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def _inferSchemaFromList(self, data, names=None):
""" Infer schema from list of Row or tuple. :param data: list of Row or tuple :param names: list of column name... |
if not data:
raise ValueError("can not infer schema from empty dataset")
first = data[0]
if type(first) is dict:
warnings.warn("inferring schema from dict is deprecated,"
"please use pyspark.sql.Row instead")
schema = reduce(_merge_type,... |
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def _inferSchema(self, rdd, samplingRatio=None, names=None):
""" Infer schema from an RDD of Row or tuple. :param rdd: an RDD of Row or tuple :param samplingRati... |
first = rdd.first()
if not first:
raise ValueError("The first row in RDD is empty, "
"can not infer schema")
if type(first) is dict:
warnings.warn("Using RDD of dict to inferSchema is deprecated. "
"Use pyspark.sql.R... |
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def _createFromRDD(self, rdd, schema, samplingRatio):
""" Create an RDD for DataFrame from an existing RDD, returns the RDD and schema. """ |
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
converter = _create_converter(struct)
rdd = rdd.map(converter)
if isinstance(schema, (list, tuple)):
for i, name in enumerate(sche... |
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def _createFromLocal(self, data, schema):
""" Create an RDD for DataFrame from a list or pandas.DataFrame, returns the RDD and schema. """ |
# make sure data could consumed multiple times
if not isinstance(data, list):
data = list(data)
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchemaFromList(data, names=schema)
converter = _create_converter(struct)
d... |
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def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
""" Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting t... |
from pyspark.serializers import ArrowStreamPandasSerializer
from pyspark.sql.types import from_arrow_type, to_arrow_type, TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
r... |
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def _create_shell_session():
""" Initialize a SparkSession for a pyspark shell session. This is called from shell.py to make error handling simpler without needi... |
import py4j
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
try:
# Try to access HiveConf, it will raise exception if Hive is not added
conf = SparkConf()
if conf.get('spark.sql.catalogImplementation', 'hive').lower() == 'h... |
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def _restore(name, fields, value):
""" Restore an object of namedtuple""" |
k = (name, fields)
cls = __cls.get(k)
if cls is None:
cls = collections.namedtuple(name, fields)
__cls[k] = cls
return cls(*value) |
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def _hack_namedtuple(cls):
""" Make class generated by namedtuple picklable """ |
name = cls.__name__
fields = cls._fields
def __reduce__(self):
return (_restore, (name, fields, tuple(self)))
cls.__reduce__ = __reduce__
cls._is_namedtuple_ = True
return cls |
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def load_stream(self, stream):
""" Load a stream of un-ordered Arrow RecordBatches, where the last iteration yields a list of indices that can be used to put the... |
# load the batches
for batch in self.serializer.load_stream(stream):
yield batch
# load the batch order indices
num = read_int(stream)
batch_order = []
for i in xrange(num):
index = read_int(stream)
batch_order.append(index)
y... |
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def _create_batch(self, series):
""" Create an Arrow record batch from the given pandas.Series or list of Series, with optional type. :param series: A single pan... |
import pandas as pd
import pyarrow as pa
from pyspark.sql.types import _check_series_convert_timestamps_internal
# Make input conform to [(series1, type1), (series2, type2), ...]
if not isinstance(series, (list, tuple)) or \
(len(series) == 2 and isinstance(serie... |
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def dump_stream(self, iterator, stream):
""" Make ArrowRecordBatches from Pandas Series and serialize. Input is a single series or a list of series accompanied b... |
batches = (self._create_batch(series) for series in iterator)
super(ArrowStreamPandasSerializer, self).dump_stream(batches, stream) |
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def load_stream(self, stream):
""" Deserialize ArrowRecordBatches to an Arrow table and return as a list of pandas.Series. """ |
batches = super(ArrowStreamPandasSerializer, self).load_stream(stream)
import pyarrow as pa
for batch in batches:
yield [self.arrow_to_pandas(c) for c in pa.Table.from_batches([batch]).itercolumns()] |
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def dump_stream(self, iterator, stream):
""" Override because Pandas UDFs require a START_ARROW_STREAM before the Arrow stream is sent. This should be sent after... |
def init_stream_yield_batches():
should_write_start_length = True
for series in iterator:
batch = self._create_batch(series)
if should_write_start_length:
write_int(SpecialLengths.START_ARROW_STREAM, stream)
should... |
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def trigger(self, processingTime=None, once=None, continuous=None):
"""Set the trigger for the stream query. If this is not set it will run the query as fast as ... |
params = [processingTime, once, continuous]
if params.count(None) == 3:
raise ValueError('No trigger provided')
elif params.count(None) < 2:
raise ValueError('Multiple triggers not allowed.')
