text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: ClashLuke/iDAF path: /src/model_creator.py
import os
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.initializers import orthogonal as initializer
from tensorflow.keras.layers import (Add, BatchNormalization,... | code_fim | hard | {
"lang": "python",
"repo": "ClashLuke/iDAF",
"path": "/src/model_creator.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: roksikonja/pyproject path: /src/pypackage/module.py
"""A module for computing geometric distances between vectors.
"""
import numpy as np
<|fim_suffix|>
def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:
"""Computes the Euclidean distance between points x and y given in Cartesia... | code_fim | medium | {
"lang": "python",
"repo": "roksikonja/pyproject",
"path": "/src/pypackage/module.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """Computes the Euclidean distance between points x and y given in Cartesian coordinates.
Args:
x: A vector.
y: A vector.
Returns:
A float representing the Euclidean distance between x and y.
"""
distance_vector: np.ndarray = x - y
distance = compute_norm(... | code_fim | medium | {
"lang": "python",
"repo": "roksikonja/pyproject",
"path": "/src/pypackage/module.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Returns:
A float representing the Euclidean distance between x and y.
"""
distance_vector: np.ndarray = x - y
distance = compute_norm(distance_vector)
return distance<|fim_prefix|># repo: roksikonja/pyproject path: /src/pypackage/module.py
"""A module for computing geometric d... | code_fim | hard | {
"lang": "python",
"repo": "roksikonja/pyproject",
"path": "/src/pypackage/module.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
'''
get the thing name from topic
assuming
topic: status/<DeviceType>/<MAC>/<AppName>/<AppId>/<type>
thing name: <AppName><AppId><MAC>
'''
def get_thing_name(topic):
if topic.count('/') != 5:
return None
topic = topic.split('/')
return "{name}{id}{mac}".format(name=topic[3], id=topic[... | code_fim | hard | {
"lang": "python",
"repo": "iotap-center/mqtt-transformer",
"path": "/mosquitto_awsiot_datashipper.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> print("Message received: ")
if msg.topic.startswith("status/camera/"):
return # This is, currently, of no use for CoSIS
name = get_thing_name(msg.topic)
if name is None:
print("Unknown status topic: {}".format(msg.topic))
return
status = msg.... | code_fim | hard | {
"lang": "python",
"repo": "iotap-center/mqtt-transformer",
"path": "/mosquitto_awsiot_datashipper.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: iotap-center/mqtt-transformer path: /mosquitto_awsiot_datashipper.py
from __future__ import print_function
import paho.mqtt.client as paho_mqtt
from AWSIoTPythonSDK.MQTTLib import AWSIoTMQTTClient
import time, argparse, re, json
import mosquitto_awsiot_config
import socket
# arguments
parser = ... | code_fim | hard | {
"lang": "python",
"repo": "iotap-center/mqtt-transformer",
"path": "/mosquitto_awsiot_datashipper.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: knightelvis/Mturk-Tracker path: /app/mturk/main/management/commands/diffs.py
import time
import logging
from utils.sql import execute_sql, query_to_tuples
log = logging.getLogger(__name__)
def hitgroups(cid):
r = execute_sql("select distinct group_id from hits_mv where crawl_id = %s", cid... | code_fim | hard | {
"lang": "python",
"repo": "knightelvis/Mturk-Tracker",
"path": "/app/mturk/main/management/commands/diffs.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> prev = execute_sql("""select hits_available from hits_mv
where
crawl_id between %s and %s and
group_id = '%s'
order by crawl_id desc
limit 1;""" % (cid - 100, cid - 1, g)).fetchall()
prev = prev[0][0] if prev e... | code_fim | medium | {
"lang": "python",
"repo": "knightelvis/Mturk-Tracker",
"path": "/app/mturk/main/management/commands/diffs.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = callback(*args, **kwargs)
loop.call_soon_threadsafe(set_result, fut, result)
fut = loop.create_future()
return fut, func_wrapper<|fim_prefix|># repo: AlexCovizzi/torrenttv path: /torrenttv/utils/async_utils/futurize.py
import asyncio
def futurize(func, args=None, kwar... | code_fim | medium | {
"lang": "python",
"repo": "AlexCovizzi/torrenttv",
"path": "/torrenttv/utils/async_utils/futurize.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AlexCovizzi/torrenttv path: /torrenttv/utils/async_utils/futurize.py
import asyncio
def futurize(func, args=None, kwargs=None, loop=None, executor=None):
loop = loop or asyncio.get_event_loop()
args = args or ()
kwargs = kwargs or {}
awaitable = loop.run_in_executor(executor, fu... | code_fim | hard | {
"lang": "python",
"repo": "AlexCovizzi/torrenttv",
"path": "/torrenttv/utils/async_utils/futurize.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AmrReda/Algorithms path: /src/Algorithm Implementation/Geometry/Convex hull/convex_hull.py
"""Computes the convex hull of a set of 2D points.
Input: an iterable sequence of (x, y) pairs representing the points.
