id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
6,108 | import os
import json
import random
import numpy as np
import torch
from torch.nn import functional as F
from torch.utils.data import Dataset
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) | null |
6,109 | from lib2to3.pgen2 import token
import os
import torch
import numpy as np
import shutil
import struct
from functools import lru_cache
from itertools import accumulate
The provided code snippet includes necessary dependencies for implementing the `print_rank_0` function. Write a Python function `def print_rank_0(*messa... | If distributed is initialized print only on rank 0. |
6,114 | import types
import copy
import torch
import math, os
from torch.nn import functional as F
import torch.nn as nn
if os.environ['RWKV_RUN_DEVICE'] == 'cuda':
T_MAX = 1024 # increase this if your ctx_len is long [NOTE: TAKES LOTS OF VRAM!]
# it's possible to go beyond CUDA limitations if you slice the ctx and pas... | null |
6,115 | import math, os
import numpy as np
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
from torch.utils.cpp_extension import load
class WKV(torch.autograd.Function):
def forward(ctx, B, T, C, w, u, k, v):
ctx.B = B
ctx.T = T
ctx.C = C
assert T <= T_... | null |
6,116 | import math, os
import numpy as np
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
print(f'\nRWKV_HEAD_QK_DIM {RWKV_HEAD_QK_DIM}\n')
from torch.utils.cpp_extension import load
def RWKV_Init(model, args): # fancy initialization of all lin & emb layer in the model
print("\n[--... | null |
6,117 | import random
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
def top_k_logits(logits, k):
v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[:, [-1]]] = -float('Inf')
return out
def top_p_probs(probs, p):
out = probs.clone()
sorted_probs, s... | null |
6,118 | import random
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) | null |
6,119 | import math
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
def RWKV_Init(module, config): # fancy initialization of all lin & emb layer in the module
for m in module.modules():
if not isinstance(m, (nn.Linear, nn.Embedding)):
continue
with torch.n... | null |
6,120 | import math
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
def rotate_half(x):
x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), -1)
def apply_rotary_pos_emb(q, k, cos, sin):
cos, sin = cos[...,:q.shape[-2],:], sin[...,:q.shape... | null |
6,122 | import json, time, random, os
import numpy as np
import torch
from torch.nn import functional as F
def FermatPrimalityTest(number):
def MillerRabinPrimalityTest(number):
def MaybeIsPrime(number):
if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number):
return True
else:
return False | null |
6,131 | import os, math, gc, importlib
import torch
import torch.nn as nn
from torch.nn import functional as F
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from pytorch_lightning.strategies import DeepSpeedStrategy
from torch.utils.cpp_extension import load
if 'x060' in ... | null |
6,132 | import os, math, gc, importlib
import torch
import torch.nn as nn
from torch.nn import functional as F
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from pytorch_lightning.strategies import DeepSpeedStrategy
from torch.utils.cpp_extension import load
if 'x060' in ... | null |
6,133 | import json, math, random, sys, time, shutil, os, string, re, fileinput
import numpy as np
from tokenizer.rwkv_tokenizer import TRIE_TOKENIZER
from src.binidx import MMapIndexedDataset
def index_file_path(prefix_path):
return prefix_path + ".idx" | null |
6,134 | import json, math, random, sys, time, shutil, os, string, re, fileinput
import numpy as np
from tokenizer.rwkv_tokenizer import TRIE_TOKENIZER
from src.binidx import MMapIndexedDataset
def data_file_path(prefix_path):
return prefix_path + ".bin" | null |
6,135 | import json, math, random, sys, time, shutil, os, string, re, fileinput
import numpy as np
from tokenizer.rwkv_tokenizer import TRIE_TOKENIZER
tokenizer = TRIE_TOKENIZER("tokenizer/rwkv_vocab_v20230424.txt")
from src.binidx import MMapIndexedDataset
cnt = 0
print(f"### Convert {IN_FILE} to {OUT_NAME}.bin/idx...")
