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
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def test_tts_convert_with_voice_settings() -> None:
"""Test TTS with custom voice settings."""
client = ElevenLabs()
audio_generator = client.text_to_speech.convert(
text=DEFAULT_TEXT,
voice_id=DEFAULT_VOICE,
model_id=DEFAULT_MODEL,
voice_settings=VoiceSettings(stability=0.71... | Test TTS with custom voice settings. | test_tts_convert_with_voice_settings | python | elevenlabs/elevenlabs-python | tests/test_tts.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_tts.py | MIT |
def test_voice_preview_generation():
"""Test generating voice previews from description."""
client = ElevenLabs()
# Test parameters
description = "A warm and friendly female voice with a slight British accent, speaking clearly and professionally"
sample_text = "This is a test message that needs to ... | Test generating voice previews from description. | test_voice_preview_generation | python | elevenlabs/elevenlabs-python | tests/test_ttv.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_ttv.py | MIT |
def test_construct_event_valid_signature():
"""Test webhook event construction with valid signature."""
# Setup
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
# Create a valid signature
body = json.dumps(payload)
timesta... | Test webhook event construction with valid signature. | test_construct_event_valid_signature | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_missing_signature():
"""Test webhook event construction with missing signature header."""
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
with pytest.raises(BadRequestError) as excinfo:
client.webhook... | Test webhook event construction with missing signature header. | test_construct_event_missing_signature | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_invalid_signature_format():
"""Test webhook event construction with invalid signature format."""
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
body = json.dumps(payload)
sig_header = "invalid_format"
... | Test webhook event construction with invalid signature format. | test_construct_event_invalid_signature_format | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_expired_timestamp():
"""Test webhook event construction with expired timestamp."""
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
# Create an expired timestamp (31 minutes old)
expired_time = int(time.ti... | Test webhook event construction with expired timestamp. | test_construct_event_expired_timestamp | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_invalid_signature():
"""Test webhook event construction with invalid signature."""
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
body = json.dumps(payload)
timestamp = str(int(time.time()))
sig_head... | Test webhook event construction with invalid signature. | test_construct_event_invalid_signature | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_missing_secret():
"""Test webhook event construction with missing secret."""
client = ElevenLabs()
payload = {"event_type": "speech.completed", "id": "123456"}
body = json.dumps(payload)
timestamp = str(int(time.time()))
sig_header = f"t={timestamp},v0=some_signature"
... | Test webhook event construction with missing secret. | test_construct_event_missing_secret | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def test_construct_event_mocked_time(mock_time):
"""Test webhook event construction with mocked time."""
mock_time.return_value = 1600000000
client = ElevenLabs()
webhook_secret = "test_secret"
payload = {"event_type": "speech.completed", "id": "123456"}
# Create a valid signature with fixed t... | Test webhook event construction with mocked time. | test_construct_event_mocked_time | python | elevenlabs/elevenlabs-python | tests/test_webhooks.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/test_webhooks.py | MIT |
def as_local_files(urls: Sequence[str]) -> Generator[str, None, None]:
"""Util to download files from urls and return local file paths"""
temp_files = []
for url in urls:
response = httpx.get(url)
temp_file = tempfile.NamedTemporaryFile()
temp_file.write(response.content)
te... | Util to download files from urls and return local file paths | as_local_files | python | elevenlabs/elevenlabs-python | tests/utils.py | https://github.com/elevenlabs/elevenlabs-python/blob/master/tests/utils.py | MIT |
def create_oss_artifact(
path,
bucket=None,
accesskey_id=None,
accesskey_secret=None,
key=None,
endpoint=None,
is_global=False,
):
"""
Configure the object as OssArtifact
OSS configuration can be found
(https://www.alibabacloud.com/help/doc-detail/32027.htm)
:param path: ... |
Configure the object as OssArtifact
OSS configuration can be found
(https://www.alibabacloud.com/help/doc-detail/32027.htm)
:param path: the local path of container
:param bucket: oss bucket
:param accesskey_id: oss user id
:param accesskey_secret: oss user ky
:param key: key of oss obj... | create_oss_artifact | python | couler-proj/couler | couler/argo.py | https://github.com/couler-proj/couler/blob/master/couler/argo.py | Apache-2.0 |
def create_s3_artifact(
path,
bucket=None,
accesskey_id=None,
accesskey_secret=None,
key=None,
endpoint=None,
is_global=False,
insecure=False,
):
"""
Configure the object as S3Artifact
:param path: the local path of container
:param bucket: s3 bucket
:param accesskey_... |
Configure the object as S3Artifact
:param path: the local path of container
:param bucket: s3 bucket
:param accesskey_id: s3 user id
:param accesskey_secret: s3 user key
:param key: key of s3 object
:param endpoint: end point of s3
:param insecure: use HTTP instead of HTTPS when True
... | create_s3_artifact | python | couler-proj/couler | couler/argo.py | https://github.com/couler-proj/couler/blob/master/couler/argo.py | Apache-2.0 |
def obtain_secret(secret_keys, namespace="default", name=None, dry_run=False):
"""Get the key from secret in k8s, and return the secret name."""