jTrigger = None
if processingTime is not None:
if... |
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def foreach(self, f):
""" Sets the output of the streaming query to be processed using the provided writer ``f``. This is often used to write the output of a str... |
from pyspark.rdd import _wrap_function
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.taskcontext import TaskContext
if callable(f):
# The provided object is a callable function that is supposed to be called on each row.
# ... |
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def dumps(obj, protocol=None):
"""Serialize obj as a string of bytes allocated in memory protocol defaults to cloudpickle.DEFAULT_PROTOCOL which is an alias to p... |
file = StringIO()
try:
cp = CloudPickler(file, protocol=protocol)
cp.dump(obj)
return file.getvalue()
finally:
file.close() |
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def _fill_function(*args):
"""Fills in the rest of function data into the skeleton function object The skeleton itself is create by _make_skel_func(). """ |
if len(args) == 2:
func = args[0]
state = args[1]
elif len(args) == 5:
# Backwards compat for cloudpickle v0.4.0, after which the `module`
# argument was introduced
func = args[0]
keys = ['globals', 'defaults', 'dict', 'closure_values']
state = dict(zip(k... |
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def _is_dynamic(module):
""" Return True if the module is special module that cannot be imported by its name. """ |
# Quick check: module that have __file__ attribute are not dynamic modules.
if hasattr(module, '__file__'):
return False
if hasattr(module, '__spec__'):
return module.__spec__ is None
else:
# Backward compat for Python 2
import imp
try:
path = None
... |
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def save_function(self, obj, name=None):
""" Registered with the dispatch to handle all function types. Determines what kind of function obj is (e.g. lambda, def... |
try:
should_special_case = obj in _BUILTIN_TYPE_CONSTRUCTORS
except TypeError:
# Methods of builtin types aren't hashable in python 2.
should_special_case = False
if should_special_case:
# We keep a special-cased cache of built-in type constructo... |
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def save_inst(self, obj):
"""Inner logic to save instance. Based off pickle.save_inst""" |
cls = obj.__class__
# Try the dispatch table (pickle module doesn't do it)
f = self.dispatch.get(cls)
if f:
f(self, obj) # Call unbound method with explicit self
return
memo = self.memo
write = self.write
save = self.save
if ha... |
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def _copy_new_parent(self, parent):
"""Copy the current param to a new parent, must be a dummy param.""" |
if self.parent == "undefined":
param = copy.copy(self)
param.parent = parent.uid
return param
else:
raise ValueError("Cannot copy from non-dummy parent %s." % parent) |
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def toList(value):
""" Convert a value to a list, if possible. """ |
if type(value) == list:
return value
elif type(value) in [np.ndarray, tuple, xrange, array.array]:
return list(value)
elif isinstance(value, Vector):
return list(value.toArray())
else:
raise TypeError("Could not convert %s to list" % value... |
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def toListFloat(value):
""" Convert a value to list of floats, if possible. """ |
if TypeConverters._can_convert_to_list(value):
value = TypeConverters.toList(value)
if all(map(lambda v: TypeConverters._is_numeric(v), value)):
return [float(v) for v in value]
raise TypeError("Could not convert %s to list of floats" % value) |
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def toListInt(value):
""" Convert a value to list of ints, if possible. """ |
if TypeConverters._can_convert_to_list(value):
value = TypeConverters.toList(value)
if all(map(lambda v: TypeConverters._is_integer(v), value)):
return [int(v) for v in value]
raise TypeError("Could not convert %s to list of ints" % value) |
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def toListString(value):
""" Convert a value to list of strings, if possible. """ |
if TypeConverters._can_convert_to_list(value):
value = TypeConverters.toList(value)
if all(map(lambda v: TypeConverters._can_convert_to_string(v), value)):
return [TypeConverters.toString(v) for v in value]
raise TypeError("Could not convert %s to list of strings... |
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def toVector(value):
""" Convert a value to a MLlib Vector, if possible. """ |
if isinstance(value, Vector):
return value
elif TypeConverters._can_convert_to_list(value):
value = TypeConverters.toList(value)
if all(map(lambda v: TypeConverters._is_numeric(v), value)):
return DenseVector(value)
raise TypeError("Could not ... |
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def toString(value):
""" Convert a value to a string, if possible. """ |
if isinstance(value, basestring):
return value
elif type(value) in [np.string_, np.str_]:
return str(value)
elif type(value) == np.unicode_:
return unicode(value)
else:
raise TypeError("Could not convert %s to string type" % type(value)) |
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def _copy_params(self):
""" Copy all params defined on the class to current object. """ |
cls = type(self)
src_name_attrs = [(x, getattr(cls, x)) for x in dir(cls)]
src_params = list(filter(lambda nameAttr: isinstance(nameAttr[1], Param), src_name_attrs))
for name, param in src_params:
setattr(self, name, param._copy_new_parent(self)) |
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def explainParam(self, param):
""" Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string. """ |
param = self._resolveParam(param)
values = []
if self.isDefined(param):
if param in self._defaultParamMap:
values.append("default: %s" % self._defaultParamMap[param])
if param in self._paramMap:
values.append("current: %s" % self._paramMap... |
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def getParam(self, paramName):
""" Gets a param by its name. """ |
param = getattr(self, paramName)
if isinstance(param, Param):
return param
else:
raise ValueError("Cannot find param with name %s." % paramName) |
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def isSet(self, param):
""" Checks whether a param is explicitly set by user. """ |
param = self._resolveParam(param)
return param in self._paramMap |
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def hasDefault(self, param):
""" Checks whether a param has a default value. """ |
param = self._resolveParam(param)
return param in self._defaultParamMap |
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def getOrDefault(self, param):
""" Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set. """ |
param = self._resolveParam(param)
if param in self._paramMap:
return self._paramMap[param]
else:
return self._defaultParamMap[param] |
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def set(self, param, value):
""" Sets a parameter in the embedded param map. """ |
self._shouldOwn(param)
try:
value = param.typeConverter(value)
except ValueError as e:
raise ValueError('Invalid param value given for param "%s". %s' % (param.name, e))
self._paramMap[param] = value |
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def _shouldOwn(self, param):
""" Validates that the input param belongs to this Params instance. """ |
if not (self.uid == param.parent and self.hasParam(param.name)):
raise ValueError("Param %r does not belong to %r." % (param, self)) |
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def _resolveParam(self, param):
""" Resolves a param and validates the ownership. :param param: param name or the param instance, which must belong to this Param... |
if isinstance(param, Param):
self._shouldOwn(param)
return param
elif isinstance(param, basestring):
return self.getParam(param)
else:
raise ValueError("Cannot resolve %r as a param." % param) |
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def _set(self, **kwargs):
""" Sets user-supplied params. """ |
for param, value in kwargs.items():
p = getattr(self, param)
if value is not None:
try:
value = p.typeConverter(value)
except TypeError as e:
raise TypeError('Invalid param value given for param "%s". %s' % (p.name,... |
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def _setDefault(self, **kwargs):
""" Sets default params. """ |
for param, value in kwargs.items():
p = getattr(self, param)
if value is not None and not isinstance(value, JavaObject):
try:
value = p.typeConverter(value)
except TypeError as e:
raise TypeError('Invalid default pa... |
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def _copyValues(self, to, extra=None):
""" Copies param values from this instance to another instance for params shared by them. :param to: the target instance :... |
paramMap = self._paramMap.copy()
if extra is not None:
paramMap.update(extra)
for param in self.params:
# copy default params
if param in self._defaultParamMap and to.hasParam(param.name):
to._defaultParamMap[to.getParam(param.name)] = self._d... |
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def _to_java_object_rdd(rdd):
""" Return an JavaRDD of Object by unpickling It will convert each Python object into Java object by Pyrolite, whenever the RDD is ... |
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
return rdd.ctx._jvm.org.apache.spark.ml.python.MLSerDe.pythonToJava(rdd._jrdd, True) |
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def value(self):
""" Return the broadcasted value """ |
if not hasattr(self, "_value") and self._path is not None:
# we only need to decrypt it here when encryption is enabled and
# if its on the driver, since executor decryption is handled already
if self._sc is not None and self._sc._encryption_enabled:
port, au... |
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def unpersist(self, blocking=False):
""" Delete cached copies of this broadcast on the executors. If the broadcast is used after this is called, it will need to ... |
if self._jbroadcast is None:
raise Exception("Broadcast can only be unpersisted in driver")
self._jbroadcast.unpersist(blocking) |
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def destroy(self, blocking=False):