print... | null |
6,136 | import json, math, random, sys, time, shutil, os, string, re, fileinput
import numpy as np
from tokenizer.rwkv_tokenizer import TRIE_TOKENIZER
from src.binidx import MMapIndexedDataset
for i in range(N_EPOCH):
print(f"Shuffle: {i+1} out of {N_EPOCH}")
random.shuffle(non_empty_lines)
for entry in non_empty_l... | null |
6,140 | import argparse
import random
import requests
import time
import sys
from urllib import parse as urlparse
import base64
import json
from uuid import uuid4
from base64 import b64encode
from Crypto.Cipher import AES, PKCS1_OAEP
from Crypto.PublicKey import RSA
from Crypto.Hash import SHA256
from termcolor import cprint
t... | null |
6,141 | import plotly.graph_objects as go
import numpy as np
from plotly.subplots import make_subplots
import streamlit as st
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatrix(a)` to solve the following problem:
Returns a LaTeX bmatrix :a: n... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,143 | import matplotlib.pyplot as plt
import numpy as np
plt.axis('equal')
plt.axis('square')
plt.axhline(y=0, color='k', linewidth = 0.25)
plt.axvline(x=0, color='k', linewidth = 0.25)
plt.xticks(np.arange(-5, 6))
plt.yticks(np.arange(-5, 6))
plt.xlabel('$x_1$')
plt.ylabel('$x_2$')
def plot_shape(X,copy = False):
if c... | null |
6,144 | import matplotlib.pyplot as plt
import numpy as np
def plot_shape(X,copy = False):
if copy:
fill_color = np.array([255,236,255])/255
edge_color = np.array([255,0,0])/255
else:
fill_color = np.array([219,238,243])/255
edge_color = np.array([0,153,255])/255
plt.fill(X[:,... | null |
6,145 | import streamlit as st
import plotly.graph_objects as go
import sympy
import numpy as np
from scipy.stats import multivariate_normal
rv = multivariate_normal([0, 0],
Sigma)
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python functi... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,146 | import streamlit as st
import plotly.express as px
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_iris
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatr... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,147 | import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_iris
def heatmap(Matrices,Titles,Ranges,Equal_tags):
M1 = Matrices[0]
M2 = Matrices[1]
M3 = Matrices[2]
Title_1 = Titles[0]
Title_2 = Titles[1]
Title_3 = Titles[2]
... | null |
6,149 | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def plot_heatmap(x,title):
fig, ax = plt.subplots()
ax = sns.heatmap(x,
cmap='RdYlBu_r',
cbar_kws={"orientation": "horizontal"}, vmin=-1, vmax=1)
ax.set_aspect("equal")
plt.title(title) | null |
6,150 | import streamlit as st
import numpy as np
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.express as px
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatrix(a)` to solve the following problem:
R... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,151 | import numpy as np
import matplotlib.pyplot as plt
plt.ylabel('$x_2$')
plt.xlabel('$x_1$')
plt.axis('scaled')
plt.show()
def draw_vector(vector,RBG):
array = np.array([[0, 0, vector[0], vector[1]]])
X, Y, U, V = zip(*array)
plt.quiver(X, Y, U, V,angles='xy', scale_units='xy',scale=1,color = RBG) | null |
6,153 | import streamlit as st
import numpy as np
import plotly.express as px
import pandas as pd
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatrix(a)` to solve the following problem:
Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX b... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,154 | import numpy as np
import matplotlib.pyplot as plt
def visualize(X_circle,X_vec,title_txt):
fig, ax = plt.subplots()
plt.plot(X_circle[0,:], X_circle[1,:],'k',
linestyle = '--',
linewidth = 0.5)
plt.quiver(0,0,X_vec[0,0],X_vec[1,0],
angles='xy', scale_... | null |
6,155 | import plotly.graph_objects as go
import streamlit as st
import numpy as np
import plotly.express as px
import pandas as pd
import sympy
from sympy import *
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatrix(a)` to solve the following... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,156 | import sympy
import numpy as np
import matplotlib.pyplot as plt
from numpy import linalg as L
xx1, xx2 = mesh_circ(0, 0, 1, 50)
def mesh_circ(c1, c2, r, num):
theta = np.linspace(0, 2*np.pi, num)
r = np.linspace(0,r, num)
theta,r = np.meshgrid(theta,r)
xx1 = np.cos(theta)*r + c1
xx2 = np.s... | null |
6,158 | import sympy
from sympy import Matrix, Transpose
import numpy as np
from sympy.functions import exp
import matplotlib.pyplot as plt
xx1, xx2 = mesh_circ(0, 0, 3, 20)
def mesh_circ(c1, c2, r, num):
theta = np.arange(0,2*np.pi+np.pi/num,np.pi/num)
r = np.arange(0,r,r/num)
theta,r = np.meshgrid(theta... | null |
6,159 | import numpy as np
import matplotlib.pyplot as plt
def visualize(X_circle,X_vec,title_txt):
fig, ax = plt.subplots()
plt.plot(X_circle[:,0], X_circle[:,1],'k',
linestyle = '--',
linewidth = 0.5)
plt.quiver(0,0,X_vec[0,0],X_vec[0,1],
angles='xy', scale_... | null |
6,160 | import numpy as np