secret_data = {}
for x in secret_keys:
secret_data[x] = ""
return create_secret(
secret_data,
namespace=namespace,
name=name,
... | Get the key from secret in k8s, and return the secret name. | obtain_secret | python | couler-proj/couler | couler/argo.py | https://github.com/couler-proj/couler/blob/master/couler/argo.py | Apache-2.0 |
def create_secret(
secret_data,
namespace="default",
name=None,
dry_run=False,
use_existing=False,
artifact_secret=False,
):
"""Store the input dict as a secret in k8s, and return the secret name."""
secret = Secret(
namespace=namespace,
data=secret_data,
name=nam... | Store the input dict as a secret in k8s, and return the secret name. | create_secret | python | couler-proj/couler | couler/argo.py | https://github.com/couler-proj/couler/blob/master/couler/argo.py | Apache-2.0 |
def check_name(name):
"""Check the name is valid or not"""
if len(name) > WorkflowCRD.NAME_MAX_LENGTH:
raise ValueError(
"Name is too long. Max length: {}, now: {}"
"".format(WorkflowCRD.NAME_MAX_LENGTH, len(name))
)
if "." in name:
... | Check the name is valid or not | check_name | python | couler-proj/couler | couler/argo_submitter.py | https://github.com/couler-proj/couler/blob/master/couler/argo_submitter.py | Apache-2.0 |
def config_defaults(name_salter=None, service_account: str = None):
"""
Config couler defaults.
:param name_salter: function to salt workflow names.
:param service_account: name of the default Kubernetes
ServiceAccount with which to run workflows
:return:
"""
if name_salter is not No... |
Config couler defaults.
:param name_salter: function to salt workflow names.
:param service_account: name of the default Kubernetes
ServiceAccount with which to run workflows
:return:
| config_defaults | python | couler-proj/couler | couler/core/config.py | https://github.com/couler-proj/couler/blob/master/couler/core/config.py | Apache-2.0 |
def config_workflow(
name=None,
user_id=None,
timeout=None,
time_to_clean=None,
cluster_config_file=None,
cron_config=None,
service_account=None,
):
"""
Config some workflow-level information.
:param name: name of the workflow.
:param user_id: user information.
:param tim... |
Config some workflow-level information.
:param name: name of the workflow.
:param user_id: user information.
:param timeout: maximum running time(seconds).
:param time_to_clean: time to keep the workflow after completed(seconds).
:param cluster_config_file: cluster specific config
:param cr... | config_workflow | python | couler-proj/couler | couler/core/config.py | https://github.com/couler-proj/couler/blob/master/couler/core/config.py | Apache-2.0 |
def _config_cron_workflow(
schedule,
concurrency_policy='"Allow"',
successful_jobs_history_limit=3, # Default 3
failed_jobs_history_limit=1, # Default 1
starting_deadline_seconds=10,
suspend="false",
timezone="Asia/Shanghai",
):
"""
Config the CronWorkflow, see example
https://... |
Config the CronWorkflow, see example
https://github.com/argoproj/argo/blob/master/docs/cron-workflows.md
| _config_cron_workflow | python | couler-proj/couler | couler/core/config.py | https://github.com/couler-proj/couler/blob/master/couler/core/config.py | Apache-2.0 |
def run_script(
image,
command=None,
source=None,
args=None,
output=None,
input=None,
env=None,
secret=None,
resources=None,
timeout=None,
retry=None,
step_name=None,
image_pull_policy=None,
pool=None,
enable_ulogfs=True,
daemon=False,
volume_mounts=No... |
Generate an Argo script template. For example,
https://github.com/argoproj/argo/tree/master/examples#scripts--results.
:param image: Docker image name
:param command: entrypoint array
:param source: reference to function name that contains code to be executed
:param args: arguments to the step... | run_script | python | couler-proj/couler | couler/core/run_templates.py | https://github.com/couler-proj/couler/blob/master/couler/core/run_templates.py | Apache-2.0 |
def run_container(
image,
command=None,
args=None,
output=None,
input=None,
env=None,
env_from=None,
secret=None,
resources=None,
timeout=None,
retry=None,
step_name=None,
image_pull_policy=None,
pool=None,
enable_ulogfs=True,
daemon=False,
volume_moun... |
Generate an Argo container template. For example, the template whalesay
in https://github.com/argoproj/argo/tree/master/examples#hello-world.