""" Destroy all data and metadata related to this broadcast variable. Use this with caution; once a broadcast variable has bee... |
if self._jbroadcast is None:
raise Exception("Broadcast can only be destroyed in driver")
self._jbroadcast.destroy(blocking)
os.unlink(self._path) |
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def _wrapped(self):
""" Wrap this udf with a function and attach docstring from func """ |
# It is possible for a callable instance without __name__ attribute or/and
# __module__ attribute to be wrapped here. For example, functools.partial. In this case,
# we should avoid wrapping the attributes from the wrapped function to the wrapper
# function. So, we take out these attri... |
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def registerJavaFunction(self, name, javaClassName, returnType=None):
"""Register a Java user-defined function as a SQL function. In addition to a name and the f... |
jdt = None
if returnType is not None:
if not isinstance(returnType, DataType):
returnType = _parse_datatype_string(returnType)
jdt = self.sparkSession._jsparkSession.parseDataType(returnType.json())
self.sparkSession._jsparkSession.udf().registerJava(nam... |
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def registerJavaUDAF(self, name, javaClassName):
"""Register a Java user-defined aggregate function as a SQL function. :param name: name of the user-defined aggr... |
self.sparkSession._jsparkSession.udf().registerJavaUDAF(name, javaClassName) |
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def getOrCreate(cls, checkpointPath, setupFunc):
""" Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. If checkpoint data... |
cls._ensure_initialized()
gw = SparkContext._gateway
# Check whether valid checkpoint information exists in the given path
ssc_option = gw.jvm.StreamingContextPythonHelper().tryRecoverFromCheckpoint(checkpointPath)
if ssc_option.isEmpty():
ssc = setupFunc()
... |
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def awaitTermination(self, timeout=None):
""" Wait for the execution to stop. @param timeout: time to wait in seconds """ |
if timeout is None:
self._jssc.awaitTermination()
else:
self._jssc.awaitTerminationOrTimeout(int(timeout * 1000)) |
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def stop(self, stopSparkContext=True, stopGraceFully=False):
""" Stop the execution of the streams, with option of ensuring all received data has been processed.... |
self._jssc.stop(stopSparkContext, stopGraceFully)
StreamingContext._activeContext = None
if stopSparkContext:
self._sc.stop() |
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def textFileStream(self, directory):
""" Create an input stream that monitors a Hadoop-compatible file system for new files and reads them as text files. Files m... |
return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer()) |
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def binaryRecordsStream(self, directory, recordLength):
""" Create an input stream that monitors a Hadoop-compatible file system for new files and reads them as ... |
return DStream(self._jssc.binaryRecordsStream(directory, recordLength), self,
NoOpSerializer()) |
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def queueStream(self, rdds, oneAtATime=True, default=None):
""" Create an input stream from a queue of RDDs or list. In each batch, it will process either one or... |
if default and not isinstance(default, RDD):
default = self._sc.parallelize(default)
if not rdds and default:
rdds = [rdds]
if rdds and not isinstance(rdds[0], RDD):
rdds = [self._sc.parallelize(input) for input in rdds]
self._check_serializers(rdds... |
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def transform(self, dstreams, transformFunc):
""" Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams. The order o... |
jdstreams = [d._jdstream for d in dstreams]
# change the final serializer to sc.serializer
func = TransformFunction(self._sc,
lambda t, *rdds: transformFunc(rdds),
*[d._jrdd_deserializer for d in dstreams])
jfunc = self._... |
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def union(self, *dstreams):
""" Create a unified DStream from multiple DStreams of the same type and same slide duration. """ |
if not dstreams:
raise ValueError("should have at least one DStream to union")
if len(dstreams) == 1:
return dstreams[0]
if len(set(s._jrdd_deserializer for s in dstreams)) > 1:
raise ValueError("All DStreams should have same serializer")
if len(set(s... |
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def from_json_file(cls, json_file):
"""Constructs a `GPT2Config` from a json file of parameters.""" |
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return cls.from_dict(json.loads(text)) |
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def to_json_file(self, json_file_path):
""" Save this instance to a json file.""" |
with open(json_file_path, "w", encoding='utf-8') as writer:
writer.write(self.to_json_string()) |
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def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputFeature`s.""" |
