_paths(graph: nx.DiGraph, s, t):
    num_of_simple_paths = np.full(graph.number_of_nodes(), np.nan)
    is_in_dfs_stack = np.full(graph.number_of_nodes(), 0)
    def _dfs(u):
        if is_in_dfs_stack[u]:
            return 0

eladn

import typing
import networkx as nx
import numpy as np
from enum import Enum


__all__ = ['Agent', 'alpha_beta_rb_minimax']


class Agent(Enum):

eladn

import os
import sys
import typing
import dataclasses
import multiprocessing
import functools
import time
import logging

eladn

/*
Based on: https://github.com/MartinPavlik/graphql-codegen-mongoose-schema
 */

import {
    GraphQLSchema, TypeInfo,
} from 'graphql';
import {
    schemaToTemplateContext,
    AstNode,

eladn

import numpy as np
from networkx import Graph, number_of_nodes
from collections import deque
from scipy.misc import comb


class GraphWithNodesMappings(Graph):
    def __init__(self, *args, **kargs):
        super().__init__(*args, **kargs)
        self.__node_name_to_idx = {}

eladn

import numpy as np
from collections import namedtuple
from itertools import chain

TaxStep = namedtuple('TaxStep', ['up_to', 'tax_percent'])

# Israeli income salary tax steps; updated on the 21.04.18; taken from:
# https://www.xn----1hcmgxnk8ede.co.il/%D7%9E%D7%93%D7%A8%D7%92%D7%95%D7%AA-%D7%9E%D7%A1
income_salary_tax_steps = [
    TaxStep(up_to=6240, tax_percent=10),

eladn

import collections

"""
This is based on HeapDict 1.0.0, but includes a few modifications.
https://pypi.org/project/HeapDict/
"""

def doc(s):
    if hasattr(s, '__call__'):
        s = s.__doc__