:param image: Docker image name
:param command: entrypoint array
:param args: arguments to the step or task
:param output: output artifact for container... | run_container | python | couler-proj/couler | couler/core/run_templates.py | https://github.com/couler-proj/couler/blob/master/couler/core/run_templates.py | Apache-2.0 |
def _cleanup():
"""Cleanup the cached fields, just used for unit test.
"""
global _secrets, _update_steps_lock, _dag_caller_line, _upstream_dag_task, _upstream_dag_depends_logic, workflow, _steps_outputs # noqa: E501
global _exit_handler_enable, _when_prefix, _when_task, _while_steps, _concurrent_func_... | Cleanup the cached fields, just used for unit test.
| _cleanup | python | couler-proj/couler | couler/core/states.py | https://github.com/couler-proj/couler/blob/master/couler/core/states.py | Apache-2.0 |
def _update_dag_tasks(
function_name,
caller_line,
dependencies,
depends_logic,
args=None,
template_name=None,
step_name=None,
):
"""
A task in DAG of Argo YAML contains name, related template and parameters.
Here we insert a single task into the global tasks.
"""
if step... |
A task in DAG of Argo YAML contains name, related template and parameters.
Here we insert a single task into the global tasks.
| _update_dag_tasks | python | couler-proj/couler | couler/core/step_update_utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/step_update_utils.py | Apache-2.0 |
def _update_steps(function_name, caller_line, args=None, template_name=None):
"""
A step in Argo YAML contains name, related template and parameters.
Here we insert a single step into the global steps.
"""
function_id = utils.invocation_name(function_name, caller_line)
# Update `steps` only if ... |
A step in Argo YAML contains name, related template and parameters.
Here we insert a single step into the global steps.
| _update_steps | python | couler-proj/couler | couler/core/step_update_utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/step_update_utils.py | Apache-2.0 |
def argo_safe_name(name):
"""Some names are to be used in the Argo YAML file. For example,
the generateName and template name in
https://github.com/argoproj/argo/blob/master/examples/hello-world.yaml. As
Argo is to use the YAML as part of Kubernetes job description
YAML, these names must follow Kube... | Some names are to be used in the Argo YAML file. For example,
the generateName and template name in
https://github.com/argoproj/argo/blob/master/examples/hello-world.yaml. As
Argo is to use the YAML as part of Kubernetes job description
YAML, these names must follow Kubernetes's convention -- no
per... | argo_safe_name | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def invocation_location():
"""If a function A in file B calls function C, which in turn calls
invocation_location(), the call returns information about the invocation,
in particular, the caller's name "A" and the line number where A
calls C. Return (B + line_number) as function_name if A doesn't exist,
... | If a function A in file B calls function C, which in turn calls
invocation_location(), the call returns information about the invocation,
in particular, the caller's name "A" and the line number where A
calls C. Return (B + line_number) as function_name if A doesn't exist,
where users directly calls C i... | invocation_location | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def body(func_obj):
"""If a function A calls body(), the call returns the Python source code of
the function definition body (not including the signature) of A.
"""
if func_obj is None:
return None
code = inspect.getsource(func_obj)
# Remove function signature
code = code[code.find("... | If a function A calls body(), the call returns the Python source code of
the function definition body (not including the signature) of A.
| body | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def workflow_filename():
"""Return the Python file that defines the workflow.
"""
stacks = inspect.stack()
frame = inspect.stack()[len(stacks) - 1]
full_path = frame[0].f_code.co_filename
filename, _ = os.path.splitext(os.path.basename(full_path))
filename = argo_safe_name(filename)
retu... | Return the Python file that defines the workflow.
| workflow_filename | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def load_cluster_config():
"""Load user provided cluster specification file.
"""
module_file = os.getenv("couler_cluster_config")
if module_file is None:
return None
spec = util.spec_from_file_location(module_file, module_file)
module = util.module_from_spec(spec)
spec.loader.exec_m... | Load user provided cluster specification file.
| load_cluster_config | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def encode_base64(s):
"""
Encode a string using base64 and return a binary string.
This function is used in Secret creation.
For example, the secrets for Argo YAML:
https://github.com/argoproj/argo/blob/master/examples/README.md#secrets
"""
bencode = base64.b64encode(s.encode("utf-8"))
r... |
Encode a string using base64 and return a binary string.
This function is used in Secret creation.
For example, the secrets for Argo YAML:
https://github.com/argoproj/argo/blob/master/examples/README.md#secrets
| encode_base64 | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def generate_parameters_run_job(env):
"""
Generate the inputs parameter for running kubernetes resource
"""
envs = []
para_index = 0
parameters = []
args = []
if env is not None:
if isinstance(env, dict):
for key in env:
value = env[key]
... |
Generate the inputs parameter for running kubernetes resource
| generate_parameters_run_job | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def convert_dict_to_env_list(d):
"""This is to convert a Python dictionary to a list, where
each list item is a dict with `name` and `value` keys.
