code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_plugin_twitter_dm_attachments_invalid_attachment(
mocker, twitter_url, good_message_response):
"""
Test case with an invalid attachment.
"""
mock_post: Mock = mocker.patch("requests.post")
mock_post.side_effect = [good_media_response, good_message_response]
# Create applicatio... |
Test case with an invalid attachment.
| test_plugin_twitter_dm_attachments_invalid_attachment | python | caronc/apprise | test/test_plugin_twitter.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_twitter.py | BSD-2-Clause |
def test_plugin_twitter_tweet_attachments_more_logging(
mock_post, twitter_url, good_media_response):
"""
NotifyTwitter() Tweet Attachment Checks - More logging
TODO: The "more logging" aspect is not verified yet?
"""
good_tweet_response = good_response({
'screen_name': TWITTER_SCR... |
NotifyTwitter() Tweet Attachment Checks - More logging
TODO: The "more logging" aspect is not verified yet?
| test_plugin_twitter_tweet_attachments_more_logging | python | caronc/apprise | test/test_plugin_twitter.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_twitter.py | BSD-2-Clause |
def test_plugin_whatsapp_auth(mock_post):
"""
NotifyWhatsApp() Auth
- account-wide auth token
- API key and its own auth token
"""
response = mock.Mock()
response.content = ''
response.status_code = requests.codes.ok
# Prepare Mock
mock_post.return_value = response
# ... |
NotifyWhatsApp() Auth
- account-wide auth token
- API key and its own auth token
| test_plugin_whatsapp_auth | python | caronc/apprise | test/test_plugin_whatsapp.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_whatsapp.py | BSD-2-Clause |
def test_plugin_windows_mocked():
"""
NotifyWindows() General Checks (via non-Windows platform)
"""
# We need to fake our windows environment for testing purposes
win32api_name = 'win32api'
win32api = types.ModuleType(win32api_name)
sys.modules[win32api_name] = win32api
win32api.GetMod... |
NotifyWindows() General Checks (via non-Windows platform)
| test_plugin_windows_mocked | python | caronc/apprise | test/test_plugin_windows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_windows.py | BSD-2-Clause |
def test_plugin_windows_native(mock_loadimage,
mock_notify,
mock_update_window):
"""
NotifyWindows() General Checks (via Windows platform)
"""
# Create our instance
obj = apprise.Apprise.instantiate('windows://', suppress_exceptions=Fal... |
NotifyWindows() General Checks (via Windows platform)
| test_plugin_windows_native | python | caronc/apprise | test/test_plugin_windows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_windows.py | BSD-2-Clause |
def test_plugin_workflows_templating_basic_success(
request_mock, workflows_url, tmpdir):
"""
NotifyWorkflows() Templating - success.
Test cases where URL and JSON is valid.
"""
template = tmpdir.join("simple.json")
template.write(cleandoc("""
{
"@type": "MessageCard",
"... |
NotifyWorkflows() Templating - success.
Test cases where URL and JSON is valid.
| test_plugin_workflows_templating_basic_success | python | caronc/apprise | test/test_plugin_workflows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_workflows.py | BSD-2-Clause |
def test_plugin_workflows_templating_load_json_failure(
request_mock, workflows_url, tmpdir):
"""
NotifyWorkflows() Templating - template loading failure.
Test a case where we can not access the file.
"""
template = tmpdir.join("empty.json")
template.write("")
obj = Apprise.instant... |
NotifyWorkflows() Templating - template loading failure.
Test a case where we can not access the file.
| test_plugin_workflows_templating_load_json_failure | python | caronc/apprise | test/test_plugin_workflows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_workflows.py | BSD-2-Clause |
def test_plugin_workflows_templating_target_success(
request_mock, workflows_url, tmpdir):
"""
NotifyWorkflows() Templating - success with target.
A more complicated example; uses a target.
"""
template = tmpdir.join("more_complicated_example.json")
template.write(cleandoc("""
{
... |
NotifyWorkflows() Templating - success with target.
A more complicated example; uses a target.
| test_plugin_workflows_templating_target_success | python | caronc/apprise | test/test_plugin_workflows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_workflows.py | BSD-2-Clause |
def test_workflows_yaml_config_missing_template_filename(
request_mock, workflows_url, simple_template, tmpdir):
"""
NotifyWorkflows() YAML Configuration Entries - Missing template reference.
"""
config = tmpdir.join("workflow01.yml")
config.write(cleandoc("""
urls:
- {url}:
... |
NotifyWorkflows() YAML Configuration Entries - Missing template reference.
| test_workflows_yaml_config_missing_template_filename | python | caronc/apprise | test/test_plugin_workflows.py | https://github.com/caronc/apprise/blob/master/test/test_plugin_workflows.py | BSD-2-Clause |
def test_notify_overflow_truncate_with_amalgamation():
"""
API: Overflow With Amalgamation Truncate Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
body_len = 1024
title_... |
API: Overflow With Amalgamation Truncate Functionality Testing
| test_notify_overflow_truncate_with_amalgamation | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_truncate_no_amalgamation():
"""
API: Overflow No Amalgamation Truncate Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
body_len = 1024
title_len ... |
API: Overflow No Amalgamation Truncate Functionality Testing
| test_notify_overflow_truncate_no_amalgamation | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_with_amalgamation():
"""
API: Overflow With Amalgamation Splits Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
body_len = 1024
title_len ... |
API: Overflow With Amalgamation Splits Functionality Testing
| test_notify_overflow_split_with_amalgamation | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_with_amalgamation_force_title_always():
"""
API: Overflow With Amalgamation (title alaways Split Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
... |
API: Overflow With Amalgamation (title alaways Split Functionality Testing
| test_notify_overflow_split_with_amalgamation_force_title_always | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_with_amalgamation_force_title_once():
"""
API: Overflow With Amalgamation (title once) Split Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
b... |
API: Overflow With Amalgamation (title once) Split Functionality Testing
| test_notify_overflow_split_with_amalgamation_force_title_once | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_no_amalgamation():
"""
API: Overflow No Amalgamation Splits Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
body_len = 1024
title_len = 10... |
API: Overflow No Amalgamation Splits Functionality Testing
| test_notify_overflow_split_no_amalgamation | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_no_amalgamation_force_title_always():
"""
API: Overflow No Amalgamation (title always) Split Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
b... |
API: Overflow No Amalgamation (title always) Split Functionality Testing
| test_notify_overflow_split_no_amalgamation_force_title_always | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def test_notify_overflow_split_no_amalgamation_force_title_once():
"""
API: Overflow No Amalgamation (title once) Split Functionality Testing
"""
#
# A little preparation
#
# Number of characters per line
row = 24
# Some variables we use to control the data we work with
body_... |
API: Overflow No Amalgamation (title once) Split Functionality Testing
| test_notify_overflow_split_no_amalgamation_force_title_once | python | caronc/apprise | test/test_rest_plugins.py | https://github.com/caronc/apprise/blob/master/test/test_rest_plugins.py | BSD-2-Clause |
def environ(*remove, **update):
"""
Temporarily updates the ``os.environ`` dictionary in-place.
The ``os.environ`` dictionary is updated in-place so that the modification
is sure to work in all situations.
:param remove: Environment variable(s) to remove.
:param update: Dictionary of environme... |
Temporarily updates the ``os.environ`` dictionary in-place.
The ``os.environ`` dictionary is updated in-place so that the modification
is sure to work in all situations.
:param remove: Environment variable(s) to remove.
:param update: Dictionary of environment variables and values to
... | environ | python | caronc/apprise | test/helpers/environment.py | https://github.com/caronc/apprise/blob/master/test/helpers/environment.py | BSD-2-Clause |
def reload_plugin(name):
"""
Reload built-in plugin module, e.g. `NotifyGnome`.
Reloading plugin modules is needed when testing module-level code of
notification plugins.
See also https://stackoverflow.com/questions/31363311.
"""
A_MGR.unload_modules()
reload(sys.modules['apprise.app... |
Reload built-in plugin module, e.g. `NotifyGnome`.
Reloading plugin modules is needed when testing module-level code of
notification plugins.
See also https://stackoverflow.com/questions/31363311.
| reload_plugin | python | caronc/apprise | test/helpers/module.py | https://github.com/caronc/apprise/blob/master/test/helpers/module.py | BSD-2-Clause |
def add(self, url, meta):
"""
Adds a test suite to our object
"""
self.__tests.append({
'url': url,
'meta': meta,
}) |
Adds a test suite to our object
| add | python | caronc/apprise | test/helpers/rest.py | https://github.com/caronc/apprise/blob/master/test/helpers/rest.py | BSD-2-Clause |
def __notify(
self,
url,
obj,
meta,
asset,
mock_request,
mock_patch,
mock_del,
mock_put,
mock_head,
mock_post,
mock_get
):
"""
Perform notification testing against object specified
"""
#
... |
Perform notification testing against object specified
| __notify | python | caronc/apprise | test/helpers/rest.py | https://github.com/caronc/apprise/blob/master/test/helpers/rest.py | BSD-2-Clause |
def extract_anthropic_prompt(prompt_and_response):
"""Extract the anthropic prompt from a prompt and response pair."""
search_term = '\n\nAssistant:'
search_term_idx = prompt_and_response.rfind(search_term)
assert search_term_idx != -1, f"Prompt and response does not contain '{search_term}'"
return ... | Extract the anthropic prompt from a prompt and response pair. | extract_anthropic_prompt | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def strip_html_tags(html_string):
"""Strip HTML tags from a string, except for <code> tags (which contain real code in the StackExchange answers)."""
# Create a BeautifulSoup object
soup = BeautifulSoup(html_string, 'html.parser')
# Initialize an empty list to store the text
text = []
for eleme... | Strip HTML tags from a string, except for <code> tags (which contain real code in the StackExchange answers). | strip_html_tags | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_se(split, silent=False, cache_dir: str = None) -> Dict[str, Dict[str, Union[List[Tuple[int, int]], List[str], str]]]:
"""Load the StackExchange dataset from Huggingface, and return a dict of prompts and responses. See get_hh for the format.
We strip the HTML tags from the responses (except for <... | Load the StackExchange dataset from Huggingface, and return a dict of prompts and responses. See get_hh for the format.
We strip the HTML tags from the responses (except for <code> tags), and we add necessary newlines.
| get_se | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_shp(split: str, silent: bool = False, cache_dir: str = None) -> Dict[str, Dict[str, Union[List[Tuple[int, int]], List[str], str]]]:
"""Load the Stanford Human Preferences dataset from Huggingface and convert it to the necessary format. See hh for the format.
We filter preference pairs to only keep p... | Load the Stanford Human Preferences dataset from Huggingface and convert it to the necessary format. See hh for the format.
We filter preference pairs to only keep pairs where the score ratio is at least 2.
For this dataset, the sft_target is the response with the highest score.
| get_shp | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_hh(split: str, silent: bool = False, cache_dir: str = None) -> Dict[str, Dict[str, Union[List[Tuple[int, int]], List[str], str]]]:
"""Load the Anthropic Helpful-Harmless dataset from Huggingface and convert it to the necessary format.
The dataset is converted to a dictionary with the following s... | Load the Anthropic Helpful-Harmless dataset from Huggingface and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt1': {
'responses': List[str],
'pairs': List[Tuple[int, int]],
's... | get_hh | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_dataset(name: str, split: str, silent: bool = False, cache_dir: str = None):
"""Load the given dataset by name. Supported by default are 'shp', 'hh', and 'se'."""
if name == 'shp':
data = get_shp(split, silent=silent, cache_dir=cache_dir)
elif name == 'hh':
data = get_hh(split, silen... | Load the given dataset by name. Supported by default are 'shp', 'hh', and 'se'. | get_dataset | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_collate_fn(tokenizer) -> Callable[[List[Dict]], Dict[str, Union[List, torch.Tensor]]]:
"""Returns a collate function for the given tokenizer.
The collate function takes a list of examples (dicts, where values are lists of
ints [tokens] or strings [the original texts]) and returns a batc... | Returns a collate function for the given tokenizer.
The collate function takes a list of examples (dicts, where values are lists of
ints [tokens] or strings [the original texts]) and returns a batch of examples,
PyTorch tensors padded to the maximum length. Strings are passed through. | get_collate_fn | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def tokenize_batch_element(prompt: str, chosen: str, rejected: str, truncation_mode: str, tokenizer, max_length: int, max_prompt_length: int) -> Dict:
"""Tokenize a single batch element.
At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
in case the prompt + c... | Tokenize a single batch element.
At this stage, we don't convert to PyTorch tensors yet; we just handle the truncation
in case the prompt + chosen or prompt + rejected responses is/are too long. First
we truncate the prompt; if we're still too long, we truncate the chosen/rejected.
... | tokenize_batch_element | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def get_batch_iterator(names: List[str],
tokenizer,
split: str = 'train',
batch_size: int = 1,
shuffle: bool = True,
max_length: int = 512,
max_prompt_length: int = 128,
... | Get an iterator over batches of data. Stops after n_epochs or n_examples, whichever comes first.
Args:
names: Names of datasets to use.
tokenizer: Tokenizer to use.
split: Which split to use.
batch_size: Batch size.
shuffle: Whether to shuffle the data after each epoch.
... | get_batch_iterator | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def strings_match_up_to_spaces(str_a: str, str_b: str) -> bool:
"""Returns True if str_a and str_b match up to spaces, False otherwise."""
for idx in range(min(len(str_a), len(str_b)) - 2):
if str_a[idx] != str_b[idx]:
if str_a[idx] != ' ' and str_b[idx] != ' ':
return False
... | Returns True if str_a and str_b match up to spaces, False otherwise. | strings_match_up_to_spaces | python | eric-mitchell/direct-preference-optimization | preference_datasets.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/preference_datasets.py | Apache-2.0 |
def worker_main(rank: int, world_size: int, config: DictConfig, policy: nn.Module, reference_model: Optional[nn.Module] = None):
"""Main function for each worker process (may be only 1 for BasicTrainer/TensorParallelTrainer)."""
if 'FSDP' in config.trainer:
init_distributed(rank, world_size, port=config... | Main function for each worker process (may be only 1 for BasicTrainer/TensorParallelTrainer). | worker_main | python | eric-mitchell/direct-preference-optimization | train.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/train.py | Apache-2.0 |
def main(config: DictConfig):
"""Main entry point for training. Validates config, creates/initializes model(s), and kicks off worker process(es)."""
# Resolve hydra references, e.g. so we don't re-compute the run directory
OmegaConf.resolve(config)
missing_keys: Set[str] = OmegaConf.missing_keys(confi... | Main entry point for training. Validates config, creates/initializes model(s), and kicks off worker process(es). | main | python | eric-mitchell/direct-preference-optimization | train.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/train.py | Apache-2.0 |
def preference_loss(policy_chosen_logps: torch.FloatTensor,
policy_rejected_logps: torch.FloatTensor,
reference_chosen_logps: torch.FloatTensor,
reference_rejected_logps: torch.FloatTensor,
beta: float,
label_smoothing: ... | Compute the DPO loss for a batch of policy and reference model log probabilities.
Args:
policy_chosen_logps: Log probabilities of the policy model for the chosen responses. Shape: (batch_size,)
policy_rejected_logps: Log probabilities of the policy model for the rejected responses. Shape: (batch_si... | preference_loss | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def concatenated_inputs(batch: Dict[str, Union[List, torch.LongTensor]]) -> Dict[str, torch.LongTensor]:
"""Concatenate the chosen and rejected inputs into a single tensor.
Args:
batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (b... | Concatenate the chosen and rejected inputs into a single tensor.
Args:
batch: A batch of data. Must contain the keys 'chosen_input_ids' and 'rejected_input_ids', which are tensors of shape (batch_size, sequence_length).
