uid stringlengths 24 24 | split stringclasses 1
value | category stringclasses 2
values | content stringlengths 5 482k | signature stringlengths 1 14k | suffix stringlengths 1 482k | prefix stringlengths 9 14k | prefix_token_count int64 3 5.01k | prefix_token_budget int64 64 256 | element_token_count int64 1 292k | signature_token_count int64 1 5.01k | prefix_context_token_count int64 0 255 | repo stringlengths 7 112 | path stringlengths 4 208 | language stringclasses 1
value | name stringlengths 1 218 | qualname stringlengths 1 218 | start_line int64 1 26.7k | end_line int64 1 26.7k | signature_start_line int64 1 26.7k | signature_end_line int64 1 26.7k | source_hash stringlengths 40 40 | source_dataset stringclasses 1
value | source_split stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fa137a44e89ab9868ebf69a6 | train | function | def test_indent():
for description, input, output in (
("Sanity check: 1 line string",
'Test', ' Test'),
("List of strings turns in to strings joined by \\n",
["Test", "Test"], ' Test\n Test'),
):
eq_.description = "indent(): %s" % description
yie... | def test_indent():
| for description, input, output in (
("Sanity check: 1 line string",
'Test', ' Test'),
("List of strings turns in to strings joined by \\n",
["Test", "Test"], ' Test\n Test'),
):
eq_.description = "indent(): %s" % description
yield eq_, indent(inpu... | @mock_streams('stderr')
@with_patched_object(output, 'warnings', True)
def test_warn():
"""
warn() should print 'Warning' plus given text
"""
warn("Test")
assert "\nWarning: Test\n\n" == sys.stderr.getvalue()
def test_indent():
| 64 | 64 | 91 | 4 | 60 | objectified/fabric | tests/test_utils.py | Python | test_indent | test_indent | 26 | 35 | 26 | 26 | 6e8a71e4234fae1545c1bc1cdd8cbee382f729c8 | bigcode/the-stack | train |
4a37b16eb79719f123b04fff | train | function | @with_fakes
def test_fastprint_calls_puts():
"""
fastprint() is just an alias to puts()
"""
text = "Some output"
fake_puts = Fake('puts', expect_call=True).with_args(
text=text, show_prefix=False, end="", flush=True
)
with patched_context(utils, 'puts', fake_puts):
fastprint(... | @with_fakes
def test_fastprint_calls_puts():
| """
fastprint() is just an alias to puts()
"""
text = "Some output"
fake_puts = Fake('puts', expect_call=True).with_args(
text=text, show_prefix=False, end="", flush=True
)
with patched_context(utils, 'puts', fake_puts):
fastprint(text)
| with env.host_string if show_prefix is False
"""
s = "my output"
h = "localhost"
puts(s, show_prefix=False)
eq_(sys.stdout.getvalue(), "%s" % (s + "\n"))
@with_fakes
def test_fastprint_calls_puts():
| 64 | 64 | 84 | 13 | 51 | objectified/fabric | tests/test_utils.py | Python | test_fastprint_calls_puts | test_fastprint_calls_puts | 118 | 128 | 118 | 119 | f237ceb605e79c9edfb4596efdc8827cd5b1b99d | bigcode/the-stack | train |
53eb773cc3e06f38d5f67a43 | train | function | def test_indent_with_strip():
for description, input, output in (
("Sanity check: 1 line string",
indent('Test', strip=True), ' Test'),
("Check list of strings",
indent(["Test", "Test"], strip=True), ' Test\n Test'),
("Check list of strings",
inde... | def test_indent_with_strip():
| for description, input, output in (
("Sanity check: 1 line string",
indent('Test', strip=True), ' Test'),
("Check list of strings",
indent(["Test", "Test"], strip=True), ' Test\n Test'),
("Check list of strings",
indent([" Test", " T... | ("List of strings turns in to strings joined by \\n",
["Test", "Test"], ' Test\n Test'),
):
eq_.description = "indent(): %s" % description
yield eq_, indent(input), output
del eq_.description
def test_indent_with_strip():
| 64 | 64 | 123 | 6 | 57 | objectified/fabric | tests/test_utils.py | Python | test_indent_with_strip | test_indent_with_strip | 38 | 50 | 38 | 38 | 8b7c3a364b93e261c1a086f78a311b5b51da6247 | bigcode/the-stack | train |
7fde2ff6339a4ef838b973e7 | train | function | @mock_streams('stdout')
def test_puts_with_user_output_off():
"""
puts() shouldn't print input to sys.stdout if "user" output level is off
"""
output.user = False
puts("You aren't reading this.")
eq_(sys.stdout.getvalue(), "")
| @mock_streams('stdout')
def test_puts_with_user_output_off():
| """
puts() shouldn't print input to sys.stdout if "user" output level is off
"""
output.user = False
puts("You aren't reading this.")
eq_(sys.stdout.getvalue(), "")
| input to sys.stdout if "user" output level is on
"""
s = "string!"
output.user = True
puts(s, show_prefix=False)
eq_(sys.stdout.getvalue(), s + "\n")
@mock_streams('stdout')
def test_puts_with_user_output_off():
| 64 | 64 | 61 | 16 | 48 | objectified/fabric | tests/test_utils.py | Python | test_puts_with_user_output_off | test_puts_with_user_output_off | 86 | 93 | 86 | 87 | 26c43d5a89619711c1263e1267b85eb2f5f85709 | bigcode/the-stack | train |
4363e41e4b85f39cfefcff29 | train | function | @mock_streams('stdout')
def test_puts_with_user_output_on():
"""
puts() should print input to sys.stdout if "user" output level is on
"""
s = "string!"
output.user = True
puts(s, show_prefix=False)
eq_(sys.stdout.getvalue(), s + "\n")
| @mock_streams('stdout')
def test_puts_with_user_output_on():
| """
puts() should print input to sys.stdout if "user" output level is on
"""
s = "string!"
output.user = True
puts(s, show_prefix=False)
eq_(sys.stdout.getvalue(), s + "\n")
| plus exception value
"""
try:
abort("Test")
except SystemExit:
pass
result = sys.stderr.getvalue()
eq_("\nFatal error: Test\n\nAborting.\n", result)
@mock_streams('stdout')
def test_puts_with_user_output_on():
| 64 | 64 | 71 | 16 | 48 | objectified/fabric | tests/test_utils.py | Python | test_puts_with_user_output_on | test_puts_with_user_output_on | 75 | 83 | 75 | 76 | dbdb861646b9d184a79bee588fc7bc06a266eeb6 | bigcode/the-stack | train |
88278d675502f293dc405789 | train | function | @mock_streams('stdout')
def test_puts_with_prefix():
"""
puts() should prefix output with env.host_string if non-empty
"""
s = "my output"
h = "localhost"
with settings(host_string=h):
puts(s)
eq_(sys.stdout.getvalue(), "[%s] %s" % (h, s + "\n"))
| @mock_streams('stdout')
def test_puts_with_prefix():
| """
puts() should prefix output with env.host_string if non-empty
"""
s = "my output"
h = "localhost"
with settings(host_string=h):
puts(s)
eq_(sys.stdout.getvalue(), "[%s] %s" % (h, s + "\n"))
| _user_output_off():
"""
puts() shouldn't print input to sys.stdout if "user" output level is off
"""
output.user = False
puts("You aren't reading this.")
eq_(sys.stdout.getvalue(), "")
@mock_streams('stdout')
def test_puts_with_prefix():
| 63 | 64 | 80 | 14 | 49 | objectified/fabric | tests/test_utils.py | Python | test_puts_with_prefix | test_puts_with_prefix | 96 | 105 | 96 | 97 | a6a80ab024a8e7abb08f6eaadceb19fa0b27c945 | bigcode/the-stack | train |
4fa640b1e2fc295d8245e17b | train | function | @mock_streams('stderr')
@with_patched_object(output, 'aborts', True)
def test_abort_message():
"""
abort() should print 'Fatal error' plus exception value
"""
try:
abort("Test")
except SystemExit:
pass
result = sys.stderr.getvalue()
eq_("\nFatal error: Test\n\nAborting.\n", r... | @mock_streams('stderr')
@with_patched_object(output, 'aborts', True)
def test_abort_message():
| """
abort() should print 'Fatal error' plus exception value
"""
try:
abort("Test")
except SystemExit:
pass
result = sys.stderr.getvalue()
eq_("\nFatal error: Test\n\nAborting.\n", result)
| yield eq_, input, output
del eq_.description
@aborts
def test_abort():
"""
abort() should raise SystemExit
"""
abort("Test")
@mock_streams('stderr')
@with_patched_object(output, 'aborts', True)
def test_abort_message():
| 64 | 64 | 84 | 25 | 39 | objectified/fabric | tests/test_utils.py | Python | test_abort_message | test_abort_message | 61 | 72 | 61 | 63 | 94df562b05bcdc77acc24dfdee4ded57c93a4026 | bigcode/the-stack | train |
63d4b293476c9767a992c54e | train | class | class training(object):
def __init__(self, options):
self.initializer = options.get('initializer')
self.activation = options.get('activation')
self.optimizer = options.get('optimizer')
self.filterSize = options.get('filterSize')
def model(self, imageTrain, convDense):
fashionModel = keras.Sequ... | class training(object):
| def __init__(self, options):
self.initializer = options.get('initializer')
self.activation = options.get('activation')
self.optimizer = options.get('optimizer')
self.filterSize = options.get('filterSize')
def model(self, imageTrain, convDense):
fashionModel = keras.Sequential()
for dense in... | import tensorflow as tf
from tensorflow import keras
import numpy as np
class training(object):
| 19 | 81 | 272 | 4 | 14 | ivokun/fashion-mnist-tensorflow | utils.py | Python | training | training | 6 | 35 | 6 | 7 | 99ead7e85a056ac2dbee253d9d73fd13fd5adfcb | bigcode/the-stack | train |
3bfbf6ce76b2fdbdb092bf94 | train | function | def main(_):
#1.load data(X:list of lint,y:int).
#if os.path.exists(FLAGS.cache_path): # 如果文件系统中存在，那么加载故事（词汇表索引化的）
# with open(FLAGS.cache_path, 'r') as data_f:
# trainX, trainY, testX, testY, vocabulary_index2word=pickle.load(data_f)
# vocab_size=len(vocabulary_index2word)
#el... | def main(_):
#1.load data(X:list of lint,y:int).
#if os.path.exists(FLAGS.cache_path): # 如果文件系统中存在，那么加载故事（词汇表索引化的）
# with open(FLAGS.cache_path, 'r') as data_f:
# trainX, trainY, testX, testY, vocabulary_index2word=pickle.load(data_f)
# vocab_size=len(vocabulary_index2word)
#el... | if 1==1:
trainX, trainY, testX, testY = None, None, None, None
vocabulary_word2index, vocabulary_index2word = create_voabulary(word2vec_model_path=FLAGS.word2vec_model_path,name_scope="rcnn") #simple='simple'
vocab_size = len(vocabulary_word2index)
print("cnn_model.vocab_size:",vocab... | #O.K.train-zhihu4-only-title-all.txt-->training-data/test-zhihu4-only-title.txt--->'training-data/train-zhihu5-only-title-multilabel.txt'
tf.flags.DEFINE_string("word2vec_model_path","zhihu-word2vec-title-desc.bin-100","word2vec's vocabulary and vectors") #zhihu-word2vec.bin-100-->zhihu-word2vec-multilabel-minicount15... | 256 | 256 | 1,251 | 104 | 152 | sliderSun/pynlp | text-classification/a03_TextRCNN/p71_TextRCNN_train.py | Python | main | main | 37 | 115 | 37 | 43 | 75d7cbfec8445a0c177e3b84fb7d5ffb706f55f2 | bigcode/the-stack | train |
ee598514d343f4e7f34eb276 | train | function | def get_label_using_logits(logits,vocabulary_index2word_label,top_number=1):
#print("get_label_using_logits.logits:",logits) #1-d array: array([-5.69036102, -8.54903221, -5.63954401, ..., -5.83969498,-5.84496021, -6.13911009], dtype=float32))
index_list=np.argsort(logits)[-top_number:]
index_list=index_list... | def get_label_using_logits(logits,vocabulary_index2word_label,top_number=1):
#print("get_label_using_logits.logits:",logits) #1-d array: array([-5.69036102, -8.54903221, -5.63954401, ..., -5.83969498,-5.84496021, -6.13911009], dtype=float32))
| index_list=np.argsort(logits)[-top_number:]
index_list=index_list[::-1]
#label_list=[]
#for index in index_list:
# label=vocabulary_index2word_label[index]
# label_list.append(label) #('get_label_using_logits.label_list:', [u'-3423450385060590478', u'2838091149470021485', u'-31749070029424... | def get_label_using_logits(logits,vocabulary_index2word_label,top_number=1):
#print("get_label_using_logits.logits:",logits) #1-d array: array([-5.69036102, -8.54903221, -5.63954401, ..., -5.83969498,-5.84496021, -6.13911009], dtype=float32))
| 85 | 64 | 204 | 85 | 0 | sliderSun/pynlp | text-classification/a03_TextRCNN/p71_TextRCNN_train.py | Python | get_label_using_logits | get_label_using_logits | 166 | 174 | 166 | 167 | bb8239a95f9cbc62f955a1e9b10326e398603c13 | bigcode/the-stack | train |
6f43449579e42f0aab3c102f | train | function | def do_eval(sess,textCNN,evalX,evalY,batch_size,vocabulary_index2word_label):
number_examples=len(evalX)
eval_loss,eval_acc,eval_counter=0.0,0.0,0
for start,end in zip(range(0,number_examples,batch_size),range(batch_size,number_examples,batch_size)):
feed_dict = {textCNN.input_x: evalX[start:end], t... | def do_eval(sess,textCNN,evalX,evalY,batch_size,vocabulary_index2word_label):
| number_examples=len(evalX)
eval_loss,eval_acc,eval_counter=0.0,0.0,0
for start,end in zip(range(0,number_examples,batch_size),range(batch_size,number_examples,batch_size)):
feed_dict = {textCNN.input_x: evalX[start:end], textCNN.dropout_keep_prob: 1}
if not FLAGS.multi_label_flag:
... | .
sess.run(t_assign_embedding)
print("word. exists embedding:", count_exist, " ;word not exist embedding:", count_not_exist)
print("using pre-trained word emebedding.ended...")