"""
if not isinstance(d, dict):
raise TypeError("The input parameter `d` is not a dict.")
env_list = []
for k, v in d.items():
if is... | This is to convert a Python dictionary to a list, where
each list item is a dict with `name` and `value` keys.
| convert_dict_to_env_list | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def gpu_requested(resources):
"""
Check whether the requested resources contains GPU.
Here resources is a dict like {"cpu": 1, "memory": 2,...}.
"""
if resources is None:
return False
if not isinstance(resources, dict):
raise TypeError("Parameter resources is required to be a dic... |
Check whether the requested resources contains GPU.
Here resources is a dict like {"cpu": 1, "memory": 2,...}.
| gpu_requested | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def bool_to_str(bool_val):
"""convert boolean to strings for YAML configuration"""
if not isinstance(bool_val, bool):
raise TypeError("The bool_val is required to be boolean type")
return "true" if bool_val else "false" | convert boolean to strings for YAML configuration | bool_to_str | python | couler-proj/couler | couler/core/utils.py | https://github.com/couler-proj/couler/blob/master/couler/core/utils.py | Apache-2.0 |
def concurrent(function_list, subtasks=False):
"""
Start different jobs at the same time
subtasks: each function F of function_list contains multiple steps.
Then, for each F, we create a sub-steps template.
"""
if not isinstance(function_list, list):
raise SyntaxError("require input func... |
Start different jobs at the same time
subtasks: each function F of function_list contains multiple steps.
Then, for each F, we create a sub-steps template.
| concurrent | python | couler-proj/couler | couler/core/syntax/concurrent.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/concurrent.py | Apache-2.0 |
def when(condition, function):
"""Generates an Argo conditional step.
For example, the coinflip example in
https://github.com/argoproj/argo/blob/master/examples/coinflip.yaml.
"""
pre = condition["pre"]
post = condition["post"]
if pre is None or post is None:
raise SyntaxError("Outpu... | Generates an Argo conditional step.
For example, the coinflip example in
https://github.com/argoproj/argo/blob/master/examples/coinflip.yaml.
| when | python | couler-proj/couler | couler/core/syntax/conditional.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/conditional.py | Apache-2.0 |
def dag(dependency_graph):
"""
Generate a DAG of Argo YAML
Note: couler.set_dependencies() is more preferable.
https://github.com/argoproj/argo/blob/master/examples/dag-coinflip.yaml
"""
if not isinstance(dependency_graph, list):
raise SyntaxError("require input as list")
states.wor... |
Generate a DAG of Argo YAML
Note: couler.set_dependencies() is more preferable.
https://github.com/argoproj/argo/blob/master/examples/dag-coinflip.yaml
| dag | python | couler-proj/couler | couler/core/syntax/dag.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/dag.py | Apache-2.0 |
def set_dependencies(step_function, dependencies=None):
"""
:param step_function: step to run
:param dependencies: the list of dependencies of this step. This can be in
either of the following forms:
1. a list of step names;
2. a string representing the enhanced depends logic that sp... |
:param step_function: step to run
:param dependencies: the list of dependencies of this step. This can be in
either of the following forms:
1. a list of step names;
2. a string representing the enhanced depends logic that specifies
dependencies based on their statuses. See the l... | set_dependencies | python | couler-proj/couler | couler/core/syntax/dag.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/dag.py | Apache-2.0 |
def set_exit_handler(status, exit_handler):
"""
Configure the workflow handler
Status would be: Succeeded, Failed, or Error.
Each status invokes one exit_handler function.
https://github.com/argoproj/argo/blob/master/examples/exit-handlers.yaml
"""
if not callable(exit_handler):
rais... |
Configure the workflow handler
Status would be: Succeeded, Failed, or Error.
Each status invokes one exit_handler function.
https://github.com/argoproj/argo/blob/master/examples/exit-handlers.yaml
| set_exit_handler | python | couler-proj/couler | couler/core/syntax/exit_handler.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/exit_handler.py | Apache-2.0 |
def exec_while(condition, inner_while):
"""
Generate the Argo recursive logic. For example
https://github.com/argoproj/argo/blob/master/examples/README.md#recursion.
"""
# _while_lock means 'exec_while' operation begins to work
# _while_steps stores logic steps inside the recursion logic
sta... |
Generate the Argo recursive logic. For example
https://github.com/argoproj/argo/blob/master/examples/README.md#recursion.
| exec_while | python | couler-proj/couler | couler/core/syntax/recursion.py | https://github.com/couler-proj/couler/blob/master/couler/core/syntax/recursion.py | Apache-2.0 |
def _container_output(step_name, template_name, output):
"""Generate output name from an Argo container template. For example,
"{{steps.generate-parameter.outputs.parameters.hello-param}}" used in
https://github.com/argoproj/argo/tree/master/examples#output-parameters.