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so t... |
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def read_examples(input_file):
"""Read a list of `InputExample`s from an input file.""" |
examples = []
unique_id = 0
with open(input_file, "r", encoding='utf-8') as reader:
while True:
line = reader.readline()
if not line:
break
line = line.strip()
text_a = None
text_b = None
m = re.match(r"^(.*) \|... |
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def read_squad_examples(input_file, is_training, version_2_with_negative):
"""Read a SQuAD json file into a list of SquadExample.""" |
with open(input_file, "r", encoding='utf-8') as reader:
input_data = json.load(reader)["data"]
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
return True
return False
examples = []
for entry in input_data:
for ... |
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
"""Returns tokenized answer spans that better match the annotated answ... |
# The SQuAD annotations are character based. We first project them to
# whitespace-tokenized words. But then after WordPiece tokenization, we can
# often find a "better match". For example:
#
# Question: What year was John Smith born?
# Context: The leader was John Smith (1895-1943).
#... |
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def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token.""" |
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ... |
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def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list.""" |
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes |
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def _compute_softmax(scores):
"""Compute softmax probability over raw logits.""" |
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum +... |
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def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file.""" |
with open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0] == 2:
line = list(unicode(cell, 'utf-8') for cell in line)
... |
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def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets.""" |
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[3]
text_b = line[4]
label = line[0]
examples.append(
InputExample(guid=guid, text_a=t... |
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def from_dict(cls, json_object):
"""Constructs a `OpenAIGPTConfig` from a Python dictionary of parameters.""" |
config = OpenAIGPTConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config |
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| def set_num_special_tokens(self, num_special_tokens):
" Update input embeddings with new embedding matrice if needed "
if self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_tokens
# Build new embeddings and initiali... |
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def set_num_special_tokens(self, num_special_tokens):
""" Update input and output embeddings with new embedding matrice Make sure we are sharing the embeddings "... |
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight) |
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def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab.""" |
ids = []
for token in tokens:
ids.append(self.vocab[token])
if len(ids) > self.max_len:
logger.warning(
"Token indices sequence length is longer than the specified maximum "
" sequence length for this BERT model ({} > {}). Running this"
... |
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def convert_ids_to_tokens(self, ids):
"""Converts a sequence of ids in wordpiece tokens using the vocab.""" |
tokens = []
for i in ids:
tokens.append(self.ids_to_tokens[i])
return tokens |
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def save_vocabulary(self, vocab_path):
"""Save the tokenizer vocabulary to a directory or file.""" |
index = 0
if os.path.isdir(vocab_path):
vocab_file = os.path.join(vocab_path, VOCAB_NAME)
with open(vocab_file, "w", encoding="utf-8") as writer:
for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
if index != token_index:
... |
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def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs):
""" Instantiate a PreTrainedBertModel from a pre-trained model file. ... |
if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP:
vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path]
if '-cased' in pretrained_model_name_or_path and kwargs.get('do_lower_case', True):
logger.warning("The pre-trained model you are lo... |
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def _run_strip_accents(self, text):
"""Strips accents from a piece of text.""" |
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output) |
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def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character.""" |
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output) |
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def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character.""" |
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabe... |