# 在验证集上做验证，报告损失、精确度
def do_eval(sess,textCNN,evalX,evalY,batch_size,vocabulary_index2word_label):
| 82 | 82 | 274 | 22 | 59 | sliderSun/pynlp | text-classification/a03_TextRCNN/p71_TextRCNN_train.py | Python | do_eval | do_eval | 150 | 163 | 150 | 150 | 92964d4dbba9b41a1a9f76f94debf7f9e7dd4c1d | bigcode/the-stack | train |
b5cbecb27886c7c86e35353a | train | function | def calculate_accuracy(labels_predicted, labels,eval_counter):
label_nozero=[]
#print("labels:",labels)
labels=list(labels)
for index,label in enumerate(labels):
if label>0:
label_nozero.append(index)
if eval_counter<2:
print("labels_predicted:",labels_predicted," ;labels... | def calculate_accuracy(labels_predicted, labels,eval_counter):
| label_nozero=[]
#print("labels:",labels)
labels=list(labels)
for index,label in enumerate(labels):
if label>0:
label_nozero.append(index)
if eval_counter<2:
print("labels_predicted:",labels_predicted," ;labels_nozero:",label_nozero)
count = 0
label_dict = {x: x fo... | u'2838091149470021485', u'-3174907002942471215', u'-1812694399780494968', u'6815248286057533876'])
return index_list
#统计预测的准确率
def calculate_accuracy(labels_predicted, labels,eval_counter):
| 64 | 64 | 141 | 12 | 51 | sliderSun/pynlp | text-classification/a03_TextRCNN/p71_TextRCNN_train.py | Python | calculate_accuracy | calculate_accuracy | 177 | 192 | 177 | 177 | 46cc0ad89e3e311af9b074e8975093922f0a5e15 | bigcode/the-stack | train |
147a5ab62c9ea9d01fac38f0 | train | function | def assign_pretrained_word_embedding(sess,vocabulary_index2word,vocab_size,textRCNN,word2vec_model_path=None):
print("using pre-trained word emebedding.started.word2vec_model_path:",word2vec_model_path)
# word2vecc=word2vec.load('word_embedding.txt') #load vocab-vector fiel.word2vecc['w91874']
word2vec_mode... | def assign_pretrained_word_embedding(sess,vocabulary_index2word,vocab_size,textRCNN,word2vec_model_path=None):
| print("using pre-trained word emebedding.started.word2vec_model_path:",word2vec_model_path)
# word2vecc=word2vec.load('word_embedding.txt') #load vocab-vector fiel.word2vecc['w91874']
word2vec_model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_model_path, binary=True)
word2vec_dict = {}
... | ,testX,testY,batch_size,vocabulary_index2word_label)
print("Epoch %d Validation Loss:%.3f\tValidation Accuracy: %.3f" % (epoch,eval_loss,eval_acc))
#save model to checkpoint
save_path=FLAGS.ckpt_dir+"model.ckpt"
saver.save(sess,save_path,global_step=epoch)... | 155 | 155 | 517 | 26 | 128 | sliderSun/pynlp | text-classification/a03_TextRCNN/p71_TextRCNN_train.py | Python | assign_pretrained_word_embedding | assign_pretrained_word_embedding | 117 | 147 | 117 | 117 | 09c847295d102b3010b4cd470610da6166ee2b1c | bigcode/the-stack | train |
a7d6ede0c03443f7c5db76ba | train | function | def generate_instance_config(metrics, template=None):
template = template if template else SNMP_CONF
instance_config = copy.copy(template)
instance_config['metrics'] = metrics
instance_config['name'] = HOST
return instance_config
| def generate_instance_config(metrics, template=None):
| template = template if template else SNMP_CONF
instance_config = copy.copy(template)
instance_config['metrics'] = metrics
instance_config['name'] = HOST
return instance_config
| "name": "needFallback"
}, {
"OID": "1.3.6.1.2.1.4.31.3.1.3.2.1",
"name": "noFallbackAndSameResult"
}
]
def generate_instance_config(metrics, template=None):
| 64 | 64 | 51 | 9 | 55 | tylerbenson/integrations-core | snmp/tests/common.py | Python | generate_instance_config | generate_instance_config | 177 | 182 | 177 | 177 | 11d4159b6ea76eb1d3c744c217b2f4c0e5a0bd52 | bigcode/the-stack | train |
9df192a274074bf00b2a421a | train | function | def generate_v3_instance_config(metrics, name=None, user=None,
auth=None, auth_key=None, priv=None, priv_key=None):
instance_config = generate_instance_config(metrics, SNMP_V3_CONF)
if name:
instance_config['name'] = name
if user:
instance_config['user'] = us... | def generate_v3_instance_config(metrics, name=None, user=None,
auth=None, auth_key=None, priv=None, priv_key=None):
| instance_config = generate_instance_config(metrics, SNMP_V3_CONF)
if name:
instance_config['name'] = name
if user:
instance_config['user'] = user
if auth:
instance_config['authProtocol'] = auth
if auth_key:
instance_config['authKey'] = auth_key
if priv:
i... | SNMP_CONF
instance_config = copy.copy(template)
instance_config['metrics'] = metrics
instance_config['name'] = HOST
return instance_config
def generate_v3_instance_config(metrics, name=None, user=None,
auth=None, auth_key=None, priv=None, priv_key=None):
| 64 | 64 | 135 | 29 | 34 | tylerbenson/integrations-core | snmp/tests/common.py | Python | generate_v3_instance_config | generate_v3_instance_config | 185 | 202 | 185 | 186 | a8444f851f7bb0550a0ea6a82319656638451a8e | bigcode/the-stack | train |
dff44001288a61c8511ef433 | train | function | @register.inclusion_tag("django_plotly_dash/plotly_direct.html", takes_context=True)
def plotly_direct(context, name=None, slug=None, da=None):
'Direct insertion of a Dash app'
da, app = _locate_daapp(name, slug, da)
view_func = app.locate_endpoint_function()
# Load embedded holder inserted by middle... | @register.inclusion_tag("django_plotly_dash/plotly_direct.html", takes_context=True)
def plotly_direct(context, name=None, slug=None, da=None):
| 'Direct insertion of a Dash app'
da, app = _locate_daapp(name, slug, da)
view_func = app.locate_endpoint_function()
# Load embedded holder inserted by middleware
eh = context.request.dpd_content_handler.embedded_holder
# Need to add in renderer launcher
renderer_launcher = '<script id="_... | ly_footer(context):
'Insert placeholder for django-plotly-dash footer content'
return context.request.dpd_content_handler.footer_placeholder
@register.inclusion_tag("django_plotly_dash/plotly_direct.html", takes_context=True)
def plotly_direct(context, name=None, slug=None, da=None):
| 64 | 64 | 166 | 35 | 28 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_direct | plotly_direct | 90 | 111 | 90 | 91 | 26c9bd172fb91ee050d849d10670cfba007c9d0b | bigcode/the-stack | train |
352d7b4800a6fb20f917a78c | train | function | @register.simple_tag(takes_context=True)
def plotly_footer(context):
'Insert placeholder for django-plotly-dash footer content'
return context.request.dpd_content_handler.footer_placeholder
| @register.simple_tag(takes_context=True)
def plotly_footer(context):
| 'Insert placeholder for django-plotly-dash footer content'
return context.request.dpd_content_handler.footer_placeholder
| _id=cache_id)
return locals()
@register.simple_tag(takes_context=True)
def plotly_header(context):
'Insert placeholder for django-plotly-dash header content'
return context.request.dpd_content_handler.header_placeholder
@register.simple_tag(takes_context=True)
def plotly_footer(context):
| 64 | 64 | 40 | 15 | 48 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_footer | plotly_footer | 85 | 88 | 85 | 86 | 953f1e42612e35c75cc846b55948c180d29a2406 | bigcode/the-stack | train |
58613e4a5e2c93c815c0b11c | train | function | @register.simple_tag(takes_context=True)
def site_root_url(context):
'Provide the root url of the demo site'
current_site_url = get_current_site(context.request)
return current_site_url.domain
| @register.simple_tag(takes_context=True)
def site_root_url(context):
| 'Provide the root url of the demo site'
current_site_url = get_current_site(context.request)
return current_site_url.domain
| 'Return a string of space-separated class names'
da, app = _locate_daapp(name, slug, da)
return app.extra_html_properties(prefix=prefix,
postfix=postfix,
template_type=template_type)
@register.simple_tag(takes_context=True)
def site_ro... | 64 | 64 | 44 | 15 | 49 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | site_root_url | site_root_url | 141 | 145 | 141 | 142 | a03eca607d417931179ecd3b8c427e7624f8a8ef | bigcode/the-stack | train |
26cba48def8ac5bb1d1459db | train | function | @register.inclusion_tag("django_plotly_dash/plotly_messaging.html", takes_context=True)
def plotly_message_pipe(context, url=None):
'Insert script for providing background websocket connection'
url = url if url else ws_default_url
return locals()
| @register.inclusion_tag("django_plotly_dash/plotly_messaging.html", takes_context=True)
def plotly_message_pipe(context, url=None):
| 'Insert script for providing background websocket connection'
url = url if url else ws_default_url
return locals()
| edded(eh)
try:
resp = view_func()
finally:
eh.add_scripts(renderer_launcher)
app.exit_embedded()
return locals()
@register.inclusion_tag("django_plotly_dash/plotly_messaging.html", takes_context=True)
def plotly_message_pipe(context, url=None):
| 64 | 64 | 56 | 31 | 33 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_message_pipe | plotly_message_pipe | 113 | 117 | 113 | 114 | e9598a123cf1cbd34fcfea8933e2f0bc8b079e69 | bigcode/the-stack | train |
05f36eef9a281e985262d91f | train | function | @register.simple_tag(takes_context=True)
def plotly_header(context):
'Insert placeholder for django-plotly-dash header content'
return context.request.dpd_content_handler.header_placeholder
| @register.simple_tag(takes_context=True)
def plotly_header(context):
| 'Insert placeholder for django-plotly-dash header content'
return context.request.dpd_content_handler.header_placeholder
| %;
height: 100%;
"""
cache_id = store_initial_arguments(context['request'], initial_arguments)
da, app = _locate_daapp(name, slug, da, cache_id=cache_id)
return locals()
@register.simple_tag(takes_context=True)
def plotly_header(context):
| 64 | 64 | 40 | 15 | 49 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_header | plotly_header | 80 | 83 | 80 | 81 | 1db44848f96df22266c6dbf86df9dbcc640db999 | bigcode/the-stack | train |
9fe498a189a4815869521f1f | train | function | def _locate_daapp(name, slug, da, cache_id=None):
app = None
if name is not None:
da, app = DashApp.locate_item(name, stateless=True, cache_id=cache_id)
if slug is not None:
da, app = DashApp.locate_item(slug, stateless=False, cache_id=cache_id)
if not app:
app = da.as_dash_i... | def _locate_daapp(name, slug, da, cache_id=None):
| app = None
if name is not None:
da, app = DashApp.locate_item(name, stateless=True, cache_id=cache_id)
if slug is not None:
da, app = DashApp.locate_item(slug, stateless=False, cache_id=cache_id)
if not app:
app = da.as_dash_instance()
return da, app
| _current_site
from django_plotly_dash.models import DashApp
from django_plotly_dash.util import pipe_ws_endpoint_name, store_initial_arguments
register = template.Library()
ws_default_url = "/%s" % pipe_ws_endpoint_name()
def _locate_daapp(name, slug, da, cache_id=None):
| 64 | 64 | 99 | 16 | 48 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | _locate_daapp | _locate_daapp | 38 | 51 | 38 | 39 | bff72a86e43c0d82f8af4be294f7086a3924c4ec | bigcode/the-stack | train |
a297a742f419683c84230af7 | train | function | @register.inclusion_tag("django_plotly_dash/plotly_app.html", takes_context=True)
def plotly_app(context, name=None, slug=None, da=None, ratio=0.1, use_frameborder=False, initial_arguments=None):
'Insert a dash application using a html iframe'
fbs = '1' if use_frameborder else '0'
dstyle = """
positio... | @register.inclusion_tag("django_plotly_dash/plotly_app.html", takes_context=True)
def plotly_app(context, name=None, slug=None, da=None, ratio=0.1, use_frameborder=False, initial_arguments=None):
| 'Insert a dash application using a html iframe'
fbs = '1' if use_frameborder else '0'
dstyle = """
position: relative;
padding-bottom: %s%%;
height: 0;
overflow:hidden;
""" % (ratio*100)
istyle = """
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100... | app = da.as_dash_instance()
return da, app
@register.inclusion_tag("django_plotly_dash/plotly_app.html", takes_context=True)
def plotly_app(context, name=None, slug=None, da=None, ratio=0.1, use_frameborder=False, initial_arguments=None):
| 64 | 64 | 189 | 50 | 13 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_app | plotly_app | 53 | 78 | 53 | 54 | a66a9c1dfe8d7d6a488d19e45e74944413c465a5 | bigcode/the-stack | train |
fcbf918ee8595245c8f45446 | train | function | @register.simple_tag()
def plotly_class(name=None, slug=None, da=None, prefix=None, postfix=None, template_type=None):
'Return a string of space-separated class names'
da, app = _locate_daapp(name, slug, da)
return app.extra_html_properties(prefix=prefix,
postfix=postf... | @register.simple_tag()
def plotly_class(name=None, slug=None, da=None, prefix=None, postfix=None, template_type=None):
| 'Return a string of space-separated class names'
da, app = _locate_daapp(name, slug, da)
return app.extra_html_properties(prefix=prefix,
postfix=postfix,
template_type=template_type)
| da)
slugified_id = app.slugified_id()
if postfix:
return "%s-%s" %(slugified_id, postfix)
return slugified_id
@register.simple_tag()
def plotly_class(name=None, slug=None, da=None, prefix=None, postfix=None, template_type=None):
| 64 | 64 | 78 | 28 | 35 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_class | plotly_class | 131 | 139 | 131 | 132 | a60f974a2ea8ddb9c8bef6bf5829d753e30864d5 | bigcode/the-stack | train |
2b2ee7f4838943ff24ba53d7 | train | function | @register.simple_tag()
def plotly_app_identifier(name=None, slug=None, da=None, postfix=None):
'Return a slug-friendly identifier'
da, app = _locate_daapp(name, slug, da)
slugified_id = app.slugified_id()
if postfix:
return "%s-%s" %(slugified_id, postfix)
return slugified_id
| @register.simple_tag()
def plotly_app_identifier(name=None, slug=None, da=None, postfix=None):
| 'Return a slug-friendly identifier'
da, app = _locate_daapp(name, slug, da)
slugified_id = app.slugified_id()
if postfix:
return "%s-%s" %(slugified_id, postfix)
return slugified_id
| essaging.html", takes_context=True)
def plotly_message_pipe(context, url=None):
'Insert script for providing background websocket connection'
url = url if url else ws_default_url
return locals()
@register.simple_tag()
def plotly_app_identifier(name=None, slug=None, da=None, postfix=None):
| 64 | 64 | 80 | 22 | 42 | aboulang/django-plotly-dash-pivot-demo | django_plotly_dash/templatetags/plotly_dash.py | Python | plotly_app_identifier | plotly_app_identifier | 119 | 129 | 119 | 120 | eacaa16139b39e935e0572ed67f333fc630e2f2c | bigcode/the-stack | train |
e4dfe4a9b51a892ce6689dac | train | class | class DirectoryRequests(object):
# This module make requests (of 'path' type) to the shakti API and convert the raw data returned from the API
# in an DataType object (data_types.py), where the data will be more easily accessible
netflix_session = None
@cache_utils.cache_output(cache_utils.CACHE_MYLIS... | class DirectoryRequests(object):
# This module make requests (of 'path' type) to the shakti API and convert the raw data returned from the API
# in an DataType object (data_types.py), where the data will be more easily accessible
| netflix_session = None
@cache_utils.cache_output(cache_utils.CACHE_MYLIST, fixed_identifier='my_list_items', ignore_self_class=True)
def req_mylist_items(self):
"""Return the 'my list' video list as videoid items"""
common.debug('Requesting "my list" video list as videoid items')
tr... | original implementation module)
Methods to make 'path' requests through the Shakti API
SPDX-License-Identifier: MIT
See LICENSES/MIT.md for more information.
"""
from __future__ import absolute_import, division, unicode_literals
from future.utils import iteritems
from resources.lib import common
from res... | 256 | 256 | 3,028 | 54 | 201 | kevenli/plugin.video.netflix | resources/lib/services/directorybuilder/dir_builder_requests.py | Python | DirectoryRequests | DirectoryRequests | 25 | 255 | 25 | 28 | 4894ca5498434addb2da3fba70d2475b5a171883 | bigcode/the-stack | train |
ff2521738ab382b22b466756 | train | class | class StorageGetBlobTest(StorageTestCase):
def _setup(self, storage_account, key):
# test chunking functionality by reducing the threshold
# for chunking and the size of each chunk, otherwise
# the tests would take too long to execute
self.bsc = BlobServiceClient(
self.ac... | class StorageGetBlobTest(StorageTestCase):
| def _setup(self, storage_account, key):
# test chunking functionality by reducing the threshold
# for chunking and the size of each chunk, otherwise
# the tests would take too long to execute
self.bsc = BlobServiceClient(
self.account_url(storage_account, "blob"),
... | # coding: utf-8
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------... | 164 | 256 | 8,506 | 9 | 155 | iscai-msft/azure-sdk-for-python | sdk/storage/azure-storage-blob/tests/test_get_blob.py | Python | StorageGetBlobTest | StorageGetBlobTest | 31 | 928 | 31 | 31 | 2b752e9e8a03fe65e8acec4ef3c235cda6544924 | bigcode/the-stack | train |
f6d9b7c6e54abe964897f6d4 | train | class | @viewer_registry("imviz-image-viewer", label="Image 2D (Imviz)")
class ImvizImageView(BqplotImageView, AstrowidgetsImageViewerMixin):
# Whether to inherit tools from glue-jupyter automatically. Set this to
# False to have full control here over which tools are shown in case new
# ones are added in glue-jup... | @viewer_registry("imviz-image-viewer", label="Image 2D (Imviz)")
class ImvizImageView(BqplotImageView, AstrowidgetsImageViewerMixin):
# Whether to inherit tools from glue-jupyter automatically. Set this to
# False to have full control here over which tools are shown in case new
# ones are added in glue-jup... | inherit_tools = False
tools = ['bqplot:home', 'jdaviz:boxzoom', 'jdaviz:boxzoommatch',
'bqplot:panzoom', 'jdaviz:panzoommatch',
'jdaviz:contrastbias', 'jdaviz:blinkonce',
'bqplot:rectangle', 'bqplot:circle', 'bqplot:ellipse']
default_class = None
def __init__(sel... | import numpy as np
from glue.core.link_helpers import LinkSame
from glue_jupyter.bqplot.image import BqplotImageView
from jdaviz.configs.imviz.helper import data_has_valid_wcs, layer_is_image_data
from jdaviz.core.astrowidgets_api import AstrowidgetsImageViewerMixin
from jdaviz.core.events import SnackbarMessage
from... | 187 | 256 | 2,055 | 88 | 99 | check-spelling/jdaviz | jdaviz/configs/imviz/plugins/viewers.py | Python | ImvizImageView | ImvizImageView | 14 | 231 | 14 | 19 | 71628f375ea7ffa894e4e1c993f5e3b02f2b83ed | bigcode/the-stack | train |
e19cdef6e5044df50947bacc | train | class | class KeyStoneInfo(object):
"""To generate Keystone Authentication information
Contrail Driver expects Keystone auth info for testing purpose.
"""
auth_protocol = 'http'
auth_host = 'host'
auth_port = 5000
admin_user = 'neutron'
admin_password = 'neutron'
admin_token = 'neutron'
... | class KeyStoneInfo(object):
| """To generate Keystone Authentication information
Contrail Driver expects Keystone auth info for testing purpose.
"""
auth_protocol = 'http'
auth_host = 'host'
auth_port = 5000
admin_user = 'neutron'
admin_password = 'neutron'
admin_token = 'neutron'
admin_tenant_name = 'neut... | .datetime.now()
self.auth_token = None
self._session = None
self._is_admin = True
self.admin = uuid.uuid4().hex.decode()
self.request_id = 'req-' + str(uuid.uuid4())
self.tenant = tenant_id
class KeyStoneInfo(object):
| 64 | 64 | 84 | 6 | 57 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | KeyStoneInfo | KeyStoneInfo | 200 | 210 | 200 | 200 | 8df280a432e978be0382e4eb5f48c1c9fd8318d0 | bigcode/the-stack | train |
ccd1afdcec0088a2e51b54e5 | train | class | class ContrailPluginTestCase(test_plugin.NeutronDbPluginV2TestCase):
_plugin_name = ('%s.NeutronPluginContrailCoreV2' % CONTRAIL_PKG_PATH)
def setUp(self, plugin=None, ext_mgr=None):
cfg.CONF.keystone_authtoken = KeyStoneInfo()
mock.patch('requests.post').start().side_effect = FAKE_SERVER.requ... | class ContrailPluginTestCase(test_plugin.NeutronDbPluginV2TestCase):
| _plugin_name = ('%s.NeutronPluginContrailCoreV2' % CONTRAIL_PKG_PATH)
def setUp(self, plugin=None, ext_mgr=None):
cfg.CONF.keystone_authtoken = KeyStoneInfo()
mock.patch('requests.post').start().side_effect = FAKE_SERVER.request
super(ContrailPluginTestCase, self).setUp(self._plugin_na... | _host = 'host'
auth_port = 5000
admin_user = 'neutron'
admin_password = 'neutron'
admin_token = 'neutron'
admin_tenant_name = 'neutron'
class ContrailPluginTestCase(test_plugin.NeutronDbPluginV2TestCase):
| 64 | 64 | 103 | 17 | 47 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | ContrailPluginTestCase | ContrailPluginTestCase | 213 | 220 | 213 | 213 | 766054985365020ebf7990e0cff2adc610d5e1f0 | bigcode/the-stack | train |
dfda30491fa9647214d4f955 | train | class | class TestContrailL3NatTestCase(ContrailPluginTestCase,
test_l3_plugin.L3NatDBIntTestCase):
mock_rescheduling = False
def setUp(self):
super(TestContrailL3NatTestCase, self).setUp()
| class TestContrailL3NatTestCase(ContrailPluginTestCase,
test_l3_plugin.L3NatDBIntTestCase):
| mock_rescheduling = False
def setUp(self):
super(TestContrailL3NatTestCase, self).setUp()
| bindings.VIF_TYPE_VROUTER
HAS_PORT_FILTER = True
def setUp(self):
super(TestContrailPortBinding, self).setUp()
class TestContrailL3NatTestCase(ContrailPluginTestCase,
test_l3_plugin.L3NatDBIntTestCase):
| 64 | 64 | 58 | 29 | 35 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailL3NatTestCase | TestContrailL3NatTestCase | 307 | 312 | 307 | 308 | 5d45c6ed693bc036548e10cee62632361d9019ee | bigcode/the-stack | train |
dfd0da4f88e0841cf1f0dc06 | train | class | class TestContrailSubnetsV2(test_plugin.TestSubnetsV2,
ContrailPluginTestCase):
def setUp(self):
super(TestContrailSubnetsV2, self).setUp()
# Support ipv6 in contrail is planned in Juno
def test_update_subnet_ipv6_attributes(self):
self.skipTest("Contrail isn't s... | class TestContrailSubnetsV2(test_plugin.TestSubnetsV2,
ContrailPluginTestCase):
| def setUp(self):
super(TestContrailSubnetsV2, self).setUp()
# Support ipv6 in contrail is planned in Juno
def test_update_subnet_ipv6_attributes(self):
self.skipTest("Contrail isn't supporting ipv6 yet")
def test_update_subnet_ipv6_inconsistent_address_attribute(self):
self.ski... | =None, ext_mgr=None):
cfg.CONF.keystone_authtoken = KeyStoneInfo()
mock.patch('requests.post').start().side_effect = FAKE_SERVER.request
super(ContrailPluginTestCase, self).setUp(self._plugin_name)
class TestContrailNetworksV2(test_plugin.TestNetworksV2,
ContrailP... | 119 | 119 | 398 | 23 | 96 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailSubnetsV2 | TestContrailSubnetsV2 | 229 | 268 | 229 | 230 | 583228ed84bb13544b9d97ada2e804efbdc5c40a | bigcode/the-stack | train |
37944d02055039c4217cf7dd | train | class | class FakeServer(db_base_plugin_v2.NeutronDbPluginV2,
external_net_db.External_net_db_mixin,
securitygroups_db.SecurityGroupDbMixin,
l3_db.L3_NAT_db_mixin):
"""FakeServer for contrail api server.
This class mocks behaviour of contrail API server.
"""
s... | class FakeServer(db_base_plugin_v2.NeutronDbPluginV2,
external_net_db.External_net_db_mixin,
securitygroups_db.SecurityGroupDbMixin,
l3_db.L3_NAT_db_mixin):
| """FakeServer for contrail api server.
This class mocks behaviour of contrail API server.