Each element of return for run_co... | Generate output name from an Argo container template. For example,
"{{steps.generate-parameter.outputs.parameters.hello-param}}" used in
https://github.com/argoproj/argo/tree/master/examples#output-parameters.
Each element of return for run_container is contacted by:
couler.step_name.template_name.outp... | _container_output | python | couler-proj/couler | couler/core/templates/output.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/output.py | Apache-2.0 |
def _script_output(step_name, template_name, output):
"""Generate output name from an Argo script template. For example,
"{{steps.generate.outputs.result}}" in
https://github.com/argoproj/argo/tree/master/examples#scripts--results
Return of run_script is contacted by:
couler.step_name.template_name... | Generate output name from an Argo script template. For example,
"{{steps.generate.outputs.result}}" in
https://github.com/argoproj/argo/tree/master/examples#scripts--results
Return of run_script is contacted by:
couler.step_name.template_name.outputs.result
| _script_output | python | couler-proj/couler | couler/core/templates/output.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/output.py | Apache-2.0 |
def _job_output(step_name, template_name):
"""
:param step_name:
:param template_name:
https://github.com/argoproj/argo/blob/master/examples/k8s-jobs.yaml#L44
Return the job name and job id for running a job
"""
job_name = "couler.%s.%s.outputs.parameters.job-name" % (
step_name,
... |
:param step_name:
:param template_name:
https://github.com/argoproj/argo/blob/master/examples/k8s-jobs.yaml#L44
Return the job name and job id for running a job
| _job_output | python | couler-proj/couler | couler/core/templates/output.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/output.py | Apache-2.0 |
def extract_step_return(step_output):
"""Extract information for run container or script output.
step_output is a list with multiple outputs
"""
ret = {}
if isinstance(step_output, list):
# The first element of outputs is used for control flow operation
step_output = step_output[0]
... | Extract information for run container or script output.
step_output is a list with multiple outputs
| extract_step_return | python | couler-proj/couler | couler/core/templates/output.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/output.py | Apache-2.0 |
def to_yaml(self):
"""Covert the secret to a secret CRD specification."""
d = OrderedDict(
{
"apiVersion": "v1",
"kind": "Secret",
"metadata": {"name": self.name, "namespace": self.namespace} if self.namespace != "default" else {"name": self.na... | Covert the secret to a secret CRD specification. | to_yaml | python | couler-proj/couler | couler/core/templates/secret.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/secret.py | Apache-2.0 |
def to_env_list(self):
"""
Convert the secret to an environment list, and can be attached to
containers.
"""
secret_envs = []
for key, _ in self.data.items():
secret_env = {
"name": key,
"valueFrom": {"secretKeyRef": {"name": se... |
Convert the secret to an environment list, and can be attached to
containers.
| to_env_list | python | couler-proj/couler | couler/core/templates/secret.py | https://github.com/couler-proj/couler/blob/master/couler/core/templates/secret.py | Apache-2.0 |
def run(
raw_template,
tuning_params,
objective,
success_condition=None,
failure_condition=None,
algorithm="random",
parallel_trial_count=3,
max_trial_count=12,
max_failed_trial_count=3,
):
"""
Args:
tuning_params: A dictionary of hyperparameters to be tuned.
... |
Args:
tuning_params: A dictionary of hyperparameters to be tuned.
objective: The dictionary of objective for model training.
success_condition: The condition to indicate when a Katib
experiment succeeds.
failure_condition: The condition to indicate when a Katib
... | run | python | couler-proj/couler | couler/steps/katib.py | https://github.com/couler-proj/couler/blob/master/couler/steps/katib.py | Apache-2.0 |
def test_workflow_security_context(self):
"""
The securityContext configuration mostly taken from
https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
"""
self.assertIsNone(couler.workflow.security_context)
flip_coin()
self.assertNotIn("secur... |
The securityContext configuration mostly taken from
https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
| test_workflow_security_context | python | couler-proj/couler | couler/tests/workflow_basic_test.py | https://github.com/couler-proj/couler/blob/master/couler/tests/workflow_basic_test.py | Apache-2.0 |
def random_code() -> None:
"""
Randomly generate a 'success' or 'fail'
to let sys.exit emulate a task final state
"""
import random
task = ['success', 'fail']
res = random.randint(0, 1)
res = task[res]
print(f'{res}')
if res == 'fail':
sys.exit(2) |
Randomly generate a 'success' or 'fail'
to let sys.exit emulate a task final state
| random_code | python | couler-proj/couler | examples/depends.py | https://github.com/couler-proj/couler/blob/master/examples/depends.py | Apache-2.0 |
def __init__(
self,
data_url,
text_features=None,
label_column=None,
data_dir="",
citation="",
url="",
label_classes=None,
process_label=lambda x: x,
**kwargs,
):
"""BuilderConfig for CLUE.
Args:
text_features:... | BuilderConfig for CLUE.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
dat... | __init__ | python | JunnYu/RoFormer_pytorch | examples/clue/classification/clue_10.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/examples/clue/classification/clue_10.py | Apache-2.0 |
def __init__(
self,
data_url,
text_features=None,
label_column=None,
data_dir="",
citation="",
url="",
label_classes=None,
process_label=lambda x: x,
**kwargs,
):
"""BuilderConfig for CLUE.
Args:
text_features:... | BuilderConfig for CLUE.
Args:
text_features: `dict[string, string]`, map from the name of the feature
dict for each text field to the name of the column in the tsv file
label_column: `string`, name of the column in the tsv file corresponding
to the label
dat... | __init__ | python | JunnYu/RoFormer_pytorch | examples/clue/classification/clue_11.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/examples/clue/classification/clue_11.py | Apache-2.0 |
def create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[1]
text_b = None
... | Creates examples for the training and dev sets. | create_examples | python | JunnYu/RoFormer_pytorch | examples/dummpy/task_text_classification_chnsenti.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/examples/dummpy/task_text_classification_chnsenti.py | Apache-2.0 |
def _init_weight(out: nn.Parameter):
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
n_pos, dim = out.shape
position_enc = np.array(
[
... |
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
| _init_weight | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(self, seq_len: int, past_key_values_length: int = 0):
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
positions = torch.arange(
past_key_values_length,
past_key_values_length + seq_len,
dtype=torch.long,
device=self.weight.device,
... | `input_ids_shape` is expected to be [bsz x seqlen]. | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def load_tf_weights_in_roformer(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFl... | Load tf checkpoints in a pytorch model. | load_tf_weights_in_roformer | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
... |
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attent... | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None... |
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
... | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`tor... |
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
... | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`tor... |
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
... | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`tor... |
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
| forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
... |
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside ... | forward | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_roformer.py | Apache-2.0 |
def build(self, input_shape: tf.TensorShape):
"""
Build shared token embedding layer Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
weight = self._init_weight... |
Build shared token embedding layer Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
| build | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def _init_weight(n_pos: int, dim: int):
"""
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
"""
position_enc = np.array(
[
[pos / np.power(10000, 2 *... |
Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in
the 2nd half of the vector. [dim // 2:]
| _init_weight | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0):
"""Input is expected to be of size [bsz x seqlen]."""
bsz, seq_len = input_shape[:2]
positions = tf.range(
past_key_values_length,
seq_len + past_key_values_length,
delta=1,
... | Input is expected to be of size [bsz x seqlen]. | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor... |
Applies embedding based on inputs tensor.
Returns:
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` ... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
config.vocab_size - 1]``.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-S... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
| call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
start_positions (:obj:`tf.Tensor` or :obj:`np.ndarray` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Pos... | call | python | JunnYu/RoFormer_pytorch | src/roformer/modeling_tf_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/modeling_tf_roformer.py | Apache-2.0 |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence h... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
... | build_inputs_with_special_tokens | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def get_special_tokens_mask(
self,
token_ids_0: List[int],
token_ids_1: Optional[List[int]] = None,
already_has_special_tokens: bool = False,
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when addin... |
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, ... | get_special_tokens_mask | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj... | create_token_type_ids_from_sequences | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer.py | Apache-2.0 |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: ``[CLS] X [SEP]``
- pair of sequences: ``[CLS] A [SEP] B [SEP]``
Args:
... | build_inputs_with_special_tokens | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer_fast.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer_fast.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
sequence pair mask has the following format:
::
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
If :obj:`token_ids_1` is :obj... | create_token_type_ids_from_sequences | python | JunnYu/RoFormer_pytorch | src/roformer/tokenization_roformer_fast.py | https://github.com/JunnYu/RoFormer_pytorch/blob/master/src/roformer/tokenization_roformer_fast.py | Apache-2.0 |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--result_dir',
help="Directory where the results are saved.")
parser.add_argument('--checkpoint_dir',
help="Directory that con... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | src/inference.py | https://github.com/salu133445/musegan/blob/master/src/inference.py | MIT |
def setup():
"""Parse command line arguments, load model parameters, load configurations
and setup environment."""
