tals/roberta_python
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msg += " as .tar files. See testing/test_data/fake_examples/celeb_a_hq "
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adjust_spines(ax, spines=['left','bottom'])
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pid['general']['WORKING_DIRECTORY'] = WORKING_DIRECTORY
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node: (offset_x + x, offset_y + y)
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execution = wps.execute(processid, inputs, output=outputs)
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return cls(func, name=name, **attrs)
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Teff = np.array((Teff,))
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output += value
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self.relax_core()
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all_rows.append(new_row)
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raise RuntimeError("Exception must be of BaseException type")
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section = parse_section_name(line)
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b = _compress_bytes(obj, level)
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request_method = request.META['REQUEST_METHOD']
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w_vec = np.zeros(x_n) # A vector of outcome weights
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f_log_avg_u += dotprod - stop(dotprod) # Add zeros_like(dot_prod).
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use_request_uri=use_request_uri,
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subadres.huisnummer_id is None
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SdotST = np.einsum('it,jt->ij', sinv, sinv)
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function = function_code_to_function_map[function_code]
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tr('Clothing')
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import_path = os.path.join(import_root, filepath)
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fields['channel'] = channel
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msg = ExtendedReceive.from_raw_message(msgraw)
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mmol_string = r.text
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geom = trimesh.creation.box((0.5, 0.5, 0.01))
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current_branch = [l for l in lines if l.startswith('* ')][0]
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return EncryptedPassportElement(**data)
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init_kwargs['long_description'] = str(readme_text)
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config = super(MQTTHandler, self).get_default_config_help()
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yvals = np.log10(model.recurrence.occur_rates)
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failhard=failhard,
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r.env.user = username
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ret['chunk_count'] = sym[CHUNK_COUNT]
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return string.format(self.name)
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a = input(prompt).lower()
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close = QtCore.QRegExp(close)
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file_subgroup.append(file_path2)
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ipam_pool = docker.types.IPAMPool(subnet=subnet_cidr)
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raise ValueError("Must specify a polygon to fill points with")
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v = _find_value(key, item)
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lines = [l.strip() for l in output if l.strip()]
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gr.send({'text': json.dumps(knock)})
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figsize = (5 + (1 if num_cax > 0 else 0), 5)
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return y - savgol_filter(y, win, 2) + np.nanmedian(y)
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K : ndarray(n, k)
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pairs_df = pairs_df[pairs_df['index_x'] > pairs_df['index_y']]
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row = [i, u'en']
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fpos = fpos + int(header['block9']['blocklength'])
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self.log.debug("ConfigMap %s deleted", cm_key)
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raise DailymotionClientError('Missing username or password in grant info for password grant type.')
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pars = page.data.get('extext')
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labels = self._varargs_as_labels(column_or_columns)
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self.signed_in.set('\n'.join(sorted(names)))
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run.url = url
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youtube_id : str
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StackName=stack_name, NextToken=next_token
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registry_value = registry_key.GetValueByName('F')
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admin_request_announcement_email(request, form, ann)
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startp=startp_gls,
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reader = csv.reader(eol_checker, skipinitialspace=True)
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return NoCountPage(items, page_number, page_size, has_next)
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log.debug("No task '%s' registration action for '%s' event", self._name, ev_type)
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kwargs["task_class"] = ScrTask
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tuning_count += step_method.tune(verbose=self.verbose)
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matching_full_hashes = set()
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config = copy.deepcopy(defaults)
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i = int(s, 10)
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parameters = request.get_nonoauth_parameters()
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return child_message
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ratio = width * 1. / bpwidth
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plt.plot([-fb-(n+1)*fs, -fb-(n+1)*fs],line_ampl,'--g', linewidth=2)
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self.resize(event.width, event.height)
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batch = tz.get_in(("metadata", "batch"), data)
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logger.debug('command websocket connected to %s', "ws://{}:{}".format(self.ip, self.port));
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gs_blocks_dir = os.path.join(data_dir, GOLD_STANDARD_BLOCKS_DIRNAME)
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rtn_format="json",
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sfile = pmag.get_named_arg("-f", reqd=True)
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obsen_lam = []
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lumi_list = self.dbsUtils2.decodeLumiIntervals(lumi_list)
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b.append(BedLine(m.bedline))
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end_mark = attr_node.yaml_node.end_mark
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print('There are orbit breaks right next to each other')
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raise Dump2PolarionException("No results read from CSV file '{}'".format(csv_file))
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cte = str(msg.get('content-transfer-encoding', '')).lower()
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out_f.write(content)
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print_warnings : bool
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sub = r.pubsub(ignore_subscribe_messages=True)
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return course, course.get_task(taskid)
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assert self.pay_partner_id, "PAY_PARTNER_ID IS EMPTY"
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logging.info('Done.')
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print('ERROR IN DELETE')
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savename = savename[:-4] + "_" + extra_title + ".png"
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rs = np.linalg.norm(self.coords_for_computations, axis=1)
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userena.send_activation_email()
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supersnps[hslice:hslice+optim, :, :] = arr
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break
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g = rbh_network(id2desc, rbh, file_name = 'rbh.network.edges.txt')
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item = (x, tuple(deps))
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D = call_MediaInfo(file_name, mediainfo_path)
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Dataset of single lines of Python code taken from the CodeSearchNet dataset.
Context
This dataset allows checking the validity of Variational-Autoencoder latent spaces by testing what percentage of random/intermediate latent points can be greedily decoded into valid Python code.
Content
Each row has a parsable line of source code. {'text': '{python source code line}'}
Most lines are < 100 characters while all are under 125 characters.
Contains 2.6 million lines.
All code is in parsable into a python3 ast.