Output: a list of vertices of the convex hull in counter-clockwise order,
... | code_fim | hard | {
"lang": "python",
"repo": "AmrReda/Algorithms",
"path": "/src/Algorithm Implementation/Geometry/Convex hull/convex_hull.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def add(self, point):
self._points.append(point)
def _get_orientation(self, origin, p1, p2):
difference = ((p2.x - origin.x) * (p1.y - origin.y)) - ((p1.x - origin.x) - (p2.y - origin.y))
return difference<|fim_prefix|># repo: AmrReda/Algorithms path: /src/Algorithm ... | code_fim | medium | {
"lang": "python",
"repo": "AmrReda/Algorithms",
"path": "/src/Algorithm Implementation/Geometry/Convex hull/convex_hull.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if not new_name.endswith(".pdf"):
new_name += ".pdf"
return new_name
def extract_from_title(filename):
with open(filename, "rb") as f:
try:
pdf = pdftotext.PDF(f)
except:
return
try:
text = pdf[0][:64].splitlines()[0]
new_n... | code_fim | hard | {
"lang": "python",
"repo": "morfismo/pdfs-rename",
"path": "/rename/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: morfismo/pdfs-rename path: /rename/utils.py
#!/usr/bin/env python3
from PyPDF2 import PdfFileReader
from slugify import slugify
import pdftotext
<|fim_suffix|> new_name = slugify(name)
if not new_name:
return
if not new_name.endswith(".pdf"):
new_name += ".pdf"
... | code_fim | hard | {
"lang": "python",
"repo": "morfismo/pdfs-rename",
"path": "/rename/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> b,c=nx.intersection_array(nx.cycle_graph(5))
assert_equal(b,[2, 1])
assert_equal(c,[1, 1])
b,c=nx.intersection_array(nx.dodecahedral_graph())
assert_equal(b,[3, 2, 1, 1, 1])
assert_equal(c,[1, 1, 1, 2, 3])
b,c=nx.intersection_array(nx.icosahedral_gra... | code_fim | medium | {
"lang": "python",
"repo": "wangyum/Anaconda",
"path": "/lib/python2.7/site-packages/networkx/algorithms/tests/test_distance_regular.py",
"mode": "spm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_intersection_array(self):
b,c=nx.intersection_array(nx.cycle_graph(5))
assert_equal(b,[2, 1])
assert_equal(c,[1, 1])
b,c=nx.intersection_array(nx.dodecahedral_graph())
assert_equal(b,[3, 2, 1, 1, 1])
assert_equal(c,[1, 1, 1, 2, 3])
b,c=n... | code_fim | hard | {
"lang": "python",
"repo": "wangyum/Anaconda",
"path": "/lib/python2.7/site-packages/networkx/algorithms/tests/test_distance_regular.py",
"mode": "spm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wangyum/Anaconda path: /lib/python2.7/site-packages/networkx/algorithms/tests/test_distance_regular.py
#!/usr/bin/env python
from nose.tools import *
import networkx as nx
class TestDistanceRegular:
def test_is_distance_regular(self):
assert_true(nx.is_distance_regular(nx.icosahedra... | code_fim | hard | {
"lang": "python",
"repo": "wangyum/Anaconda",
"path": "/lib/python2.7/site-packages/networkx/algorithms/tests/test_distance_regular.py",
"mode": "psm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if prefix:
if target_group_name.startswith(prefix):
if not target_group_load_balancers:
punt = True
else:
if not target_group_load_balancers:
punt = True
if punt:
... | code_fim | hard | {
"lang": "python",
"repo": "Signiant/aws-target-group-cleanup",
"path": "/src/aws-target-group-cleanup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Signiant/aws-target-group-cleanup path: /src/aws-target-group-cleanup.py
import sys
import boto3
import argparse
import pprint
from time import sleep
def remove_target_group(arn, elb_client):
request_id = None
response = None
try:
response = elb_client.delete_target_group(
... | code_fim | hard | {
"lang": "python",
"repo": "Signiant/aws-target-group-cleanup",
"path": "/src/aws-target-group-cleanup.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def main(argv):
plugin_results = dict()
groups_removed_count = 0
prefix = ""
parser = argparse.ArgumentParser(description='Remove ALB target groups not assigned to load balancers')
parser.add_argument('-f','--force', help='Perform the actual deletes (will run in dryrun mode by defaul... | code_fim | hard | {
"lang": "python",
"repo": "Signiant/aws-target-group-cleanup",
"path": "/src/aws-target-group-cleanup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return value1 + value2<|fim_prefix|># repo: jashburn8020/circleci-tutorial path: /src/maths.py
class Maths:
def addition(value1, value2):
<|fim_middle|> """Add 2 integer values. Raises `TypeError` if arguments are non-integer."""
if not isinstance(value1, int) or not isinstance... | code_fim | hard | {
"lang": "python",
"repo": "jashburn8020/circleci-tutorial",
"path": "/src/maths.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jashburn8020/circleci-tutorial path: /src/maths.py
class Maths:
def addition(value1, value2):
<|fim_suffix|> return value1 + value2<|fim_middle|> """Add 2 integer values. Raises `TypeError` if arguments are non-integer."""
if not isinstance(value1, int) or not isinstance... | code_fim | hard | {
"lang": "python",
"repo": "jashburn8020/circleci-tutorial",
"path": "/src/maths.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def get_conll_ner_datasets(vocab, char_vocab, tag_vocab, data_dir, lang):
print(f'Loading CoNLL NER data for {lang} Language..')