# Parse the command line arguments
args = parse_arguments()
# Load parameters
params = load_yaml(args.params)
# Load training configurations
config = load_yaml(args.config... | Parse command line arguments, load model parameters, load configurations
and setup environment. | setup | python | salu133445/musegan | src/inference.py | https://github.com/salu133445/musegan/blob/master/src/inference.py | MIT |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--result_dir',
help="Directory where the results are saved.")
parser.add_argument('--checkpoint_dir',
help="Directory that con... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def setup():
"""Parse command line arguments, load model parameters, load configurations
and setup environment."""
# Parse the command line arguments
args = parse_arguments()
# Load parameters
params = load_yaml(args.params)
# Load training configurations
config = load_yaml(args.config... | Parse command line arguments, load model parameters, load configurations
and setup environment. | setup | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def slerp(a, b, t):
"""Return the spherical linear interpolation of point `a` and `b` at
specific ratio `t`."""
omega = np.arccos(np.dot(a / np.linalg.norm(a), b / np.linalg.norm(b)))
so = np.sin(omega)
return np.sin((1 - t) * omega) / so * a + np.sin(t * omega) / so * b | Return the spherical linear interpolation of point `a` and `b` at
specific ratio `t`. | slerp | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def lerp(a, b1, b2, t1, t2):
"""Return the 2D linear interpolation of point `a`, `b1` and `b2` at
specific ratio `t1` and `t2`."""
inter1 = a * (1 - t1) + b1 * t1
inter2 = b2 * (1 - t1) + (b2 + (b1 - a)) * t1
return inter1 * (1 - t2) + t2 * inter2 | Return the 2D linear interpolation of point `a`, `b1` and `b2` at
specific ratio `t1` and `t2`. | lerp | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def get_input_z(config, params):
"""Return input latent code to the generator."""
if ((config['mode'] == 'slerp') and (config['rows'] > 1)
and (config['columns'] > 1)):
raise ValueError("Only supports 1D interpolation in 'slerp' mode.")
a = scipy.stats.truncnorm.rvs(
config['low... | Return input latent code to the generator. | get_input_z | python | salu133445/musegan | src/interpolation.py | https://github.com/salu133445/musegan/blob/master/src/interpolation.py | MIT |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument("filepath", help="Path to the data file.")
parser.add_argument(
"--name",
help="File name to save in SharedArray. Defaults to the original file name.",
)
... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | src/process_data.py | https://github.com/salu133445/musegan/blob/master/src/process_data.py | MIT |
def create_shared_array(name, shape, dtype):
"""Create shared array. Prompt if a file with the same name existed."""
try:
return sa.create(name, shape, dtype)
except FileExistsError:
response = ""
while response.lower() not in ["y", "n", "yes", "no"]:
response = input(
... | Create shared array. Prompt if a file with the same name existed. | create_shared_array | python | salu133445/musegan | src/process_data.py | https://github.com/salu133445/musegan/blob/master/src/process_data.py | MIT |
def main():
"""Load and save an array to shared memory."""
filepath, name, prefix, dtype = parse_arguments()
if name is None:
name = os.path.splitext(os.path.basename(filepath))[0]
if prefix is not None:
name = prefix + "_" + name
print("Loading data from '{}'.".format(file... | Load and save an array to shared memory. | main | python | salu133445/musegan | src/process_data.py | https://github.com/salu133445/musegan/blob/master/src/process_data.py | MIT |
def parse_arguments():
"""Parse and return the command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument('--exp_dir', help="Directory to save all the results.")
parser.add_argument('--params', help="Path to the model parameter file.")
parser.add_argument('--config', help="Pat... | Parse and return the command line arguments. | parse_arguments | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def setup_dirs(config):
"""Setup an experiment directory structure and update the `params`
dictionary with the directory paths."""
# Get experiment directory structure
config['exp_dir'] = os.path.realpath(config['exp_dir'])
config['src_dir'] = os.path.join(config['exp_dir'], 'src')
config['eval_... | Setup an experiment directory structure and update the `params`
dictionary with the directory paths. | setup_dirs | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def setup():
"""Parse command line arguments, load model parameters, load configurations,
setup environment and setup loggers."""
# Parse the command line arguments
args = parse_arguments()
# Load parameters
params = load_yaml(args.params)
if params.get('is_accompaniment') and params.get('c... | Parse command line arguments, load model parameters, load configurations,
setup environment and setup loggers. | setup | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_training_data(params, config):
"""Load and return the training data."""
# Load data
if params['is_conditional']:
raise ValueError("Not supported yet.")
else:
labels = None
LOGGER.info("Loading training data.")
data = load_data(config['data_source'], config['data_filename... | Load and return the training data. | load_training_data | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_or_create_samples(params, config):
"""Load or create the samples used as the sampler inputs."""
