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from torch.distributions import Categorical
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
import torch.nn.functional as F


def embed_inputs(embedding, logits, device='cuda', print_entropy=False):
    '''
    embeds inputs in a dense representation, before passing them to the model
    '''

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from transformers import MT5Config, MT5ForConditionalGeneration, MT5Tokenizer
from transformers.models.t5.modeling_t5 import load_tf_weights_in_t5

model_name = "persiannlp/mt5-base-parsinlu-opus-translation_fa_en"
tokenizer = MT5Tokenizer.from_pretrained(model_name)
model = MT5ForConditionalGeneration.from_pretrained(model_name)

def run_model(input_string, **generator_args):
    input_ids = tokenizer.encode(input_string, return_tensors="pt")
    res = model.generate(input_ids, **generator_args)

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#!/usr/bin/env python
from typing import Iterable
from collections import Counter
import os
import logging
import sys
import json
import click
import datasets
import numpy as np

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import json
import argparse
from typing import Optional, Union, Tuple
import torch

torch.manual_seed(0)

from transformers import BertModel, BertTokenizer, PreTrainedModel, BertConfig
from transformers.modeling_outputs import MultipleChoiceModelOutput

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sortMapByValue(map) {
    var tupleArray = [];
    for (var key in map) tupleArray.push([key, map[key]]);
    tupleArray.sort(function (a, b) {
        return b[1] - a[1]
    });
    var sortedMap = {};
    tupleArray.forEach(function (el) {
        sortedMap[el[0]] = el[1]
    });

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import json
import os

all_types_to_idx = {
    'Task': 0,
    'Method': 1,
    'Material': 2,
    'Metric': 3,
    'OtherScientificTerm': 4,
    'Generic': 5

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import random
from collections import Counter
from urllib.parse import urlparse
import json
import os
import re

from tqdm import tqdm

urls_counts = {}

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from typing import List
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import scipy
import random

class NormalGammaPrior():
    """"
    Suppose X is distributed according to a normal distribution:  X ~ N(mu, tau^{-1})

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import statistics as st

import scipy.stats
import numpy as np

def metric1(scores, row_aggregator, column_aggregator, cell_aggregator):
    row_values = []
    for row_idx, row1 in enumerate(scores):
        diagonal_x = row1[row_idx]
        row_values.append(

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show_unpublished_scores: true
datasets:
  blind_labels: danielk/genie_labels
evaluator:
  image: jbragg/genie-evaluator
  input_path: /preds/
  predictions_filename: predictions.json
  label_path: /labels/
  output_path: /results
  arguments: