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|---|---|---|---|---|---|---|---|---|---|
8860e188554203beabc2e92904b07383ced04bc27175a2b983a3d2428e08dca6 | def updateMetaData(self):
'Override.'
return True | Override. | extra_foam/gui/ctrl_widgets/image_ctrl_widget.py | updateMetaData | zhujun98/EXtra-foam | 0 | python | def updateMetaData(self):
return True | def updateMetaData(self):
return True<|docstring|>Override.<|endoftext|> |
92c5bc7bf6fd086acf6ef6f428392d4aa0fbeff2f2b1b68343f9c9c58a9c179e | def loadMetaData(self):
'Override.'
pass | Override. | extra_foam/gui/ctrl_widgets/image_ctrl_widget.py | loadMetaData | zhujun98/EXtra-foam | 0 | python | def loadMetaData(self):
pass | def loadMetaData(self):
pass<|docstring|>Override.<|endoftext|> |
8bcaf35aad18c205c8843a7c974d0ec495dc273122840b869593f7b0245509ff | def is_new_to_wildlifelicensing(request=None):
'\n Verify request user holds minimum details to use Wildlife Licensing.\n '
from wildlifecompliance.management.securebase_manager import SecureBaseUtils
has_user_details = (True if (request.user.first_name and request.user.last_name and request.user.dob and request.user.residential_address and (request.user.phone_number or request.user.mobile_number) and request.user.identification) else False)
if (not SecureBaseUtils.is_wildlifelicensing_request(request)):
has_user_details = True
if is_internal(request):
has_user_details = True
return (not has_user_details) | Verify request user holds minimum details to use Wildlife Licensing. | wildlifecompliance/helpers.py | is_new_to_wildlifelicensing | jawaidm/wildlifecompliance | 1 | python | def is_new_to_wildlifelicensing(request=None):
'\n \n '
from wildlifecompliance.management.securebase_manager import SecureBaseUtils
has_user_details = (True if (request.user.first_name and request.user.last_name and request.user.dob and request.user.residential_address and (request.user.phone_number or request.user.mobile_number) and request.user.identification) else False)
if (not SecureBaseUtils.is_wildlifelicensing_request(request)):
has_user_details = True
if is_internal(request):
has_user_details = True
return (not has_user_details) | def is_new_to_wildlifelicensing(request=None):
'\n \n '
from wildlifecompliance.management.securebase_manager import SecureBaseUtils
has_user_details = (True if (request.user.first_name and request.user.last_name and request.user.dob and request.user.residential_address and (request.user.phone_number or request.user.mobile_number) and request.user.identification) else False)
if (not SecureBaseUtils.is_wildlifelicensing_request(request)):
has_user_details = True
if is_internal(request):
has_user_details = True
return (not has_user_details)<|docstring|>Verify request user holds minimum details to use Wildlife Licensing.<|endoftext|> |
52148cf684ca840ea83ead84c79c3d3ab1d734bd763c0d2c8b93b0145e76ef5d | def belongs_to(user, group_name):
'\n Check if the user belongs to the given group.\n :param user:\n :param group_name:\n :return:\n '
return user.groups.filter(name=group_name).exists() | Check if the user belongs to the given group.
:param user:
:param group_name:
:return: | wildlifecompliance/helpers.py | belongs_to | jawaidm/wildlifecompliance | 1 | python | def belongs_to(user, group_name):
'\n Check if the user belongs to the given group.\n :param user:\n :param group_name:\n :return:\n '
return user.groups.filter(name=group_name).exists() | def belongs_to(user, group_name):
'\n Check if the user belongs to the given group.\n :param user:\n :param group_name:\n :return:\n '
return user.groups.filter(name=group_name).exists()<|docstring|>Check if the user belongs to the given group.
:param user:
:param group_name:
:return:<|endoftext|> |
67228f2c2cf5d3d75a60644f25e7aa3d4e1a6cde496305efc5873b8ceb993a8d | def belongs_to_list(user, group_names):
'\n Check if the user belongs to the given list of groups.\n :param user:\n :param list_of_group_names:\n :return:\n '
return user.groups.filter(name__in=group_names).exists() | Check if the user belongs to the given list of groups.
:param user:
:param list_of_group_names:
:return: | wildlifecompliance/helpers.py | belongs_to_list | jawaidm/wildlifecompliance | 1 | python | def belongs_to_list(user, group_names):
'\n Check if the user belongs to the given list of groups.\n :param user:\n :param list_of_group_names:\n :return:\n '
return user.groups.filter(name__in=group_names).exists() | def belongs_to_list(user, group_names):
'\n Check if the user belongs to the given list of groups.\n :param user:\n :param list_of_group_names:\n :return:\n '
return user.groups.filter(name__in=group_names).exists()<|docstring|>Check if the user belongs to the given list of groups.
:param user:
:param list_of_group_names:
:return:<|endoftext|> |
84c23ecefe1a55b3030292926b89f5cf62b3ee0842180b48a6c48ea0769c347c | def is_wildlifecompliance_payment_officer(request):
'\n Check user for request has payment officer permissions.\n\n :return: boolean\n '
PAYMENTS_GROUP_NAME = 'Wildlife Compliance - Payment Officers'
is_payment_officer = (request.user.is_authenticated() and is_model_backend(request) and in_dbca_domain(request) and request.user.groups.filter(name__in=[PAYMENTS_GROUP_NAME]).exists())
return is_payment_officer | Check user for request has payment officer permissions.
:return: boolean | wildlifecompliance/helpers.py | is_wildlifecompliance_payment_officer | jawaidm/wildlifecompliance | 1 | python | def is_wildlifecompliance_payment_officer(request):
'\n Check user for request has payment officer permissions.\n\n :return: boolean\n '
PAYMENTS_GROUP_NAME = 'Wildlife Compliance - Payment Officers'
is_payment_officer = (request.user.is_authenticated() and is_model_backend(request) and in_dbca_domain(request) and request.user.groups.filter(name__in=[PAYMENTS_GROUP_NAME]).exists())
return is_payment_officer | def is_wildlifecompliance_payment_officer(request):
'\n Check user for request has payment officer permissions.\n\n :return: boolean\n '
PAYMENTS_GROUP_NAME = 'Wildlife Compliance - Payment Officers'
is_payment_officer = (request.user.is_authenticated() and is_model_backend(request) and in_dbca_domain(request) and request.user.groups.filter(name__in=[PAYMENTS_GROUP_NAME]).exists())
return is_payment_officer<|docstring|>Check user for request has payment officer permissions.
