body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
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@classmethod
def build_model(cls, args, task):
'Build a new model instance.'
base_architecture(args)
if (not hasattr(args, 'max_source_positions')):
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if (not hasattr(args, 'max_target_positions')):
args.max_target_positions = DEFAUL... | 2,152,914,506,224,695,600 | Build a new model instance. | models/transformer.py | build_model | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | @classmethod
def build_model(cls, args, task):
base_architecture(args)
if (not hasattr(args, 'max_source_positions')):
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if (not hasattr(args, 'max_target_positions')):
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
(s... |
def forward(self, src_tokens, src_lengths):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n Returns:\n ... | 8,721,051,598,448,000,000 | Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch,... | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, src_tokens, src_lengths):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n Returns:\n ... |
def reorder_encoder_out(self, encoder_out, bert_outs, new_order):
'\n Reorder encoder output according to *new_order*.\n\n Args:\n encoder_out: output from the ``forward()`` method\n new_order (LongTensor): desired order\n\n Returns:\n *encoder_out* rearranged a... | -4,539,497,251,350,111,000 | Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order* | models/transformer.py | reorder_encoder_out | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def reorder_encoder_out(self, encoder_out, bert_outs, new_order):
'\n Reorder encoder output according to *new_order*.\n\n Args:\n encoder_out: output from the ``forward()`` method\n new_order (LongTensor): desired order\n\n Returns:\n *encoder_out* rearranged a... |
def max_positions(self):
'Maximum input length supported by the encoder.'
if (self.embed_positions is None):
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions()) | -5,228,954,016,557,509,000 | Maximum input length supported by the encoder. | models/transformer.py | max_positions | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def max_positions(self):
if (self.embed_positions is None):
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions()) |
def upgrade_state_dict_named(self, state_dict, name):
'Upgrade a (possibly old) state dict for new versions of fairseq.'
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del sta... | 6,001,696,072,670,587,000 | Upgrade a (possibly old) state dict for new versions of fairseq. | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del state_dict[weights_key]
state_dict['{}.embed_positions._float... |
def forward(self, src_tokens, src_lengths, bert_encoder_out):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n ... | 546,859,358,158,450,940 | Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch,... | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, src_tokens, src_lengths, bert_encoder_out):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n ... |
def encodeMLM(self, src_tokens, src_lengths, bert_encoder_out):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n ... | -6,241,553,142,829,516,000 | Args:
src_tokens (LongTensor): tokens in the source language of shape
`(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
Returns:
dict:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch,... | models/transformer.py | encodeMLM | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def encodeMLM(self, src_tokens, src_lengths, bert_encoder_out):
"\n Args:\n src_tokens (LongTensor): tokens in the source language of shape\n `(batch, src_len)`\n src_lengths (torch.LongTensor): lengths of each source sentence of\n shape `(batch)`\n\n ... |
def reorder_encoder_out(self, encoder_out, bert_outs, new_order):
'\n Reorder encoder output according to *new_order*.\n\n Args:\n encoder_out: output from the ``forward()`` method\n new_order (LongTensor): desired order\n\n Returns:\n *encoder_out* rearranged a... | -4,539,497,251,350,111,000 | Reorder encoder output according to *new_order*.
Args:
encoder_out: output from the ``forward()`` method
new_order (LongTensor): desired order
Returns:
*encoder_out* rearranged according to *new_order* | models/transformer.py | reorder_encoder_out | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def reorder_encoder_out(self, encoder_out, bert_outs, new_order):
'\n Reorder encoder output according to *new_order*.\n\n Args:\n encoder_out: output from the ``forward()`` method\n new_order (LongTensor): desired order\n\n Returns:\n *encoder_out* rearranged a... |
def max_positions(self):
'Maximum input length supported by the encoder.'
if (self.embed_positions is None):
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions()) | -5,228,954,016,557,509,000 | Maximum input length supported by the encoder. | models/transformer.py | max_positions | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def max_positions(self):
if (self.embed_positions is None):
return self.max_source_positions
return min(self.max_source_positions, self.embed_positions.max_positions()) |
def upgrade_state_dict_named(self, state_dict, name):
'Upgrade a (possibly old) state dict for new versions of fairseq.'
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del sta... | 6,001,696,072,670,587,000 | Upgrade a (possibly old) state dict for new versions of fairseq. | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del state_dict[weights_key]
state_dict['{}.embed_positions._float... |
def forward(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Args:\n prev_output_tokens (LongTensor): previous decoder outputs of shape\n `(batch, tgt_len)`, for input feeding/teacher forcing\n encoder_out (Tensor, ... | 8,584,525,393,747,568,000 | Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:... | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Args:\n prev_output_tokens (LongTensor): previous decoder outputs of shape\n `(batch, tgt_len)`, for input feeding/teacher forcing\n encoder_out (Tensor, ... |
def extract_features(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Similar to *forward* but only return features.\n\n Returns:\n tuple:\n - the decoder's features of shape `(batch, tgt_len, embed_dim)`\n ... | -1,850,262,369,369,789,400 | Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs | models/transformer.py | extract_features | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def extract_features(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Similar to *forward* but only return features.\n\n Returns:\n tuple:\n - the decoder's features of shape `(batch, tgt_len, embed_dim)`\n ... |
def output_layer(self, features, **kwargs):
'Project features to the vocabulary size.'
if (self.adaptive_softmax is None):
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
else:
... | -3,345,116,721,218,766,300 | Project features to the vocabulary size. | models/transformer.py | output_layer | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def output_layer(self, features, **kwargs):
if (self.adaptive_softmax is None):
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
else:
return features |
def max_positions(self):
'Maximum output length supported by the decoder.'
if (self.embed_positions is None):
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions()) | -778,445,331,605,681,300 | Maximum output length supported by the decoder. | models/transformer.py | max_positions | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def max_positions(self):
if (self.embed_positions is None):
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions()) |
def upgrade_state_dict_named(self, state_dict, name):
'Upgrade a (possibly old) state dict for new versions of fairseq.'
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del sta... | -8,691,086,455,940,025,000 | Upgrade a (possibly old) state dict for new versions of fairseq. | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del state_dict[weights_key]
state_dict['{}.embed_positions._float... |
def forward(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Args:\n prev_output_tokens (LongTensor): previous decoder outputs of shape\n `(batch, tgt_len)`, for input feeding/teacher forcing\n encoder_out (Tensor, ... | 8,584,525,393,747,568,000 | Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for input feeding/teacher forcing
encoder_out (Tensor, optional): output from the encoder, used for
encoder-side attention
incremental_state (dict): dictionary used for storing state during
:... | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Args:\n prev_output_tokens (LongTensor): previous decoder outputs of shape\n `(batch, tgt_len)`, for input feeding/teacher forcing\n encoder_out (Tensor, ... |
def extract_features(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Similar to *forward* but only return features.\n\n Returns:\n tuple:\n - the decoder's features of shape `(batch, tgt_len, embed_dim)`\n ... | -1,850,262,369,369,789,400 | Similar to *forward* but only return features.
Returns:
tuple:
- the decoder's features of shape `(batch, tgt_len, embed_dim)`
- a dictionary with any model-specific outputs | models/transformer.py | extract_features | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def extract_features(self, prev_output_tokens, encoder_out=None, bert_encoder_out=None, incremental_state=None, **unused):
"\n Similar to *forward* but only return features.\n\n Returns:\n tuple:\n - the decoder's features of shape `(batch, tgt_len, embed_dim)`\n ... |
def output_layer(self, features, **kwargs):
'Project features to the vocabulary size.'
if (self.adaptive_softmax is None):
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
else:
... | -3,345,116,721,218,766,300 | Project features to the vocabulary size. | models/transformer.py | output_layer | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def output_layer(self, features, **kwargs):
if (self.adaptive_softmax is None):
if self.share_input_output_embed:
return F.linear(features, self.embed_tokens.weight)
else:
return F.linear(features, self.embed_out)
else:
return features |
def max_positions(self):
'Maximum output length supported by the decoder.'
if (self.embed_positions is None):
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions()) | -778,445,331,605,681,300 | Maximum output length supported by the decoder. | models/transformer.py | max_positions | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def max_positions(self):
if (self.embed_positions is None):
return self.max_target_positions
return min(self.max_target_positions, self.embed_positions.max_positions()) |
def upgrade_state_dict_named(self, state_dict, name):
'Upgrade a (possibly old) state dict for new versions of fairseq.'
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del sta... | -8,691,086,455,940,025,000 | Upgrade a (possibly old) state dict for new versions of fairseq. | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
weights_key = '{}.embed_positions.weights'.format(name)
if (weights_key in state_dict):
del state_dict[weights_key]
state_dict['{}.embed_positions._float... |
def upgrade_state_dict_named(self, state_dict, name):
'\n Rename layer norm states from `...layer_norms.0.weight` to\n `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to\n `...final_layer_norm.weight`\n '
layer_norm_map = {'0': 'self_attn_layer_norm', '1': 'final_layer... | -3,313,760,067,142,762,500 | Rename layer norm states from `...layer_norms.0.weight` to
`...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
`...final_layer_norm.weight` | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
'\n Rename layer norm states from `...layer_norms.0.weight` to\n `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to\n `...final_layer_norm.weight`\n '
layer_norm_map = {'0': 'self_attn_layer_norm', '1': 'final_layer... |
def forward(self, x, encoder_padding_mask):
'\n Args:\n x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`\n encoder_padding_mask (ByteTensor): binary ByteTensor of shape\n `(batch, src_len)` where padding elements are indicated by ``1``.\n\n Ret... | -4,786,362,497,684,975,000 | Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)` | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, x, encoder_padding_mask):
'\n Args:\n x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`\n encoder_padding_mask (ByteTensor): binary ByteTensor of shape\n `(batch, src_len)` where padding elements are indicated by ``1``.\n\n Ret... |
def upgrade_state_dict_named(self, state_dict, name):
'\n Rename layer norm states from `...layer_norms.0.weight` to\n `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to\n `...final_layer_norm.weight`\n '
layer_norm_map = {'0': 'self_attn_layer_norm', '1': 'final_layer... | -3,313,760,067,142,762,500 | Rename layer norm states from `...layer_norms.0.weight` to
`...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to
`...final_layer_norm.weight` | models/transformer.py | upgrade_state_dict_named | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def upgrade_state_dict_named(self, state_dict, name):
'\n Rename layer norm states from `...layer_norms.0.weight` to\n `...self_attn_layer_norm.weight` and `...layer_norms.1.weight` to\n `...final_layer_norm.weight`\n '
layer_norm_map = {'0': 'self_attn_layer_norm', '1': 'final_layer... |
def forward(self, x, encoder_padding_mask, bert_encoder_out, bert_encoder_padding_mask):
'\n Args:\n x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`\n encoder_padding_mask (ByteTensor): binary ByteTensor of shape\n `(batch, src_len)` where padding el... | -6,319,351,232,305,081,000 | Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)` | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, x, encoder_padding_mask, bert_encoder_out, bert_encoder_padding_mask):
'\n Args:\n x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`\n encoder_padding_mask (ByteTensor): binary ByteTensor of shape\n `(batch, src_len)` where padding el... |
def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... | 4,665,933,914,785,590,000 | Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)` | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... |
def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... | -4,948,189,932,445,657,000 | Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)` | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... |
def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... | 4,853,743,620,221,757,000 | Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)` | models/transformer.py | forward | NCTUMLlab/Adversarial-Masking-Transformers-for-Language-Understanding | python | def forward(self, x, encoder_out=None, encoder_padding_mask=None, bert_encoder_out=None, bert_encoder_padding_mask=None, incremental_state=None, prev_self_attn_state=None, prev_attn_state=None, self_attn_mask=None, self_attn_padding_mask=None):
'\n Args:\n x (Tensor): input to the layer of shape `... |
def __init__(self, graph, session, y, x, tmp_ckpt_path='/tmp/guided_backprop_ckpt'):
'Constructs a GuidedBackprop SaliencyMask.'
super(GuidedBackprop, self).__init__(graph, session, y, x)
self.x = x
if (GuidedBackprop.GuidedReluRegistered is False):
@tf.RegisterGradient('GuidedRelu')
de... | 7,949,305,015,942,975,000 | Constructs a GuidedBackprop SaliencyMask. | saliency/guided_backprop.py | __init__ | aliabd/history-of-interpretation | python | def __init__(self, graph, session, y, x, tmp_ckpt_path='/tmp/guided_backprop_ckpt'):
super(GuidedBackprop, self).__init__(graph, session, y, x)
self.x = x
if (GuidedBackprop.GuidedReluRegistered is False):
@tf.RegisterGradient('GuidedRelu')
def _GuidedReluGrad(op, grad):
ga... |
def GetMask(self, x_value, feed_dict={}):
'Returns a GuidedBackprop mask.'
with self.guided_graph.as_default():
guided_feed_dict = {}
for tensor in feed_dict:
guided_feed_dict[tensor.name] = feed_dict[tensor]
guided_feed_dict[self.x.name] = [x_value]
return self.guided_se... | 1,790,101,936,076,309,000 | Returns a GuidedBackprop mask. | saliency/guided_backprop.py | GetMask | aliabd/history-of-interpretation | python | def GetMask(self, x_value, feed_dict={}):
with self.guided_graph.as_default():
guided_feed_dict = {}
for tensor in feed_dict:
guided_feed_dict[tensor.name] = feed_dict[tensor]
guided_feed_dict[self.x.name] = [x_value]
return self.guided_sess.run(self.guided_grads_node, f... |
def validate_url(url):
'\n Auxiliary method to validate an urllib\n :param url: An url to be validated\n :type url: string\n :returns: True if the url is valid\n :rtype: bool\n '
scheme = url.split('://')[0].lower()
if (scheme not in url_schemes):
return False
if (not bool(url_... | 2,019,927,503,990,452,000 | Auxiliary method to validate an urllib
:param url: An url to be validated
:type url: string
:returns: True if the url is valid
:rtype: bool | app/utils/onelogin/saml2/settings.py | validate_url | nycrecords/intranet | python | def validate_url(url):
'\n Auxiliary method to validate an urllib\n :param url: An url to be validated\n :type url: string\n :returns: True if the url is valid\n :rtype: bool\n '
scheme = url.split('://')[0].lower()
if (scheme not in url_schemes):
return False
if (not bool(url_... |
def __init__(self, settings=None, custom_base_path=None, sp_validation_only=False):
'\n Initializes the settings:\n - Sets the paths of the different folders\n - Loads settings info from settings file or array/object provided\n\n :param settings: SAML Toolkit Settings\n :type sett... | -4,102,128,884,988,414,500 | Initializes the settings:
- Sets the paths of the different folders
- Loads settings info from settings file or array/object provided
:param settings: SAML Toolkit Settings
:type settings: dict
:param custom_base_path: Path where are stored the settings file and the cert folder
:type custom_base_path: string
:param ... | app/utils/onelogin/saml2/settings.py | __init__ | nycrecords/intranet | python | def __init__(self, settings=None, custom_base_path=None, sp_validation_only=False):
'\n Initializes the settings:\n - Sets the paths of the different folders\n - Loads settings info from settings file or array/object provided\n\n :param settings: SAML Toolkit Settings\n :type sett... |
def __load_paths(self, base_path=None):
'\n Set the paths of the different folders\n '
if (base_path is None):
base_path = dirname(dirname(dirname(__file__)))
if (not base_path.endswith(sep)):
base_path += sep
self.__paths = {'base': base_path, 'cert': ((base_path + 'certs'... | -7,425,753,098,019,775,000 | Set the paths of the different folders | app/utils/onelogin/saml2/settings.py | __load_paths | nycrecords/intranet | python | def __load_paths(self, base_path=None):
'\n \n '
if (base_path is None):
base_path = dirname(dirname(dirname(__file__)))
if (not base_path.endswith(sep)):
base_path += sep
self.__paths = {'base': base_path, 'cert': ((base_path + 'certs') + sep), 'lib': ((base_path + 'lib') ... |
def __update_paths(self, settings):
'\n Set custom paths if necessary\n '
if (not isinstance(settings, dict)):
return
if ('custom_base_path' in settings):
base_path = settings['custom_base_path']
base_path = join(dirname(__file__), base_path)
self.__load_paths(b... | -851,702,267,590,710,300 | Set custom paths if necessary | app/utils/onelogin/saml2/settings.py | __update_paths | nycrecords/intranet | python | def __update_paths(self, settings):
'\n \n '
if (not isinstance(settings, dict)):
return
if ('custom_base_path' in settings):
base_path = settings['custom_base_path']
base_path = join(dirname(__file__), base_path)
self.__load_paths(base_path) |
def get_base_path(self):
'\n Returns base path\n\n :return: The base toolkit folder path\n :rtype: string\n '
return self.__paths['base'] | -3,251,405,120,382,975,500 | Returns base path
:return: The base toolkit folder path
:rtype: string | app/utils/onelogin/saml2/settings.py | get_base_path | nycrecords/intranet | python | def get_base_path(self):
'\n Returns base path\n\n :return: The base toolkit folder path\n :rtype: string\n '
return self.__paths['base'] |
def get_cert_path(self):
'\n Returns cert path\n\n :return: The cert folder path\n :rtype: string\n '
return self.__paths['cert'] | -2,868,438,709,607,504,400 | Returns cert path
:return: The cert folder path
:rtype: string | app/utils/onelogin/saml2/settings.py | get_cert_path | nycrecords/intranet | python | def get_cert_path(self):
'\n Returns cert path\n\n :return: The cert folder path\n :rtype: string\n '
return self.__paths['cert'] |
def get_lib_path(self):
'\n Returns lib path\n\n :return: The library folder path\n :rtype: string\n '
return self.__paths['lib'] | -3,343,954,377,221,302,300 | Returns lib path
:return: The library folder path
:rtype: string | app/utils/onelogin/saml2/settings.py | get_lib_path | nycrecords/intranet | python | def get_lib_path(self):
'\n Returns lib path\n\n :return: The library folder path\n :rtype: string\n '
return self.__paths['lib'] |
def get_ext_lib_path(self):
'\n Returns external lib path\n\n :return: The external library folder path\n :rtype: string\n '
return self.__paths['extlib'] | -7,216,888,168,113,255,000 | Returns external lib path
:return: The external library folder path
:rtype: string | app/utils/onelogin/saml2/settings.py | get_ext_lib_path | nycrecords/intranet | python | def get_ext_lib_path(self):
'\n Returns external lib path\n\n :return: The external library folder path\n :rtype: string\n '
return self.__paths['extlib'] |
def get_schemas_path(self):
'\n Returns schema path\n\n :return: The schema folder path\n :rtype: string\n '
return (self.__paths['lib'] + 'schemas/') | -4,632,891,345,904,846,000 | Returns schema path
:return: The schema folder path
:rtype: string | app/utils/onelogin/saml2/settings.py | get_schemas_path | nycrecords/intranet | python | def get_schemas_path(self):
'\n Returns schema path\n\n :return: The schema folder path\n :rtype: string\n '
return (self.__paths['lib'] + 'schemas/') |
def __load_settings_from_dict(self, settings):
'\n Loads settings info from a settings Dict\n\n :param settings: SAML Toolkit Settings\n :type settings: dict\n\n :returns: True if the settings info is valid\n :rtype: boolean\n '
errors = self.check_settings(settings)
... | 9,101,060,825,214,802,000 | Loads settings info from a settings Dict
:param settings: SAML Toolkit Settings
:type settings: dict
:returns: True if the settings info is valid
:rtype: boolean | app/utils/onelogin/saml2/settings.py | __load_settings_from_dict | nycrecords/intranet | python | def __load_settings_from_dict(self, settings):
'\n Loads settings info from a settings Dict\n\n :param settings: SAML Toolkit Settings\n :type settings: dict\n\n :returns: True if the settings info is valid\n :rtype: boolean\n '
errors = self.check_settings(settings)
... |
def __load_settings_from_file(self):
'\n Loads settings info from the settings json file\n\n :returns: True if the settings info is valid\n :rtype: boolean\n '
filename = (self.get_base_path() + 'settings.json')
if (not exists(filename)):
raise OneLogin_Saml2_Error('Setti... | 3,696,009,825,667,300,400 | Loads settings info from the settings json file
:returns: True if the settings info is valid
:rtype: boolean | app/utils/onelogin/saml2/settings.py | __load_settings_from_file | nycrecords/intranet | python | def __load_settings_from_file(self):
'\n Loads settings info from the settings json file\n\n :returns: True if the settings info is valid\n :rtype: boolean\n '
filename = (self.get_base_path() + 'settings.json')
if (not exists(filename)):
raise OneLogin_Saml2_Error('Setti... |
def __add_default_values(self):
'\n Add default values if the settings info is not complete\n '
self.__sp.setdefault('assertionConsumerService', {})
self.__sp['assertionConsumerService'].setdefault('binding', OneLogin_Saml2_Constants.BINDING_HTTP_POST)
self.__sp.setdefault('attributeConsum... | 8,789,121,310,913,046,000 | Add default values if the settings info is not complete | app/utils/onelogin/saml2/settings.py | __add_default_values | nycrecords/intranet | python | def __add_default_values(self):
'\n \n '
self.__sp.setdefault('assertionConsumerService', {})
self.__sp['assertionConsumerService'].setdefault('binding', OneLogin_Saml2_Constants.BINDING_HTTP_POST)
self.__sp.setdefault('attributeConsumingService', {})
self.__sp.setdefault('singleLogout... |
def check_settings(self, settings):
'\n Checks the settings info.\n\n :param settings: Dict with settings data\n :type settings: dict\n\n :returns: Errors found on the settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((not isinst... | 6,683,541,287,288,272,000 | Checks the settings info.
:param settings: Dict with settings data
:type settings: dict
:returns: Errors found on the settings data
:rtype: list | app/utils/onelogin/saml2/settings.py | check_settings | nycrecords/intranet | python | def check_settings(self, settings):
'\n Checks the settings info.\n\n :param settings: Dict with settings data\n :type settings: dict\n\n :returns: Errors found on the settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((not isinst... |
def check_idp_settings(self, settings):
'\n Checks the IdP settings info.\n :param settings: Dict with settings data\n :type settings: dict\n :returns: Errors found on the IdP settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((no... | 8,715,893,815,939,197,000 | Checks the IdP settings info.
:param settings: Dict with settings data
:type settings: dict
:returns: Errors found on the IdP settings data
:rtype: list | app/utils/onelogin/saml2/settings.py | check_idp_settings | nycrecords/intranet | python | def check_idp_settings(self, settings):
'\n Checks the IdP settings info.\n :param settings: Dict with settings data\n :type settings: dict\n :returns: Errors found on the IdP settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((no... |
def check_sp_settings(self, settings):
'\n Checks the SP settings info.\n :param settings: Dict with settings data\n :type settings: dict\n :returns: Errors found on the SP settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((not i... | -2,496,403,301,086,345,700 | Checks the SP settings info.
:param settings: Dict with settings data
:type settings: dict
:returns: Errors found on the SP settings data
:rtype: list | app/utils/onelogin/saml2/settings.py | check_sp_settings | nycrecords/intranet | python | def check_sp_settings(self, settings):
'\n Checks the SP settings info.\n :param settings: Dict with settings data\n :type settings: dict\n :returns: Errors found on the SP settings data\n :rtype: list\n '
assert isinstance(settings, dict)
errors = []
if ((not i... |
def check_sp_certs(self):
'\n Checks if the x509 certs of the SP exists and are valid.\n :returns: If the x509 certs of the SP exists and are valid\n :rtype: boolean\n '
key = self.get_sp_key()
cert = self.get_sp_cert()
return ((key is not None) and (cert is not None)) | -7,812,650,226,965,906,000 | Checks if the x509 certs of the SP exists and are valid.
:returns: If the x509 certs of the SP exists and are valid
:rtype: boolean | app/utils/onelogin/saml2/settings.py | check_sp_certs | nycrecords/intranet | python | def check_sp_certs(self):
'\n Checks if the x509 certs of the SP exists and are valid.\n :returns: If the x509 certs of the SP exists and are valid\n :rtype: boolean\n '
key = self.get_sp_key()
cert = self.get_sp_cert()
return ((key is not None) and (cert is not None)) |
def get_sp_key(self):
'\n Returns the x509 private key of the SP.\n :returns: SP private key\n :rtype: string or None\n '
key = self.__sp.get('privateKey')
key_file_name = (self.__paths['cert'] + 'sp.key')
if ((not key) and exists(key_file_name)):
with open(key_file_n... | -4,037,297,485,836,650,000 | Returns the x509 private key of the SP.
:returns: SP private key
:rtype: string or None | app/utils/onelogin/saml2/settings.py | get_sp_key | nycrecords/intranet | python | def get_sp_key(self):
'\n Returns the x509 private key of the SP.\n :returns: SP private key\n :rtype: string or None\n '
key = self.__sp.get('privateKey')
key_file_name = (self.__paths['cert'] + 'sp.key')
if ((not key) and exists(key_file_name)):
with open(key_file_n... |
def get_sp_cert(self):
'\n Returns the x509 public cert of the SP.\n :returns: SP public cert\n :rtype: string or None\n '
cert = self.__sp.get('x509cert')
cert_file_name = (self.__paths['cert'] + 'sp.crt')
if ((not cert) and exists(cert_file_name)):
with open(cert_fi... | 3,085,101,086,525,962,000 | Returns the x509 public cert of the SP.
:returns: SP public cert
:rtype: string or None | app/utils/onelogin/saml2/settings.py | get_sp_cert | nycrecords/intranet | python | def get_sp_cert(self):
'\n Returns the x509 public cert of the SP.\n :returns: SP public cert\n :rtype: string or None\n '
cert = self.__sp.get('x509cert')
cert_file_name = (self.__paths['cert'] + 'sp.crt')
if ((not cert) and exists(cert_file_name)):
with open(cert_fi... |
def get_sp_cert_new(self):
'\n Returns the x509 public of the SP planned\n to be used soon instead the other public cert\n :returns: SP public cert new\n :rtype: string or None\n '
cert = self.__sp.get('x509certNew')
cert_file_name = (self.__paths['cert'] + 'sp_new.crt')
... | -1,498,846,491,623,402,500 | Returns the x509 public of the SP planned
to be used soon instead the other public cert
:returns: SP public cert new
:rtype: string or None | app/utils/onelogin/saml2/settings.py | get_sp_cert_new | nycrecords/intranet | python | def get_sp_cert_new(self):
'\n Returns the x509 public of the SP planned\n to be used soon instead the other public cert\n :returns: SP public cert new\n :rtype: string or None\n '
cert = self.__sp.get('x509certNew')
cert_file_name = (self.__paths['cert'] + 'sp_new.crt')
... |
def get_idp_cert(self):
'\n Returns the x509 public cert of the IdP.\n :returns: IdP public cert\n :rtype: string\n '
return self.__idp.get('x509cert') | 4,088,177,130,043,971,000 | Returns the x509 public cert of the IdP.
:returns: IdP public cert
:rtype: string | app/utils/onelogin/saml2/settings.py | get_idp_cert | nycrecords/intranet | python | def get_idp_cert(self):
'\n Returns the x509 public cert of the IdP.\n :returns: IdP public cert\n :rtype: string\n '
return self.__idp.get('x509cert') |
def get_idp_data(self):
'\n Gets the IdP data.\n\n :returns: IdP info\n :rtype: dict\n '
return self.__idp | -3,116,901,750,163,315,000 | Gets the IdP data.
:returns: IdP info
:rtype: dict | app/utils/onelogin/saml2/settings.py | get_idp_data | nycrecords/intranet | python | def get_idp_data(self):
'\n Gets the IdP data.\n\n :returns: IdP info\n :rtype: dict\n '
return self.__idp |
def get_sp_data(self):
'\n Gets the SP data.\n\n :returns: SP info\n :rtype: dict\n '
return self.__sp | -7,802,741,565,788,101,000 | Gets the SP data.
:returns: SP info
:rtype: dict | app/utils/onelogin/saml2/settings.py | get_sp_data | nycrecords/intranet | python | def get_sp_data(self):
'\n Gets the SP data.\n\n :returns: SP info\n :rtype: dict\n '
return self.__sp |
def get_security_data(self):
'\n Gets security data.\n\n :returns: Security info\n :rtype: dict\n '
return self.__security | 1,365,572,923,322,081,000 | Gets security data.
:returns: Security info
:rtype: dict | app/utils/onelogin/saml2/settings.py | get_security_data | nycrecords/intranet | python | def get_security_data(self):
'\n Gets security data.\n\n :returns: Security info\n :rtype: dict\n '
return self.__security |
def get_contacts(self):
'\n Gets contact data.\n\n :returns: Contacts info\n :rtype: dict\n '
return self.__contacts | -8,828,376,608,204,893,000 | Gets contact data.
:returns: Contacts info
:rtype: dict | app/utils/onelogin/saml2/settings.py | get_contacts | nycrecords/intranet | python | def get_contacts(self):
'\n Gets contact data.\n\n :returns: Contacts info\n :rtype: dict\n '
return self.__contacts |
def get_organization(self):
'\n Gets organization data.\n\n :returns: Organization info\n :rtype: dict\n '
return self.__organization | 7,596,810,709,364,819,000 | Gets organization data.
:returns: Organization info
:rtype: dict | app/utils/onelogin/saml2/settings.py | get_organization | nycrecords/intranet | python | def get_organization(self):
'\n Gets organization data.\n\n :returns: Organization info\n :rtype: dict\n '
return self.__organization |
def get_sp_metadata(self):
'\n Gets the SP metadata. The XML representation.\n :returns: SP metadata (xml)\n :rtype: string\n '
metadata = OneLogin_Saml2_Metadata.builder(self.__sp, self.__security['authnRequestsSigned'], self.__security['wantAssertionsSigned'], self.__security['meta... | -4,473,593,141,927,607,300 | Gets the SP metadata. The XML representation.
:returns: SP metadata (xml)
:rtype: string | app/utils/onelogin/saml2/settings.py | get_sp_metadata | nycrecords/intranet | python | def get_sp_metadata(self):
'\n Gets the SP metadata. The XML representation.\n :returns: SP metadata (xml)\n :rtype: string\n '
metadata = OneLogin_Saml2_Metadata.builder(self.__sp, self.__security['authnRequestsSigned'], self.__security['wantAssertionsSigned'], self.__security['meta... |
def validate_metadata(self, xml):
"\n Validates an XML SP Metadata.\n\n :param xml: Metadata's XML that will be validate\n :type xml: string\n\n :returns: The list of found errors\n :rtype: list\n "
assert isinstance(xml, compat.text_types)
if (len(xml) == 0):
... | 3,210,722,307,556,125,000 | Validates an XML SP Metadata.
:param xml: Metadata's XML that will be validate
:type xml: string
:returns: The list of found errors
:rtype: list | app/utils/onelogin/saml2/settings.py | validate_metadata | nycrecords/intranet | python | def validate_metadata(self, xml):
"\n Validates an XML SP Metadata.\n\n :param xml: Metadata's XML that will be validate\n :type xml: string\n\n :returns: The list of found errors\n :rtype: list\n "
assert isinstance(xml, compat.text_types)
if (len(xml) == 0):
... |
def format_idp_cert(self):
'\n Formats the IdP cert.\n '
self.__idp['x509cert'] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509cert']) | 154,846,901,795,047,100 | Formats the IdP cert. | app/utils/onelogin/saml2/settings.py | format_idp_cert | nycrecords/intranet | python | def format_idp_cert(self):
'\n \n '
self.__idp['x509cert'] = OneLogin_Saml2_Utils.format_cert(self.__idp['x509cert']) |
def format_idp_cert_multi(self):
'\n Formats the Multple IdP certs.\n '
if ('x509certMulti' in self.__idp):
if ('signing' in self.__idp['x509certMulti']):
for idx in range(len(self.__idp['x509certMulti']['signing'])):
self.__idp['x509certMulti']['signing'][idx] ... | -5,308,441,926,965,280,000 | Formats the Multple IdP certs. | app/utils/onelogin/saml2/settings.py | format_idp_cert_multi | nycrecords/intranet | python | def format_idp_cert_multi(self):
'\n \n '
if ('x509certMulti' in self.__idp):
if ('signing' in self.__idp['x509certMulti']):
for idx in range(len(self.__idp['x509certMulti']['signing'])):
self.__idp['x509certMulti']['signing'][idx] = OneLogin_Saml2_Utils.format_... |
def format_sp_cert(self):
'\n Formats the SP cert.\n '
self.__sp['x509cert'] = OneLogin_Saml2_Utils.format_cert(self.__sp['x509cert']) | -8,799,753,653,561,585,000 | Formats the SP cert. | app/utils/onelogin/saml2/settings.py | format_sp_cert | nycrecords/intranet | python | def format_sp_cert(self):
'\n \n '
self.__sp['x509cert'] = OneLogin_Saml2_Utils.format_cert(self.__sp['x509cert']) |
def format_sp_cert_new(self):
'\n Formats the SP cert.\n '
self.__sp['x509certNew'] = OneLogin_Saml2_Utils.format_cert(self.__sp['x509certNew']) | -4,849,810,101,938,704,000 | Formats the SP cert. | app/utils/onelogin/saml2/settings.py | format_sp_cert_new | nycrecords/intranet | python | def format_sp_cert_new(self):
'\n \n '
self.__sp['x509certNew'] = OneLogin_Saml2_Utils.format_cert(self.__sp['x509certNew']) |
def format_sp_key(self):
'\n Formats the private key.\n '
self.__sp['privateKey'] = OneLogin_Saml2_Utils.format_private_key(self.__sp['privateKey']) | 240,490,718,022,734,240 | Formats the private key. | app/utils/onelogin/saml2/settings.py | format_sp_key | nycrecords/intranet | python | def format_sp_key(self):
'\n \n '
self.__sp['privateKey'] = OneLogin_Saml2_Utils.format_private_key(self.__sp['privateKey']) |
def get_errors(self):
'\n Returns an array with the errors, the array is empty when the settings is ok.\n\n :returns: Errors\n :rtype: list\n '
return self.__errors | -450,769,031,362,331,000 | Returns an array with the errors, the array is empty when the settings is ok.
:returns: Errors
:rtype: list | app/utils/onelogin/saml2/settings.py | get_errors | nycrecords/intranet | python | def get_errors(self):
'\n Returns an array with the errors, the array is empty when the settings is ok.\n\n :returns: Errors\n :rtype: list\n '
return self.__errors |
def set_strict(self, value):
'\n Activates or deactivates the strict mode.\n\n :param value: Strict parameter\n :type value: boolean\n '
assert isinstance(value, bool)
self.__strict = value | -1,835,184,990,267,616,000 | Activates or deactivates the strict mode.
:param value: Strict parameter
:type value: boolean | app/utils/onelogin/saml2/settings.py | set_strict | nycrecords/intranet | python | def set_strict(self, value):
'\n Activates or deactivates the strict mode.\n\n :param value: Strict parameter\n :type value: boolean\n '
assert isinstance(value, bool)
self.__strict = value |
def is_strict(self):
"\n Returns if the 'strict' mode is active.\n\n :returns: Strict parameter\n :rtype: boolean\n "
return self.__strict | -5,834,133,523,043,742,000 | Returns if the 'strict' mode is active.
:returns: Strict parameter
:rtype: boolean | app/utils/onelogin/saml2/settings.py | is_strict | nycrecords/intranet | python | def is_strict(self):
"\n Returns if the 'strict' mode is active.\n\n :returns: Strict parameter\n :rtype: boolean\n "
return self.__strict |
def is_debug_active(self):
'\n Returns if the debug is active.\n\n :returns: Debug parameter\n :rtype: boolean\n '
return self.__debug | 2,389,600,388,186,807,000 | Returns if the debug is active.
:returns: Debug parameter
:rtype: boolean | app/utils/onelogin/saml2/settings.py | is_debug_active | nycrecords/intranet | python | def is_debug_active(self):
'\n Returns if the debug is active.\n\n :returns: Debug parameter\n :rtype: boolean\n '
return self.__debug |
@pytest.mark.ui
def test_apply_mag_field_view1(exopy_qtbot, root_view, task_workbench):
'Test ApplyMagFieldView widget outisde of a LoopTask.\n\n '
task = ApplyMagFieldTask(name='Test')
root_view.task.add_child_task(0, task)
show_and_close_widget(exopy_qtbot, ApplyMagFieldView(task=task, root=root_vi... | 8,545,179,322,454,801,000 | Test ApplyMagFieldView widget outisde of a LoopTask. | tests/tasks/tasks/instr/test_apply_mag_field_task.py | test_apply_mag_field_view1 | Qcircuits/exopy_hqc_legacy | python | @pytest.mark.ui
def test_apply_mag_field_view1(exopy_qtbot, root_view, task_workbench):
'\n\n '
task = ApplyMagFieldTask(name='Test')
root_view.task.add_child_task(0, task)
show_and_close_widget(exopy_qtbot, ApplyMagFieldView(task=task, root=root_view)) |
@pytest.mark.ui
def test_apply_mag_field_view2(exopy_qtbot, root_view, task_workbench):
'Test ApplyMagFieldView widget inside of a LoopTask.\n\n '
task = ApplyMagFieldTask(name='Test')
loop = LoopTask(name='r', task=task)
root_view.task.add_child_task(0, loop)
show_and_close_widget(exopy_qtbot, L... | 4,429,556,137,842,436,600 | Test ApplyMagFieldView widget inside of a LoopTask. | tests/tasks/tasks/instr/test_apply_mag_field_task.py | test_apply_mag_field_view2 | Qcircuits/exopy_hqc_legacy | python | @pytest.mark.ui
def test_apply_mag_field_view2(exopy_qtbot, root_view, task_workbench):
'\n\n '
task = ApplyMagFieldTask(name='Test')
loop = LoopTask(name='r', task=task)
root_view.task.add_child_task(0, loop)
show_and_close_widget(exopy_qtbot, LoopView(task=loop, root=root_view)) |
def test_check1(self):
'Simply test that everything is ok if field can be evaluated.\n\n '
self.task.field = '3.0'
(test, traceback) = self.task.check(test_instr=True)
assert test
assert (not traceback)
assert (self.task.get_from_database('Test_field') == 3.0) | 3,313,670,270,641,834,000 | Simply test that everything is ok if field can be evaluated. | tests/tasks/tasks/instr/test_apply_mag_field_task.py | test_check1 | Qcircuits/exopy_hqc_legacy | python | def test_check1(self):
'\n\n '
self.task.field = '3.0'
(test, traceback) = self.task.check(test_instr=True)
assert test
assert (not traceback)
assert (self.task.get_from_database('Test_field') == 3.0) |