"""
supported_extension_aliases = ['external-net', 'router', 'floatingip']
@property
def _core_plugin(self):
return self
def create_port(self, context, port):
self._ensure_default_sec... |
import uuid
import mock
import netaddr
from oslo.config import cfg
from testtools import matchers
import webob.exc
from neutron.api import extensions
from neutron.api.v2 import attributes as attr
from neutron.api.v2 import base as api_base
from neutron.common import exceptions as exc
from neutron import context as n... | 256 | 256 | 1,197 | 46 | 210 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | FakeServer | FakeServer | 48 | 179 | 48 | 51 | 7c201ad78e53d1d4a4a313aea19796e95bc94787 | bigcode/the-stack | train |
ae6d52d7f0403f03f8ed6d5c | train | class | class TestContrailPortsV2(test_plugin.TestPortsV2,
ContrailPluginTestCase):
def setUp(self):
super(TestContrailPortsV2, self).setUp()
def test_delete_ports_by_device_id(self):
self.skipTest("This method tests rpc API of "
"which contrail isn't usi... | class TestContrailPortsV2(test_plugin.TestPortsV2,
ContrailPluginTestCase):
| def setUp(self):
super(TestContrailPortsV2, self).setUp()
def test_delete_ports_by_device_id(self):
self.skipTest("This method tests rpc API of "
"which contrail isn't using")
def test_delete_ports_by_device_id_second_call_failure(self):
self.skipTest("This me... | validate_subnet,
neutron_context.get_admin_context(
load_admin_roles=False),
subnet)
self.assertThat(
str(error),
matchers.Not(matchers.Contains('built-in fun... | 63 | 64 | 137 | 21 | 42 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailPortsV2 | TestContrailPortsV2 | 271 | 286 | 271 | 272 | 1f179ef8d0d1f5109f0ae822d47d27c6714dd9ca | bigcode/the-stack | train |
dcbdca9228454faf2de2b116 | train | class | class TestContrailSecurityGroups(test_sg.TestSecurityGroups,
ContrailPluginTestCase):
def setUp(self, plugin=None, ext_mgr=None):
super(TestContrailSecurityGroups, self).setUp(self._plugin_name,
ext_mgr)
ext_mgr =... | class TestContrailSecurityGroups(test_sg.TestSecurityGroups,
ContrailPluginTestCase):
| def setUp(self, plugin=None, ext_mgr=None):
super(TestContrailSecurityGroups, self).setUp(self._plugin_name,
ext_mgr)
ext_mgr = extensions.PluginAwareExtensionManager.get_instance()
self.ext_api = test_extensions.setup_extensions_middlewa... | API of "
"which contrail isn't using")
def test_delete_ports_ignores_port_not_found(self):
self.skipTest("This method tests private method of "
"which contrail isn't using")
class TestContrailSecurityGroups(test_sg.TestSecurityGroups,
... | 64 | 64 | 80 | 20 | 44 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailSecurityGroups | TestContrailSecurityGroups | 289 | 295 | 289 | 290 | ea572a382963d7778188eff26eb2b8de01a7c292 | bigcode/the-stack | train |
d499a68e305a0a0d88dd5883 | train | class | class Context(object):
def __init__(self, tenant_id=''):
self.read_only = False
self.show_deleted = False
self.roles = [u'admin', u'KeystoneServiceAdmin', u'KeystoneAdmin']
self._read_deleted = 'no'
self.timestamp = datetime.datetime.now()
self.auth_token = None
... | class Context(object):
| def __init__(self, tenant_id=''):
self.read_only = False
self.show_deleted = False
self.roles = [u'admin', u'KeystoneServiceAdmin', u'KeystoneAdmin']
self._read_deleted = 'no'
self.timestamp = datetime.datetime.now()
self.auth_token = None
self._session = None... | if data.get('id'):
obj['id'] = data.get('id')
response = mock.MagicMock()
response.status_code = code
def return_obj():
return obj
response.json = return_obj
return response
FAKE_SERVER = FakeServer()
class Context(object):
| 64 | 64 | 123 | 4 | 60 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | Context | Context | 185 | 197 | 185 | 185 | 0bc846c74e1cd55f8ccaf79e68110ddc866bfc4e | bigcode/the-stack | train |
b5754d813c78a4c03f440d93 | train | class | class TestContrailPortBinding(ContrailPluginTestCase,
test_bindings.PortBindingsTestCase):
VIF_TYPE = portbindings.VIF_TYPE_VROUTER
HAS_PORT_FILTER = True
def setUp(self):
super(TestContrailPortBinding, self).setUp()
| class TestContrailPortBinding(ContrailPluginTestCase,
test_bindings.PortBindingsTestCase):
| VIF_TYPE = portbindings.VIF_TYPE_VROUTER
HAS_PORT_FILTER = True
def setUp(self):
super(TestContrailPortBinding, self).setUp()
| SecurityGroups, self).setUp(self._plugin_name,
ext_mgr)
ext_mgr = extensions.PluginAwareExtensionManager.get_instance()
self.ext_api = test_extensions.setup_extensions_middleware(ext_mgr)
class TestContrailPortBinding(ContrailPluginTestCase,
... | 64 | 64 | 63 | 22 | 42 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailPortBinding | TestContrailPortBinding | 298 | 304 | 298 | 299 | c7e4fe249aa02177cd976e00f302e696c8f18228 | bigcode/the-stack | train |
570dd891ab7bd77a1fc7aa90 | train | class | class TestContrailNetworksV2(test_plugin.TestNetworksV2,
ContrailPluginTestCase):
def setUp(self):
super(TestContrailNetworksV2, self).setUp()
| class TestContrailNetworksV2(test_plugin.TestNetworksV2,
ContrailPluginTestCase):
| def setUp(self):
super(TestContrailNetworksV2, self).setUp()
| _authtoken = KeyStoneInfo()
mock.patch('requests.post').start().side_effect = FAKE_SERVER.request
super(ContrailPluginTestCase, self).setUp(self._plugin_name)
class TestContrailNetworksV2(test_plugin.TestNetworksV2,
ContrailPluginTestCase):
| 64 | 64 | 41 | 21 | 43 | armando-migliaccio/neutron-1 | neutron/tests/unit/opencontrail/test_contrail_plugin.py | Python | TestContrailNetworksV2 | TestContrailNetworksV2 | 223 | 226 | 223 | 224 | 97a7c1b1ab2f0bcc2cfec9859012643f58b28d2e | bigcode/the-stack | train |
c17c60cb034bc6ffdffb0660 | train | function | def bad():
nonlocal missing
global missing
| def bad():
| nonlocal missing
global missing
| ##Patterns: E0115
##Err: E0115
def bad():
| 17 | 64 | 12 | 3 | 13 | mrfyda/codacy-pylint-python3 | docs/tests/E0115.py | Python | bad | bad | 4 | 6 | 4 | 4 | dafb70f6df6f12e5db01c30983976b6347f1f11d | bigcode/the-stack | train |
8335f2954adf4a3fda3a8eb2 | train | function | def good():
nonlocal missing
def test():
global missing
return test
| def good():
| nonlocal missing
def test():
global missing
return test
| ##Patterns: E0115
##Err: E0115
def bad():
nonlocal missing
global missing
def good():
| 29 | 64 | 20 | 3 | 25 | mrfyda/codacy-pylint-python3 | docs/tests/E0115.py | Python | good | good | 8 | 12 | 8 | 8 | f235a7b5e4f17aa557134fb78f8982611a5a20b9 | bigcode/the-stack | train |
c00de702e3933de6a379ccef | train | function | def uplink_callback(msg, client):
print("Received uplink from ", msg.dev_id)
print(msg)
| def uplink_callback(msg, client):
| print("Received uplink from ", msg.dev_id)
print(msg)
| 1")
USERNAME = os.environ.get("USERNAME")
PASSWORD = os.environ.get("PASSWORD")
HOSTNAME = os.environ.get("HOSTNAME")
PORT = os.environ.get("PORT")
app_id = os.environ.get("APP_ID")
access_key = os.environ.get("ACCESS_KEY")
def uplink_callback(msg, client):
| 64 | 64 | 24 | 8 | 56 | didx-xyz/pypeerdid | bitlinq_peerdid/old_test_scripts/ttn_api_gw.py | Python | uplink_callback | uplink_callback | 21 | 23 | 21 | 21 | 94ad1713cfd5039aa4f70a53fe343aac7ccf6b33 | bigcode/the-stack | train |
2b8207539683339a3ca02fef | train | function | def deck_list(ctx, organization_id: str = None, project_id: str = None) -> Union[None, str]:
# GraphQL
try:
graph_ql = GraphQL(authentication=ctx.auth)
data = graph_ql.query(
"""
query($organization_id: UUID, $project_id: UUID) {
allDecks(organizationId: $... | def deck_list(ctx, organization_id: str = None, project_id: str = None) -> Union[None, str]:
# GraphQL
| try:
graph_ql = GraphQL(authentication=ctx.auth)
data = graph_ql.query(
"""
query($organization_id: UUID, $project_id: UUID) {
allDecks(organizationId: $organization_id, projectId: $project_id) {
results {
title
... | from typing import Union
import src.cli.console as console
from src.cli.console.input import get_identifier_or_pass
from src.context.helper import convert_deck_argument_to_uuid
from src.graphql import GraphQL
def deck_list(ctx, organization_id: str = None, project_id: str = None) -> Union[None, str]:
# GraphQL
| 74 | 94 | 316 | 31 | 42 | blackappsolutions/cli | src/cli/console/deck.py | Python | deck_list | deck_list | 9 | 57 | 9 | 10 | a26317701037de5e1c3b6e388c05bd7dc5b47a1b | bigcode/the-stack | train |
2d6bbfe3abfef7ea54c38a14 | train | class | class Gitlab(GitServer):
tokenSpace = 'gitlab'
baseUrl = 'https://gitlab.com/api/v4'
def open(
self,
title: str,
body: str,
fromBranch: Branch,
toBranch: Branch
) -> bool:
token = self.configPersonal.getToken(self.tokenSpace)
if token is not None... | class Gitlab(GitServer):
| tokenSpace = 'gitlab'
baseUrl = 'https://gitlab.com/api/v4'
def open(
self,
title: str,
body: str,
fromBranch: Branch,
toBranch: Branch
) -> bool:
token = self.configPersonal.getToken(self.tokenSpace)
if token is not None:
projectId =... | from gitcd.git.server import GitServer
from gitcd.git.branch import Branch
from gitcd.exceptions import GitcdGithubApiException
import requests
class Gitlab(GitServer):
| 38 | 256 | 1,356 | 7 | 30 | pchr-srf/gitcd | gitcd/git/server/gitlab.py | Python | Gitlab | Gitlab | 9 | 221 | 9 | 10 | 3ac7dbe36a7fde5529dd46eab5261081eddd8c20 | bigcode/the-stack | train |
76b6d1ad7db17d7e3d3fc2ee | train | function | def _any_start_rule(tag, rules):
"Return any rule that has the given tag as a start tag or None"
try:
return next(rule for rule in rules if rule.is_starttag(tag))
except StopIteration:
return None
| def _any_start_rule(tag, rules):
| "Return any rule that has the given tag as a start tag or None"
try:
return next(rule for rule in rules if rule.is_starttag(tag))
except StopIteration:
return None
| taking
a list of all tags except the end tag.
"""
def is_starttag(self, tag):
return tag.__class__ == self.startcls
def is_endtag(self, tag):
return tag.__class__ == self.endcls
def _any_start_rule(tag, rules):
| 64 | 64 | 53 | 9 | 54 | ulikoehler/ODBPy | ODBPy/Treeifier.py | Python | _any_start_rule | _any_start_rule | 37 | 42 | 37 | 37 | 2446dc435bca540f9b21a30a5f2cecb9f0ff265e | bigcode/the-stack | train |
2fed383c9d2d6a3d3c771623 | train | class | class TreeifierRule(namedtuple("TreeifierRule", ["startcls", "endcls", "function"])):
"""
A rule for the treefier that idenfies the start and corresponding end element of
a nested structure.
Once the end tag is encountered, the unary "function" is executed taking
a list of all tags except the e... | class TreeifierRule(namedtuple("TreeifierRule", ["startcls", "endcls", "function"])):
| """
A rule for the treefier that idenfies the start and corresponding end element of
a nested structure.
Once the end tag is encountered, the unary "function" is executed taking
a list of all tags except the end tag.
"""
def is_starttag(self, tag):
return tag.__class__ == self.s... | every time and end tag is encountered,
the innermost element is processed on the fly.
"""
from collections import namedtuple, deque
__all__ = ["TreeifierRule", "treeify"]
class TreeifierRule(namedtuple("TreeifierRule", ["startcls", "endcls", "function"])):
| 64 | 64 | 122 | 23 | 41 | ulikoehler/ODBPy | ODBPy/Treeifier.py | Python | TreeifierRule | TreeifierRule | 23 | 34 | 23 | 23 | 584ea03671067cd70cbe01b14fa51133bb62ac1c | bigcode/the-stack | train |
a33472b801c0c0dc7f9627ac | train | function | def treeify(tags, rules):
"""
From a flattened list of tag-like objects (i.e. parsed lines) generate a
nested tree by using start-tag/end-tag rule pairs.
"""
hierarchy = deque()
elementlist = deque([[]]) # Contains toplevel element list
for tag in tags:
# Check if this is an end tag
... | def treeify(tags, rules):
| """
From a flattened list of tag-like objects (i.e. parsed lines) generate a
nested tree by using start-tag/end-tag rule pairs.