:return: boolean<|endoftext|> |
5d63dda6a1f505007a827594d95179399bc008d45a36a9e320856d424bb62584 | def is_reception(request):
'\n A check whether request is performed by Wildlife Licensing Reception.\n '
from wildlifecompliance.components.licences.models import WildlifeLicenceReceptionEmail
is_reception_email = WildlifeLicenceReceptionEmail.objects.filter(email=request.user.email).exists()
return (request.user.is_authenticated() and is_reception_email) | A check whether request is performed by Wildlife Licensing Reception. | wildlifecompliance/helpers.py | is_reception | jawaidm/wildlifecompliance | 1 | python | def is_reception(request):
'\n \n '
from wildlifecompliance.components.licences.models import WildlifeLicenceReceptionEmail
is_reception_email = WildlifeLicenceReceptionEmail.objects.filter(email=request.user.email).exists()
return (request.user.is_authenticated() and is_reception_email) | def is_reception(request):
'\n \n '
from wildlifecompliance.components.licences.models import WildlifeLicenceReceptionEmail
is_reception_email = WildlifeLicenceReceptionEmail.objects.filter(email=request.user.email).exists()
return (request.user.is_authenticated() and is_reception_email)<|docstring|>A check whether request is performed by Wildlife Licensing Reception.<|endoftext|> |
24f7f1ffebd025f015802b0541452979802efba5d2d64f4d89b43fa06ee64677 | @cli.group('replicas')
@click.option('--count', type=int, expose_value=True, help='Number of replicas the indices should have.')
@click.pass_context
def replicas(ctx, count):
'Replica Count Per-shard'
if (count == None):
click.echo('{0}'.format(ctx.get_help()))
click.echo(click.style('Missing required parameter --count', fg='red', bold=True))
sys.exit(1) | Replica Count Per-shard | curator/cli/replicas.py | replicas | ferki/curator | 0 | python | @cli.group('replicas')
@click.option('--count', type=int, expose_value=True, help='Number of replicas the indices should have.')
@click.pass_context
def replicas(ctx, count):
if (count == None):
click.echo('{0}'.format(ctx.get_help()))
click.echo(click.style('Missing required parameter --count', fg='red', bold=True))
sys.exit(1) | @cli.group('replicas')
@click.option('--count', type=int, expose_value=True, help='Number of replicas the indices should have.')
@click.pass_context
def replicas(ctx, count):
if (count == None):
click.echo('{0}'.format(ctx.get_help()))
click.echo(click.style('Missing required parameter --count', fg='red', bold=True))
sys.exit(1)<|docstring|>Replica Count Per-shard<|endoftext|> |
1e5bd9f5db73e705ee70c161737987ecec4b2c05ec2a4ca141c7074710a085a5 | def __init__(__self__, *, family: pulumi.Input[str], description: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None):
'\n The set of arguments for constructing a ClusterParameterGroup resource.\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[\'ClusterParameterGroupParameterArgs\']]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
pulumi.set(__self__, 'family', family)
if (description is not None):
pulumi.set(__self__, 'description', description)
if (name is not None):
pulumi.set(__self__, 'name', name)
if (name_prefix is not None):
pulumi.set(__self__, 'name_prefix', name_prefix)
if (parameters is not None):
pulumi.set(__self__, 'parameters', parameters)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) | The set of arguments for constructing a ClusterParameterGroup resource.
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | __init__ | jen20/pulumi-aws | 0 | python | def __init__(__self__, *, family: pulumi.Input[str], description: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None):
'\n The set of arguments for constructing a ClusterParameterGroup resource.\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[\'ClusterParameterGroupParameterArgs\']]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
pulumi.set(__self__, 'family', family)
if (description is not None):
pulumi.set(__self__, 'description', description)
if (name is not None):
pulumi.set(__self__, 'name', name)
if (name_prefix is not None):
pulumi.set(__self__, 'name_prefix', name_prefix)
if (parameters is not None):
pulumi.set(__self__, 'parameters', parameters)
if (tags is not None):
pulumi.set(__self__, 'tags', tags) | def __init__(__self__, *, family: pulumi.Input[str], description: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None):
'\n The set of arguments for constructing a ClusterParameterGroup resource.\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[\'ClusterParameterGroupParameterArgs\']]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
pulumi.set(__self__, 'family', family)
if (description is not None):
pulumi.set(__self__, 'description', description)
if (name is not None):
pulumi.set(__self__, 'name', name)
if (name_prefix is not None):
pulumi.set(__self__, 'name_prefix', name_prefix)
if (parameters is not None):
pulumi.set(__self__, 'parameters', parameters)
if (tags is not None):
pulumi.set(__self__, 'tags', tags)<|docstring|>The set of arguments for constructing a ClusterParameterGroup resource.
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.<|endoftext|> |
fcebe3e5868e3971f8220d829c52637b53042fccdf9a1c0bd3495f5f15ce642d | @property
@pulumi.getter
def family(self) -> pulumi.Input[str]:
'\n The family of the documentDB cluster parameter group.\n '
return pulumi.get(self, 'family') | The family of the documentDB cluster parameter group. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | family | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def family(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'family') | @property
@pulumi.getter
def family(self) -> pulumi.Input[str]:
'\n \n '
return pulumi.get(self, 'family')<|docstring|>The family of the documentDB cluster parameter group.<|endoftext|> |
dc3888658fdd6360c92bae24f944e137469b0c94d211e25a7e6ddee476dc142c | @property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
'\n The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n '
return pulumi.get(self, 'description') | The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi". | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | description | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'description') | @property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'description')<|docstring|>The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".<|endoftext|> |
beb351adfc8efcbd2dd2b6955740f14de3409cdcc537231abc06ae8154b219ce | @property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
'\n The name of the documentDB parameter.\n '
return pulumi.get(self, 'name') | The name of the documentDB parameter. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | name | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>The name of the documentDB parameter.<|endoftext|> |
f1b492a53529f0cb4485884fa763ebcbf854a87cd8b4f10afa7904a89fe340d5 | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> Optional[pulumi.Input[str]]:
'\n Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n '
return pulumi.get(self, 'name_prefix') | Creates a unique name beginning with the specified prefix. Conflicts with `name`. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | name_prefix | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'name_prefix') | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> Optional[pulumi.Input[str]]:
'\n \n '
return pulumi.get(self, 'name_prefix')<|docstring|>Creates a unique name beginning with the specified prefix. Conflicts with `name`.<|endoftext|> |
4c9a40aa5e3fdc2b064cf484395734e9ca2c9ca62655aa4b8e19fd755d430757 | @property
@pulumi.getter
def parameters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]:
'\n A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n '
return pulumi.get(self, 'parameters') | A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | parameters | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def parameters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]:
'\n \n '
return pulumi.get(self, 'parameters') | @property
@pulumi.getter
def parameters(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ClusterParameterGroupParameterArgs']]]]:
'\n \n '
return pulumi.get(self, 'parameters')<|docstring|>A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.<|endoftext|> |
93f559ff90e2f960b3004f862bd462633ee849c089398c8d34d32d46319efd91 | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n A map of tags to assign to the resource.\n '
return pulumi.get(self, 'tags') | A map of tags to assign to the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | tags | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>A map of tags to assign to the resource.<|endoftext|> |
f9f16ac3d72edba6e07a8bfd68aa3efa902555d1dfa42040453f18738b2bd0d1 | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, __props__=None, __name__=None, __opts__=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
... | Manages a DocumentDB Cluster Parameter Group
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.docdb.ClusterParameterGroup("example",
description="docdb cluster parameter group",
family="docdb3.6",
parameters=[aws.docdb.ClusterParameterGroupParameterArgs(
name="tls",
value="enabled",
)])
```
## Import
DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.