from matplotlib import pyplot as plt
plt.rcParams['image.cmap'] = 'RdBu_r'
import seaborn as sns
def svd(X):
full_matrices = True
U, s, Vt = np.linalg.svd(X,full_matrices = full_matrices)
# Put the vector singular values into a padded matrix
if full_matrices:
S = np.zeros(X.sh... | null |
6,161 | import plotly.graph_objects as go
import streamlit as st
import numpy as np
import plotly.express as px
import pandas as pd
import sympy
from scipy.spatial import distance
def fcn_Minkowski(xx, yy, mu, p = 2, Chebychev = False):
if Chebychev:
zz = np.maximum(np.abs(xx - mu[0]),np.abs(yy - mu[... | null |
6,162 | import plotly.graph_objects as go
import streamlit as st
import numpy as np
import plotly.express as px
import pandas as pd
import sympy
from scipy.spatial import distance
def fcn_mahal(xx, yy, mu, Sigma, standardized = False):
if standardized:
D = np.diag(np.diag(Sigma))
Sigma_inv = ... | null |
6,163 | import streamlit as st
import plotly.graph_objects as go
import sympy
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `bmatrix` function. Write a Python function `def bmatrix(a)` to solve the following problem:
Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bm... | Returns a LaTeX bmatrix :a: numpy array :returns: LaTeX bmatrix as a string |
6,164 | import sympy
import numpy as np
import matplotlib.pyplot as plt
xx1, xx2 = mesh_circ(0, 0, 4, 20)
def mesh_circ(c1, c2, r, num):
theta = np.arange(0,2*np.pi+np.pi/num,np.pi/num)
r = np.arange(0,r,r/num)
theta,r = np.meshgrid(theta,r)
xx1 = np.cos(theta)*r + c1
xx2 = np.sin(theta)*r + c2
... | null |
6,165 | import numpy as np
def is_pos_def(A):
if np.array_equal(A, A.T):
try:
np.linalg.cholesky(A)
return True
except np.linalg.LinAlgError:
return False
else:
return False | null |
6,166 | import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
PRECISION = 3
np.random.seed(1)
U, S, V = svd(X, full_matrices = True)
U, S, V = svd(X, full_matrices = False)
import copy
def svd(X,full_matrices):
U, s, Vt = np.linalg.svd(X,full_matrices = full_matrices)
# Put the vector sin... | null |
6,167 | from typing import Dict, List, Any, Union, Optional
from langchain import PromptTemplate, LLMChain
from langchain.schema import LLMResult, BaseOutputParser, Generation
from langchain.llms import OpenAI, BaseLLM
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.chat_models import ChatOpenAI
de... | null |
6,168 | import sys
from io import StringIO
from typing import List, Optional, Dict, Tuple
from langchain.schema import LLMResult, BaseOutputParser
from pydantic.fields import Field
from pydantic.main import BaseModel
def get_n_tokens(input: str, model_name: str = 'gpt-3.5-turbo'):
import tiktoken
enc = tiktoken.encodin... | null |
6,169 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import tokenization
from pretrain import pretrain_data
The provided code snippet includes necessary dependenc... | Gathers the vectors at the specific positions over a minibatch. Args: sequence: A [batch_size, seq_length] or [batch_size, seq_length, depth] tensor of values positions: A [batch_size, n_positions] tensor of indices Returns: A [batch_size, n_positions] or [batch_size, n_positions, depth] tensor of the values at the ind... |
6,170 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import tokenization
from pretrain import pretrain_data
def scatter_update(sequence, updates, positions):
"""... | Implementation of dynamic masking. The optional arguments aren't needed for BERT/ELECTRA and are from early experiments in "strategically" masking out tokens instead of uniformly at random. Args: config: configure_pretraining.PretrainingConfig inputs: pretrain_data.Inputs containing input input_ids/input_mask mask_prob... |
6,171 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import tokenization
from pretrain import pretrain_data
def scatter_update(sequence, updates, positions):
"""... | null |
6,172 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import tokenization
from pretrain import pretrain_data
def sample_from_softmax(logits, disallow=None):
if d... | null |
6,173 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import tokenization
from util import utils
Inputs = collections.namedtuple(
"Inputs", ["input_i... | Pretty-print model inputs. |
6,174 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
The provided code snippet includes necessary dependencies for implementing the `get_span_labels` function. Write a Python function `def get_span_labels(sentence_tags, inv_label_mapping=None)` to solve the follo... | Go from token-level labels to list of entities (start, end, class). |
6,175 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
The provided code snippet includes necessary dependencies for implementing the `get_tags` function. Write a Python function `def get_tags(span_labels, length, encoding)` to solve the following problem:
Converts... | Converts a list of entities to token-label labels based on the provided encoding (e.g., BIOES). |
6,176 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