"""
hierarchy = deque()
elementlist = deque([[]]) # Contains toplevel element list
for tag in tags:
# Check if this is an end tag
if len(hierarchy) ... | class__ == self.endcls
def _any_start_rule(tag, rules):
"Return any rule that has the given tag as a start tag or None"
try:
return next(rule for rule in rules if rule.is_starttag(tag))
except StopIteration:
return None
def treeify(tags, rules):
| 67 | 67 | 225 | 7 | 59 | ulikoehler/ODBPy | ODBPy/Treeifier.py | Python | treeify | treeify | 45 | 69 | 45 | 45 | 5e20bf50e40a01fdc2332e00943da85a2149c8b1 | bigcode/the-stack | train |
2cac855fe0bb8d10de2a0200 | train | class | class AnnouncementAPI(APIView):
def get(self, request):
announcements = Announcement1.objects.filter(visible=True)
return self.success(self.paginate_data(request, announcements, AnnouncementSerializer))
| class AnnouncementAPI(APIView):
| def get(self, request):
announcements = Announcement1.objects.filter(visible=True)
return self.success(self.paginate_data(request, announcements, AnnouncementSerializer))
| from utils.api import APIView
from companyAnnounce1.models import Announcement1
from companyAnnounce1.serializers import AnnouncementSerializer
class AnnouncementAPI(APIView):
| 34 | 64 | 39 | 6 | 27 | scintiller/OnlineJudge | companyAnnounce1/views/oj.py | Python | AnnouncementAPI | AnnouncementAPI | 7 | 10 | 7 | 7 | f08f674f15781139547a5860d8a98f6d2259da69 | bigcode/the-stack | train |
c0bb14ffc060c9a07d9bdde8 | train | class | class MurphiTokens:
ssp_prefix = "SSP_"
mutex = "mutex"
defaccess = "none"
defload = "load"
defstore = "store"
defval = "undefined"
defset = "undefine"
statesuf = "s_"
vectorsuf = "v_"
countsuf = "cnt_"
instsuf = "i_"
TemplateDir = "MurphiTemp"
fconst = "const.m"
... | class MurphiTokens:
| ssp_prefix = "SSP_"
mutex = "mutex"
defaccess = "none"
defload = "load"
defstore = "store"
defval = "undefined"
defset = "undefine"
statesuf = "s_"
vectorsuf = "v_"
countsuf = "cnt_"
instsuf = "i_"
TemplateDir = "MurphiTemp"
fconst = "const.m"
# Lock framewor... | class MurphiTokens:
| 5 | 256 | 1,481 | 5 | 0 | icsa-caps/HieraGen | Murphi/ModularMurphi/MurphiTokens.py | Python | MurphiTokens | MurphiTokens | 1 | 202 | 1 | 1 | 2fc22f168be7eab526978939c2074466d33fcef0 | bigcode/the-stack | train |
45e7118faa94bc846289852e | train | function | def generate(hop_size=256):
while True:
shuffled = sklearn.utils.shuffle(files)
for f in shuffled:
with open(f, 'rb') as fopen:
wav, f0, mel = pickle.load(fopen)
batch_max_steps = random.randint(16384, 55125)
batch_max_frames = batch_max_steps // ... | def generate(hop_size=256):
| while True:
shuffled = sklearn.utils.shuffle(files)
for f in shuffled:
with open(f, 'rb') as fopen:
wav, f0, mel = pickle.load(fopen)
batch_max_steps = random.randint(16384, 55125)
batch_max_frames = batch_max_steps // hop_size
if len... | ')
sr = 22050
def pad_seq(x, base=8):
len_out = int(base * ceil(float(x.shape[0]) / base))
len_pad = len_out - x.shape[0]
assert len_pad >= 0
return np.pad(x, ((0, len_pad), (0, 0)), 'constant'), x.shape[0]
def generate(hop_size=256):
| 87 | 87 | 291 | 8 | 79 | ishine/malaya-speech | pretrained-model/speechsplit-conversion/speechsplit.py | Python | generate | generate | 29 | 62 | 29 | 29 | 72234dfd002e348abb0f336d851c041491f2d834 | bigcode/the-stack | train |
660fe303ce84348e63d9bb8d | train | function | def pad_seq(x, base=8):
len_out = int(base * ceil(float(x.shape[0]) / base))
len_pad = len_out - x.shape[0]
assert len_pad >= 0
return np.pad(x, ((0, len_pad), (0, 0)), 'constant'), x.shape[0]
| def pad_seq(x, base=8):
| len_out = int(base * ceil(float(x.shape[0]) / base))
len_pad = len_out - x.shape[0]
assert len_pad >= 0
return np.pad(x, ((0, len_pad), (0, 0)), 'constant'), x.shape[0]
| aya_speech import train
import malaya_speech
import sklearn
import pickle
speaker_model = malaya_speech.speaker_vector.deep_model('vggvox-v2')
files = glob('speechsplit-dataset/*.pkl')
sr = 22050
def pad_seq(x, base=8):
| 64 | 64 | 72 | 9 | 54 | ishine/malaya-speech | pretrained-model/speechsplit-conversion/speechsplit.py | Python | pad_seq | pad_seq | 22 | 26 | 22 | 22 | 43d73084d403780435731906f34fadf2fb19f06f | bigcode/the-stack | train |
46b151c7e185933225fb4fda | train | function | def model_fn(features, labels, mode, params):
vectors = features['v']
X = features['mel']
len_X = features['mel_length'][:, 0]
X_f0 = features['f0']
len_X_f0 = features['f0_length'][:, 0]
hparams = speechsplit.hparams
interplnr = speechsplit.InterpLnr(hparams)
model = speechsplit.Model(h... | def model_fn(features, labels, mode, params):
| vectors = features['v']
X = features['mel']
len_X = features['mel_length'][:, 0]
X_f0 = features['f0']
len_X_f0 = features['f0_length'][:, 0]
hparams = speechsplit.hparams
interplnr = speechsplit.InterpLnr(hparams)
model = speechsplit.Model(hparams)
model_F0 = speechsplit.Model_F0(hp... | ={
'audio': tf.TensorShape([None]),
'mel': tf.TensorShape([None, 80]),
'mel_length': tf.TensorShape([None]),
'f0': tf.TensorShape([None, 1]),
'f0_length': tf.TensorShape([None]),
'v': tf.TensorShape([512]),
},
... | 195 | 195 | 653 | 11 | 183 | ishine/malaya-speech | pretrained-model/speechsplit-conversion/speechsplit.py | Python | model_fn | model_fn | 114 | 181 | 114 | 114 | 0366de5086480eabe95aa1280067e1693bd2e7b4 | bigcode/the-stack | train |
124fd5c78cf5788abf393b35 | train | function | def get_dataset(batch_size=4):
def get():
dataset = tf.data.Dataset.from_generator(
generate,
{
'mel': tf.float32,
'mel_length': tf.int32,
'f0': tf.float32,
'f0_length': tf.int32,
'audio': tf.float32,
... | def get_dataset(batch_size=4):
| def get():
dataset = tf.data.Dataset.from_generator(
generate,
{
'mel': tf.float32,
'mel_length': tf.int32,
'f0': tf.float32,
'f0_length': tf.int32,
'audio': tf.float32,
'v': tf.float32,
... | pad_seq(f0)
wav_16k = malaya_speech.resample(wav, sr, 16000)
v = speaker_model([wav_16k])[0]
v = v / v.max()
yield {
'mel': mel,
'mel_length': [len(mel)],
'f0': f0,
'f0_length': [len(f0)],
... | 107 | 107 | 358 | 8 | 99 | ishine/malaya-speech | pretrained-model/speechsplit-conversion/speechsplit.py | Python | get_dataset | get_dataset | 65 | 108 | 65 | 65 | 1e9928586843ff3d578763318130f107d9676156 | bigcode/the-stack | train |
2b56dcc9df9f176dc6223adc | train | class | class TestUpdater(TestCase):
def setUp(self):
self.sentry_app = self.create_sentry_app()
self.service_hook = self.create_service_hook(
application=self.sentry_app.application
)
self.updater = Updater(service_hook=self.service_hook)
def test_updates_application(self)... | class TestUpdater(TestCase):
| def setUp(self):
self.sentry_app = self.create_sentry_app()
self.service_hook = self.create_service_hook(
application=self.sentry_app.application
)
self.updater = Updater(service_hook=self.service_hook)
def test_updates_application(self):
app = self.create_s... | from __future__ import absolute_import
from sentry.mediators.service_hooks import Updater
from sentry.testutils import TestCase
class TestUpdater(TestCase):
| 35 | 78 | 260 | 6 | 28 | detouched/sentry | tests/sentry/mediators/service_hooks/test_updater.py | Python | TestUpdater | TestUpdater | 7 | 42 | 7 | 7 | 1a71474d0db4ad24359c8b3595448e3d7889ba2f | bigcode/the-stack | train |
d820d91f73e085c083d691a8 | train | class | class ManafaMethodCoverageAnalyzer(AbstractAnalyzer):
"""Implements AbstractAnalyzer interface to allow analyze results with EManafa profiler.
Calculate statistics about the produced results to analyze, validate and characterize executions.
"""
def __init__(self, profiler):
self.supported_filte... | class ManafaMethodCoverageAnalyzer(AbstractAnalyzer):
| """Implements AbstractAnalyzer interface to allow analyze results with EManafa profiler.
Calculate statistics about the produced results to analyze, validate and characterize executions.
"""
def __init__(self, profiler):
self.supported_filters = {"method_coverage"}
super(ManafaMethodCov... | import json
import os
from anadroid.results_analysis.AbstractAnalyzer import AbstractAnalyzer
from anadroid.utils.Utils import loge, mega_find
class ManafaMethodCoverageAnalyzer(AbstractAnalyzer):
| 40 | 256 | 1,225 | 9 | 30 | greensoftwarelab/PyAnaDroid | anadroid/results_analysis/ManafaMethodCoverageAnalyzer.py | Python | ManafaMethodCoverageAnalyzer | ManafaMethodCoverageAnalyzer | 8 | 136 | 8 | 8 | 9a8e4247150562201353a75fa8cc9128000c076a | bigcode/the-stack | train |
1f80c89ea0587f057621d4b0 | train | class | class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
| class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
| MultipleChoice(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
FlaxAutoModelForMultipleChoice = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class FlaxAutoModelForNextSentencePrediction(_BaseAutoModelClass):
| 64 | 64 | 33 | 15 | 49 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForNextSentencePrediction | FlaxAutoModelForNextSentencePrediction | 280 | 281 | 280 | 280 | f7872a6164ebf6a04952309053aed768aaad3ec0 | bigcode/the-stack | train |
cc784f03b686290042c8f3b1 | train | class | class FlaxAutoModelForVision2Seq(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
| class FlaxAutoModelForVision2Seq(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
| Classification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
FlaxAutoModelForImageClassification = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class FlaxAutoModelForVision2Seq(_BaseAutoModelClass):
| 64 | 64 | 31 | 15 | 49 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForVision2Seq | FlaxAutoModelForVision2Seq | 298 | 299 | 298 | 298 | 6bbe289f9df5a600f30d129eef011c841bad9a9a | bigcode/the-stack | train |
9e1972c0d005e81b4eb2f9e2 | train | class | class FlaxAutoModelForPreTraining(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING
| class FlaxAutoModelForPreTraining(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING
| FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
class FlaxAutoModel(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_MAPPING
FlaxAutoModel = auto_class_update(FlaxAutoModel)
class FlaxAutoModelForPreTraining(_BaseAutoModelClass):
| 64 | 64 | 28 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForPreTraining | FlaxAutoModelForPreTraining | 218 | 219 | 218 | 218 | 8c32c4410c5c0c2ea6c89896e10b6fb7e70be402 | bigcode/the-stack | train |
3d358e8ad273cc660d6f6406 | train | class | class FlaxAutoModel(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_MAPPING
| class FlaxAutoModel(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_MAPPING
| _MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
class FlaxAutoModel(_BaseAutoModelClass):
| 64 | 64 | 21 | 11 | 53 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModel | FlaxAutoModel | 211 | 212 | 211 | 211 | bcded07c585623ef5aa060be219e520e503dc20d | bigcode/the-stack | train |
19746c13dffe533d1a4c009c | train | class | class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
| class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
| USAL_LM_MAPPING
FlaxAutoModelForSeq2SeqLM = auto_class_update(
FlaxAutoModelForSeq2SeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class FlaxAutoModelForSequenceClassification(_BaseAutoModelClass):
| 64 | 64 | 28 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForSequenceClassification | FlaxAutoModelForSequenceClassification | 248 | 249 | 248 | 248 | e29b5b3d8f72217c98d5815a2eadf39c13358e2e | bigcode/the-stack | train |
e72aad913e8f66a7ac85ad06 | train | class | class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
| class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
| Classification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
FlaxAutoModelForSequenceClassification = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class FlaxAutoModelForQuestionAnswering(_BaseAutoModelClass):
| 64 | 64 | 31 | 15 | 49 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForQuestionAnswering | FlaxAutoModelForQuestionAnswering | 257 | 258 | 257 | 257 | f8db87ebb51171f119810fbd9b189848671a794e | bigcode/the-stack | train |
06389d0c532472745aae196a | train | class | class FlaxAutoModelForMaskedLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING
| class FlaxAutoModelForMaskedLM(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING
| AutoModelClass):
_model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
FlaxAutoModelForCausalLM = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class FlaxAutoModelForMaskedLM(_BaseAutoModelClass):
| 64 | 64 | 29 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForMaskedLM | FlaxAutoModelForMaskedLM | 232 | 233 | 232 | 232 | 81c120de6c9aff0d77defde2295aaf5775faff3e | bigcode/the-stack | train |
ba999f68302d9bbf3fd07b9b | train | class | class FlaxAutoModelForTokenClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
| class FlaxAutoModelForTokenClassification(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
| (_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
FlaxAutoModelForQuestionAnswering = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class FlaxAutoModelForTokenClassification(_BaseAutoModelClass):
| 64 | 64 | 28 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForTokenClassification | FlaxAutoModelForTokenClassification | 264 | 265 | 264 | 264 | 720950c38050497a3d86319437e4a8ea3bc4ea53 | bigcode/the-stack | train |
1561d2855228cb42a5aeabe4 | train | class | class FlaxAutoModelForImageClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
| class FlaxAutoModelForImageClassification(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
| ):
_model_mapping = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
FlaxAutoModelForNextSentencePrediction = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class FlaxAutoModelForImageClassification(_BaseAutoModelClass):
| 64 | 64 | 28 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForImageClassification | FlaxAutoModelForImageClassification | 289 | 290 | 289 | 289 | a9d34a73a494129241cc289d78222fe03099c442 | bigcode/the-stack | train |
