flax-community/t5-vae-python
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return "\n".join(new_body)
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date_format = '%Y-%m-%d-%H-%M-%S' if has_time else '%Y-%m-%d'
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help='Write additional debugging information '
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epoch_one, epoch_two,
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assert errback is not None
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delattr(self, '_lazy_sftp')
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raise ValueError("Error in config file.")
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assert issubclass(initialState.dtype.type, np.integer) and initialState.ndim==1, "initialState %r is not a one-dimensional integer numpy array" % initialState
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del ERROR_CLASS_MAP[klass]
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nos1 = ib1[0] * ps1 + os1 # real offset for image 1
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b = np.append(b, [self.get_single_score(p, centroids=c, sd=sd)])
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opts['file_roots'] = {'base': [syspaths.BASE_FILE_ROOTS_DIR]}
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display_name = '.'.join(components[1:])
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bSizer1 = wx.StaticBoxSizer(wx.StaticBox(self.panel, wx.ID_ANY, "Import data to working directory"), wx.HORIZONTAL)
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print("{:10} {}".format(*c))
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self._ar_keyword = self._format_keyword(split_line[1])
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options[constants.LABEL_CONFIG],
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sentence = self._sentence(random.randint(5, 16))
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items.append(_ETreeXmlToObject.fill_instance_element(item_element, item_type))
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self.add(servicegroup)
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return self.pin_mask(color, square) != BB_ALL
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return (out,err.value)
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cell.set_property('foreground', "white")
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ax.text(xy[0],xy[1],txt,fontdict=fontdict,color=clr,zorder=zorder)
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stamp, pos = load_le32(buf, pos)
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digits = list(map(int, string))
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response = requests.post(url, data=data, timeout=timeout)
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subplot(2, 3, 6)
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self.error(403)
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vote_update["count"] = VOTE_COUNT
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t = Token
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indices = np.array([], dtype=np.uint64)
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state_m = model if isinstance(model.core_element, State) else model.parent
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newivmlist.append(file[1])
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install(package, execute=not opts.dry_run)
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labels = [tr('Global (%s)'), tr('Do not report'), tr('Custom')]
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body.set('order', kwargs['order'])
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uninstallable=uninstallable,
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subscription=full_subscription, body={'ackIds': ack_ids}
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aggregate = list()
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result._bq_source_format = 'NEWLINE_DELIMITED_JSON'
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time_step = 0
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bk.write("mode: midWayWordInSentence(+SID,-WID).\n")
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array([ 11.])
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x = tf.layers.conv2d(x, output_filters, (3, 3), padding="SAME", name="conv2")
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lb = pst.parameter_data.parlbnd.copy()
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r = _apply(ramap[k], v, status=status, path=path+[k])
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new_panel['genes'] = new_genes
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url = self._url + "/lengths"
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magic_method_codes=MagRec["magic_method_codes"].split(":")
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path = try_
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route_map : folium.folium.Map
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r = _call_cache[key]
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_return_freqs=False,
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ttactor.SetCamera(followcam)
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dW1 = np.dot(dh.T, epx)
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regionfile : str
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self.msg("Please answer y or n.")
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xyz_x = 0.0
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count_no_shape_fit = 0
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time_array, [channel1, channel2]
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return np.sum(counts > 1) / float(counts.shape[0])
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started = super(Node, self).start(timeout=timeout)
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type_ = type_.lower()
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hobj.axon = [h.Section(name='axon[0]'), h.Section(name='axon[1]')]
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['author', 'description', 'title'],
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entity.put_annotation(key, value)
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raise Exception("Unable to find child node %s" % current_name)
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_logger.debug("Received command %s", message)
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check_args = ['checktrans']
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tf, note = tfi.create_db_entry(release.comment)
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fmtstr = '[generate2] executing {} {} tasks using {} {} procs'
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raise BinAsciiError(str(e))
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priority = GLib.PRIORITY_LOW
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self.modifiers[modifier] = False
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counts = sorted(cell_barcode_counts.values(), reverse=True)
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self._writer.write((self._username + '\n').encode('ascii'))
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relative_jimage = np.subtract(to_jimage, jimage)
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p = subprocess.Popen(scp_command)
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key_columns_array = []
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self._permutations = vars['permutations']
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dc = ioloop.DelayedCallback(purge, self.registration_timeout, self.loop)
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logger.debug("Not a valid JID: {0!r}".format(name))
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receiver=transceiver.receiver,
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analysis_id = "AN{}".format(source.zfill(6))
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ad_path = os.path.join(neurommsig_excel_dir, 'alzheimers', 'alzheimers.xlsx')
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raise ValueError("Can't write to file object %r" % fileobj)
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return recipes
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outfiles.append(Specout)
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[ float(only_warning_count), 'warning', 'had warnings' ],
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raise NameError('Unrecognised output request!')
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self.mgrremove.execute(conn, migration_rqst)
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self[key] = OrderedSet(self[key])
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date_tokens = dictionary.split(date_string)
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remove_headers = ('Content-Length',)
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filename, fileext = os.path.splitext(os.path.basename(rawfilename))
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errmsg = "'{}'; this pipeline can only deal with .bam, .fastq, " \
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letter
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chars.append(_escape_char(c, escape_char))
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return rnPattern
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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.