```sh
$ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | __init__ | jen20/pulumi-aws | 0 | python | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, __props__=None, __name__=None, __opts__=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
... | @overload
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None, __props__=None, __name__=None, __opts__=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
...<|docstring|>Manages a DocumentDB Cluster Parameter Group
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.docdb.ClusterParameterGroup("example",
description="docdb cluster parameter group",
family="docdb3.6",
parameters=[aws.docdb.ClusterParameterGroupParameterArgs(
name="tls",
value="enabled",
)])
```
## Import
DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.
```sh
$ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.<|endoftext|> |
d608c30d22baac5fd65395c0c9bd56f561be79e52eefc991080e6043499cb439 | @overload
def __init__(__self__, resource_name: str, args: ClusterParameterGroupArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param ClusterParameterGroupArgs args: The arguments to use to populate this resource\'s properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... | Manages a DocumentDB Cluster Parameter Group
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.docdb.ClusterParameterGroup("example",
description="docdb cluster parameter group",
family="docdb3.6",
parameters=[aws.docdb.ClusterParameterGroupParameterArgs(
name="tls",
value="enabled",
)])
```
## Import
DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.
```sh
$ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1
```
:param str resource_name: The name of the resource.
:param ClusterParameterGroupArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | __init__ | jen20/pulumi-aws | 0 | python | @overload
def __init__(__self__, resource_name: str, args: ClusterParameterGroupArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param ClusterParameterGroupArgs args: The arguments to use to populate this resource\'s properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
... | @overload
def __init__(__self__, resource_name: str, args: ClusterParameterGroupArgs, opts: Optional[pulumi.ResourceOptions]=None):
'\n Manages a DocumentDB Cluster Parameter Group\n\n ## Example Usage\n\n ```python\n import pulumi\n import pulumi_aws as aws\n\n example = aws.docdb.ClusterParameterGroup("example",\n description="docdb cluster parameter group",\n family="docdb3.6",\n parameters=[aws.docdb.ClusterParameterGroupParameterArgs(\n name="tls",\n value="enabled",\n )])\n ```\n\n ## Import\n\n DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.\n\n ```sh\n $ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1\n ```\n\n :param str resource_name: The name of the resource.\n :param ClusterParameterGroupArgs args: The arguments to use to populate this resource\'s properties.\n :param pulumi.ResourceOptions opts: Options for the resource.\n '
...<|docstring|>Manages a DocumentDB Cluster Parameter Group
## Example Usage
```python
import pulumi
import pulumi_aws as aws
example = aws.docdb.ClusterParameterGroup("example",
description="docdb cluster parameter group",
family="docdb3.6",
parameters=[aws.docdb.ClusterParameterGroupParameterArgs(
name="tls",
value="enabled",
)])
```
## Import
DocumentDB Cluster Parameter Groups can be imported using the `name`, e.g.
```sh
$ pulumi import aws:docdb/clusterParameterGroup:ClusterParameterGroup cluster_pg production-pg-1
```
:param str resource_name: The name of the resource.
:param ClusterParameterGroupArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.<|endoftext|> |
a05fe3eeb243687e88e4101e0d6e83409eeb70b640c9d98c9ada5ed2b226d088 | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, arn: Optional[pulumi.Input[str]]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None) -> 'ClusterParameterGroup':
'\n Get an existing ClusterParameterGroup resource\'s state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] arn: The ARN of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['arn'] = arn
__props__['description'] = description
__props__['family'] = family
__props__['name'] = name
__props__['name_prefix'] = name_prefix
__props__['parameters'] = parameters
__props__['tags'] = tags
return ClusterParameterGroup(resource_name, opts=opts, __props__=__props__) | Get an existing ClusterParameterGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] arn: The ARN of the documentDB cluster parameter group.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | get | jen20/pulumi-aws | 0 | python | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, arn: Optional[pulumi.Input[str]]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None) -> 'ClusterParameterGroup':
'\n Get an existing ClusterParameterGroup resource\'s state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] arn: The ARN of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['arn'] = arn
__props__['description'] = description
__props__['family'] = family
__props__['name'] = name
__props__['name_prefix'] = name_prefix
__props__['parameters'] = parameters
__props__['tags'] = tags
return ClusterParameterGroup(resource_name, opts=opts, __props__=__props__) | @staticmethod
def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None, arn: Optional[pulumi.Input[str]]=None, description: Optional[pulumi.Input[str]]=None, family: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, name_prefix: Optional[pulumi.Input[str]]=None, parameters: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]]]=None, tags: Optional[pulumi.Input[Mapping[(str, pulumi.Input[str])]]]=None) -> 'ClusterParameterGroup':
'\n Get an existing ClusterParameterGroup resource\'s state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] arn: The ARN of the documentDB cluster parameter group.\n :param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n :param pulumi.Input[str] family: The family of the documentDB cluster parameter group.\n :param pulumi.Input[str] name: The name of the documentDB parameter.\n :param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType[\'ClusterParameterGroupParameterArgs\']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.\n '
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__['arn'] = arn
__props__['description'] = description
__props__['family'] = family
__props__['name'] = name
__props__['name_prefix'] = name_prefix
__props__['parameters'] = parameters
__props__['tags'] = tags
return ClusterParameterGroup(resource_name, opts=opts, __props__=__props__)<|docstring|>Get an existing ClusterParameterGroup resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] arn: The ARN of the documentDB cluster parameter group.
:param pulumi.Input[str] description: The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".
:param pulumi.Input[str] family: The family of the documentDB cluster parameter group.
:param pulumi.Input[str] name: The name of the documentDB parameter.
:param pulumi.Input[str] name_prefix: Creates a unique name beginning with the specified prefix. Conflicts with `name`.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ClusterParameterGroupParameterArgs']]]] parameters: A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.