train_set = ConllDataset(os.path.join(data_dir, f'{lang}.train'),
vocab, char_vocab, tag_vocab, update_vocab=True, remove_empty=opt.remove_emp... | code_fim | hard | {
"lang": "python",
"repo": "microsoft/Multilingual-Model-Transfer",
"path": "/data_prep/bio_dataset.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: microsoft/Multilingual-Model-Transfer path: /data_prep/bio_dataset.py
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
import copy
import os
import random
import torch
from torch.utils.data import Dataset
from options import opt
from utils import re... | code_fim | hard | {
"lang": "python",
"repo": "microsoft/Multilingual-Model-Transfer",
"path": "/data_prep/bio_dataset.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> with instance_for_test() as instance:
run_config = load_yaml_from_globs(
file_relative_path(__file__, "../../docs_snippets/deploying/dask_hello_world.yaml")
)
result = execute_pipeline(
reconstructable(dask_pipeline),
run_config=run_config,
... | code_fim | medium | {
"lang": "python",
"repo": "JBrVJxsc/dagster",
"path": "/examples/docs_snippets/docs_snippets_tests/deploying_tests/test_dask.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JBrVJxsc/dagster path: /examples/docs_snippets/docs_snippets_tests/deploying_tests/test_dask.py
from dagster import execute_pipeline, file_relative_path, reconstructable
from dagster.core.test_utils import instance_for_test
from dagster.utils.yaml_utils import load_yaml_from_globs
from docs_snipp... | code_fim | medium | {
"lang": "python",
"repo": "JBrVJxsc/dagster",
"path": "/examples/docs_snippets/docs_snippets_tests/deploying_tests/test_dask.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> devices = device_list.split(",")
devices = [int(x) for x in devices]
devices.sort()
process_device_map = dict()
for process_id, device_id in enumerate(devices):
process_device_map[process_id] = device_id
return process_device_map
d... | code_fim | hard | {
"lang": "python",
"repo": "Ascend/ModelZoo-PyTorch",
"path": "/ACL_PyTorch/contrib/cv/gan/CycleGAN/parse.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ascend/ModelZoo-PyTorch path: /ACL_PyTorch/contrib/cv/gan/CycleGAN/parse.py
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are per... | code_fim | hard | {
"lang": "python",
"repo": "Ascend/ModelZoo-PyTorch",
"path": "/ACL_PyTorch/contrib/cv/gan/CycleGAN/parse.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bcgov/mds path: /services/core-api/app/api/now_applications/models/activity_summary/underground_exploration.py
from sqlalchemy.dialects.postgresql import UUID
from sqlalchemy.schema import FetchedValue
from sqlalchemy.ext.associationproxy import association_proxy
from sqlalchemy.ext.hybrid import... | code_fim | hard | {
"lang": "python",
"repo": "bcgov/mds",
"path": "/services/core-api/app/api/now_applications/models/activity_summary/underground_exploration.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> __tablename__ = "underground_exploration"
__mapper_args__ = {
'polymorphic_identity': 'underground_exploration', ## type code
}
activity_summary_id = db.Column(
db.Integer, db.ForeignKey('activity_summary.activity_summary_id'), primary_key=True)
total_ore_amount = db.C... | code_fim | medium | {
"lang": "python",
"repo": "bcgov/mds",
"path": "/services/core-api/app/api/now_applications/models/activity_summary/underground_exploration.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>print("%d %d %d" % (len(l), neg_count, pos_count))
outp = open("train.json", 'w', encoding="utf-8")
outp.write(json.dumps(l[ : int(len(l) / 5 * 4)], indent=4, ensure_ascii=False))
outp.close()
outp = open("test.json", 'w', encoding="utf-8")
outp.write(json.dumps(l[int(len(l) / 5 * 4) : ], indent=4, ensu... | code_fim | hard | {
"lang": "python",
"repo": "UglyDogIsDog/VectorizeBlockchainNews",
"path": "/scripts/split.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: UglyDogIsDog/VectorizeBlockchainNews path: /scripts/split.py
import json
import random
inp = open("data.json", "rb")
passages = json.load(inp)
inp.close()
<|fim_suffix|>outp = open("train.json", 'w', encoding="utf-8")
outp.write(json.dumps(l[ : int(len(l) / 5 * 4)], indent=4, ensure_ascii=False... | code_fim | hard | {
"lang": "python",
"repo": "UglyDogIsDog/VectorizeBlockchainNews",
"path": "/scripts/split.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lypnol/adventofcode-2017 path: /day-22/part-2/silvestre.py
from collections import deque
from submission import Submission
class SilvestreSubmission(Submission):
def run(self, s):
current_grid = self.read_input(s)
virus_pos = [len(current_grid)//2 for i in range(2)]
... | code_fim | hard | {
"lang": "python",
"repo": "lypnol/adventofcode-2017",
"path": "/day-22/part-2/silvestre.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> State :
0 - Clean
1 - Weakened
2 - Infected
3 - Flagged
"""
# Step 1 & 2
curr_x = virus_pos[0]
curr_y = virus_pos[1]
assert 0 <= curr_x < len(current_grid) and 0 <= curr_y < len(current_grid)
if current_grid[curr_x][cu... | code_fim | hard | {
"lang": "python",
"repo": "lypnol/adventofcode-2017",
"path": "/day-22/part-2/silvestre.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def read_input(self, s):
"""
On crée une grid. Une list de list (une deque de deque)
"""
grid = deque()
for str_row in s.split("\n"):
grid.append(deque(list(map(int,str_row.replace('.','0').replace('#','2')))))