def test_check2(self):
'Check handling a wrong field.\n\n '
self.task.field = '*1.0*'
(test, traceback) = self.task.check(test_instr=True)
assert (not test)
assert (len(traceback) == 1)
assert ('root/Test-field' in traceback)
assert (self.task.get_from_database('Test_field') == 0.01) | 7,797,678,657,480,563,000 | Check handling a wrong field. | tests/tasks/tasks/instr/test_apply_mag_field_task.py | test_check2 | Qcircuits/exopy_hqc_legacy | python | def test_check2(self):
'\n\n '
self.task.field = '*1.0*'
(test, traceback) = self.task.check(test_instr=True)
assert (not test)
assert (len(traceback) == 1)
assert ('root/Test-field' in traceback)
assert (self.task.get_from_database('Test_field') == 0.01) |
def test_perform1(self):
'Simple test when everything is right.\n\n '
self.task.field = '2.0'
self.root.prepare()
self.task.perform()
assert (self.root.get_from_database('Test_field') == 2.0) | -9,060,059,394,285,495,000 | Simple test when everything is right. | tests/tasks/tasks/instr/test_apply_mag_field_task.py | test_perform1 | Qcircuits/exopy_hqc_legacy | python | def test_perform1(self):
'\n\n '
self.task.field = '2.0'
self.root.prepare()
self.task.perform()
assert (self.root.get_from_database('Test_field') == 2.0) |
def _skip_date_tokens(tokens):
'\n Based on RFC 3164 + RFC 5424 and real-world logs\n '
if (tokens and tokens[0].startswith('<')):
tokens.pop(0)
while (tokens and ((not tokens[0]) or tokens[0][:1].isdigit())):
tokens.pop(0) | -6,881,443,272,031,541,000 | Based on RFC 3164 + RFC 5424 and real-world logs | syslog.py | _skip_date_tokens | Bun/ha-syslog-devtracker | python | def _skip_date_tokens(tokens):
'\n \n '
if (tokens and tokens[0].startswith('<')):
tokens.pop(0)
while (tokens and ((not tokens[0]) or tokens[0][:1].isdigit())):
tokens.pop(0) |
def parse_syslog_line(line):
'Parses lines created by hostapd and dnsmasq DHCP'
tokens = line.split(' ')
_skip_date_tokens(tokens)
process = _find_process(tokens)
if ((not process) or (not tokens)):
_LOGGER.debug('Unable to process line: %r', line)
return
if (process == 'hostapd'... | -6,569,647,656,581,431,000 | Parses lines created by hostapd and dnsmasq DHCP | syslog.py | parse_syslog_line | Bun/ha-syslog-devtracker | python | def parse_syslog_line(line):
tokens = line.split(' ')
_skip_date_tokens(tokens)
process = _find_process(tokens)
if ((not process) or (not tokens)):
_LOGGER.debug('Unable to process line: %r', line)
return
if (process == 'hostapd'):
if (len(tokens) == 3):
if (... |
def _add_conv_fc_branch(self, num_branch_convs, num_branch_fcs, in_channels, is_shared=False):
'Add shared or separable branch.\n\n convs -> avg pool (optional) -> fcs\n '
last_layer_dim = in_channels
branch_convs = nn.ModuleList()
if (num_branch_convs > 0):
for i in range(num_bran... | -8,176,502,878,514,245,000 | Add shared or separable branch.
convs -> avg pool (optional) -> fcs | mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py | _add_conv_fc_branch | marcovalenti/mmdetection | python | def _add_conv_fc_branch(self, num_branch_convs, num_branch_fcs, in_channels, is_shared=False):
'Add shared or separable branch.\n\n convs -> avg pool (optional) -> fcs\n '
last_layer_dim = in_channels
branch_convs = nn.ModuleList()
if (num_branch_convs > 0):
for i in range(num_bran... |
def get_targets(self, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg, reference_labels, classes, concat=True):
"Calculate the ground truth for all samples in a batch according to\n the sampling_results.\n Almost the same as the implementation in bbox_head, we passed\n additional paramet... | -50,011,819,819,159,050 | Calculate the ground truth for all samples in a batch according to
the sampling_results.
Almost the same as the implementation in bbox_head, we passed
additional parameters pos_inds_list and neg_inds_list to
`_get_target_single` function.
Args:
sampling_results (List[obj:SamplingResults]): Assign results of
... | mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py | get_targets | marcovalenti/mmdetection | python | def get_targets(self, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg, reference_labels, classes, concat=True):
"Calculate the ground truth for all samples in a batch according to\n the sampling_results.\n Almost the same as the implementation in bbox_head, we passed\n additional paramet... |
def test_publisher_structure():
'\n The module.publisher itself is a bit of a skeleton...\n '
params = {'1': 1, '2': 2, 'channel_name': 'test'}
test_publisher = Publisher(params)
assert (test_publisher.get_parameters() == {'1': 1, '2': 2, 'channel_name': 'test'})
test_publisher.set_data_block(... | 2,516,664,684,722,745,000 | The module.publisher itself is a bit of a skeleton... | src/decisionengine/framework/modules/tests/test_Publisher.py | test_publisher_structure | BrunoCoimbra/decisionengine | python | def test_publisher_structure():
'\n \n '
params = {'1': 1, '2': 2, 'channel_name': 'test'}
test_publisher = Publisher(params)
assert (test_publisher.get_parameters() == {'1': 1, '2': 2, 'channel_name': 'test'})
test_publisher.set_data_block('example')
assert (test_publisher.get_data_block(... |
def create_dataset_from_tf_record_files(input_file_pattern, pre_batch_size, batch_size, is_training=True):
'Creates dataset from (tf)records files for training/evaluation.'
files = tf.data.Dataset.list_files(input_file_pattern, shuffle=is_training)
def make_dataset(files_dataset, shard_index):
'Ret... | -4,105,225,009,478,872,600 | Creates dataset from (tf)records files for training/evaluation. | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | create_dataset_from_tf_record_files | Ezra-H/autodist | python | def create_dataset_from_tf_record_files(input_file_pattern, pre_batch_size, batch_size, is_training=True):
files = tf.data.Dataset.list_files(input_file_pattern, shuffle=is_training)
def make_dataset(files_dataset, shard_index):
'Returns dataset for sharded tf record files.'
if (pre_batch_... |
def create_dataset_from_data_producer(producer, params):
'Return dataset online-generating data.'
def preprocess_train_input(features, labels):
'Pre-process the training data.\n\n This is needed because\n - The label needs to be extended to be used in the loss fn\n - We need the sa... | -7,371,762,913,903,107,000 | Return dataset online-generating data. | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | create_dataset_from_data_producer | Ezra-H/autodist | python | def create_dataset_from_data_producer(producer, params):
def preprocess_train_input(features, labels):
'Pre-process the training data.\n\n This is needed because\n - The label needs to be extended to be used in the loss fn\n - We need the same inputs for training and eval so addin... |
def create_ncf_input_data(params, producer=None, input_meta_data=None, strategy=None):
'Creates NCF training/evaluation dataset.\n\n Args:\n params: Dictionary containing parameters for train/evaluation data.\n producer: Instance of BaseDataConstructor that generates data online. Must\n not be N... | -6,474,459,502,976,197,000 | Creates NCF training/evaluation dataset.