# Load sample_z
LOGGER.info("Loading sample_z.")
sample_z_path = os.path.join(config['model_dir'], 'sample_z.npy')
if os.path.exists(sample_z_path):
sample_z = np.load(sample_z_path)
... | Load or create the samples used as the sampler inputs. | load_or_create_samples | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def get_n_params(var_list):
"""Return the number of variables in a variable list."""
return int(np.sum([np.product(
[x.value for x in var.get_shape()]) for var in var_list])) | Return the number of variables in a variable list. | get_n_params | python | salu133445/musegan | src/train.py | https://github.com/salu133445/musegan/blob/master/src/train.py | MIT |
def load_data_from_npz(filename):
"""Load and return the training data from a npz file (sparse format)."""
with np.load(filename) as f:
data = np.zeros(f['shape'], np.bool_)
data[[x for x in f['nonzero']]] = True
return data | Load and return the training data from a npz file (sparse format). | load_data_from_npz | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def load_data(data_source, data_filename):
"""Load and return the training data."""
if data_source == 'sa':
import SharedArray as sa
return sa.attach(data_filename)
if data_source == 'npy':
return load_data_from_npy(data_filename)
if data_source == 'npz':
return load_data... | Load and return the training data. | load_data | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def get_samples(n_samples, data, labels=None, use_random_transpose=False):
"""Return some random samples of the training data."""
indices = np.random.choice(len(data), n_samples, False)
if np.issubdtype(data.dtype, np.bool_):
sample_data = data[indices] * 2. - 1.
else:
sample_data = data... | Return some random samples of the training data. | get_samples | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def get_dataset(data, labels=None, batch_size=None, data_shape=None,
use_random_transpose=False, num_threads=1):
"""Create and return a tensorflow dataset from an array."""
if labels is None:
dataset = tf.data.Dataset.from_generator(
lambda: _gen_data(data), tf.float32)
... | Create and return a tensorflow dataset from an array. | get_dataset | python | salu133445/musegan | src/musegan/data.py | https://github.com/salu133445/musegan/blob/master/src/musegan/data.py | MIT |
def vector_to_image(array, inverted=True):
"""
Convert a batched vector array to an image array.
Arguments
---------
array : `np.array`, ndim=2
The vector array.
Returns
-------
image : `np.array`, ndim=4
The image array.
"""
if array.ndim != 2:
raise Va... |
Convert a batched vector array to an image array.
Arguments
---------
array : `np.array`, ndim=2
The vector array.
Returns
-------
image : `np.array`, ndim=4
The image array.
| vector_to_image | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def pianoroll_to_image(pianoroll, colormap=None, inverted=True,
boundary_width=1, boundary_color=0, frame=False,
gamma=1.):
"""
Convert a batched pianoroll array to an image array.
Arguments
---------
pianoroll : `np.array`, ndim=5
The pianoroll... |
Convert a batched pianoroll array to an image array.
Arguments
---------
pianoroll : `np.array`, ndim=5
The pianoroll array. The shape is (n_pianorolls, n_bars, n_timestep,
n_pitches, n_tracks).
boundary_width : int
Linewidth of the boundary lines. Default to 0.
boundar... | pianoroll_to_image | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def image_pair(image1, image2, mode='side-by-side', boundary_width=1,
boundary_color=0, frame=False):
"""
Pair two image arrays to one single image array.
Arguments
---------
image1 : `np.array`, ndim=4
The image array at the left in 'side-by-side' mode or at the top in
... |
Pair two image arrays to one single image array.
Arguments
---------
image1 : `np.array`, ndim=4
The image array at the left in 'side-by-side' mode or at the top in
'top-bottom' mode.
image2 : `np.array`, ndim=4
The image array at the right in 'side-by-side' mode or at the ... | image_pair | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def image_grid(image, grid_shape, grid_width=3, grid_color=0, frame=True):
"""
Convert a batched image array to one merged grid image array.
Arguments
---------
pianoroll : `np.array`, ndim=4
The pianoroll array. The first axis is the batch axis. The second and
third axes are the ti... |
Convert a batched image array to one merged grid image array.
Arguments
---------
pianoroll : `np.array`, ndim=4
The pianoroll array. The first axis is the batch axis. The second and
third axes are the time and pitch axes, respectively, of the pianorolls.
The last axis is the t... | image_grid | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
def save_pianoroll(filename, pianoroll, programs, is_drums, tempo,
beat_resolution, lowest_pitch):
"""Saves a batched pianoroll array to a npz file."""
if not np.issubdtype(pianoroll.dtype, np.bool_):
raise TypeError("Input pianoroll array must have a boolean dtype.")
if pianoroll... | Saves a batched pianoroll array to a npz file. | save_pianoroll | python | salu133445/musegan | src/musegan/io_utils.py | https://github.com/salu133445/musegan/blob/master/src/musegan/io_utils.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.