import os
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.tagging import tagging_metrics
from... | Splits up words into subword-level tokens. |
6,177 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def parse_args():
parser = argparse... | null |
6,178 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
OPTS = None
def set_opts(config: conf... | null |
6,179 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def make_qid_to_has_ans(dataset):
q... | null |
6,180 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def normalize_answer(s):
"""Lower te... | null |
6,181 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def apply_no_ans_threshold(scores, na... | null |
6,182 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def make_eval_dict(exact_scores, f1_s... | null |
6,183 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def merge_eval(main_eval, new_eval, pr... | null |
6,184 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def histogram_na_prob(na_probs, qid_l... | null |
6,185 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
import tensorflow.compat.v1 as tf
import configure_finetuning
def find_best_thresh(preds, scores, na... | null |
6,186 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
import json
import os
import six
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.qa import qa... | Check if this is the 'max context' doc span for the token. |
6,187 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
import json
import os
import six
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.qa import qa... | Returns tokenized answer spans that better match the annotated answer. |
6,188 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import collections
import json
import os
import six
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.qa import qa... | null |
6,189 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import six
import configure_finetuning
from finetune import scorer
from finetune.qa import mrqa_official_eval
from finetune.qa import squad_official_eval
from finetune.qa im... | Get the n-best logits from a list. |
6,190 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import six
import configure_finetuning
from finetune import scorer
from finetune.qa import mrqa_official_eval
from finetune.qa import squad_official_eval
from finetune.qa im... | Compute softmax probability over raw logits. |
6,191 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import six
import configure_finetuning
from finetune import scorer
from finetune.qa import mrqa_official_eval
from finetune.qa import squad_official_eval
from finetune.qa im... | Project the tokenized prediction back to the original text. |
6,192 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import string
import re
import json
import tensorflow.compat.v1 as tf
from collections import Counter
import configure_finetuning
def read_predictions(prediction_file):
with tf.io.gfile.GFile(predic... | null |
6,193 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import string
import re
import json
import tensorflow.compat.v1 as tf
from collections import Counter
import configure_finetuning
def read_answers(gold_file):
answers = {}
with tf.io.gfile.GFile(g... | null |
6,194 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import string
import re
import json
import tensorflow.compat.v1 as tf
from collections import Counter
import configure_finetuning
def f1_score(prediction, ground_truth):
prediction_tokens = normalize... | null |
6,195 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import Counter
import string
import re
import json
import sys
import os
import collections
import tensorflow.compat.v1 as tf
import configure_finetuning
def f1_score(prediction, ground_truth):
... | null |
6,196 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import csv
import os
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.classification import classification_metric... | Truncates a sequence pair in place to the maximum length. |
6,197 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import csv
import os
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import feature_spec
from finetune import task
from finetune.classification import classification_metric... | Reads a tab separated value file. |
6,198 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import configure_finetuning
class FeatureSpec(object):
"""Defines a feature passed as input to the model."""
def __init__(self, name, shape, default_value... | Non-task-specific model inputs. |
6,199 | import argparse
import multiprocessing
import os
import random
import time
import tensorflow.compat.v1 as tf
from model import tokenization
from util import utils
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return feature | null |
6,200 | import argparse
import multiprocessing
import os
import random
import time
import tensorflow.compat.v1 as tf
from model import tokenization
from util import utils
class ExampleWriter(object):
"""Writes pre-training examples to disk."""