return grid<|fim_prefix|># repo... | code_fim | hard | {
"lang": "python",
"repo": "lypnol/adventofcode-2017",
"path": "/day-22/part-2/silvestre.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
self.new_credential.credential_create()
test_credentials = Credentials("MySpace", "Ghostke99", "daimaMkenya001")
test_credentials.credential_create()
search_duplicate = Credentials.search_duplicate("MySpace")
self.assertTrue(search_duplicate)
if __name_... | code_fim | hard | {
"lang": "python",
"repo": "k-wayne/PasswordVault",
"path": "/test_credential.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: k-wayne/PasswordVault path: /test_credential.py
import unittest
from credential import Credentials
import pyperclip
class TestUser(unittest.TestCase):
def setUp(self):
"""
#method to run before each test
"""
# instantiate an object by populating with dummy va... | code_fim | hard | {
"lang": "python",
"repo": "k-wayne/PasswordVault",
"path": "/test_credential.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: open-toontown/open-toontown path: /toontown/ai/CrashedLeaderBoardDecorator.py
from direct.directnotify import DirectNotifyGlobal
from direct.distributed.ClockDelta import *
from direct.interval.IntervalGlobal import *
from . import HolidayDecorator
from toontown.toonbase import ToontownGlobals
fr... | code_fim | hard | {
"lang": "python",
"repo": "open-toontown/open-toontown",
"path": "/toontown/ai/CrashedLeaderBoardDecorator.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> if isinstance(base.cr.playGame.getPlace().loader.hood, GSHood.GSHood):
base.cr.playGame.getPlace().loader.stopSmokeEffect()
def undecorate(self):
if base.config.GetBool('want-crashedLeaderBoard-Smoke', 1):
self.stopSmokeEffect()
holidayIds = base.cr.new... | code_fim | medium | {
"lang": "python",
"repo": "open-toontown/open-toontown",
"path": "/toontown/ai/CrashedLeaderBoardDecorator.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shurrey/ask-an-expert path: /slackmodule/SlackService.py
import Config
import slack
class SlackService():
def __init__(self):
print(str(slack))
self.client = slack.WebClient(token=Config.config['slack_token'])
def sendExpertMessage(self, channel, fname, gname, expert_u... | code_fim | hard | {
"lang": "python",
"repo": "shurrey/ask-an-expert",
"path": "/slackmodule/SlackService.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> print("Response: " + str(response))
def composeMessage(self, fname, gname, expert_url, institution, product, question):
return(f"We have a question!\r\n" \
f"\r\n" \
f"User: {fname} {gname}\r\n" \
f"Institution: {institution}\r\n" \
... | code_fim | hard | {
"lang": "python",
"repo": "shurrey/ask-an-expert",
"path": "/slackmodule/SlackService.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: milenpenev/Python_Advanced path: /Exam preparation/Python Advanced Retake Exam - 14 April 2021/01-pizza-orders.py
from collections import deque
pizza_orders = [int(el) for el in input().split(", ")]
employees = [int(el) for el in input().split(", ")]
completed_orders = []
pizza_orders = deque(pi... | code_fim | hard | {
"lang": "python",
"repo": "milenpenev/Python_Advanced",
"path": "/Exam preparation/Python Advanced Retake Exam - 14 April 2021/01-pizza-orders.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if employees:
print("All orders are successfully completed!")
print(f"Total pizzas made: {sum(completed_orders)}")
print("Employees: ", end="")
print(*employees, sep=", ")
else:
print("Not all orders are completed.")
print("Orders left: ", end="")
print(*pizza_orders, sep=", "... | code_fim | hard | {
"lang": "python",
"repo": "milenpenev/Python_Advanced",
"path": "/Exam preparation/Python Advanced Retake Exam - 14 April 2021/01-pizza-orders.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> conv21 = tf.layers.conv2d(conv20, 1024, (3, 3), (1, 1), padding='same', activation=leaky_relu)
pad2 = tf.keras.layers.ZeroPadding2D((1,1))(conv21)
conv22 = tf.layers.conv2d(pad2, 1024, (3, 3), (2, 2), padding='valid', activation=leaky_relu)
conv23 = tf.layers.conv2d(conv22,... | code_fim | hard | {
"lang": "python",
"repo": "cersar/BasicNetwork",
"path": "/network/YoloV1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def compute_loss(y_true,y_hat,lambd_coord=5,lambd_nonObj=.5):
probes_hat, confs_hat, boxes_cord_hat = y_hat
obj_mask = y_true[..., 0]
confs_true = tf.expand_dims(obj_mask,axis=2)
boxes_cord_true = tf.expand_dims(y_true[...,1:5],axis=2)
probes_true = y_true[...,5:]
IOU = compute_I... | code_fim | hard | {
"lang": "python",
"repo": "cersar/BasicNetwork",
"path": "/network/YoloV1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cersar/BasicNetwork path: /network/YoloV1.py
import tensorflow as tf
from util.process_box import compute_IOU
import numpy as np
def YoloV1(input_shape,class_num=20,box_num=2):
iw, ih, c = input_shape
net = tf.Graph()
with net.as_default():
x = tf.placeholder(tf.float32, sha... | code_fim | hard | {
"lang": "python",
"repo": "cersar/BasicNetwork",
"path": "/network/YoloV1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tensorflow/tensorflow path: /tensorflow/python/framework/type_utils.py
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/tensorflow",
"path": "/tensorflow/python/framework/type_utils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># LINT.IfChange(_specs_for_flat_tensors)
def _specs_for_flat_tensors(element_spec):
"""Return a flat list of type specs for element_spec.