:param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: A map of tags to assign to the resource.<|endoftext|> |
9659c08d4406d0d16c7b4155b88ad71bc71e6de666cb74ccbf8db484bf1f33af | @property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
'\n The ARN of the documentDB cluster parameter group.\n '
return pulumi.get(self, 'arn') | The ARN of the documentDB cluster parameter group. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | arn | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'arn') | @property
@pulumi.getter
def arn(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'arn')<|docstring|>The ARN of the documentDB cluster parameter group.<|endoftext|> |
46c44c2c1e15ef5bc8d2772ca06c909cfd9474f090a1b61b67a3d952258a22b6 | @property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
'\n The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".\n '
return pulumi.get(self, 'description') | The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi". | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | description | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'description') | @property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
'\n \n '
return pulumi.get(self, 'description')<|docstring|>The description of the documentDB cluster parameter group. Defaults to "Managed by Pulumi".<|endoftext|> |
a614c72469d84521879d8754a8a13f0373741eaeae2b57851425eaca3dfa5d06 | @property
@pulumi.getter
def family(self) -> pulumi.Output[str]:
'\n The family of the documentDB cluster parameter group.\n '
return pulumi.get(self, 'family') | The family of the documentDB cluster parameter group. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | family | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def family(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'family') | @property
@pulumi.getter
def family(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'family')<|docstring|>The family of the documentDB cluster parameter group.<|endoftext|> |
5d92bbb764d6f52e07844ffc9205465a5dd29ed3e442c23b6b424efb3e689547 | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n The name of the documentDB parameter.\n '
return pulumi.get(self, 'name') | The name of the documentDB parameter. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | name | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name') | @property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name')<|docstring|>The name of the documentDB parameter.<|endoftext|> |
5ea02d2cfd78240c2047d5e469988b82276474d9b14bac6004d7a098437918c5 | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> pulumi.Output[str]:
'\n Creates a unique name beginning with the specified prefix. Conflicts with `name`.\n '
return pulumi.get(self, 'name_prefix') | Creates a unique name beginning with the specified prefix. Conflicts with `name`. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | name_prefix | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name_prefix') | @property
@pulumi.getter(name='namePrefix')
def name_prefix(self) -> pulumi.Output[str]:
'\n \n '
return pulumi.get(self, 'name_prefix')<|docstring|>Creates a unique name beginning with the specified prefix. Conflicts with `name`.<|endoftext|> |
c05c9de4e1e4495324612c6e776008dfd69ffdb111db17076763f3bf5e29d492 | @property
@pulumi.getter
def parameters(self) -> pulumi.Output[Optional[Sequence['outputs.ClusterParameterGroupParameter']]]:
'\n A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.\n '
return pulumi.get(self, 'parameters') | A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | parameters | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def parameters(self) -> pulumi.Output[Optional[Sequence['outputs.ClusterParameterGroupParameter']]]:
'\n \n '
return pulumi.get(self, 'parameters') | @property
@pulumi.getter
def parameters(self) -> pulumi.Output[Optional[Sequence['outputs.ClusterParameterGroupParameter']]]:
'\n \n '
return pulumi.get(self, 'parameters')<|docstring|>A list of documentDB parameters to apply. Setting parameters to system default values may show a difference on imported resources.<|endoftext|> |
f00e5ea8a558dd35375d601d76efbd8ba3270f53689afc47a3b121fae9db0f54 | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n A map of tags to assign to the resource.\n '
return pulumi.get(self, 'tags') | A map of tags to assign to the resource. | sdk/python/pulumi_aws/docdb/cluster_parameter_group.py | tags | jen20/pulumi-aws | 0 | python | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags') | @property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[(str, str)]]]:
'\n \n '
return pulumi.get(self, 'tags')<|docstring|>A map of tags to assign to the resource.<|endoftext|> |
4f59022283ca279ed2a32c8100aa6a503878e2138654fe687fbe4d7b41ff4c22 | def get_row(m, i):
' Returns array of 9 SudokuSets. '
return m[(i * 9):((i * 9) + 9)] | Returns array of 9 SudokuSets. | solver.py | get_row | joneser005/sudoku | 0 | python | def get_row(m, i):
' '
return m[(i * 9):((i * 9) + 9)] | def get_row(m, i):
' '
return m[(i * 9):((i * 9) + 9)]<|docstring|>Returns array of 9 SudokuSets.<|endoftext|> |
ac418a6db96f161c95a71d00f90de96eb7a1326b96ac6a96ff8d401a3f7fbf71 | def get_col(m, i):
' Returns array of 9 SudokuSets. '
result = []
for j in range(9):
result.append(m[((j * 9) + i)])
return result | Returns array of 9 SudokuSets. | solver.py | get_col | joneser005/sudoku | 0 | python | def get_col(m, i):
' '
result = []
for j in range(9):
result.append(m[((j * 9) + i)])
return result | def get_col(m, i):
' '
result = []
for j in range(9):
result.append(m[((j * 9) + i)])
return result<|docstring|>Returns array of 9 SudokuSets.<|endoftext|> |
27d3a49a46f215562ab9ddf953379e5a3df889f2c523e13160abe61afec6d94f | def get_box(m, bi):
' \n Returns array of 9 SudokuSets.\n \n 0 1 2\n 3 4 5\n 6 7 8\n x offset = (i%3)*3\n y offset = int(i/3) # possible values are 0,1,2\n m[0] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[2] => (0-2)+6+(0*9) + (0-2)+6+9 + (0-2)+6+18\n m[4] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[i] => \n '
result = []
xoff = ((bi % 3) * 3)
yoff = int((bi / 3))
for i in range((xoff + (yoff * 27)), ((xoff + (yoff * 27)) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 9), (((xoff + (yoff * 27)) + 9) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 18), (((xoff + (yoff * 27)) + 18) + 3)):
result.append(m[i])
return result | Returns array of 9 SudokuSets.
0 1 2
3 4 5
6 7 8
x offset = (i%3)*3
y offset = int(i/3) # possible values are 0,1,2
m[0] => (0-2)+0 + (0-2)+9 + (0-2)+18
m[2] => (0-2)+6+(0*9) + (0-2)+6+9 + (0-2)+6+18
m[4] => (0-2)+0 + (0-2)+9 + (0-2)+18
m[i] => | solver.py | get_box | joneser005/sudoku | 0 | python | def get_box(m, bi):
' \n Returns array of 9 SudokuSets.\n \n 0 1 2\n 3 4 5\n 6 7 8\n x offset = (i%3)*3\n y offset = int(i/3) # possible values are 0,1,2\n m[0] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[2] => (0-2)+6+(0*9) + (0-2)+6+9 + (0-2)+6+18\n m[4] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[i] => \n '
result = []
xoff = ((bi % 3) * 3)
yoff = int((bi / 3))
for i in range((xoff + (yoff * 27)), ((xoff + (yoff * 27)) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 9), (((xoff + (yoff * 27)) + 9) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 18), (((xoff + (yoff * 27)) + 18) + 3)):
result.append(m[i])
return result | def get_box(m, bi):
' \n Returns array of 9 SudokuSets.\n \n 0 1 2\n 3 4 5\n 6 7 8\n x offset = (i%3)*3\n y offset = int(i/3) # possible values are 0,1,2\n m[0] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[2] => (0-2)+6+(0*9) + (0-2)+6+9 + (0-2)+6+18\n m[4] => (0-2)+0 + (0-2)+9 + (0-2)+18\n m[i] => \n '
result = []
xoff = ((bi % 3) * 3)
yoff = int((bi / 3))
for i in range((xoff + (yoff * 27)), ((xoff + (yoff * 27)) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 9), (((xoff + (yoff * 27)) + 9) + 3)):
result.append(m[i])
for i in range(((xoff + (yoff * 27)) + 18), (((xoff + (yoff * 27)) + 18) + 3)):
result.append(m[i])
return result<|docstring|>Returns array of 9 SudokuSets.