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def get_next_line(self):
""" Gets next line of random_file and starts over when reaching end of file""" |
try:
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
self.current_random_doc = self.current_random_doc + 1
line = next(self.random_file).st... |
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def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_list):
"""Creates the predictions for the masked LM objective. This is mo... |
cand_indices = []
for (i, token) in enumerate(tokens):
if token == "[CLS]" or token == "[SEP]":
continue
cand_indices.append(i)
num_to_mask = min(max_predictions_per_seq,
max(1, int(round(len(tokens) * masked_lm_prob))))
shuffle(cand_indices)
mask_... |
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def build_tf_to_pytorch_map(model, config):
""" A map of modules from TF to PyTorch. This time I use a map to keep the PyTorch model as identical to the original... |
tf_to_pt_map = {}
if hasattr(model, 'transformer'):
# We are loading in a TransfoXLLMHeadModel => we will load also the Adaptive Softmax
tf_to_pt_map.update({
"transformer/adaptive_softmax/cutoff_0/cluster_W": model.crit.cluster_weight,
"transformer/adaptive_softmax/cut... |
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def to_offset(freq):
""" Return DateOffset object from string or tuple representation or datetime.timedelta object Parameters freq : str, tuple, datetime.timedel... |
if freq is None:
return None
if isinstance(freq, DateOffset):
return freq
if isinstance(freq, tuple):
name = freq[0]
stride = freq[1]
if isinstance(stride, str):
name, stride = stride, name
name, _ = libfreqs._base_and_stride(name)
delta... |
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def get_offset(name):
""" Return DateOffset object associated with rule name Examples -------- get_offset('EOM') --> BMonthEnd(1) """ |
if name not in libfreqs._dont_uppercase:
name = name.upper()
name = libfreqs._lite_rule_alias.get(name, name)
name = libfreqs._lite_rule_alias.get(name.lower(), name)
else:
name = libfreqs._lite_rule_alias.get(name, name)
if name not in _offset_map:
try:
... |
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def infer_freq(index, warn=True):
""" Infer the most likely frequency given the input index. If the frequency is uncertain, a warning will be printed. Parameters... |
import pandas as pd
if isinstance(index, ABCSeries):
values = index._values
if not (is_datetime64_dtype(values) or
is_timedelta64_dtype(values) or
values.dtype == object):
raise TypeError("cannot infer freq from a non-convertible dtype "
... |
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def get_freq(self):
""" Find the appropriate frequency string to describe the inferred frequency of self.values Returns ------- str or None """ |
if not self.is_monotonic or not self.index._is_unique:
return None
delta = self.deltas[0]
if _is_multiple(delta, _ONE_DAY):
return self._infer_daily_rule()
# Business hourly, maybe. 17: one day / 65: one weekend
if self.hour_deltas in ([1, 17], [1, 65],... |
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def load(fh, encoding=None, is_verbose=False):
"""load a pickle, with a provided encoding if compat is True: fake the old class hierarchy if it works, then retur... |
try:
fh.seek(0)
if encoding is not None:
up = Unpickler(fh, encoding=encoding)
else:
up = Unpickler(fh)
up.is_verbose = is_verbose
return up.load()
except (ValueError, TypeError):
raise |
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def ensure_index_from_sequences(sequences, names=None):
""" Construct an index from sequences of data. A single sequence returns an Index. Many sequences returns... |
from .multi import MultiIndex
if len(sequences) == 1:
if names is not None:
names = names[0]
return Index(sequences[0], name=names)
else:
return MultiIndex.from_arrays(sequences, names=names) |
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def ensure_index(index_like, copy=False):
""" Ensure that we have an index from some index-like object. Parameters index : sequence An Index or other sequence co... |
if isinstance(index_like, Index):
if copy:
index_like = index_like.copy()
return index_like
if hasattr(index_like, 'name'):
return Index(index_like, name=index_like.name, copy=copy)
if is_iterator(index_like):
index_like = list(index_like)
# must check for ... |
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def _simple_new(cls, values, name=None, dtype=None, **kwargs):
""" We require that we have a dtype compat for the values. If we are passed a non-dtype compat, th... |
if not hasattr(values, 'dtype'):
if (values is None or not len(values)) and dtype is not None:
values = np.empty(0, dtype=dtype)
else:
values = np.array(values, copy=False)
if is_object_dtype(values):
values = cls(value... |
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def _shallow_copy_with_infer(self, values, **kwargs):
""" Create a new Index inferring the class with passed value, don't copy the data, use the same object attr... |
attributes = self._get_attributes_dict()
attributes.update(kwargs)
attributes['copy'] = False