Returns:
A dictionary containing the concatenated inputs under... | concatenated_inputs | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def __init__(self, policy: nn.Module, config: DictConfig, seed: int, run_dir: str, reference_model: Optional[nn.Module] = None, rank: int = 0, world_size: int = 1):
"""A trainer for a language model, supporting either SFT or DPO training.
If multiple GPUs are present, naively splits the m... | A trainer for a language model, supporting either SFT or DPO training.
If multiple GPUs are present, naively splits the model across them, effectively
offering N times available memory, but without any parallel computation.
| __init__ | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def get_batch_samples(self, batch: Dict[str, torch.LongTensor]) -> Tuple[str, str]:
"""Generate samples from the policy (and reference model, if doing DPO training) for the given batch of inputs."""
# FSDP generation according to https://github.com/pytorch/pytorch/issues/100069
ctx = lambda: (F... | Generate samples from the policy (and reference model, if doing DPO training) for the given batch of inputs. | get_batch_samples | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def concatenated_forward(self, model: nn.Module, batch: Dict[str, Union[List, torch.LongTensor]]) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
"""Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
We do this to avoid doing two forward ... | Run the given model on the given batch of inputs, concatenating the chosen and rejected inputs together.
We do this to avoid doing two forward passes, because it's faster for FSDP.
| concatenated_forward | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def get_batch_metrics(self, batch: Dict[str, Union[List, torch.LongTensor]], loss_config: DictConfig, train=True):
"""Compute the SFT or DPO loss and other metrics for the given batch of inputs."""
metrics = {}
train_test = 'train' if train else 'eval'
if loss_config.name in {'dpo', 'i... | Compute the SFT or DPO loss and other metrics for the given batch of inputs. | get_batch_metrics | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def train(self):
"""Begin either SFT or DPO training, with periodic evaluation."""
rank0_print(f'Using {self.config.optimizer} optimizer')
self.optimizer = getattr(torch.optim, self.config.optimizer)(self.policy.parameters(), lr=self.config.lr)
self.scheduler = torch.optim.lr_scheduler.... | Begin either SFT or DPO training, with periodic evaluation. | train | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def save(self, output_dir: Optional[str] = None, metrics: Optional[Dict] = None):
"""Save policy, optimizer, and scheduler state to disk."""
policy_state_dict = self.policy.state_dict()
self.write_state_dict(self.example_counter, policy_state_dict, metrics, 'policy.pt', output_dir)
del ... | Save policy, optimizer, and scheduler state to disk. | save | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def __init__(self, policy: nn.Module, config: DictConfig, seed: int, run_dir: str, reference_model: Optional[nn.Module] = None, rank: int = 0, world_size: int = 1):
"""A trainer subclass that uses PyTorch FSDP to shard the model across multiple GPUs.
This trainer will shard both the policy a... | A trainer subclass that uses PyTorch FSDP to shard the model across multiple GPUs.
This trainer will shard both the policy and reference model across all available GPUs.
Models are sharded at the block level, where the block class name is provided in the config.
| __init__ | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def save(self, output_dir=None, metrics=None):
"""Save policy, optimizer, and scheduler state to disk, gathering from all processes and saving only on the rank 0 process."""
save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
with FSDP.state_dict_type(self.policy, StateDictTy... | Save policy, optimizer, and scheduler state to disk, gathering from all processes and saving only on the rank 0 process. | save | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def __init__(self, policy, config, seed, run_dir, reference_model=None, rank=0, world_size=1):
"""A trainer subclass that uses TensorParallel to shard the model across multiple GPUs.
Based on https://github.com/BlackSamorez/tensor_parallel. Note sampling is extremely slow,
see https://... | A trainer subclass that uses TensorParallel to shard the model across multiple GPUs.
Based on https://github.com/BlackSamorez/tensor_parallel. Note sampling is extremely slow,
see https://github.com/BlackSamorez/tensor_parallel/issues/66.
| __init__ | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def save(self, output_dir=None, metrics=None):
"""Save (unsharded) policy state to disk."""
with tp.save_tensor_parallel(self.policy):
policy_state_dict = self.policy.state_dict()
self.write_state_dict(self.example_counter, policy_state_dict, metrics, 'policy.pt', output_dir)
... | Save (unsharded) policy state to disk. | save | python | eric-mitchell/direct-preference-optimization | trainers.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/trainers.py | Apache-2.0 |
def get_local_dir(prefixes_to_resolve: List[str]) -> str:
"""Return the path to the cache directory for this user."""
for prefix in prefixes_to_resolve:
if os.path.exists(prefix):
return f"{prefix}/{getpass.getuser()}"
os.makedirs(prefix)
return f"{prefix}/{getpass.getuser()}" | Return the path to the cache directory for this user. | get_local_dir | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def get_local_run_dir(exp_name: str, local_dirs: List[str]) -> str:
"""Create a local directory to store outputs for this run, and return its path."""
now = datetime.now()
timestamp = now.strftime("%Y-%m-%d_%H-%M-%S_%f")
run_dir = f"{get_local_dir(local_dirs)}/{exp_name}_{timestamp}"
os.makedirs(run... | Create a local directory to store outputs for this run, and return its path. | get_local_run_dir | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def slice_and_move_batch_for_device(batch: Dict, rank: int, world_size: int, device: str) -> Dict:
"""Slice a batch into chunks, and move each chunk to the specified device."""
chunk_size = len(list(batch.values())[0]) // world_size
start = chunk_size * rank
end = chunk_size * (rank + 1)
sliced = {k... | Slice a batch into chunks, and move each chunk to the specified device. | slice_and_move_batch_for_device | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def all_gather_if_needed(values: torch.Tensor, rank: int, world_size: int) -> torch.Tensor:
"""Gather and stack/cat values from all processes, if there are multiple processes."""
if world_size == 1:
return values
all_values = [torch.empty_like(values).to(rank) for _ in range(world_size)]
dist.a... | Gather and stack/cat values from all processes, if there are multiple processes. | all_gather_if_needed | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def print_gpu_memory(rank: int = None, message: str = ''):
"""Print the amount of GPU memory currently allocated for each GPU."""
if torch.cuda.is_available():
device_count = torch.cuda.device_count()
for i in range(device_count):
device = torch.device(f'cuda:{i}')
alloca... | Print the amount of GPU memory currently allocated for each GPU. | print_gpu_memory | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def get_block_class_from_model(model: torch.nn.Module, block_class_name: str) -> torch.nn.Module:
"""Get the class of a block from a model, using the block's class name."""
for module in model.modules():
if module.__class__.__name__ == block_class_name:
return module.__class__
raise Valu... | Get the class of a block from a model, using the block's class name. | get_block_class_from_model | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def __init__(self, seed):
"""Temporarily set the random seed, and then restore it when exiting the context."""
self.seed = seed
self.stored_state = None
self.stored_np_state = None | Temporarily set the random seed, and then restore it when exiting the context. | __init__ | python | eric-mitchell/direct-preference-optimization | utils.py | https://github.com/eric-mitchell/direct-preference-optimization/blob/master/utils.py | Apache-2.0 |
def __init__(self, node: Node, text_buf: bytes, override_nodes: list["DTNode"] | None = None):
"""
Initialize a node from its name (which may be in the form of `label:name`)
and `parse` which contains the node itself.
"""
self.node = node
self.text_buf = text_buf
... |
Initialize a node from its name (which may be in the form of `label:name`)
and `parse` which contains the node itself.
| __init__ | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_string(self, property_re: str) -> str | None:
"""Extract last defined value for a `string` type property matching the `property_re` regex."""
if (nodes := self._get_property(property_re)) is None:
return None
return self._get_content(nodes[0]).strip('"') | Extract last defined value for a `string` type property matching the `property_re` regex. | get_string | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_array(self, property_re: str) -> list[str] | None:
"""Extract last defined values for a `array` type property matching the `property_re` regex."""
if (nodes := self._get_property(property_re)) is None:
return None
return list(
chain.from_iterable(
... | Extract last defined values for a `array` type property matching the `property_re` regex. | get_array | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_phandle_array(self, property_re: str) -> list[str] | None:
"""Extract last defined values for a `phandle-array` type property matching the `property_re` regex."""
if array_vals := self.get_array(property_re):
return [
f"&{stripped}"
for binding in " ".... | Extract last defined values for a `phandle-array` type property matching the `property_re` regex. | get_phandle_array | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_path(self, property_re: str) -> str | None:
"""
Extract last defined value for a `path` type property matching the `property_re` regex.
Only supports phandle paths `&p` rather than path types `"/a/b"` right now.
"""
if (nodes := self._get_property(property_re)) is None:
... |
Extract last defined value for a `path` type property matching the `property_re` regex.
Only supports phandle paths `&p` rather than path types `"/a/b"` right now.
| get_path | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def __init__(
self,
in_str: str,
file_name: str | None = None,
preprocess: bool = True,
preamble: str | None = None,
additional_includes: list[str] | None = None,
):
"""
Given an input DTS string `in_str` and `file_name` it is read from, parse it to be... |
Given an input DTS string `in_str` and `file_name` it is read from, parse it to be
able to get `compatible` and `chosen` nodes.
For performance reasons, the whole tree isn't parsed into DTNode's.
If `preamble` is set to a non-empty string, prepend it to the read buffer.
| __init__ | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_compatible_nodes(self, compatible_value: str) -> list[DTNode]:
"""Return a list of nodes that have the given compatible value."""
query = TS_LANG.query(
rf"""
(node
(property name: (identifier) @prop value: (string_literal) @propval)
(#eq? @pro... | Return a list of nodes that have the given compatible value. | get_compatible_nodes | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def get_chosen_property(self, property_name: str) -> str | None:
"""Return phandle for a given property in the /chosen node."""
phandle = None
for node in self.chosen_nodes:
if (val := node.get_path(re.escape(property_name))) is not None:
phandle = val
return ... | Return phandle for a given property in the /chosen node. | get_chosen_property | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def preprocess_extra_data(self, data: str) -> str:
"""
Given a string containing data, preprocess it in the same context as the
original input buffer by appending the data to it and extracting the result
afterwards.
TODO(perf): Figure out a good interface to achieve this without... |
Given a string containing data, preprocess it in the same context as the
original input buffer by appending the data to it and extracting the result
afterwards.
TODO(perf): Figure out a good interface to achieve this without running preprocessing
twice.
| preprocess_extra_data | python | caksoylar/keymap-drawer | keymap_drawer/dts.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/dts.py | MIT |
def from_key_spec(cls, key_spec: dict | str | int | None) -> "LayoutKey":
"""Derive full params from a string/int (for tap), a full spec or null (empty key)."""
match key_spec:
case dict():
return cls(**key_spec)
case str():
return cls(tap=key_spec... | Derive full params from a string/int (for tap), a full spec or null (empty key). | from_key_spec | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def serialize_model(self) -> str | dict[str, str]:
"""Custom serializer to output string-only for simple legends."""
if self.hold or self.shifted or self.left or self.right or self.type:
return {
k: v
for k, v in (
("t", self.tap),
... | Custom serializer to output string-only for simple legends. | serialize_model | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def apply_formatter(self, formatter: Callable[[str], str]) -> None:
"""Add a formatter function (str -> str) to all non-empty fields."""
if self.tap:
self.tap = formatter(self.tap)
if self.hold:
self.hold = formatter(self.hold)
if self.shifted:
self.sh... | Add a formatter function (str -> str) to all non-empty fields. | apply_formatter | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def normalize_fields(cls, spec_dict: dict) -> dict:
"""Normalize spec_dict so that each field uses its alias and key is parsed to LayoutKey."""
for name, field in cls.model_fields.items():
if name in spec_dict:
spec_dict[field.alias or name] = spec_dict.pop(name)
if k... | Normalize spec_dict so that each field uses its alias and key is parsed to LayoutKey. | normalize_fields | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def validate_trigger_spec(self):
"""Make sure either positions or trigger keys are specified."""
assert (not self.key_positions and self.trigger_keys) or (
self.key_positions and not self.trigger_keys
), "Need to specify exactly one of `key_positions` or `trigger_keys` for combo"
... | Make sure either positions or trigger keys are specified. | validate_trigger_spec | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def get_combos_per_layer(self, layers: Iterable[str] | None = None) -> dict[str, list[ComboSpec]]:
"""Return a mapping of layer names to combos that are present on that layer, if they aren't drawn separately."""
assert self.config is not None
if layers is None:
layers = self.layers
... | Return a mapping of layer names to combos that are present on that layer, if they aren't drawn separately. | get_combos_per_layer | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def get_separate_combos(self) -> list[ComboSpec]:
"""Return a list of combos that are meant to be drawn separately."""
assert self.config is not None
return [
combo
for combo in self.combos
if combo.draw_separate or (combo.draw_separate is None and self.config... | Return a list of combos that are meant to be drawn separately. | get_separate_combos | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def dump(self, num_cols: int = 0) -> dict:
"""Returns a dict-valued dump of the keymap representation."""
dump = self.model_dump(exclude_defaults=True, exclude_unset=True, by_alias=True)
if num_cols > 0:
dump["layers"] = {
name: [layer_keys[i : i + num_cols] for i in ... | Returns a dict-valued dump of the keymap representation. | dump | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def rebase(self, base: "KeymapData") -> None:
"""
Rebase a keymap on a "base" one: This mostly preserves the fields with the fields from
the keymap, however for layers and combos it inherits fields from base that are not
specified in the new keymap. For example this can be used to take a... |
Rebase a keymap on a "base" one: This mostly preserves the fields with the fields from
the keymap, however for layers and combos it inherits fields from base that are not
specified in the new keymap. For example this can be used to take an old keymap and update
with a new parse output, ... | rebase | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def parse_layers(cls, val) -> dict[str, list[LayoutKey]]:
"""Parse each key on layer from its key spec, flattening the spec if it contains sublists."""
return {
layer_name: [
val if isinstance(val, LayoutKey) else LayoutKey.from_key_spec(val)
for val in chain.... | Parse each key on layer from its key spec, flattening the spec if it contains sublists. | parse_layers | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def create_layout(cls, vals):
"""Create layout with type given by layout param."""
if vals["layout"] is None: # ignore for no-layout mode
return vals
if isinstance(vals["layout"], PhysicalLayout): # already provided a valid object
return vals
vals["layout"] = Ph... | Create layout with type given by layout param. | create_layout | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def check_combos(self):
"""Resolve trigger keys if specified then validate combo positions are legitimate ones we can draw."""
for combo in self.combos:
if combo.trigger_keys:
self._resolve_key_positions_from_trigger_keys(combo)
assert self.layout is None or all(... | Resolve trigger keys if specified then validate combo positions are legitimate ones we can draw. | check_combos | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def check_dimensions(self):
"""Validate that physical layout and layers have the same number of keys."""
if self.layout is None: # only check self-consistency for no-layout mode
if len(set(len(layer) for layer in self.layers.values())) > 1:
counts = {layer_name: len(layer) f... | Validate that physical layout and layers have the same number of keys. | check_dimensions | python | caksoylar/keymap-drawer | keymap_drawer/keymap.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/keymap.py | MIT |
def from_qmk_spec(
cls, scale: float, pos: Point, width: float, height: float, rotation: float, rotation_pos: Point
) -> "PhysicalKey":
"""
Create a PhysicalKey from QMK-format key definition. `pos` is the top left corner coordinates,
`rotation_pos` is the coordinates around which th... |
Create a PhysicalKey from QMK-format key definition. `pos` is the top left corner coordinates,
`rotation_pos` is the coordinates around which the rectangle is rotated. `scale` maps from `1u` dimensions
to pixel dimensions.
During construction of PhysicalKey, uses `rotation_pos` to re-ad... | from_qmk_spec | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def normalize(self) -> "PhysicalLayout":
"""Normalize the layout so that the keys are all in (0, 0) to (width, height) coordinates."""
min_pt = Point(
min(k.pos.x - k.bounding_width / 2 for k in self.keys),
min(k.pos.y - k.bounding_height / 2 for k in self.keys),
)
... | Normalize the layout so that the keys are all in (0, 0) to (width, height) coordinates. | normalize | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def handle_file_overrides(self):
"""Allow certain spec combinations where one overrides the other."""
if self.qmk_info_json and self.qmk_keyboard:
logger.warning("qmk_info_json is overriding qmk_keyboard specification")
self.qmk_keyboard = None
if self.dts_layout and self... | Allow certain spec combinations where one overrides the other. | handle_file_overrides | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def check_specs(self):
"""Check that exactly one layout type is specified."""
if (
sum(
spec is not None
for spec in (
self.qmk_keyboard,
self.zmk_keyboard,
self.zmk_shared_layout,
... | Check that exactly one layout type is specified. | check_specs | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def handle_qmk_layout(self):
"""Check and set the layout name to account for deprecated qmk_layout field."""
if self.qmk_layout is not None:
logger.warning('"qmk_layout" is deprecated, please use "layout_name" instead')
assert self.layout_name is None, '"qmk_layout" cannot be use... | Check and set the layout name to account for deprecated qmk_layout field. | handle_qmk_layout | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def generate(self) -> PhysicalLayout:
"""Generate a physical layout given config and layout specs."""
draw_cfg, parse_cfg = self.config.draw_config, self.config.parse_config
if self.qmk_keyboard or self.qmk_info_json:
if self.qmk_keyboard:
qmk_info = _get_qmk_info(se... | Generate a physical layout given config and layout specs. | generate | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def check_thumbs(self):
"""Check that the number of thumb keys is specified correctly."""
if self.thumbs:
if isinstance(self.thumbs, int):
assert self.thumbs <= self.columns, "Number of thumbs should not be greater than columns"
assert self.split, "Cannot proc... | Check that the number of thumb keys is specified correctly. | check_thumbs | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def check_drops(self):
"""Check that drop_pinky or drop_index are only used with split layouts."""
if self.drop_pinky or self.drop_inner:
assert self.split, '"drop_*" properties can only be used with split layouts'
return self | Check that drop_pinky or drop_index are only used with split layouts. | check_drops | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def generate(self, key_w: float, key_h: float, split_gap: float) -> PhysicalLayout:
"""Generate a list of PhysicalKeys from given ortho specifications."""
logger.debug("generating OrthoLayout-based physical layout for spec %s", self.model_dump())
nrows = self.rows
if not isinstance(self... | Generate a list of PhysicalKeys from given ortho specifications. | generate | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def spec_validator(cls, val: str) -> str:
"""Split spec string by spaces or underscores then validate each part."""
assert all(
cls.part_pattern.match(part) for part in cls._split_spec(val)
), "Cols+thumbs `spec` value does not match the expected syntax, please double check"
... | Split spec string by spaces or underscores then validate each part. | spec_validator | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def generate(self, key_w: float, key_h: float, split_gap: float) -> PhysicalLayout:
"""Generate a list of PhysicalKeys from given CPT specification."""
logger.debug("generating CPT-based physical layout for spec %s", self.spec)
parts = [match.groupdict() for part in self._split_spec(self.spec) ... | Generate a list of PhysicalKeys from given CPT specification. | generate | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def generate(self, layout_name: str | None, key_size: float) -> PhysicalLayout:
"""Generate a sequence of PhysicalKeys from QmkKeys."""
logger.debug("generating QMK-based physical layout for layout name %s", layout_name)
assert self.layouts, "QmkLayout.layouts cannot be empty"
if layout_... | Generate a sequence of PhysicalKeys from QmkKeys. | generate | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def _get_qmk_info(qmk_keyboard: str, use_local_cache: bool = False) -> dict:
"""
Get a QMK info.json file from either self-maintained folder of layouts,
local file cache if enabled, or from QMK keyboards metadata API.
"""
qmk_keyboard = _map_qmk_keyboard(qmk_keyboard)
local_path = QMK_LAYOUTS_PA... |
Get a QMK info.json file from either self-maintained folder of layouts,
local file cache if enabled, or from QMK keyboards metadata API.
| _get_qmk_info | python | caksoylar/keymap-drawer | keymap_drawer/physical_layout.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/physical_layout.py | MIT |
def draw(args: Namespace, config: Config) -> None:
"""Draw the keymap in SVG format to stdout."""
yaml_data: dict[str, dict] = {}
for yaml_arg in args.keymap_yaml:
yaml_data = _merge_keymaps(yaml_data, yaml.safe_load(yaml_arg))
cli_layout = {
k: v
for k, v in (
("qmk... | Draw the keymap in SVG format to stdout. | draw | python | caksoylar/keymap-drawer | keymap_drawer/__main__.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/__main__.py | MIT |
def parse(args: Namespace, config: Config) -> None:
"""Call the appropriate parser for given args and dump YAML keymap representation to stdout."""
if args.base_keymap:
yaml_data = yaml.safe_load(args.base_keymap)
base = KeymapData(
layers=yaml_data.get("layers", {}), combos=yaml_dat... | Call the appropriate parser for given args and dump YAML keymap representation to stdout. | parse | python | caksoylar/keymap-drawer | keymap_drawer/__main__.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/__main__.py | MIT |
def dump_config(args: Namespace, config: Config) -> None:
"""Dump the currently active config, either default or parsed from args."""