0 1 2
3 4 5
6 7 8
x offset = (i%3)*3
y offset = int(i/3) # possible values are 0,1,2
m[0] => (0-2)+0 + (0-2)+9 + (0-2)+18
m[2] => (0-2)+6+(0*9) + (0-2)+6+9 + (0-2)+6+18
m[4] => (0-2)+0 + (0-2)+9 + (0-2)+18
m[i] =><|endoftext|> |
3b83cd183d9e81ca404d720c23278dbb385955e4ccbdd7ab055d8460e5905cfc | def makekey(s):
" Returns string from set of ints, ordered low to high.\n Example: if set is (5,2,6), result will be '256'. "
so = sorted(s)
s = ''
for x in so:
s += str(x)
return s | Returns string from set of ints, ordered low to high.
Example: if set is (5,2,6), result will be '256'. | solver.py | makekey | joneser005/sudoku | 0 | python | def makekey(s):
" Returns string from set of ints, ordered low to high.\n Example: if set is (5,2,6), result will be '256'. "
so = sorted(s)
s =
for x in so:
s += str(x)
return s | def makekey(s):
" Returns string from set of ints, ordered low to high.\n Example: if set is (5,2,6), result will be '256'. "
so = sorted(s)
s =
for x in so:
s += str(x)
return s<|docstring|>Returns string from set of ints, ordered low to high.
Example: if set is (5,2,6), result will be '256'.<|endoftext|> |
76e4d4d59165acf313a12f565719305d52d189bd638976a7354d88f52130a7c8 | def get_symcc_build_dir(target_directory):
'Return path to uninstrumented target directory.'
return os.path.join(target_directory, 'uninstrumented') | Return path to uninstrumented target directory. | fuzzers/symcc_aflplusplus_single/fuzzer.py | get_symcc_build_dir | andreafioraldi/fuzzbench | 800 | python | def get_symcc_build_dir(target_directory):
return os.path.join(target_directory, 'uninstrumented') | def get_symcc_build_dir(target_directory):
return os.path.join(target_directory, 'uninstrumented')<|docstring|>Return path to uninstrumented target directory.<|endoftext|> |
97847e273ab9e3f0ad29bf400930f1cb27b4055fb1e98437468e6f7dc5b3975d | def build():
'Build an AFL version and SymCC version of the benchmark'
print('Step 1: Building with AFL and SymCC')
build_directory = os.environ['OUT']
src = os.getenv('SRC')
work = os.getenv('WORK')
with utils.restore_directory(src), utils.restore_directory(work):
aflplusplus_fuzzer.build('tracepc', 'symcc')
print('Step 2: Completed AFL build')
shutil.copy('/afl/afl-fuzz', build_directory)
shutil.copy('/afl/afl-showmap', build_directory)
print('Step 3: Copying SymCC files')
symcc_build_dir = get_symcc_build_dir(os.environ['OUT'])
shutil.copy('/symcc/build//SymRuntime-prefix/src/SymRuntime-build/libSymRuntime.so', symcc_build_dir)
shutil.copy('/usr/lib/libz3.so', os.path.join(symcc_build_dir, 'libz3.so'))
shutil.copy('/libcxx_native_build/lib/libc++.so.1', symcc_build_dir)
shutil.copy('/libcxx_native_build/lib/libc++abi.so.1', symcc_build_dir)
shutil.copy('/rust/bin/symcc_fuzzing_helper', symcc_build_dir) | Build an AFL version and SymCC version of the benchmark | fuzzers/symcc_aflplusplus_single/fuzzer.py | build | andreafioraldi/fuzzbench | 800 | python | def build():
print('Step 1: Building with AFL and SymCC')
build_directory = os.environ['OUT']
src = os.getenv('SRC')
work = os.getenv('WORK')
with utils.restore_directory(src), utils.restore_directory(work):
aflplusplus_fuzzer.build('tracepc', 'symcc')
print('Step 2: Completed AFL build')
shutil.copy('/afl/afl-fuzz', build_directory)
shutil.copy('/afl/afl-showmap', build_directory)
print('Step 3: Copying SymCC files')
symcc_build_dir = get_symcc_build_dir(os.environ['OUT'])
shutil.copy('/symcc/build//SymRuntime-prefix/src/SymRuntime-build/libSymRuntime.so', symcc_build_dir)
shutil.copy('/usr/lib/libz3.so', os.path.join(symcc_build_dir, 'libz3.so'))
shutil.copy('/libcxx_native_build/lib/libc++.so.1', symcc_build_dir)
shutil.copy('/libcxx_native_build/lib/libc++abi.so.1', symcc_build_dir)
shutil.copy('/rust/bin/symcc_fuzzing_helper', symcc_build_dir) | def build():
print('Step 1: Building with AFL and SymCC')
build_directory = os.environ['OUT']
src = os.getenv('SRC')
work = os.getenv('WORK')
with utils.restore_directory(src), utils.restore_directory(work):
aflplusplus_fuzzer.build('tracepc', 'symcc')
print('Step 2: Completed AFL build')
shutil.copy('/afl/afl-fuzz', build_directory)
shutil.copy('/afl/afl-showmap', build_directory)
print('Step 3: Copying SymCC files')
symcc_build_dir = get_symcc_build_dir(os.environ['OUT'])
shutil.copy('/symcc/build//SymRuntime-prefix/src/SymRuntime-build/libSymRuntime.so', symcc_build_dir)
shutil.copy('/usr/lib/libz3.so', os.path.join(symcc_build_dir, 'libz3.so'))
shutil.copy('/libcxx_native_build/lib/libc++.so.1', symcc_build_dir)
shutil.copy('/libcxx_native_build/lib/libc++abi.so.1', symcc_build_dir)
shutil.copy('/rust/bin/symcc_fuzzing_helper', symcc_build_dir)<|docstring|>Build an AFL version and SymCC version of the benchmark<|endoftext|> |
d71050f363eb32dfa93ede9a74fcf180c04bf61582e54ad3374392c1662fd497 | def launch_afl_thread(input_corpus, output_corpus, target_binary, additional_flags):
' Simple wrapper for running AFL. '
afl_thread = threading.Thread(target=afl_fuzzer.run_afl_fuzz, args=(input_corpus, output_corpus, target_binary, additional_flags))
afl_thread.start()
return afl_thread | Simple wrapper for running AFL. | fuzzers/symcc_aflplusplus_single/fuzzer.py | launch_afl_thread | andreafioraldi/fuzzbench | 800 | python | def launch_afl_thread(input_corpus, output_corpus, target_binary, additional_flags):
' '
afl_thread = threading.Thread(target=afl_fuzzer.run_afl_fuzz, args=(input_corpus, output_corpus, target_binary, additional_flags))
afl_thread.start()
return afl_thread | def launch_afl_thread(input_corpus, output_corpus, target_binary, additional_flags):
' '
afl_thread = threading.Thread(target=afl_fuzzer.run_afl_fuzz, args=(input_corpus, output_corpus, target_binary, additional_flags))
afl_thread.start()
return afl_thread<|docstring|>Simple wrapper for running AFL.<|endoftext|> |
246ea4c68e6915b834fb2e3bfdb24a785d7f837959131c33cb1d2c4228cc408d | def fuzz(input_corpus, output_corpus, target_binary):
'\n Launches a master and a secondary instance of AFL, as well as\n the symcc helper.\n '
target_binary_dir = os.path.dirname(target_binary)
symcc_workdir = get_symcc_build_dir(target_binary_dir)
target_binary_name = os.path.basename(target_binary)