8e4a6ded8cb2f5161e53784d | train | class | class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
| class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
| TokenClassification(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
FlaxAutoModelForTokenClassification = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class FlaxAutoModelForMultipleChoice(_BaseAutoModelClass):
| 64 | 64 | 29 | 14 | 50 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForMultipleChoice | FlaxAutoModelForMultipleChoice | 273 | 274 | 273 | 273 | 6531bee8fb72fc69874810ad445b87f5a8eecd39 | bigcode/the-stack | train |
08d1ccefe60ef9cf05b2162a | train | class | class FlaxAutoModelForCausalLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
| class FlaxAutoModelForCausalLM(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
| ForPreTraining(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_PRETRAINING_MAPPING
FlaxAutoModelForPreTraining = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class FlaxAutoModelForCausalLM(_BaseAutoModelClass):
| 64 | 64 | 31 | 15 | 49 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForCausalLM | FlaxAutoModelForCausalLM | 225 | 226 | 225 | 225 | b9e10350625e4a50d853e22b16e7e48a38dcae83 | bigcode/the-stack | train |
c6e21d1a4598c8f455fe024b | train | class | class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
| class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass):
| _model_mapping = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
| (_BaseAutoModelClass):
_model_mapping = FLAX_MODEL_FOR_MASKED_LM_MAPPING
FlaxAutoModelForMaskedLM = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class FlaxAutoModelForSeq2SeqLM(_BaseAutoModelClass):
| 64 | 64 | 35 | 16 | 48 | liminghao1630/transformers | src/transformers/models/auto/modeling_flax_auto.py | Python | FlaxAutoModelForSeq2SeqLM | FlaxAutoModelForSeq2SeqLM | 239 | 240 | 239 | 239 | f3b5c6194b0414cf9f49a424a131e47b553dc001 | bigcode/the-stack | train |
18bea6e6398ddf8ac9c7ede6 | train | function | def shape(fig, alpha, color, edge_c, edge_w, grid, sides, edges, multi_pi, radius, height):
# Definition of x
def x_(u, v):
x = u
return x
# Definition of y
def y_(u, v):
y = (a * cos(v)) / u
return y
# Definition of z
def z_(u, v):
z = (a * sin(v)) /u
return z
a = radius # changes radius of the ... | def shape(fig, alpha, color, edge_c, edge_w, grid, sides, edges, multi_pi, radius, height):
# Definition of x
| def x_(u, v):
x = u
return x
# Definition of y
def y_(u, v):
y = (a * cos(v)) / u
return y
# Definition of z
def z_(u, v):
z = (a * sin(v)) /u
return z
a = radius # changes radius of the entire thing
h = height
# Value of the angles
s = sides
u = linspace(1, h, s + 1)
v = linspace(0, 2 * pi... | # A Gabriel's Horn, brought to you by PharaohCola13
import mpl_toolkits.mplot3d.axes3d as p3
import matplotlib.pyplot as plt
from matplotlib import *
from numpy import *
from mpl_toolkits.mplot3d.art3d import *
from matplotlib.animation import *
name = "Gabriel's-Horn"
def shape(fig, alpha, color, edge_c, edge_w, gri... | 104 | 109 | 364 | 33 | 71 | gitter-badger/GeoMetrics | src/GUI/compile_space/gabriel_horn.py | Python | shape | shape | 12 | 64 | 12 | 13 | dc5276c87a367e2d6d1c9abecc125bee1de2bc10 | bigcode/the-stack | train |
f058f1eb3c4f725b1d523149 | train | function | def init_unix_connection_engine(
db_user: str,
db_pass: str,
db_name: str,
instance_connection_name: str,
db_socket_dir: str,
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-m... | def init_unix_connection_engine(
db_user: str,
db_pass: str,
db_name: str,
instance_connection_name: str,
db_socket_dir: str,
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-m... | pool = sqlalchemy.create_engine(
# Equivalent URL:
# mpostgresql+pg8000://<db_user>:<db_pass>@/<db_name>?unix_socket=<socket_path>/<cloud_sql_instance_name>
sqlalchemy.engine.url.URL.create(
drivername="postgresql+pg8000",
username=db_user, # e.g. "my-database-user"
... | def init_unix_connection_engine(
db_user: str,
db_pass: str,
db_name: str,
instance_connection_name: str,
db_socket_dir: str,
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-m... | 85 | 82 | 276 | 85 | 0 | InstantDomain/python-docs-samples | cloud-sql/postgres/client-side-encryption/snippets/cloud_sql_connection_pool.py | Python | init_unix_connection_engine | init_unix_connection_engine | 46 | 73 | 46 | 56 | b1416f34badeb90185cc515c6392792d6c5be9d5 | bigcode/the-stack | train |
ed592939dc6eeb2c97484083 | train | function | def init_db(
db_user: str,
db_pass: str,
db_name: str,
table_name: str,
instance_connection_name: str = None,
db_socket_dir: str = None,
db_host: str = None,
) -> sqlalchemy.engine.base.Engine:
if db_host:
db = init_tcp_connection_engine(db_user, db_pass, db_name, db_host)
e... | def init_db(
db_user: str,
db_pass: str,
db_name: str,
table_name: str,
instance_connection_name: str = None,
db_socket_dir: str = None,
db_host: str = None,
) -> sqlalchemy.engine.base.Engine:
| if db_host:
db = init_tcp_connection_engine(db_user, db_pass, db_name, db_host)
else:
db = init_unix_connection_engine(
db_user, db_pass, db_name, instance_connection_name, db_socket_dir
)
# Create tables (if they don't already exist)
with db.connect() as conn:
... | return pool
def init_db(
db_user: str,
db_pass: str,
db_name: str,
table_name: str,
instance_connection_name: str = None,
db_socket_dir: str = None,
db_host: str = None,
) -> sqlalchemy.engine.base.Engine:
| 64 | 64 | 216 | 61 | 2 | InstantDomain/python-docs-samples | cloud-sql/postgres/client-side-encryption/snippets/cloud_sql_connection_pool.py | Python | init_db | init_db | 76 | 103 | 76 | 85 | 045deafee25924021f357573ec1c18f8003069a9 | bigcode/the-stack | train |
e41307d939ea3bde300b03e1 | train | function | def init_tcp_connection_engine(
db_user: str, db_pass: str, db_name: str, db_host: str
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-manager/docs/overview to help keep
# secrets secret.... | def init_tcp_connection_engine(
db_user: str, db_pass: str, db_name: str, db_host: str
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-manager/docs/overview to help keep
# secrets secret.... | host_args = db_host.split(":")
db_hostname, db_port = host_args[0], int(host_args[1])
pool = sqlalchemy.create_engine(
# Equivalent URL:
# postgresql+pg8000://<db_user>:<db_pass>@<db_host>:<db_port>/<db_name>
sqlalchemy.engine.url.URL.create(
drivername="postgresql+pg800... | def init_tcp_connection_engine(
db_user: str, db_pass: str, db_name: str, db_host: str
) -> sqlalchemy.engine.base.Engine:
# Remember - storing secrets in plaintext is potentially unsafe. Consider using
# something like https://cloud.google.com/secret-manager/docs/overview to help keep
# secrets secret.... | 83 | 79 | 265 | 83 | 0 | InstantDomain/python-docs-samples | cloud-sql/postgres/client-side-encryption/snippets/cloud_sql_connection_pool.py | Python | init_tcp_connection_engine | init_tcp_connection_engine | 19 | 43 | 19 | 26 | f1796e86b57cc83485283238513aefd3198ad828 | bigcode/the-stack | train |
e8ac0f9fd84ec6806bf12bac | train | class | class TestDungeons(TestVanillaOWG):
def testFirstDungeonChests(self):
self.run_location_tests([
["Hyrule Castle - Map Chest", True, []],
["Sanctuary", True, []],
["Sewers - Secret Room - Left", False, []],
["Sewers - Secret Room - Left", True, ['Progressive... | class TestDungeons(TestVanillaOWG):
| def testFirstDungeonChests(self):
self.run_location_tests([
["Hyrule Castle - Map Chest", True, []],
["Sanctuary", True, []],
["Sewers - Secret Room - Left", False, []],
["Sewers - Secret Room - Left", True, ['Progressive Glove']],
["Sewers - Sec... | from test.owg.TestVanillaOWG import TestVanillaOWG
class TestDungeons(TestVanillaOWG):
| 27 | 256 | 2,399 | 10 | 16 | RoflCopter69/MultiWorld-Utilities | test/owg/TestDungeons.py | Python | TestDungeons | TestDungeons | 4 | 131 | 4 | 5 | d1b80ae6ce21bb2ebb71d510a855d2baba68bd0e | bigcode/the-stack | train |
c55fac6b604d82849f42a9f5 | train | class | class DueDiligenceController(BaseController):
"""Due Diligence Controller class"""
CHOICES_COMMANDS = ["load", "oi", "active", "change", "nonzero", "eb"]
SPECIFIC_CHOICES = {
"cp": [
"events",
"twitter",
"ex",
"mkt",
"ps",
"ba... | class DueDiligenceController(BaseController):
| """Due Diligence Controller class"""
CHOICES_COMMANDS = ["load", "oi", "active", "change", "nonzero", "eb"]
SPECIFIC_CHOICES = {
"cp": [
"events",
"twitter",
"ex",
"mkt",
"ps",
"basic",
],
"cg": [
"... | List
from datetime import datetime, timedelta
import pandas as pd
from prompt_toolkit.completion import NestedCompleter
from gamestonk_terminal.rich_config import console
from gamestonk_terminal.parent_classes import BaseController
from gamestonk_terminal.cryptocurrency.due_diligence import (
coinglass_model,
... | 255 | 256 | 7,923 | 9 | 246 | JakubPluta/GamestonkTerminal | gamestonk_terminal/cryptocurrency/due_diligence/dd_controller.py | Python | DueDiligenceController | DueDiligenceController | 44 | 1,210 | 44 | 44 | 5e8bcaf6fdb6a5c2930dacc7a38b8a3eb6b4ba5e | bigcode/the-stack | train |
9d60f71ef362f5f23fa015ba | train | class | class icons:
logo = b'x\xda-V\xc7\x1a\xa20\x10~ \x0f\x02\x02\xcaa\x0f\t\xbd7A\xe0&-A\xaa\x14\x01\x9f~q\xbf\r\x04\xc8\xf4\xf9g\x92\x8f*\x84\xb6\xb7\x12\xba\x8czp\x0c\xcb\x0f\xb0\x18\xa0\xe3Kv\x8f\x07\xacy`\xfe\xde\x8a#\x87\xedO\x00\xca9\xbc\x07"\x00\x86\xec\xf0\xe7\r\xc3\x9f\x18\x90\xbeA\x9d\xf3@\xbf\xda\x8a]\x1fk\xa... | class icons:
| logo = b'x\xda-V\xc7\x1a\xa20\x10~ \x0f\x02\x02\xcaa\x0f\t\xbd7A\xe0&-A\xaa\x14\x01\x9f~q\xbf\r\x04\xc8\xf4\xf9g\x92\x8f*\x84\xb6\xb7\x12\xba\x8czp\x0c\xcb\x0f\xb0\x18\xa0\xe3Kv\x8f\x07\xacy`\xfe\xde\x8a#\x87\xedO\x00\xca9\xbc\x07"\x00\x86\xec\xf0\xe7\r\xc3\x9f\x18\x90\xbeA\x9d\xf3@\xbf\xda\x8a]\x1fk\xa3\x0f\xc4-\xf0... | except OSError:
temp_dir = Path(Path(__file__).parent.resolve(), 'temp')
os.mkdir(temp_dir+'\\Whirledit\\')
configuration = """
Key Bindings:
Close: <Control-w>
Fullscreen: <F11>
New: <Control-n>
Open: <Control-o>
Open cmd: <Control-Shift-t>
Run: <F5>
Save: <Control-s>
Lo... | 256 | 256 | 3,086 | 3 | 252 | Redysz/WhirlEdit | data.py | Python | icons | icons | 50 | 62 | 50 | 50 | 7f419e3f4e74810a00faed807ae0b6bb381ec775 | bigcode/the-stack | train |
0ad75d375d38ea12bde7653d | train | function | def execute_testcase(testcase, session=None, request_options={}):
"""
Executes a testcase
"""
# Construct a new session if one has not been provided, whose scope will#
# only last through this testcase
if not session:
session = requests.Session()
# Set a default User-Agent (this ca... | def execute_testcase(testcase, session=None, request_options={}):
| """
Executes a testcase
"""
# Construct a new session if one has not been provided, whose scope will#
# only last through this testcase
if not session:
session = requests.Session()
# Set a default User-Agent (this can be overriden by user options)
headers = {
"User-Agen... | from __future__ import absolute_import, division, print_function, with_statement, unicode_literals
import catnap
import requests
# Python2/3 compatible way of coercing values to unicode via str()
try:
str = unicode
except NameError:
pass
def execute_testcase(testcase, session=None, request_options={}):
| 70 | 190 | 634 | 14 | 55 | dailymuse/catnap | catnap/worker.py | Python | execute_testcase | execute_testcase | 12 | 89 | 12 | 12 | dd276d536a590bf677dda38e830bf6931b2ba289 | bigcode/the-stack | train |
902c6a3675f5fba663185800 | train | function | def _get_defun_inputs_from_args(args, names, flat_shapes=None):
"""Maps Python function positional args to graph-construction inputs."""
return _get_defun_inputs(
args, names, structure=args, flat_shapes=flat_shapes)
| def _get_defun_inputs_from_args(args, names, flat_shapes=None):
| """Maps Python function positional args to graph-construction inputs."""
return _get_defun_inputs(
args, names, structure=args, flat_shapes=flat_shapes)
| shape = value.shape
with ops.control_dependencies(None):
placeholder = graph_placeholder(
dtype=dtype or value.dtype, shape=shape, name=name)
custom_gradient.copy_handle_data(value, placeholder)
return placeholder
def _get_defun_inputs_from_args(args, names, flat_shapes=None):
| 64 | 64 | 51 | 16 | 47 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | _get_defun_inputs_from_args | _get_defun_inputs_from_args | 1,145 | 1,148 | 1,145 | 1,145 | 6f26d05dff979d32146b29876942898bad449e3e | bigcode/the-stack | train |
7d90f4ae2b86f0d6a54f1231 | train | function | def pack_sequence_as(structure, flat_sequence):
"""Like `nest.pack_sequence_as` but also builds TensorArrays from flows.
Args:
structure: The structure to pack into. May contain Tensors,
CompositeTensors, or TensorArrays.
flat_sequence: An iterable containing tensors.
Returns:
A nested structu... | def pack_sequence_as(structure, flat_sequence):
| """Like `nest.pack_sequence_as` but also builds TensorArrays from flows.
Args:
structure: The structure to pack into. May contain Tensors,
CompositeTensors, or TensorArrays.
flat_sequence: An iterable containing tensors.
Returns:
A nested structure.
Raises:
AssertionError if `structure`... | nest.flatten(sequence, expand_composites=True)
return [
item.flow if isinstance(item, tensor_array_ops.TensorArray) else item
for item in flat_sequence]
# TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this.
def pack_sequence_as(structure, flat_sequence):
| 64 | 64 | 211 | 10 | 54 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | pack_sequence_as | pack_sequence_as | 1,107 | 1,129 | 1,107 | 1,107 | 41ed6836d9ea469f54296661cca00438207d3ab1 | bigcode/the-stack | train |
d1ba3dc873c6956d80589976 | train | function | def maybe_captured(tensor):
"""If t is a captured value placeholder, returns the original captured value.
Args:
tensor: Tensor.
Returns:
A tensor, potentially from a different Graph/FuncGraph.