Note that "flat" in this function and in `_flat_tensor_specs` is a nickname
for the "batchable tensor list" encoding used by datasets and map_fn
internally (in... | code_fim | hard | {
"lang": "python",
"repo": "tensorflow/tensorflow",
"path": "/tensorflow/python/framework/type_utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def generate_chunk_pairs(self, pair: SamplePair) -> Iterable[Tuple[str, str]]:
len_a = len(pair.chunks_a)
len_b = len(pair.chunks_b)
sampled_items = []
if len_b < len_a:
for b in pair.chunks_b:
while True:
... | code_fim | hard | {
"lang": "python",
"repo": "av-pt/unmasking",
"path": "/authorship_unmasking/features/sampling.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: av-pt/unmasking path: /authorship_unmasking/features/sampling.py
# Copyright (C) 2017-2019 Janek Bevendorff, Webis Group
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | code_fim | hard | {
"lang": "python",
"repo": "av-pt/unmasking",
"path": "/authorship_unmasking/features/sampling.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vfdev-5/ignite-examples path: /classification/imaterialist_challenge_furniture_2018/models/inceptionresnetv2_ssd_like.py
import torch
import torch.nn as nn
from torch.nn import Module, Linear, ModuleList, AdaptiveAvgPool2d, ReLU, Dropout
from torch.nn.init import normal_, constant_
from pretraine... | code_fim | hard | {
"lang": "python",
"repo": "vfdev-5/ignite-examples",
"path": "/classification/imaterialist_challenge_furniture_2018/models/inceptionresnetv2_ssd_like.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> )
self.smooth2 = nn.Sequential(
nn.Conv2d(1088, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.ReLU()
)
self.smooth3 = nn.Sequential(
nn.Conv2d(2080... | code_fim | hard | {
"lang": "python",
"repo": "vfdev-5/ignite-examples",
"path": "/classification/imaterialist_challenge_furniture_2018/models/inceptionresnetv2_ssd_like.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert type(coord) is tuple, "expected a tuple as coordinate"
assert len(coord) == 3, "expected 3 elements in coordinate"
for v in coord:
assert type(v) is float or type(v) is np.float64, "expected type float in elements of coordinate"
point = np.array([co... | code_fim | hard | {
"lang": "python",
"repo": "Natasja1992/ifc-citygml-2-envi",
"path": "/conversion_tool/georeferencing.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> point = np.array([coord[0], coord[1], coord[2], 1])
transformed_point = self._T_inv.dot(point)
return transformed_point[0], transformed_point[1], transformed_point[2]
def transform_point_reverse(self, coord):
assert type(coord) is tuple, "expected a tuple as coord... | code_fim | hard | {
"lang": "python",
"repo": "Natasja1992/ifc-citygml-2-envi",
"path": "/conversion_tool/georeferencing.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Natasja1992/ifc-citygml-2-envi path: /conversion_tool/georeferencing.py
from osgeo.osr import SpatialReference, CoordinateTransformation
import numpy as np
class Transform(object):
def __init__(self):
pass
def transform_point(self, coord):
raise NotImplementedE... | code_fim | hard | {
"lang": "python",
"repo": "Natasja1992/ifc-citygml-2-envi",
"path": "/conversion_tool/georeferencing.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: wangyum/Anaconda path: /lib/python2.7/site-packages/binstar_client/commands/logout.py
'''
Log out from binstar
'''
import getpass
from binstar_client.utils import get_server_api, remove_token
import logging
from binstar_client import errors
log = logging.getLogger('binstar.logout')
def main(ar... | code_fim | medium | {
"lang": "python",
"repo": "wangyum/Anaconda",
"path": "/lib/python2.7/site-packages/binstar_client/commands/logout.py",
"mode": "psm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def add_parser(subparsers):
subparser = subparsers.add_parser('logout',
help='Log out from Anaconda Cloud',
description=__doc__)
subparser.set_defaults(main=main)<|fim_prefix|># repo: wangyum/Anaconda path: /lib/python2... | code_fim | hard | {
"lang": "python",
"repo": "wangyum/Anaconda",
"path": "/lib/python2.7/site-packages/binstar_client/commands/logout.py",
"mode": "spm",
"license": "Python-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> Args:
period: Period in milliseconds for running the work
periodically.
action: Action to be executed.
state: [Optional] Initial state passed to the action upon
the first iteration.
Returns:
The disposable obj... | code_fim | hard | {
"lang": "python",
"repo": "markusj1201/RxPY",
"path": "/rx/concurrency/mainloopscheduler/wxscheduler.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: markusj1201/RxPY path: /rx/concurrency/mainloopscheduler/wxscheduler.py
import logging
from typing import Any
from rx.disposable import Disposable
from rx.core import typing
from rx.disposable import SingleAssignmentDisposable, CompositeDisposable
from rx.concurrency.schedulerbase import Schedul... | code_fim | hard | {
"lang": "python",
"repo": "markusj1201/RxPY",
"path": "/rx/concurrency/mainloopscheduler/wxscheduler.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def get_config(self, budget): # pylint: disable=unused-argument
return self.configspace.sample_configuration().get_dictionary(), {}
class RandomSearch(hp_transfer_optimizers.core.master.Master):
def __init__(
self, **kwargs,
):
super().__init__(**kwargs)
sel... | code_fim | hard | {
"lang": "python",
"repo": "hp-transfer/ht_optimizers",
"path": "/hp_transfer_optimizers/random_search.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hp-transfer/ht_optimizers path: /hp_transfer_optimizers/random_search.py
import numpy as np
import hp_transfer_optimizers.core.master
from hp_transfer_optimizers.core.successivehalving import SuccessiveHalving
class _RandomSampler:
"""
class to implement random sampling from a Con... | code_fim | hard | {
"lang": "python",
"repo": "hp-transfer/ht_optimizers",
"path": "/hp_transfer_optimizers/random_search.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self, **kwargs,
):
super().__init__(**kwargs)