symcc_target_binary = os.path.join(symcc_workdir, target_binary_name)
os.environ['AFL_DISABLE_TRIM'] = '1'
print('[run_fuzzer] Running AFL for SymCC')
afl_fuzzer.prepare_fuzz_environment(input_corpus)
launch_afl_thread(input_corpus, output_corpus, target_binary, ['-S', 'afl-secondary'])
time.sleep(5)
print('Starting the SymCC helper')
new_environ = os.environ.copy()
new_environ['LD_LIBRARY_PATH'] = symcc_workdir
cmd = [os.path.join(symcc_workdir, 'symcc_fuzzing_helper'), '-o', output_corpus, '-a', 'afl-secondary', '-n', 'symcc', '-m', '--', symcc_target_binary, '@@']
subprocess.Popen(cmd, env=new_environ) | Launches a master and a secondary instance of AFL, as well as
the symcc helper. | fuzzers/symcc_aflplusplus_single/fuzzer.py | fuzz | andreafioraldi/fuzzbench | 800 | python | def fuzz(input_corpus, output_corpus, target_binary):
'\n Launches a master and a secondary instance of AFL, as well as\n the symcc helper.\n '
target_binary_dir = os.path.dirname(target_binary)
symcc_workdir = get_symcc_build_dir(target_binary_dir)
target_binary_name = os.path.basename(target_binary)
symcc_target_binary = os.path.join(symcc_workdir, target_binary_name)
os.environ['AFL_DISABLE_TRIM'] = '1'
print('[run_fuzzer] Running AFL for SymCC')
afl_fuzzer.prepare_fuzz_environment(input_corpus)
launch_afl_thread(input_corpus, output_corpus, target_binary, ['-S', 'afl-secondary'])
time.sleep(5)
print('Starting the SymCC helper')
new_environ = os.environ.copy()
new_environ['LD_LIBRARY_PATH'] = symcc_workdir
cmd = [os.path.join(symcc_workdir, 'symcc_fuzzing_helper'), '-o', output_corpus, '-a', 'afl-secondary', '-n', 'symcc', '-m', '--', symcc_target_binary, '@@']
subprocess.Popen(cmd, env=new_environ) | def fuzz(input_corpus, output_corpus, target_binary):
'\n Launches a master and a secondary instance of AFL, as well as\n the symcc helper.\n '
target_binary_dir = os.path.dirname(target_binary)
symcc_workdir = get_symcc_build_dir(target_binary_dir)
target_binary_name = os.path.basename(target_binary)
symcc_target_binary = os.path.join(symcc_workdir, target_binary_name)
os.environ['AFL_DISABLE_TRIM'] = '1'
print('[run_fuzzer] Running AFL for SymCC')
afl_fuzzer.prepare_fuzz_environment(input_corpus)
launch_afl_thread(input_corpus, output_corpus, target_binary, ['-S', 'afl-secondary'])
time.sleep(5)
print('Starting the SymCC helper')
new_environ = os.environ.copy()
new_environ['LD_LIBRARY_PATH'] = symcc_workdir
cmd = [os.path.join(symcc_workdir, 'symcc_fuzzing_helper'), '-o', output_corpus, '-a', 'afl-secondary', '-n', 'symcc', '-m', '--', symcc_target_binary, '@@']
subprocess.Popen(cmd, env=new_environ)<|docstring|>Launches a master and a secondary instance of AFL, as well as
the symcc helper.<|endoftext|> |
9c5dac2aa26ae2b03d437be07836f5e28905597f352ac1fbaa4977a803735547 | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.beneficiary = None
" Plan Beneficiary.\n Type `FHIRReference` referencing `['Patient']` (represented as `dict` in JSON). "
self.contract = None
" Contract details.\n List of `FHIRReference` items referencing `['Contract']` (represented as `dict` in JSON). "
self.dependent = None
' Dependent number.\n Type `str`. '
self.grouping = None
' Additional coverage classifications.\n Type `CoverageGrouping` (represented as `dict` in JSON). '
self.identifier = None
' The primary coverage ID.\n List of `Identifier` items (represented as `dict` in JSON). '
self.network = None
' Insurer network.\n Type `str`. '
self.order = None
' Relative order of the coverage.\n Type `int`. '
self.payor = None
" Identifier for the plan or agreement issuer.\n List of `FHIRReference` items referencing `['Organization'], ['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.period = None
' Coverage start and end dates.\n Type `Period` (represented as `dict` in JSON). '
self.policyHolder = None
" Owner of the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson'], ['Organization']` (represented as `dict` in JSON). "
self.relationship = None
' Beneficiary relationship to the Subscriber.\n Type `CodeableConcept` (represented as `dict` in JSON). '
self.sequence = None
' The plan instance or sequence counter.\n Type `str`. '
self.status = None
' active | cancelled | draft | entered-in-error.\n Type `str`. '
self.subscriber = None
" Subscriber to the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.subscriberId = None
' ID assigned to the Subscriber.\n Type `str`. '
self.type = None
' Type of coverage such as medical or accident.\n Type `CodeableConcept` (represented as `dict` in JSON). '
super(Coverage, self).__init__(jsondict=jsondict, strict=strict) | Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError | fhir/resources/STU3/coverage.py | __init__ | mmabey/fhir.resources | 0 | python | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.beneficiary = None
" Plan Beneficiary.\n Type `FHIRReference` referencing `['Patient']` (represented as `dict` in JSON). "
self.contract = None
" Contract details.\n List of `FHIRReference` items referencing `['Contract']` (represented as `dict` in JSON). "
self.dependent = None
' Dependent number.\n Type `str`. '
self.grouping = None
' Additional coverage classifications.\n Type `CoverageGrouping` (represented as `dict` in JSON). '
self.identifier = None
' The primary coverage ID.\n List of `Identifier` items (represented as `dict` in JSON). '
self.network = None
' Insurer network.\n Type `str`. '
self.order = None
' Relative order of the coverage.\n Type `int`. '
self.payor = None
" Identifier for the plan or agreement issuer.\n List of `FHIRReference` items referencing `['Organization'], ['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.period = None
' Coverage start and end dates.\n Type `Period` (represented as `dict` in JSON). '
self.policyHolder = None
" Owner of the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson'], ['Organization']` (represented as `dict` in JSON). "
self.relationship = None
' Beneficiary relationship to the Subscriber.\n Type `CodeableConcept` (represented as `dict` in JSON). '
self.sequence = None
' The plan instance or sequence counter.\n Type `str`. '
self.status = None