"""
if (not isinstance(tensor, ops.EagerTensor) and
tensor.op.graph.building_function and tensor.op.t... | def maybe_captured(tensor):
| """If t is a captured value placeholder, returns the original captured value.
Args:
tensor: Tensor.
Returns:
A tensor, potentially from a different Graph/FuncGraph.
"""
if (not isinstance(tensor, ops.EagerTensor) and
tensor.op.graph.building_function and tensor.op.type == "Placeholder"):
f... | None)
func_graph.variables = variables
if add_control_dependencies:
func_graph.control_outputs.extend(deps_control_manager.ops_which_must_run)
func_graph.collective_manager_ids_used = (
deps_control_manager.collective_manager_ids_used)
return func_graph
def maybe_captured(tensor):
| 64 | 64 | 122 | 7 | 56 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | maybe_captured | maybe_captured | 1,046 | 1,061 | 1,046 | 1,046 | 825cbecfb64aa5f7f613241c6b1548ddf9eea6f3 | bigcode/the-stack | train |
7fae1c1e6688fe75870e16ea | train | function | def dismantle_func_graph(func_graph):
"""Removes reference cycles in `func_graph` FuncGraph.
Helpful for making sure the garbage collector doesn't need to run when
the FuncGraph goes out of scope, e.g. in tests using defun with
@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True).
Args:
... | def dismantle_func_graph(func_graph):
| """Removes reference cycles in `func_graph` FuncGraph.
Helpful for making sure the garbage collector doesn't need to run when
the FuncGraph goes out of scope, e.g. in tests using defun with
@test_util.run_in_graph_and_eager_modes(assert_no_eager_garbage=True).
Args:
func_graph: A `FuncGraph` object to d... | args to graph-construction inputs."""
if kwargs:
names, args = zip(*sorted(kwargs.items()))
else:
names = []
args = []
return _get_defun_inputs(
args, names, structure=kwargs, flat_shapes=flat_shapes)
def dismantle_func_graph(func_graph):
| 64 | 64 | 124 | 8 | 56 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | dismantle_func_graph | dismantle_func_graph | 1,281 | 1,293 | 1,281 | 1,281 | 59b270d4a63fa89884af3672ca416c3b65efab41 | bigcode/the-stack | train |
d8366e4847fa692d998dfbf7 | train | function | def _create_substitute_placeholder(value, name=None, dtype=None, shape=None):
"""Creates a placeholder for `value` and propagates shape info to it."""
# Note: setting ops.control_dependencies(None) ensures we always put
# capturing placeholders outside of any control flow context.
if shape is None:
shape = ... | def _create_substitute_placeholder(value, name=None, dtype=None, shape=None):
| """Creates a placeholder for `value` and propagates shape info to it."""
# Note: setting ops.control_dependencies(None) ensures we always put
# capturing placeholders outside of any control flow context.
if shape is None:
shape = value.shape
with ops.control_dependencies(None):
placeholder = graph_pla... | [i] = tensor_array_ops.build_ta_with_new_flow(
old_ta=flattened_structure[i], flow=flat_sequence[i])
return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True)
def _create_substitute_placeholder(value, name=None, dtype=None, shape=None):
| 64 | 64 | 114 | 17 | 47 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | _create_substitute_placeholder | _create_substitute_placeholder | 1,132 | 1,142 | 1,132 | 1,132 | 86d814727b84d99be6e23f403e98c421431a05fe | bigcode/the-stack | train |
b1b15e23f20f6123a5d9f0e9 | train | function | def _get_defun_inputs_from_kwargs(kwargs, flat_shapes):
"""Maps Python function keyword args to graph-construction inputs."""
if kwargs:
names, args = zip(*sorted(kwargs.items()))
else:
names = []
args = []
return _get_defun_inputs(
args, names, structure=kwargs, flat_shapes=flat_shapes)
| def _get_defun_inputs_from_kwargs(kwargs, flat_shapes):
| """Maps Python function keyword args to graph-construction inputs."""
if kwargs:
names, args = zip(*sorted(kwargs.items()))
else:
names = []
args = []
return _get_defun_inputs(
args, names, structure=kwargs, flat_shapes=flat_shapes)
| saw arg: '%s', shape: '%s'. args: %s"
% (arg, shape, args))
function_inputs.append(arg)
return nest.pack_sequence_as(structure, function_inputs,
expand_composites=True)
def _get_defun_inputs_from_kwargs(kwargs, flat_shapes):
| 64 | 64 | 75 | 13 | 51 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | _get_defun_inputs_from_kwargs | _get_defun_inputs_from_kwargs | 1,270 | 1,278 | 1,270 | 1,270 | db2cd3a43271786c465ead7759597ba83f9249eb | bigcode/the-stack | train |
ae7cf425670b60e7c78f2f7a | train | function | def _get_defun_inputs(args, names, structure, flat_shapes=None):
"""Maps python function args to graph-construction inputs.
Args:
args: A flat list of user-specified arguments.
names: A list of strings with user-specified argument names, same length as
`args`. May be `None`, in which case a generic n... | def _get_defun_inputs(args, names, structure, flat_shapes=None):
| """Maps python function args to graph-construction inputs.
Args:
args: A flat list of user-specified arguments.
names: A list of strings with user-specified argument names, same length as
`args`. May be `None`, in which case a generic name is used.
structure: The original argument list or diction... | ])
return nest.pack_sequence_as(structure, flat_sequence, expand_composites=True)
def _create_substitute_placeholder(value, name=None, dtype=None, shape=None):
"""Creates a placeholder for `value` and propagates shape info to it."""
# Note: setting ops.control_dependencies(None) ensures we always put
# captur... | 256 | 256 | 1,073 | 16 | 240 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | _get_defun_inputs | _get_defun_inputs | 1,157 | 1,267 | 1,157 | 1,157 | 1e32aefff62150e485d1065e9da27781ca5dca43 | bigcode/the-stack | train |
23a23b0fd9cc2db65b47ca0e | train | function | def flatten(sequence):
"""Like nest.flatten w/ expand_composites, but returns flow for TensorArrays.
Args:
sequence: A nested structure of Tensors, CompositeTensors, and
TensorArrays.
Returns:
A list of tensors.
"""
flat_sequence = nest.flatten(sequence, expand_composites=True)
return [
... | def flatten(sequence):
| """Like nest.flatten w/ expand_composites, but returns flow for TensorArrays.
Args:
sequence: A nested structure of Tensors, CompositeTensors, and
TensorArrays.
Returns:
A list of tensors.
"""
flat_sequence = nest.flatten(sequence, expand_composites=True)
return [
item.flow if isinstan... | in zip(nest.flatten(n1, expand_composites=True),
nest.flatten(n2, expand_composites=True)):
if arg1 is not arg2:
raise ValueError(errmsg)
# TODO(edloper): If TensorArray becomes a CompositeTensor, then delete this.
def flatten(sequence):
| 64 | 64 | 97 | 4 | 60 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | flatten | flatten | 1,090 | 1,103 | 1,090 | 1,090 | e482e6172498cf5263b717c520c2248b42501f21 | bigcode/the-stack | train |
2e49da2f9a249205fec3c234 | train | function | def check_mutation(n1, n2, func):
"""Check if two list of arguments are exactly the same."""
func_name = getattr(func, "__name__", func)
errmsg = ("{}() should not modify its Python input arguments."
" Check if it modifies any lists or dicts passed as"
" arguments. Modifying a copy is all... | def check_mutation(n1, n2, func):
| """Check if two list of arguments are exactly the same."""
func_name = getattr(func, "__name__", func)
errmsg = ("{}() should not modify its Python input arguments."
" Check if it modifies any lists or dicts passed as"
" arguments. Modifying a copy is allowed.".format(func_name))
try:
... | return tensor
def device_stack_has_callable(device_stack):
"""Checks whether a device stack contains a callable."""
return any(callable(spec._device_name_or_function) # pylint: disable=protected-access
for spec in device_stack.peek_objs())
def check_mutation(n1, n2, func):
| 64 | 64 | 179 | 12 | 52 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | check_mutation | check_mutation | 1,070 | 1,086 | 1,070 | 1,070 | f86c3b7384bbb7d4eacd79e7a1d1510d288bb1bf | bigcode/the-stack | train |
0ffd0b04e8b7e2d48dc7447f | train | function | def override_func_graph_name_scope(func_graph, name_scope):
func_graph._name_stack = name_scope # pylint: disable=protected-access
| def override_func_graph_name_scope(func_graph, name_scope):
| func_graph._name_stack = name_scope # pylint: disable=protected-access
| bage=True).
Args:
func_graph: A `FuncGraph` object to destroy. `func_graph` is unusable
after this function.
"""
func_graph.clear_captures()
ops.dismantle_graph(func_graph)
def override_func_graph_name_scope(func_graph, name_scope):
| 64 | 64 | 30 | 12 | 52 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | override_func_graph_name_scope | override_func_graph_name_scope | 1,296 | 1,297 | 1,296 | 1,296 | b88aa1c7e3cb76b6bf05052080bc055094566329 | bigcode/the-stack | train |
894fc98417ce8c166b6a9b9b | train | function | def _get_composite_tensor_spec(x):
"""Returns the TypeSpec for x if it's a composite tensor, or x otherwise."""
return (x._type_spec # pylint: disable=protected-access
if isinstance(x, composite_tensor.CompositeTensor) else x)
| def _get_composite_tensor_spec(x):
| """Returns the TypeSpec for x if it's a composite tensor, or x otherwise."""
return (x._type_spec # pylint: disable=protected-access
if isinstance(x, composite_tensor.CompositeTensor) else x)
| return placeholder
def _get_defun_inputs_from_args(args, names, flat_shapes=None):
"""Maps Python function positional args to graph-construction inputs."""
return _get_defun_inputs(
args, names, structure=args, flat_shapes=flat_shapes)
def _get_composite_tensor_spec(x):
| 64 | 64 | 56 | 9 | 55 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | _get_composite_tensor_spec | _get_composite_tensor_spec | 1,151 | 1,154 | 1,151 | 1,151 | 169a4c6536679e2f9356b6a85f30ea5f84972579 | bigcode/the-stack | train |
05e5d5aa5fa109d3d6c15a68 | train | class | class FuncGraph(ops.Graph):
"""Graph representing a function body.
Attributes:
name: The name of the function.
inputs: Placeholder tensors representing the inputs to this function. The
tensors are in this FuncGraph. This represents "regular" inputs as well as
captured inputs (i.e. the values of... | class FuncGraph(ops.Graph):
| """Graph representing a function body.
Attributes:
name: The name of the function.
inputs: Placeholder tensors representing the inputs to this function. The
tensors are in this FuncGraph. This represents "regular" inputs as well as
captured inputs (i.e. the values of self.captures), with the re... | "/".join(str(p) for p in path)
return resource_variable_ops.VariableSpec(arg.shape, arg.dtype, name)
if isinstance(arg, (
int,
float,
bool,
type(None),
dtypes.DType,
tensor_spec.TensorSpec,
type_spec.TypeSpec,
)):
return arg
return Unknown... | 256 | 256 | 6,145 | 7 | 249 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | FuncGraph | FuncGraph | 135 | 807 | 135 | 135 | a7bb4ddd67f9015b9efbd21e758081d355cfcdad | bigcode/the-stack | train |
494bf01067273036b8d181ea | train | function | def convert_structure_to_signature(structure, arg_names=None):
"""Convert a potentially nested structure to a signature.
Args:
structure: Structure to convert, where top level collection is a list or a
tuple.
arg_names: Optional list of arguments that has equal number of elements as
`structure`... | def convert_structure_to_signature(structure, arg_names=None):
| """Convert a potentially nested structure to a signature.
Args:
structure: Structure to convert, where top level collection is a list or a
tuple.
arg_names: Optional list of arguments that has equal number of elements as
`structure` and is used for naming corresponding TensorSpecs.
Returns:
... | _ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.util import compat
from tensorflow.python.util import memory
from tensorflow.python.util import nest
from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_deco... | 167 | 167 | 557 | 12 | 154 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | convert_structure_to_signature | convert_structure_to_signature | 70 | 132 | 70 | 70 | 1a9fa120e88571fef66cbc2b93eb802ed6eb7b43 | bigcode/the-stack | train |
79a260d52df688f97641ea8c | train | function | def device_stack_has_callable(device_stack):
"""Checks whether a device stack contains a callable."""
return any(callable(spec._device_name_or_function) # pylint: disable=protected-access
for spec in device_stack.peek_objs())
| def device_stack_has_callable(device_stack):
| """Checks whether a device stack contains a callable."""
return any(callable(spec._device_name_or_function) # pylint: disable=protected-access
for spec in device_stack.peek_objs())
| .op.graph.building_function and tensor.op.type == "Placeholder"):
for input_t, placeholder_t in tensor.op.graph.captures:
if tensor == placeholder_t:
return maybe_captured(input_t)
# pylint: enable=protected-access
return tensor
def device_stack_has_callable(device_stack):
| 64 | 64 | 49 | 8 | 55 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | device_stack_has_callable | device_stack_has_callable | 1,064 | 1,067 | 1,064 | 1,064 | 231242fe4e2a103358c9a973198e28dec4774620 | bigcode/the-stack | train |
57b8916302b7e143e0a54be8 | train | class | class UnknownArgument(object):
"""Signifies an argument which is not currently handled."""
pass
| class UnknownArgument(object):
| """Signifies an argument which is not currently handled."""
pass
| .LOCAL_VARIABLES,
ops.GraphKeys.TRAINABLE_VARIABLES,
variable_scope._VARSTORE_KEY, # pylint: disable=protected-access
variable_scope._VARSCOPESTORE_KEY # pylint: disable=protected-access
]
_EAGER_CONST_THRESHOLD = 128
class UnknownArgument(object):
| 64 | 64 | 20 | 5 | 58 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | UnknownArgument | UnknownArgument | 65 | 67 | 65 | 65 | 81dda575f92cb008ee093c6f7fe0a942a306dedf | bigcode/the-stack | train |
10c96299b614d3ce68328915 | train | function | def func_graph_from_py_func(name,
python_func,
args,
kwargs,
signature=None,
func_graph=None,
autograph=False,
autograph_opt... | def func_graph_from_py_func(name,
python_func,
args,
kwargs,
signature=None,
func_graph=None,
autograph=False,
autograph_opt... | """Returns a `FuncGraph` generated from `python_func`.