self.config_generator = None
# Hyperband related stuff from original hpbandster code, we keep this as we might
# support multi fidelity in the future.
self.eta = eta = 3
self.min_budget = min_budget... | code_fim | hard | {
"lang": "python",
"repo": "hp-transfer/ht_optimizers",
"path": "/hp_transfer_optimizers/random_search.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: iskandr/msmhc path: /tests/test_extract_peptides.py
from msmhc.sequence import Sequence
from msmhc.peptides import extract_peptides
from nose.tools import eq_
<|fim_suffix|> seq = Sequence(name="test-seq", amino_acids="SIINFEKL")
peptide_dict = extract_peptides([seq], min_length=7, max_le... | code_fim | easy | {
"lang": "python",
"repo": "iskandr/msmhc",
"path": "/tests/test_extract_peptides.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> seq = Sequence(name="test-seq", amino_acids="SIINFEKL")
peptide_dict = extract_peptides([seq], min_length=7, max_length=8)
eq_(set(peptide_dict.keys()), {
"SIINFEKL",
"SIINFEK",
"IINFEKL"
})<|fim_prefix|># repo: iskandr/msmhc path: /tests/test_extract_peptides.py
f... | code_fim | easy | {
"lang": "python",
"repo": "iskandr/msmhc",
"path": "/tests/test_extract_peptides.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def output(monkeypatch, terminal_size, testdata, explicit_pager, expect_pager):
global clickoutput
clickoutput = ""
m = LiteCli(liteclirc=default_config_file)
class TestOutput:
def get_size(self):
size = namedtuple("Size", "rows columns")
size.columns, size... | code_fim | hard | {
"lang": "python",
"repo": "dbcli/litecli",
"path": "/tests/test_main.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dbcli/litecli path: /tests/test_main.py
import os
from collections import namedtuple
from textwrap import dedent
from tempfile import NamedTemporaryFile
import shutil
import click
from click.testing import CliRunner
from litecli.main import cli, LiteCli
from litecli.packages.special.main import... | code_fim | hard | {
"lang": "python",
"repo": "dbcli/litecli",
"path": "/tests/test_main.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: WaruiAlfred/instagram-clone path: /authentication/views.py
from django.shortcuts import render,redirect
from . forms import UserRegistrationForm,UserUpdateForm,ProfileUpdateForm
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from app_activities.model... | code_fim | hard | {
"lang": "python",
"repo": "WaruiAlfred/instagram-clone",
"path": "/authentication/views.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> else:
message = "That user doesn't exist"
return render(request, 'search.html',{"message":message})
def follow(request):
if request.method == 'POST':
value = request.POST['value']
user = request.POST['user']
follower = request.POST['follower']
if value == 'follow':
... | code_fim | hard | {
"lang": "python",
"repo": "WaruiAlfred/instagram-clone",
"path": "/authentication/views.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kshitijgoel007/ros_utils path: /src/ros_utils/util.py
import numpy as np
import rospy
from scipy.spatial.transform import Rotation as R
from geometry_msgs.msg import Vector3
from quadrotor_msgs.msg import RPMCommand
def tonp(obj):
if type(obj) == list:
return np.array([tonp(x) for x in ... | code_fim | medium | {
"lang": "python",
"repo": "kshitijgoel007/ros_utils",
"path": "/src/ros_utils/util.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return np.hstack((
tonp(imu.orientation).as_euler('ZYX')[::-1],
tonp(imu.angular_velocity),
tonp(imu.linear_acceleration)))
def rpmstoros(rpms):
rpm_msg = RPMCommand()
for i in range(0, len(rpms)):
rpm_msg.motor_rpm[i] = int(rpms[i])
return rpm_msg<|fim_prefix|># repo: k... | code_fim | medium | {
"lang": "python",
"repo": "kshitijgoel007/ros_utils",
"path": "/src/ros_utils/util.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> rpm_msg = RPMCommand()
for i in range(0, len(rpms)):
rpm_msg.motor_rpm[i] = int(rpms[i])
return rpm_msg<|fim_prefix|># repo: kshitijgoel007/ros_utils path: /src/ros_utils/util.py
import numpy as np
import rospy
from scipy.spatial.transform import Rotation as R
from geometry_msgs.ms... | code_fim | medium | {
"lang": "python",
"repo": "kshitijgoel007/ros_utils",
"path": "/src/ros_utils/util.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dchudz/bokeh-maps path: /code_examples/map_example.py
from bokeh.plotting import *
from PIL import Image
import numpy as np
import pandas as pd
import pyproj
from MapArea import rgba_to_array2d, get_stamen_maptile, add_maparea_to_plot
def get_world_capitals():
world_capitals = pd.read_html("... | code_fim | hard | {
"lang": "python",
"repo": "dchudz/bokeh-maps",
"path": "/code_examples/map_example.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def convert_lat_lon_to_x_y(lon, lat):
#output is currently in meters. Need to convert it to the right units (adjusted degrees?)
#tiles.mapbox.com uses EPSG:3857
web_mercator=pyproj.Proj("+init=EPSG:3857")
return(web_mercator(lon, lat))
world_capitals = get_world_capitals()
world_capital... | code_fim | medium | {
"lang": "python",
"repo": "dchudz/bokeh-maps",
"path": "/code_examples/map_example.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ReyesDeJong/CC5114 path: /Ex1/perceptron.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 6 23:19:22 2017
class Perceptron as AND, OR & NAND gates
@author: Esteban Reyes de Jong
"""
class Perceptron:
<|fim_suffix|>class AND(Perceptron):
def __init__(self):
... | code_fim | hard | {
"lang": "python",
"repo": "ReyesDeJong/CC5114",
"path": "/Ex1/perceptron.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self):
super().__init__(-0.5, -0.5, 0.75)
#small tests
if __name__ == "__main__":
# main()
p_AND = AND()
out_AND = p_AND.act([1,1])
p_OR = OR()
out_OR = p_OR.act([0,0])
p_NAND = NAND()
out_NAND = p_NAND.act([0,0])<|fim_prefix|># repo: ReyesDeJong/... | code_fim | medium | {
"lang": "python",
"repo": "ReyesDeJong/CC5114",
"path": "/Ex1/perceptron.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cfhamlet/os-qdb-protocal path: /src/os_qdb_protocal/__init__.py
import pkgutil
import inspect
import sys
from .protocal import Protocal
from importlib import import_module
_PROTOCALS = {}
<|fim_suffix|>
__all__ = ['__version__', 'version_info', 'create_protocal']
__version__ = pkgutil.get_dat... | code_fim | hard | {
"lang": "python",
"repo": "cfhamlet/os-qdb-protocal",
"path": "/src/os_qdb_protocal/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>__all__ = ['__version__', 'version_info', 'create_protocal']
__version__ = pkgutil.get_data(__package__, 'VERSION').decode('ascii').strip()
version_info = tuple(int(v) if v.isdigit() else v
for v in __version__.split('.'))