' active | cancelled | draft | entered-in-error.\n Type `str`. '
self.subscriber = None
" Subscriber to the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.subscriberId = None
' ID assigned to the Subscriber.\n Type `str`. '
self.type = None
' Type of coverage such as medical or accident.\n Type `CodeableConcept` (represented as `dict` in JSON). '
super(Coverage, self).__init__(jsondict=jsondict, strict=strict) | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.beneficiary = None
" Plan Beneficiary.\n Type `FHIRReference` referencing `['Patient']` (represented as `dict` in JSON). "
self.contract = None
" Contract details.\n List of `FHIRReference` items referencing `['Contract']` (represented as `dict` in JSON). "
self.dependent = None
' Dependent number.\n Type `str`. '
self.grouping = None
' Additional coverage classifications.\n Type `CoverageGrouping` (represented as `dict` in JSON). '
self.identifier = None
' The primary coverage ID.\n List of `Identifier` items (represented as `dict` in JSON). '
self.network = None
' Insurer network.\n Type `str`. '
self.order = None
' Relative order of the coverage.\n Type `int`. '
self.payor = None
" Identifier for the plan or agreement issuer.\n List of `FHIRReference` items referencing `['Organization'], ['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.period = None
' Coverage start and end dates.\n Type `Period` (represented as `dict` in JSON). '
self.policyHolder = None
" Owner of the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson'], ['Organization']` (represented as `dict` in JSON). "
self.relationship = None
' Beneficiary relationship to the Subscriber.\n Type `CodeableConcept` (represented as `dict` in JSON). '
self.sequence = None
' The plan instance or sequence counter.\n Type `str`. '
self.status = None
' active | cancelled | draft | entered-in-error.\n Type `str`. '
self.subscriber = None
" Subscriber to the policy.\n Type `FHIRReference` referencing `['Patient'], ['RelatedPerson']` (represented as `dict` in JSON). "
self.subscriberId = None
' ID assigned to the Subscriber.\n Type `str`. '
self.type = None
' Type of coverage such as medical or accident.\n Type `CodeableConcept` (represented as `dict` in JSON). '
super(Coverage, self).__init__(jsondict=jsondict, strict=strict)<|docstring|>Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError<|endoftext|> |
29e33034240a75ad2541d2a3a690949c9fcae485e7f11011e6b4af1c304c74ef | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.classDisplay = None
' Display text for the class.\n Type `str`. '
self.class_fhir = None
' An identifier for the class.\n Type `str`. '
self.group = None
' An identifier for the group.\n Type `str`. '
self.groupDisplay = None
' Display text for an identifier for the group.\n Type `str`. '
self.plan = None
' An identifier for the plan.\n Type `str`. '
self.planDisplay = None
' Display text for the plan.\n Type `str`. '
self.subClass = None
' An identifier for the subsection of the class.\n Type `str`. '
self.subClassDisplay = None
' Display text for the subsection of the subclass.\n Type `str`. '
self.subGroup = None
' An identifier for the subsection of the group.\n Type `str`. '
self.subGroupDisplay = None
' Display text for the subsection of the group.\n Type `str`. '
self.subPlan = None
' An identifier for the subsection of the plan.\n Type `str`. '
self.subPlanDisplay = None
' Display text for the subsection of the plan.\n Type `str`. '
super(CoverageGrouping, self).__init__(jsondict=jsondict, strict=strict) | Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError | fhir/resources/STU3/coverage.py | __init__ | mmabey/fhir.resources | 0 | python | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.classDisplay = None
' Display text for the class.\n Type `str`. '
self.class_fhir = None
' An identifier for the class.\n Type `str`. '
self.group = None
' An identifier for the group.\n Type `str`. '
self.groupDisplay = None
' Display text for an identifier for the group.\n Type `str`. '
self.plan = None
' An identifier for the plan.\n Type `str`. '
self.planDisplay = None
' Display text for the plan.\n Type `str`. '
self.subClass = None
' An identifier for the subsection of the class.\n Type `str`. '
self.subClassDisplay = None
' Display text for the subsection of the subclass.\n Type `str`. '
self.subGroup = None
' An identifier for the subsection of the group.\n Type `str`. '
self.subGroupDisplay = None
' Display text for the subsection of the group.\n Type `str`. '
self.subPlan = None
' An identifier for the subsection of the plan.\n Type `str`. '
self.subPlanDisplay = None
' Display text for the subsection of the plan.\n Type `str`. '
super(CoverageGrouping, self).__init__(jsondict=jsondict, strict=strict) | def __init__(self, jsondict=None, strict=True):
' Initialize all valid properties.\n\n :raises: FHIRValidationError on validation errors, unless strict is False\n :param dict jsondict: A JSON dictionary to use for initialization\n :param bool strict: If True (the default), invalid variables will raise a TypeError\n '
self.classDisplay = None
' Display text for the class.\n Type `str`. '
self.class_fhir = None
' An identifier for the class.\n Type `str`. '
self.group = None
' An identifier for the group.\n Type `str`. '
self.groupDisplay = None
' Display text for an identifier for the group.\n Type `str`. '
self.plan = None
' An identifier for the plan.\n Type `str`. '
self.planDisplay = None
' Display text for the plan.\n Type `str`. '
self.subClass = None
' An identifier for the subsection of the class.\n Type `str`. '
self.subClassDisplay = None
' Display text for the subsection of the subclass.\n Type `str`. '
self.subGroup = None
' An identifier for the subsection of the group.\n Type `str`. '
self.subGroupDisplay = None
' Display text for the subsection of the group.\n Type `str`. '
self.subPlan = None
' An identifier for the subsection of the plan.\n Type `str`. '
self.subPlanDisplay = None
' Display text for the subsection of the plan.\n Type `str`. '
super(CoverageGrouping, self).__init__(jsondict=jsondict, strict=strict)<|docstring|>Initialize all valid properties.