Args:
name: an identifier for the function.
python_func: the Python function to trace.
args: the positional args with which the Python function should be called;
ignored if a signature is provided.
kwargs: the keyword args with which the ... | _message = [error_message]
self._saving_errors.update(error_message)
@property
def saveable(self):
"""Returns whether this FuncGraph is saveable."""
return self._saveable
@property
def saving_errors(self):
"""Returns set of errors preventing this FuncGraph from being saved."""
return self.... | 256 | 256 | 2,195 | 70 | 186 | RickeyEstes/tensorflow | tensorflow/python/framework/func_graph.py | Python | func_graph_from_py_func | func_graph_from_py_func | 810 | 1,043 | 810 | 823 | 11fc636fe9b5e2f89d914c5fa08213120f815858 | bigcode/the-stack | train |
9d6bd4fbd466712131b11626 | train | function | def flush(proc, proc_log):
while True:
proc_out = proc.stdout.readline()
if proc_out == "" and proc.poll() is not None:
proc_log.close()
break
elif proc_out:
sys.stdout.write(proc_out)
proc_log.write(proc_out)
proc_log.flush()
| def flush(proc, proc_log):
| while True:
proc_out = proc.stdout.readline()
if proc_out == "" and proc.poll() is not None:
proc_log.close()
break
elif proc_out:
sys.stdout.write(proc_out)
proc_log.write(proc_out)
proc_log.flush()
| import os
import argparse
import time
from dask.distributed import Client
import sys, uuid
import threading
import subprocess
import socket
import mlflow
from notebook.notebookapp import list_running_servers
def flush(proc, proc_log):
| 52 | 64 | 65 | 7 | 44 | sewald101/azureml-examples | cli/jobs/single-step/dask/nyctaxi/src/startDask.py | Python | flush | flush | 14 | 23 | 14 | 14 | 038b6f132f82a189e89eebd88687fb67e34305c5 | bigcode/the-stack | train |
b16018d6fab38c3dbe06f96c | train | function | def to_omrs_date(value):
"""
Drop the time and timezone to export date-only values
>>> to_omrs_date('2017-06-27T12:00:00+0530') == '2017-06-27'
True
"""
if isinstance(value, six.string_types):
soft_assert_type_text(value)
if not re.match(r'\d{4}-\d{2}-\d{2}', value):
... | def to_omrs_date(value):
| """
Drop the time and timezone to export date-only values
>>> to_omrs_date('2017-06-27T12:00:00+0530') == '2017-06-27'
True
"""
if isinstance(value, six.string_types):
soft_assert_type_text(value)
if not re.match(r'\d{4}-\d{2}-\d{2}', value):
raise ValueError('"{}" ... | otech.openmrs.const import (
OPENMRS_DATA_TYPE_BOOLEAN,
OPENMRS_DATA_TYPE_DATE,
OPENMRS_DATA_TYPE_DATETIME,
)
from corehq.motech.serializers import serializers
from corehq.util.python_compatibility import soft_assert_type_text
def to_omrs_date(value):
| 64 | 64 | 151 | 8 | 55 | kkrampa/commcare-hq | corehq/motech/openmrs/serializers.py | Python | to_omrs_date | to_omrs_date | 24 | 38 | 24 | 24 | b1c7035399049ce701d925b01fac99001daafa00 | bigcode/the-stack | train |
12b1b08b7e42590682783de1 | train | function | def to_omrs_boolean(value):
if (
isinstance(value, six.string_types)
and value.lower() in ('false', '0')
):
return False
return bool(value)
| def to_omrs_boolean(value):
| if (
isinstance(value, six.string_types)
and value.lower() in ('false', '0')
):
return False
return bool(value)
| RS
tz = value.strftime('%z') or '+0000' # If we don't know, lie
return value.strftime('%Y-%m-%dT%H:%M:%S.{f}{z}'.format(f=micros, z=tz))
def to_omrs_boolean(value):
| 64 | 64 | 42 | 8 | 56 | kkrampa/commcare-hq | corehq/motech/openmrs/serializers.py | Python | to_omrs_boolean | to_omrs_boolean | 60 | 66 | 60 | 60 | fe1a2da64a752a06ae4b246d7d852a903de37a94 | bigcode/the-stack | train |
863a848417d215198db64e91 | train | function | def to_omrs_datetime(value):
"""
Converts CommCare dates and datetimes to OpenMRS datetimes.
>>> to_omrs_datetime('2017-06-27') == '2017-06-27T00:00:00.000+0000'
True
"""
if isinstance(value, six.string_types):
soft_assert_type_text(value)
if not re.match(r'\d{4}-\d{2}-\d{2}', ... | def to_omrs_datetime(value):
| """
Converts CommCare dates and datetimes to OpenMRS datetimes.
>>> to_omrs_datetime('2017-06-27') == '2017-06-27T00:00:00.000+0000'
True
"""
if isinstance(value, six.string_types):
soft_assert_type_text(value)
if not re.match(r'\d{4}-\d{2}-\d{2}', value):
raise Val... | d{2}', value):
raise ValueError('"{}" is not recognised as a date or a datetime'.format(value))
value = dateutil_parser.parse(value)
if isinstance(value, (datetime.date, datetime.datetime)):
return value.strftime('%Y-%m-%d')
def to_omrs_datetime(value):
| 66 | 66 | 221 | 8 | 58 | kkrampa/commcare-hq | corehq/motech/openmrs/serializers.py | Python | to_omrs_datetime | to_omrs_datetime | 41 | 57 | 41 | 41 | 71f4c68f3116ed3f88a8b20225b30dadd14ee186 | bigcode/the-stack | train |
9d40f22da42589011162a4ee | train | function | def omrs_datetime_to_date(value):
"""
Converts an OpenMRS datetime to a CommCare date
>>> omrs_datetime_to_date('2017-06-27T00:00:00.000+0000') == '2017-06-27'
True
"""
if value and 'T' in value:
return value.split('T')[0]
return value
| def omrs_datetime_to_date(value):
| """
Converts an OpenMRS datetime to a CommCare date
>>> omrs_datetime_to_date('2017-06-27T00:00:00.000+0000') == '2017-06-27'
True
"""
if value and 'T' in value:
return value.split('T')[0]
return value
| f}{z}'.format(f=micros, z=tz))
def to_omrs_boolean(value):
if (
isinstance(value, six.string_types)
and value.lower() in ('false', '0')
):
return False
return bool(value)
def omrs_datetime_to_date(value):
| 64 | 64 | 86 | 8 | 56 | kkrampa/commcare-hq | corehq/motech/openmrs/serializers.py | Python | omrs_datetime_to_date | omrs_datetime_to_date | 69 | 79 | 69 | 69 | e05cb4c87738508e1ee7235b7f07fa8132b6048d | bigcode/the-stack | train |
24410ccaf792725707065101 | train | function | def omrs_boolean_to_text(value):
return 'true' if value else 'false'
| def omrs_boolean_to_text(value):
| return 'true' if value else 'false'
| ('2017-06-27T00:00:00.000+0000') == '2017-06-27'
True
"""
if value and 'T' in value:
return value.split('T')[0]
return value
def omrs_boolean_to_text(value):
| 64 | 64 | 19 | 8 | 55 | kkrampa/commcare-hq | corehq/motech/openmrs/serializers.py | Python | omrs_boolean_to_text | omrs_boolean_to_text | 82 | 83 | 82 | 82 | 3ac75e0db5f7b8abcfbdfc257ff7322fa37fbe2f | bigcode/the-stack | train |
849623e72eaaa7b827d78a83 | train | function | @app.route('/', methods=['POST'])
def sms():
number = request.form['From']
message_body = request.form['Body']
response_str = mpd_controller.handle_sms_request(request)
resp = MessagingResponse()
resp.message(response_str)
return str(resp)
| @app.route('/', methods=['POST'])
def sms():
| number = request.form['From']
message_body = request.form['Body']
response_str = mpd_controller.handle_sms_request(request)
resp = MessagingResponse()
resp.message(response_str)
return str(resp)
| from flask import Flask, request
from twilio.twiml.messaging_response import Message, MessagingResponse
from MPDController import MPDController
app = Flask(__name__)
mpd_controller = MPDController()
@app.route('/', methods=['POST'])
def sms():
| 54 | 64 | 57 | 10 | 44 | phuston/sms-mpd | run.py | Python | sms | sms | 9 | 19 | 9 | 10 | b809674d5676154c66716ab164421dcfc53a39f6 | bigcode/the-stack | train |
ab4ae150ba5a77b2737b91d3 | train | class | class Test(BaseGPUTest):
def test_simple_input_internal_inf(self):
net = BasicModel_MultiLayer(inplace=True).cuda()
inp = torch.tensor(
[
[0.0, 100.0, 0.0],
[20.0, 100.0, 120.0],
[30.0, 10.0, 0.0],
[0.0, 0.0, 2.0],
... | class Test(BaseGPUTest):
| def test_simple_input_internal_inf(self):
net = BasicModel_MultiLayer(inplace=True).cuda()
inp = torch.tensor(
[
[0.0, 100.0, 0.0],
[20.0, 100.0, 120.0],
[30.0, 10.0, 0.0],
[0.0, 0.0, 2.0],
]
).cuda()
... | from captum.attr._core.layer.layer_gradient_x_activation import LayerGradientXActivation
from captum.attr._core.layer.layer_deep_lift import LayerDeepLift, LayerDeepLiftShap
from captum.attr._core.layer.layer_gradient_shap import LayerGradientShap
from captum.attr._core.neuron.neuron_conductance import NeuronConductan... | 256 | 256 | 4,222 | 7 | 248 | gorogoroyasu/captum | tests/attr/test_data_parallel.py | Python | Test | Test | 39 | 549 | 39 | 39 | d9061167ddee3ec7e538548a76349d21970b9f59 | bigcode/the-stack | train |
6e482d0d6f17e6360462208e | train | class | class MB8CoinRPC:
def __init__(self, host, port, username, password):
authpair = "%s:%s" % (username, password)
self.authhdr = "Basic %s" % (base64.b64encode(authpair))
self.conn = httplib.HTTPConnection(host, port, False, 30)
def execute(self, obj):
self.conn.request('POST', '/', json.dumps(obj),
{ 'Auth... | class MB8CoinRPC:
| def __init__(self, host, port, username, password):
authpair = "%s:%s" % (username, password)
self.authhdr = "Basic %s" % (base64.b64encode(authpair))
self.conn = httplib.HTTPConnection(host, port, False, 30)
def execute(self, obj):
self.conn.request('POST', '/', json.dumps(obj),
{ 'Authorization' : self.... | c) 2013-2014 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
#
from __future__ import print_function
import json
import struct
import re
import base64
import httplib
import sys
settings = {}
class MB8C... | 78 | 78 | 261 | 6 | 72 | MB8Coin/mb8coin-core | contrib/linearize/linearize-hashes.py | Python | MB8CoinRPC | MB8CoinRPC | 20 | 53 | 20 | 20 | 5286bf792bde0451bc1e2282a53d05425f8fe476 | bigcode/the-stack | train |
bf86f2935442a01905ae9435 | train | function | def get_block_hashes(settings, max_blocks_per_call=10000):
rpc = MB8CoinRPC(settings['host'], settings['port'],
settings['rpcuser'], settings['rpcpassword'])
height = settings['min_height']
while height < settings['max_height']+1:
num_blocks = min(settings['max_height']+1-height, max_blocks_per_call)
batch ... | def get_block_hashes(settings, max_blocks_per_call=10000):
| rpc = MB8CoinRPC(settings['host'], settings['port'],
settings['rpcuser'], settings['rpcpassword'])
height = settings['min_height']
while height < settings['max_height']+1:
num_blocks = min(settings['max_height']+1-height, max_blocks_per_call)
batch = []
for x in range(num_blocks):
batch.append(rpc.buil... | ['params'] = []
else:
obj['params'] = params
return obj
@staticmethod
def response_is_error(resp_obj):
return 'error' in resp_obj and resp_obj['error'] is not None
def get_block_hashes(settings, max_blocks_per_call=10000):
| 64 | 64 | 198 | 15 | 48 | MB8Coin/mb8coin-core | contrib/linearize/linearize-hashes.py | Python | get_block_hashes | get_block_hashes | 55 | 75 | 55 | 55 | 8e757591b83464df1e3880b416b7d61dd49ee8ce | bigcode/the-stack | train |
2c81ac96dbca7d92791dce4f | train | function | def max_canon(expr, args):
x = args[0]
shape = expr.shape
axis = expr.axis
t = Variable(shape)
if axis is None: # shape = (1, 1)
promoted_t = promote(t, x.shape)
elif axis == 0: # shape = (1, n)
promoted_t = Constant(np.ones((x.shape[0], 1))) * reshape(
... | def max_canon(expr, args):
| x = args[0]
shape = expr.shape
axis = expr.axis
t = Variable(shape)
if axis is None: # shape = (1, 1)
promoted_t = promote(t, x.shape)
elif axis == 0: # shape = (1, n)
promoted_t = Constant(np.ones((x.shape[0], 1))) * reshape(
... | or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from cvxpy.atoms import promote, reshape
from cvxpy.expressions.constants import Constant
from cvxpy.expressions.variable import Variable
import numpy as np
def max_canon(expr, args):
| 64 | 64 | 164 | 8 | 55 | mostafaelaraby/cvxpy | cvxpy/reductions/eliminate_pwl/atom_canonicalizers/max_canon.py | Python | max_canon | max_canon | 23 | 39 | 23 | 23 | cfa26b429e7c9d7eabf7c8832e0ba40aef19aa75 | bigcode/the-stack | train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.