Args:
params: Dictionary containing parameters for train/evaluation data.
producer: Instance of BaseDataConstructor that generates data online. Must
not be None when params['train_dataset_path'] or
params['eval_dataset_path'] is not specified.
input_meta_data: A d... | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | create_ncf_input_data | Ezra-H/autodist | python | def create_ncf_input_data(params, producer=None, input_meta_data=None, strategy=None):
'Creates NCF training/evaluation dataset.\n\n Args:\n params: Dictionary containing parameters for train/evaluation data.\n producer: Instance of BaseDataConstructor that generates data online. Must\n not be N... |
def make_dataset(files_dataset, shard_index):
'Returns dataset for sharded tf record files.'
if (pre_batch_size != batch_size):
raise ValueError('Pre-batch ({}) size is not equal to batch size ({})'.format(pre_batch_size, batch_size))
files_dataset = files_dataset.shard(NUM_SHARDS, shard_index)
... | -8,816,612,933,234,172,000 | Returns dataset for sharded tf record files. | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | make_dataset | Ezra-H/autodist | python | def make_dataset(files_dataset, shard_index):
if (pre_batch_size != batch_size):
raise ValueError('Pre-batch ({}) size is not equal to batch size ({})'.format(pre_batch_size, batch_size))
files_dataset = files_dataset.shard(NUM_SHARDS, shard_index)
dataset = files_dataset.interleave(tf.data.TFR... |
def preprocess_train_input(features, labels):
'Pre-process the training data.\n\n This is needed because\n - The label needs to be extended to be used in the loss fn\n - We need the same inputs for training and eval so adding fake inputs\n for DUPLICATE_MASK in training data.\n\n ... | 1,766,241,002,850,631,000 | Pre-process the training data.
This is needed because
- The label needs to be extended to be used in the loss fn
- We need the same inputs for training and eval so adding fake inputs
for DUPLICATE_MASK in training data.
Args:
features: Dictionary of features for training.
labels: Training labels.
Returns:
Pr... | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | preprocess_train_input | Ezra-H/autodist | python | def preprocess_train_input(features, labels):
'Pre-process the training data.\n\n This is needed because\n - The label needs to be extended to be used in the loss fn\n - We need the same inputs for training and eval so adding fake inputs\n for DUPLICATE_MASK in training data.\n\n ... |
def preprocess_eval_input(features):
'Pre-process the eval data.\n\n This is needed because:\n - The label needs to be extended to be used in the loss fn\n - We need the same inputs for training and eval so adding fake inputs\n for VALID_PT_MASK in eval data.\n\n Args:\n ... | 5,271,456,008,588,656,000 | Pre-process the eval data.
This is needed because:
- The label needs to be extended to be used in the loss fn
- We need the same inputs for training and eval so adding fake inputs
for VALID_PT_MASK in eval data.
Args:
features: Dictionary of features for evaluation.
Returns:
Processed evaluation features. | examples/benchmark/utils/recommendation/ncf_input_pipeline.py | preprocess_eval_input | Ezra-H/autodist | python | def preprocess_eval_input(features):
'Pre-process the eval data.\n\n This is needed because:\n - The label needs to be extended to be used in the loss fn\n - We need the same inputs for training and eval so adding fake inputs\n for VALID_PT_MASK in eval data.\n\n Args:\n ... |
def playbook_path(playbook_name):
'\n Get the path to the named playbook. To allow for\n as much brevity as possible in the given playbook\n name, we will attempt to search under:\n - oct/playbooks\n - openshift-ansible/playbooks\n\n :param playbook_name: the name of the playbook\n :type pl... | -6,925,699,650,955,053,000 | Get the path to the named playbook. To allow for
as much brevity as possible in the given playbook
name, we will attempt to search under:
- oct/playbooks
- openshift-ansible/playbooks
:param playbook_name: the name of the playbook
:type playbook_name: str
:return: the path to the playbook
:rtype: str
:raises Cli... | oct/util/playbook.py | playbook_path | DennisPeriquet/origin-ci-tool | python | def playbook_path(playbook_name):
'\n Get the path to the named playbook. To allow for\n as much brevity as possible in the given playbook\n name, we will attempt to search under:\n - oct/playbooks\n - openshift-ansible/playbooks\n\n :param playbook_name: the name of the playbook\n :type pl... |
def test_location_handles_reused(instance, monkeypatch, grpc_server_registry):
'\n verifies that only one repository location is created when two queued runs from the same\n location are dequeued in the same iteration\n '
create_run(instance, run_id='queued-run', status=PipelineRunStatus.QUEUED)
cr... | 7,905,741,415,444,816,000 | verifies that only one repository location is created when two queued runs from the same
location are dequeued in the same iteration | python_modules/dagster/dagster_tests/daemon_tests/test_queued_run_coordinator_daemon.py | test_location_handles_reused | PenguinToast/dagster | python | def test_location_handles_reused(instance, monkeypatch, grpc_server_registry):
'\n verifies that only one repository location is created when two queued runs from the same\n location are dequeued in the same iteration\n '
create_run(instance, run_id='queued-run', status=PipelineRunStatus.QUEUED)
cr... |
def _update_dag_tasks(function_name, caller_line, dependencies, depends_logic, args=None, template_name=None, step_name=None):
'\n A task in DAG of Argo YAML contains name, related template and parameters.\n Here we insert a single task into the global tasks.\n '
if (step_name is None):
functio... | 2,822,368,232,564,738,600 | A task in DAG of Argo YAML contains name, related template and parameters.
Here we insert a single task into the global tasks. | couler/core/step_update_utils.py | _update_dag_tasks | javoweb/couler | python | def _update_dag_tasks(function_name, caller_line, dependencies, depends_logic, args=None, template_name=None, step_name=None):
'\n A task in DAG of Argo YAML contains name, related template and parameters.\n Here we insert a single task into the global tasks.\n '
if (step_name is None):
functio... |
def _update_steps(function_name, caller_line, args=None, template_name=None):
'\n A step in Argo YAML contains name, related template and parameters.\n Here we insert a single step into the global steps.\n '
function_id = utils.invocation_name(function_name, caller_line)
if states._update_steps_loc... | -1,058,683,839,742,048,800 | A step in Argo YAML contains name, related template and parameters.
Here we insert a single step into the global steps. | couler/core/step_update_utils.py | _update_steps | javoweb/couler | python | def _update_steps(function_name, caller_line, args=None, template_name=None):
'\n A step in Argo YAML contains name, related template and parameters.\n Here we insert a single step into the global steps.\n '
function_id = utils.invocation_name(function_name, caller_line)
if states._update_steps_loc... |
@contextmanager
def silence_stderr():
' Redirect stderr. '
if DEBUG:
(yield)
else:
with threading.Lock():
stderr = sys.stderr
sys.stderr = StringIO()
(yield)
with threading.Lock():
sys.stderr = stderr | -2,169,894,562,685,810,000 | Redirect stderr. | bundle/python-mode/pymode/utils.py | silence_stderr | Jenkin0603/myvim | python | @contextmanager
def silence_stderr():
' '
if DEBUG:
(yield)
else:
with threading.Lock():
stderr = sys.stderr
sys.stderr = StringIO()
(yield)
with threading.Lock():
sys.stderr = stderr |
def patch_paths():
' Function description. '
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs'))
if PY2:
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs2'))
else:
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs3')) | -1,636,797,612,643,356,200 | Function description. | bundle/python-mode/pymode/utils.py | patch_paths | Jenkin0603/myvim | python | def patch_paths():
' '
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs'))
if PY2:
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs2'))
else:
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'libs3')) |
@abstractmethod
def eval(self, tokens: List[str]) -> bool:
'If the given tokens sequence matches the given command execute it\n and return True. Otherwise, return False.\n\n Parameters\n ----------\n tokens: list(string)\n List of tokens in the command line\n\n Returns\... | -3,451,811,905,667,464,000 | If the given tokens sequence matches the given command execute it
and return True. Otherwise, return False.
Parameters
----------
tokens: list(string)
List of tokens in the command line
Returns
-------
bool | vizier/api/client/cli/command.py | eval | VizierDB/web-api-async | python | @abstractmethod
def eval(self, tokens: List[str]) -> bool:
'If the given tokens sequence matches the given command execute it\n and return True. Otherwise, return False.\n\n Parameters\n ----------\n tokens: list(string)\n List of tokens in the command line\n\n Returns\... |
@abstractmethod
def help(self) -> None:
'Print a simple help statement for the command.'
raise NotImplementedError() | 592,158,047,742,548,200 | Print a simple help statement for the command. | vizier/api/client/cli/command.py | help | VizierDB/web-api-async | python | @abstractmethod
def help(self) -> None:
raise NotImplementedError() |
def output(self, rows):
'Output the given rows in tabular format. Each rows is a list of\n string values. All rows are expected to have the sam elength. The first\n row is the table header.\n\n Parameters\n ----------\n rows: list(string)\n List of rows in the table\n ... | 8,790,974,993,255,597,000 | Output the given rows in tabular format. Each rows is a list of
string values. All rows are expected to have the sam elength. The first
row is the table header.