del pkgutil
del Protocal
del import_module
del inspect
del in... | code_fim | medium | {
"lang": "python",
"repo": "cfhamlet/os-qdb-protocal",
"path": "/src/os_qdb_protocal/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: OptimusPrimus/dcase2019_task1b path: /utils/training/lr_scheduler.py
from torch.optim.lr_scheduler import _LRScheduler
class LinearLR(_LRScheduler):
<|fim_suffix|> self.initial_hold = initial_hold
self.step_size = (-initial_lr) / (nr_epochs - initial_hold)
super(LinearLR,... | code_fim | medium | {
"lang": "python",
"repo": "OptimusPrimus/dcase2019_task1b",
"path": "/utils/training/lr_scheduler.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self.last_epoch <= self.initial_hold:
return self.base_lrs
return [group['lr'] + self.step_size for group in self.optimizer.param_groups]<|fim_prefix|># repo: OptimusPrimus/dcase2019_task1b path: /utils/training/lr_scheduler.py
from torch.optim.lr_scheduler import _LRSchedu... | code_fim | medium | {
"lang": "python",
"repo": "OptimusPrimus/dcase2019_task1b",
"path": "/utils/training/lr_scheduler.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>042836, 7.885294, 0.260368, 3.042539, 0.000000],
],
"Be2+": [
[3.055430, -2.372617, 1.044914, 0.544233, 0.381737, -0.653773],
[0.001226, 0.001227, 1.542106, 0.456279, 4.047479, 0.000000],
],
"Cval": [
[1.258489, 0.728215, 1.119856, 2.168133, 0.705239, 0.019722],
... | code_fim | hard | {
"lang": "python",
"repo": "ExcitedStates/qfit-3.0",
"path": "/src/qfit/atomsf.py",
"mode": "spm",
"license": "Artistic-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>, 1.086542],
[2.025174, 0.176650, 3.573822, 7.685848, 16.677574, 0.000000],
],
"Br1-": [
[17.714310, 6.466926, 6.947385, 4.402674, -0.697279, 1.152674],
[2.122554, 19.050768, 0.152708, 58.690361, 58.690372, 0.000000],
],
"Rb1+": [
[17.684320, 7.761588, 6.680... | code_fim | hard | {
"lang": "python",
"repo": "ExcitedStates/qfit-3.0",
"path": "/src/qfit/atomsf.py",
"mode": "spm",
"license": "Artistic-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: antoinemadec/coc-fzf path: /script/get_workspace_symbols.py
#!/usr/bin/env python3
import argparse
import re
from urllib.parse import unquote
from pynvim import attach
# --------------------------------------------------------------
# functions
# ----------------------------------------------... | code_fim | hard | {
"lang": "python",
"repo": "antoinemadec/coc-fzf",
"path": "/script/get_workspace_symbols.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def file_is_excluded(filename, exclude_re_patterns):
for pattern in exclude_re_patterns:
if re.match(pattern, filename):
return True
return False
# --------------------------------------------------------------
# execution
# ---------------------------------------------------... | code_fim | hard | {
"lang": "python",
"repo": "antoinemadec/coc-fzf",
"path": "/script/get_workspace_symbols.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> state = {
'job_counts': {
'pending': 0,
'running': 0,
'complete': 0
},
'jobs': {}
}
job_ids = list(self._jobs.keys())
for job_id in job_ids:
job = self._jobs[job_id]
... | code_fim | hard | {
"lang": "python",
"repo": "stjordanis/hither",
"path": "/hither2/scriptdir_runner.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> import yaml
self._directory = directory
self._jobs: Dict[str, ScriptDirRunnerJob] = {}
config_path = f'{directory}/config.yaml'
with open(config_path, 'r') as f:
config = yaml.safe_load(f)
def iterate(self):
jobs_path = f'{self._directory}/j... | code_fim | hard | {
"lang": "python",
"repo": "stjordanis/hither",
"path": "/hither2/scriptdir_runner.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stjordanis/hither path: /hither2/scriptdir_runner.py
import os
import shutil
from typing import Dict, Union
import json
class ScriptDirRunnerJob:
def __init__(self, directory):
import kachery_client as kc
self._directory = directory
self._status = ''
self._sc... | code_fim | hard | {
"lang": "python",
"repo": "stjordanis/hither",
"path": "/hither2/scriptdir_runner.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: brazil-data-cube/forest-monitor path: /forest_monitor/models/base_sql.py
# pylint: disable=E0239
from sqlalchemy import create_engine, MetaData
from sqlalchemy.ext.declarative import declarative_base
from forest_monitor.config import getCurrentConfig
def getDatabase():
database = create_e... | code_fim | hard | {
"lang": "python",
"repo": "brazil-data-cube/forest-monitor",
"path": "/forest_monitor/models/base_sql.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class BaseModel(declarative_base(metadata=MetaData()), DBO):
"""
Abstract class for ORM model.