def cfg_str_representer(dumper, in_str):
if "\n" in in_str: # use '|' style for multiline strings
return dumper.represent_scalar("tag:yaml.org,2002:str", in... | Dump the currently active config, either default or parsed from args. | dump_config | python | caksoylar/keymap-drawer | keymap_drawer/__main__.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/__main__.py | MIT |
def main() -> None:
"""Parse the configuration and print SVG using KeymapDrawer."""
parser = ArgumentParser(description=__doc__)
parser.add_argument("-v", "--version", action="version", version=version("keymap-drawer"))
parser.add_argument("-d", "--debug", action="store_true")
parser.add_argument(
... | Parse the configuration and print SVG using KeymapDrawer. | main | python | caksoylar/keymap-drawer | keymap_drawer/__main__.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/__main__.py | MIT |
def print_combo(self, combo: ComboSpec, combo_ind: int) -> tuple[Point, Point]: # pylint: disable=too-many-locals
"""
Print SVG code for a rectangle with text representing a combo specification, which contains the key positions
that trigger it and what it does when triggered. The position of th... |
Print SVG code for a rectangle with text representing a combo specification, which contains the key positions
that trigger it and what it does when triggered. The position of the rectangle depends on the alignment
specified, along with whether dendrons are drawn going to each key position from ... | print_combo | python | caksoylar/keymap-drawer | keymap_drawer/draw/combo.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/combo.py | MIT |
def print_combos_for_layer(self, combos: Sequence[ComboSpec]) -> tuple[float | None, float | None]:
"""
Print SVG for all given combos, relative to that point.
Return min and max y-coordinates of combo boxes, for bounding box calculations.
"""
combo_pts = [self.print_combo(combo,... |
Print SVG for all given combos, relative to that point.
Return min and max y-coordinates of combo boxes, for bounding box calculations.
| print_combos_for_layer | python | caksoylar/keymap-drawer | keymap_drawer/draw/combo.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/combo.py | MIT |
def create_combo_diagrams(
self, scale_factor: int, ghost_keys: Sequence[int] | None = None
) -> tuple[PhysicalLayout | None, dict[str, list[LayoutKey]]]:
"""
Create and return both a shrunk down physical layout and layers representing combo
locations with held key highlighting.
... |
Create and return both a shrunk down physical layout and layers representing combo
locations with held key highlighting.
| create_combo_diagrams | python | caksoylar/keymap-drawer | keymap_drawer/draw/combo.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/combo.py | MIT |
def print_layer_header(self, p: Point, header: str) -> None:
"""Print a layer header that precedes the layer visualization."""
text = header + ":" if self.cfg.append_colon_to_layer_header else header
self.out.write(
f'<text x="{round(p.x)}" y="{round(p.y)}" class="label" id="{self._s... | Print a layer header that precedes the layer visualization. | print_layer_header | python | caksoylar/keymap-drawer | keymap_drawer/draw/draw.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/draw.py | MIT |
def print_footer(self, p: Point) -> None:
"""Print a footer with text given by cfg.footer_text, with CSS class `footer` for bottom-right alignment."""
self.output_stream.write(
f'<text x="{p.x - self.cfg.outer_pad_w}" y="{p.y - self.cfg.outer_pad_h / 2}" class="footer">'
f"{self.... | Print a footer with text given by cfg.footer_text, with CSS class `footer` for bottom-right alignment. | print_footer | python | caksoylar/keymap-drawer | keymap_drawer/draw/draw.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/draw.py | MIT |
def print_key(self, p_key: PhysicalKey, l_key: LayoutKey, key_ind: int) -> None:
"""
Print SVG code for a rectangle with text representing the key, which is described by its physical
representation (p_key) and what it does in the given layer (l_key).
"""
p, w, h, r = (
... |
Print SVG code for a rectangle with text representing the key, which is described by its physical
representation (p_key) and what it does in the given layer (l_key).
| print_key | python | caksoylar/keymap-drawer | keymap_drawer/draw/draw.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/draw.py | MIT |
def print_layers( # pylint: disable=too-many-locals
self,
p: Point,
layout: PhysicalLayout,
layers: Mapping[str, Sequence[LayoutKey]],
combos_per_layer: Mapping[str, Sequence[ComboSpec]],
n_cols: int = 1,
draw_header: bool = True,
pad_divisor: int = 1,
... |
Print SVG code for keys for all layers (including combos on them) starting at coordinate p,
into n_cols columns and return the bottom right coordinate.
| print_layers | python | caksoylar/keymap-drawer | keymap_drawer/draw/draw.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/draw.py | MIT |
def print_board( # pylint: disable=too-many-locals
self,
draw_layers: Sequence[str] | None = None,
keys_only: bool = False,
combos_only: bool = False,
ghost_keys: Sequence[int] | None = None,
) -> None:
"""Print SVG code representing the keymap."""
# get fina... | Print SVG code representing the keymap. | print_board | python | caksoylar/keymap-drawer | keymap_drawer/draw/draw.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/draw.py | MIT |
def init_glyphs(self) -> None:
"""Preprocess all glyphs in the keymap to get their name to SVG mapping."""
def find_key_glyph_names(key: LayoutKey) -> set[str]:
return {
glyph
for field in (key.tap, key.hold, key.shifted, key.left, key.right)
... | Preprocess all glyphs in the keymap to get their name to SVG mapping. | init_glyphs | python | caksoylar/keymap-drawer | keymap_drawer/draw/glyph.py | https://github.com/caksoylar/keymap-drawer/blob/master/keymap_drawer/draw/glyph.py | MIT |
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