if not len(values) and 'dtype' not in kwargs:
attributes['dtype'] = self.dtype
if self._infer_as_myclass:
try:
return self._constructor(values, *... |
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def is_(self, other):
""" More flexible, faster check like ``is`` but that works through views. Note: this is *not* the same as ``Index.identical()``, which chec... |
# use something other than None to be clearer
return self._id is getattr(
other, '_id', Ellipsis) and self._id is not None |
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def _assert_take_fillable(self, values, indices, allow_fill=True, fill_value=None, na_value=np.nan):
""" Internal method to handle NA filling of take. """ |
indices = ensure_platform_int(indices)
# only fill if we are passing a non-None fill_value
if allow_fill and fill_value is not None:
if (indices < -1).any():
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1... |
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def _format_data(self, name=None):
""" Return the formatted data as a unicode string. """ |
# do we want to justify (only do so for non-objects)
is_justify = not (self.inferred_type in ('string', 'unicode') or
(self.inferred_type == 'categorical' and
is_object_dtype(self.categories)))
return format_object_summary(self, self._forma... |
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def format(self, name=False, formatter=None, **kwargs):
""" Render a string representation of the Index. """ |
header = []
if name:
header.append(pprint_thing(self.name,
escape_chars=('\t', '\r', '\n')) if
self.name is not None else '')
if formatter is not None:
return header + list(self.map(formatter))
re... |
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def to_native_types(self, slicer=None, **kwargs):
""" Format specified values of `self` and return them. Parameters slicer : int, array-like An indexer into `sel... |
values = self
if slicer is not None:
values = values[slicer]
return values._format_native_types(**kwargs) |
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def _format_native_types(self, na_rep='', quoting=None, **kwargs):
""" Actually format specific types of the index. """ |
mask = isna(self)
if not self.is_object() and not quoting:
values = np.asarray(self).astype(str)
else:
values = np.array(self, dtype=object, copy=True)
values[mask] = na_rep
return values |
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def to_series(self, index=None, name=None):
""" Create a Series with both index and values equal to the index keys useful with map for returning an indexer based... |
from pandas import Series
if index is None:
index = self._shallow_copy()
if name is None:
name = self.name
return Series(self.values.copy(), index=index, name=name) |
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def to_frame(self, index=True, name=None):
""" Create a DataFrame with a column containing the Index. .. versionadded:: 0.24.0 Parameters index : boolean, defaul... |
from pandas import DataFrame
if name is None:
name = self.name or 0
result = DataFrame({name: self._values.copy()})
if index:
result.index = self
return result |
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def _validate_names(self, name=None, names=None, deep=False):
""" Handles the quirks of having a singular 'name' parameter for general Index and plural 'names' p... |
from copy import deepcopy
if names is not None and name is not None:
raise TypeError("Can only provide one of `names` and `name`")
elif names is None and name is None:
return deepcopy(self.names) if deep else self.names
elif names is not None:
if not ... |
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def set_names(self, names, level=None, inplace=False):
""" Set Index or MultiIndex name. Able to set new names partially and by level. Parameters names : label o... |
if level is not None and not isinstance(self, ABCMultiIndex):
raise ValueError('Level must be None for non-MultiIndex')
if level is not None and not is_list_like(level) and is_list_like(
names):
msg = "Names must be a string when a single level is provided."
... |
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def rename(self, name, inplace=False):
""" Alter Index or MultiIndex name. Able to set new names without level. Defaults to returning new index. Length of names ... |
return self.set_names([name], inplace=inplace) |
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def _validate_index_level(self, level):
""" Validate index level. For single-level Index getting level number is a no-op, but some verification must be done like... |
if isinstance(level, int):
if level < 0 and level != -1:
raise IndexError("Too many levels: Index has only 1 level,"
" %d is not a valid level number" % (level, ))
elif level > 0:
raise IndexError("Too many levels:"
... |
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def sortlevel(self, level=None, ascending=True, sort_remaining=None):
""" For internal compatibility with with the Index API. Sort the Index. This is for compat ... |
return self.sort_values(return_indexer=True, ascending=ascending) |
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