Injects both `created_at` and `updated_at` fields in table
"""
__abstract__ = True
def __init__(self, **kwargs):
for key, value in kwargs.items():
setattr(self, key,... | code_fim | hard | {
"lang": "python",
"repo": "brazil-data-cube/forest-monitor",
"path": "/forest_monitor/models/base_sql.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># In [72]: env.action_space.
# env.action_space.contains env.action_space.n env.action_space.to_jsonable
# env.action_space.from_jsonable env.action_space.sample
# pick an action
action = env.action_space.sample()
# do an action
observation, reward, done, info = env.step(action)
# ... | code_fim | hard | {
"lang": "python",
"repo": "vicb1/deep-learning",
"path": "/1-notebook-examples/keras-udemy-course/rl2/gym_tutorial.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: vicb1/deep-learning path: /1-notebook-examples/keras-udemy-course/rl2/gym_tutorial.py
# https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python
# https://www.udemy.com/deep-reinforcement-learning-in-python
import gym
# Wiki:
# https://github.com/openai/gym/wiki/CartPole-v0
# Envir... | code_fim | medium | {
"lang": "python",
"repo": "vicb1/deep-learning",
"path": "/1-notebook-examples/keras-udemy-course/rl2/gym_tutorial.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># do an action
observation, reward, done, info = env.step(action)
# run through an episode
done = False
while not done:
observation, reward, done, _ = env.step(env.action_space.sample())<|fim_prefix|># repo: vicb1/deep-learning path: /1-notebook-examples/keras-udemy-course/rl2/gym_tutorial.py
# https... | code_fim | hard | {
"lang": "python",
"repo": "vicb1/deep-learning",
"path": "/1-notebook-examples/keras-udemy-course/rl2/gym_tutorial.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: materialsproject/dash-mp-components path: /dash_mp_components/test_api/utils.py
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
def resize_browser_window(width, height, drive... | code_fim | hard | {
"lang": "python",
"repo": "materialsproject/dash-mp-components",
"path": "/dash_mp_components/test_api/utils.py",
"mode": "psm",
"license": "0BSD",
"source": "the-stack-v2"
} |
<|fim_suffix|> action = ActionChains(driver)
action.move_to_element_with_offset(el, x, y)
action.perform()
# move to dedicated file
class element_has_css_class(object):
"""An expectation for checking that an element has a particular css class.
locator - used to find the element
returns the Web... | code_fim | hard | {
"lang": "python",
"repo": "materialsproject/dash-mp-components",
"path": "/dash_mp_components/test_api/utils.py",
"mode": "spm",
"license": "0BSD",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: koolhead17/subsample path: /subsample/main.py
'''
Sample lines from text files (for example, rows of a .csv or .tsv file)
from the command line.
'''
from __future__ import print_function
import argparse
from sys import stderr
from itertools import chain
import logging
import random
from .algor... | code_fim | hard | {
"lang": "python",
"repo": "koolhead17/subsample",
"path": "/subsample/main.py",
"mode": "psm",
"license": "Zlib",
"source": "the-stack-v2"
} |
<|fim_suffix|> if args.seed is not None:
random.seed(args.seed)
if args.approximate:
if args.fraction is None:
args.fraction = DEFAULT_FRACTION
sample = approximate_sample(fi, args.fraction)
elif args.two_pass:
if args.fraction:
sample = two_pass_samp... | code_fim | hard | {
"lang": "python",
"repo": "koolhead17/subsample",
"path": "/subsample/main.py",
"mode": "spm",
"license": "Zlib",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def test_time_classes_max_inline():
# test support for 64bit literals
dti = pd.DatetimeIndex(["2020-01-01", "2020-01-02", "2020-01-04", "2020-01-05"])
write_buffer(
{"root": dti},
write_kwargs={"all_array_storage": "inline"},
)<|fim_prefix|># repo: BAMWelDX/weldx path: /w... | code_fim | hard | {
"lang": "python",
"repo": "BAMWelDX/weldx",
"path": "/weldx/tests/asdf_tests/test_asdf_time.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BAMWelDX/weldx path: /weldx/tests/asdf_tests/test_asdf_time.py
"""Test time schema implementation."""
import numpy as np
import pandas as pd
import pytest
from weldx.asdf.util import write_buffer, write_read_buffer_context
from weldx.time import Time
@pytest.mark.parametrize(
"inputs",
... | code_fim | hard | {
"lang": "python",
"repo": "BAMWelDX/weldx",
"path": "/weldx/tests/asdf_tests/test_asdf_time.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> t1 = Time(inputs, time_ref)
with write_read_buffer_context({"root": t1}) as data:
t2 = data["root"]
assert t1.equals(t2)
def test_time_classes_max_inline():
# test support for 64bit literals
dti = pd.DatetimeIndex(["2020-01-01", "2020-01-02", "2020-01-04", "2020-01-05"])
... | code_fim | hard | {
"lang": "python",
"repo": "BAMWelDX/weldx",
"path": "/weldx/tests/asdf_tests/test_asdf_time.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: arewellborn/s2cnn path: /examples/molecules/run_experiment.py
# pylint: disable=E1101,R,C
import argparse
import torch
import torch.nn as nn
from torch.autograd import Variable
from s2cnn_model import S2CNNRegressor
from baseline_model import BaselineRegressor
from utils import load_data, IndexBa... | code_fim | hard | {
"lang": "python",
"repo": "arewellborn/s2cnn",
"path": "/examples/molecules/run_experiment.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
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