def __init__(self, job_id, vocab_file, output_dir, max_seq_length,
... | A single process creating and writing out pre-processed examples. |
6,201 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import optimization
from pretrain import pretrain_data
from pre... | Get model config for the generator network. |
6,202 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import optimization
from pretrain import pretrain_data
from pre... | Run pre-training or evaluate the pre-trained model. |
6,203 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import tensorflow.compat.v1 as tf
import configure_pretraining
from model import modeling
from model import optimization
from pretrain import pretrain_data
from pre... | Builds an ELECTRA model an trains it for one step; useful for debugging. |
6,204 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import preprocessing
from finetune import task_builder
from model import modeling
from m... | Returns `model_fn` closure for TPUEstimator. |
6,205 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import json
import tensorflow.compat.v1 as tf
import configure_finetuning
from finetune import preprocessing
from finetune import task_builder
from model import modeling
from m... | Run finetuning. |
6,206 | from __future__ import print_function
from collections import Counter, OrderedDict
import string
import re
import argparse
import json
import sys
sys.setdefaultencoding('utf8')
import nltk
import pdb
def calc_f1_score(answers, prediction):
f1_scores = []
for ans in answers:
ans_segs = mixed_segmentation(ans, rm_pun... | null |
6,207 | import argparse
import multiprocessing
import os
import random
import tarfile
import time
import tensorflow.compat.v1 as tf
import build_pretraining_dataset
from util import utils
The provided code snippet includes necessary dependencies for implementing the `write_examples` function. Write a Python function `def writ... | A single process creating and writing out pre-processed examples. |
6,208 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
... | Loads a vocabulary file into a dictionary. |
6,209 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
def convert_by_vocab(vocab, items):
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens) | null |
6,210 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in ite... | null |
6,211 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` function. Write a Python f... | Runs basic whitespace cleaning and splitting on a piece of text. |
6,212 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python functi... | Checks whether `chars` is a whitespace character. |
6,213 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function ... | Checks whether `chars` is a control character. |
6,214 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python funct... | Checks whether `chars` is a punctuation character. |
6,215 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
def gelu(input_tensor):
... | Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. Args: activation_string: String name of the activation function. Returns: A Python function corresponding to the activation function. If `activation_string` is None, empty, or "linear", this will return None. If `activation_string` is not a string, it wi... |
6,216 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
def create_initializer(ini... | Looks up words embeddings for id tensor. Args: input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids. vocab_size: int. Size of the embedding vocabulary. embedding_size: int. Width of the word embeddings. initializer_range: float. Embedding initialization range. word_embedding_name: string. Name ... |
6,217 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
def layer_norm_and_dropout... | Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size]. use_token_type: bool. Whether to add embeddings for `token_type_ids`. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. Must be specified if `use_toke... |
6,218 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
def get_shape_list(tensor,... | Create 3D attention mask from a 2D tensor mask. Args: from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...]. to_mask: int32 Tensor of shape [batch_size, to_seq_length]. Returns: float Tensor of shape [batch_size, from_seq_length, to_seq_length]. |
6,219 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
def gelu(input_tensor):
... | Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder. See the original paper: https://arxiv.org/abs/1706.03762 Also see: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py Args: input_t... |
6,220 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import pickle
import sys
import tensorflow.compat.v1 as tf
def load_pickle(path):
with tf.io.gfile.GFile(path, "rb") as f:
return pickle.load(f) | null |
6,221 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import pickle
import sys
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `nest_dict` function. Write a Python function `def nest_dict... | Go from {prefix_key: value} to {prefix: {key: value}}. |
6,222 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import datetime
import re
import time
import tensorflow.compat.v1 as tf
from model import modeling
from util import utils
def secs_to_str(secs):
s = str(datetime.timedelta(seconds=int(round(secs))))
s = re.... | null |
6,223 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import datetime
import re
import time
import tensorflow.compat.v1 as tf
from model import modeling
from util import utils
The provided code snippet includes necessary dependencies for implementing the `get_bert... | Get model hyperparameters based on a pretraining/finetuning config |
6,224 | from contextlib import contextmanager
import datetime
import os
import time
import json
import re
from colorama import Fore
from XAgent.workflow.base_query import AutoGPTQuery
from XAgent.config import XAgentConfig
from XAgentServer.database.connect import SessionLocal
from XAgentServer.loggers.logs import Logger
from ... | common |
6,225 | from contextlib import contextmanager
import datetime
import os
import time
import json
import re
from colorama import Fore
from XAgent.workflow.base_query import AutoGPTQuery
from XAgent.config import XAgentConfig
from XAgentServer.database.connect import SessionLocal
from XAgentServer.loggers.logs import Logger
from ... | Provide a transactional scope around a series of operations. |
6,226 | import os
import time
import json
import yaml
import uuid
import logging
from copy import deepcopy
from colorama import Fore, Style
from XAgent.logs import logger
from XAgent.workflow.base_query import AutoGPTQuery
from XAgent.config import XAgentConfig, CONFIG
The provided code snippet includes necessary dependencies... | Serialize commonly used data types, like str, int, float, bool, dictionaries, and lists. Args: object (Any): The object to serialize. Returns: object: The cpickled object. |
6,227 | import logging
import os
import random
import re
import time
import json
import abc
from logging import LogRecord
from typing import Any
import uuid
from threading import Lock
from colorama import Fore, Style
from XAgent.utils import Singleton, TaskSaveItem
def remove_color_codes(s: str) -> str:
if not isinstance(... | null |
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