Parameters
----------
rows: list(string)
List of rows in the table | vizier/api/client/cli/command.py | output | VizierDB/web-api-async | python | def output(self, rows):
'Output the given rows in tabular format. Each rows is a list of\n string values. All rows are expected to have the sam elength. The first\n row is the table header.\n\n Parameters\n ----------\n rows: list(string)\n List of rows in the table\n ... |
def ProcessTag(self, line, type):
'\n Process a single string tag.\n \n :param line: an array of lines making a single string tag.\n :param type: the tag type, such as TYPE_STR or TYPE_PLUR\n :return: an array of lines representing the processed tag.\n '
return line | -3,578,550,112,715,211,000 | Process a single string tag.
:param line: an array of lines making a single string tag.
:param type: the tag type, such as TYPE_STR or TYPE_PLUR
:return: an array of lines representing the processed tag. | scripts/translations/base_string_script.py | ProcessTag | 1y445rc/FirebaseUI-Android | python | def ProcessTag(self, line, type):
'\n Process a single string tag.\n \n :param line: an array of lines making a single string tag.\n :param type: the tag type, such as TYPE_STR or TYPE_PLUR\n :return: an array of lines representing the processed tag.\n '
return line |
def ProcessFile(self, file_name):
'\n Process and write a file of string resources.\n \n :param file_name: path to the file to process.\n :return: None.\n '
lines = []
state = self.STATE_SEARCHING
curr_tag = []
pending_process_type = None
with open(file_name, 'r') as myfile:
... | -1,617,482,522,694,352,400 | Process and write a file of string resources.
:param file_name: path to the file to process.
:return: None. | scripts/translations/base_string_script.py | ProcessFile | 1y445rc/FirebaseUI-Android | python | def ProcessFile(self, file_name):
'\n Process and write a file of string resources.\n \n :param file_name: path to the file to process.\n :return: None.\n '
lines = []
state = self.STATE_SEARCHING
curr_tag = []
pending_process_type = None
with open(file_name, 'r') as myfile:
... |
def WriteFile(self, file_name, file_contents):
'\n Overwrite the contents of a file.\n \n :param file_name: path to the file to write.\n :param file_contents: string containing new file contents. \n :return: None\n '
with open(file_name, 'w') as myfile:
myfile.write(file_contents) | 3,607,157,379,932,279,000 | Overwrite the contents of a file.
:param file_name: path to the file to write.
:param file_contents: string containing new file contents.
:return: None | scripts/translations/base_string_script.py | WriteFile | 1y445rc/FirebaseUI-Android | python | def WriteFile(self, file_name, file_contents):
'\n Overwrite the contents of a file.\n \n :param file_name: path to the file to write.\n :param file_contents: string containing new file contents. \n :return: None\n '
with open(file_name, 'w') as myfile:
myfile.write(file_contents) |
def __init__(self, config_entry: config_entries.ConfigEntry) -> None:
'Init object.'
self.config_entry = config_entry | -2,559,020,865,665,428,000 | Init object. | homeassistant/components/netgear/config_flow.py | __init__ | 2004happy/core | python | def __init__(self, config_entry: config_entries.ConfigEntry) -> None:
self.config_entry = config_entry |
async def async_step_init(self, user_input=None):
'Manage the options.'
if (user_input is not None):
return self.async_create_entry(title='', data=user_input)
settings_schema = vol.Schema({vol.Optional(CONF_CONSIDER_HOME, default=self.config_entry.options.get(CONF_CONSIDER_HOME, DEFAULT_CONSIDER_HOM... | 6,396,657,444,366,347,000 | Manage the options. | homeassistant/components/netgear/config_flow.py | async_step_init | 2004happy/core | python | async def async_step_init(self, user_input=None):
if (user_input is not None):
return self.async_create_entry(title=, data=user_input)
settings_schema = vol.Schema({vol.Optional(CONF_CONSIDER_HOME, default=self.config_entry.options.get(CONF_CONSIDER_HOME, DEFAULT_CONSIDER_HOME.total_seconds())): in... |
def __init__(self):
'Initialize the netgear config flow.'
self.placeholders = {CONF_HOST: DEFAULT_HOST, CONF_PORT: DEFAULT_PORT, CONF_USERNAME: DEFAULT_USER, CONF_SSL: False}
self.discovered = False | -8,401,081,290,602,939,000 | Initialize the netgear config flow. | homeassistant/components/netgear/config_flow.py | __init__ | 2004happy/core | python | def __init__(self):
self.placeholders = {CONF_HOST: DEFAULT_HOST, CONF_PORT: DEFAULT_PORT, CONF_USERNAME: DEFAULT_USER, CONF_SSL: False}
self.discovered = False |
@staticmethod
@callback
def async_get_options_flow(config_entry: config_entries.ConfigEntry) -> OptionsFlowHandler:
'Get the options flow.'
return OptionsFlowHandler(config_entry) | 46,051,271,963,521,970 | Get the options flow. | homeassistant/components/netgear/config_flow.py | async_get_options_flow | 2004happy/core | python | @staticmethod
@callback
def async_get_options_flow(config_entry: config_entries.ConfigEntry) -> OptionsFlowHandler:
return OptionsFlowHandler(config_entry) |
async def _show_setup_form(self, user_input=None, errors=None):
'Show the setup form to the user.'
if (not user_input):
user_input = {}
if self.discovered:
data_schema = _discovery_schema_with_defaults(user_input)
else:
data_schema = _user_schema_with_defaults(user_input)
ret... | 5,815,990,346,358,273,000 | Show the setup form to the user. | homeassistant/components/netgear/config_flow.py | _show_setup_form | 2004happy/core | python | async def _show_setup_form(self, user_input=None, errors=None):
if (not user_input):
user_input = {}
if self.discovered:
data_schema = _discovery_schema_with_defaults(user_input)
else:
data_schema = _user_schema_with_defaults(user_input)
return self.async_show_form(step_id='... |
async def async_step_ssdp(self, discovery_info: ssdp.SsdpServiceInfo) -> FlowResult:
'Initialize flow from ssdp.'
updated_data: dict[(str, ((str | int) | bool))] = {}
device_url = urlparse(discovery_info.ssdp_location)
if (hostname := device_url.hostname):
hostname = cast(str, hostname)
... | -1,430,843,196,014,504,200 | Initialize flow from ssdp. | homeassistant/components/netgear/config_flow.py | async_step_ssdp | 2004happy/core | python | async def async_step_ssdp(self, discovery_info: ssdp.SsdpServiceInfo) -> FlowResult:
updated_data: dict[(str, ((str | int) | bool))] = {}
device_url = urlparse(discovery_info.ssdp_location)
if (hostname := device_url.hostname):
hostname = cast(str, hostname)
updated_data[CONF_HOST] = ho... |
async def async_step_user(self, user_input=None):
'Handle a flow initiated by the user.'
errors = {}
if (user_input is None):
return (await self._show_setup_form())
host = user_input.get(CONF_HOST, self.placeholders[CONF_HOST])
port = self.placeholders[CONF_PORT]
ssl = self.placeholders[... | -6,443,958,358,770,587,000 | Handle a flow initiated by the user. | homeassistant/components/netgear/config_flow.py | async_step_user | 2004happy/core | python | async def async_step_user(self, user_input=None):
errors = {}
if (user_input is None):
return (await self._show_setup_form())
host = user_input.get(CONF_HOST, self.placeholders[CONF_HOST])
port = self.placeholders[CONF_PORT]
ssl = self.placeholders[CONF_SSL]
username = user_input.ge... |
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