:raises: FHIRValidationError on validation errors, unless strict is False
:param dict jsondict: A JSON dictionary to use for initialization
:param bool strict: If True (the default), invalid variables will raise a TypeError<|endoftext|> |
90958978df33d7f3cca144d265757f2d604beca30ccbb11154ab77cfab1f3af0 | def register_message(pt_type, parser):
'Register new message and a new handler.'
if (pt_type not in PT_TYPES):
PT_TYPES[pt_type] = parser
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = [] | Register new message and a new handler. | empower/managers/ranmanager/vbsp/__init__.py | register_message | 5g-empower/empower-runtime | 52 | python | def register_message(pt_type, parser):
if (pt_type not in PT_TYPES):
PT_TYPES[pt_type] = parser
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = [] | def register_message(pt_type, parser):
if (pt_type not in PT_TYPES):
PT_TYPES[pt_type] = parser
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = []<|docstring|>Register new message and a new handler.<|endoftext|> |
ee675e1549b029b1d5c3b4a52fc8754a83380a6fc62ac1bca288acb1761fb91f | def register_callbacks(app, callback_str='handle_'):
'Register callbacks.'
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].append(handler) | Register callbacks. | empower/managers/ranmanager/vbsp/__init__.py | register_callbacks | 5g-empower/empower-runtime | 52 | python | def register_callbacks(app, callback_str='handle_'):
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].append(handler) | def register_callbacks(app, callback_str='handle_'):
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].append(handler)<|docstring|>Register callbacks.<|endoftext|> |
253505f077c237184df5ca33d3a201b83a6a5a31368e24d1dd90f192a09dc8ec | def unregister_callbacks(app, callback_str='handle_'):
'Unregister callbacks.'
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].remove(handler) | Unregister callbacks. | empower/managers/ranmanager/vbsp/__init__.py | unregister_callbacks | 5g-empower/empower-runtime | 52 | python | def unregister_callbacks(app, callback_str='handle_'):
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].remove(handler) | def unregister_callbacks(app, callback_str='handle_'):
for pt_type in PT_TYPES_HANDLERS:
if (not PT_TYPES[pt_type]):
handler_name = (callback_str + pt_type)
else:
handler_name = (callback_str + PT_TYPES[pt_type][1])
if hasattr(app, handler_name):
handler = getattr(app, handler_name)
PT_TYPES_HANDLERS[pt_type].remove(handler)<|docstring|>Unregister callbacks.<|endoftext|> |
6793f447409330a962753f9f90e5a95e8dd6553e168087ef39499a540b87042c | def register_callback(pt_type, handler):
'Register new message and a new handler.'
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = []
PT_TYPES_HANDLERS[pt_type].append(handler) | Register new message and a new handler. | empower/managers/ranmanager/vbsp/__init__.py | register_callback | 5g-empower/empower-runtime | 52 | python | def register_callback(pt_type, handler):
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = []
PT_TYPES_HANDLERS[pt_type].append(handler) | def register_callback(pt_type, handler):
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
PT_TYPES_HANDLERS[pt_type] = []
PT_TYPES_HANDLERS[pt_type].append(handler)<|docstring|>Register new message and a new handler.<|endoftext|> |
438afff592d6b53d8569d7bed60150716583f93b7e9c96a9779f7d25fce429ba | def unregister_callback(pt_type, handler):
'Register new message and a new handler.'
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
return
PT_TYPES_HANDLERS[pt_type].remove(handler) | Register new message and a new handler. | empower/managers/ranmanager/vbsp/__init__.py | unregister_callback | 5g-empower/empower-runtime | 52 | python | def unregister_callback(pt_type, handler):
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
return
PT_TYPES_HANDLERS[pt_type].remove(handler) | def unregister_callback(pt_type, handler):
if (pt_type not in PT_TYPES):
raise KeyError('Packet type %u undefined')
if (pt_type not in PT_TYPES_HANDLERS):
return
PT_TYPES_HANDLERS[pt_type].remove(handler)<|docstring|>Register new message and a new handler.<|endoftext|> |
e11fcb0bb1f0d8f62795221aea4939f547a4156a24cdba60d412c2444033fe0d | def decode_msg(msg_type, crud_result):
'Return the tuple (msg_type, crud_result).'
if (int(msg_type) == MSG_TYPE_REQUEST):
msg_type_str = 'request'
if (crud_result == OP_UNDEFINED):
crud_result_str = 'undefined'
elif (crud_result == OP_CREATE):
crud_result_str = 'create'
elif (crud_result == OP_UPDATE):
crud_result_str = 'update'
elif (crud_result == OP_DELETE):
crud_result_str = 'delete'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str)
msg_type_str = 'response'
if (crud_result == RESULT_SUCCESS):
crud_result_str = 'success'
elif (crud_result == RESULT_FAIL):
crud_result_str = 'fail'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str) | Return the tuple (msg_type, crud_result). | empower/managers/ranmanager/vbsp/__init__.py | decode_msg | 5g-empower/empower-runtime | 52 | python | def decode_msg(msg_type, crud_result):
if (int(msg_type) == MSG_TYPE_REQUEST):
msg_type_str = 'request'
if (crud_result == OP_UNDEFINED):
crud_result_str = 'undefined'
elif (crud_result == OP_CREATE):
crud_result_str = 'create'
elif (crud_result == OP_UPDATE):
crud_result_str = 'update'
elif (crud_result == OP_DELETE):
crud_result_str = 'delete'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str)
msg_type_str = 'response'
if (crud_result == RESULT_SUCCESS):
crud_result_str = 'success'
elif (crud_result == RESULT_FAIL):
crud_result_str = 'fail'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str) | def decode_msg(msg_type, crud_result):
if (int(msg_type) == MSG_TYPE_REQUEST):
msg_type_str = 'request'
if (crud_result == OP_UNDEFINED):
crud_result_str = 'undefined'
elif (crud_result == OP_CREATE):
crud_result_str = 'create'
elif (crud_result == OP_UPDATE):
crud_result_str = 'update'
elif (crud_result == OP_DELETE):
crud_result_str = 'delete'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str)
msg_type_str = 'response'
if (crud_result == RESULT_SUCCESS):
crud_result_str = 'success'
elif (crud_result == RESULT_FAIL):
crud_result_str = 'fail'
else:
crud_result_str = 'unknown'
return (msg_type_str, crud_result_str)<|docstring|>Return the tuple (msg_type, crud_result).<|endoftext|> |
ee608ebada079a6962348733cbe7bef52527145b6ce8aaa884d9ed4cdd5afca3 | @cli.command()
def status():
'Show status information for all available devices.' | Show status information for all available devices. | projects/clusterctrl/src/python/clusterctrl/__main__.py | status | arrdem/source | 4 | python | @cli.command()
def status():
| @cli.command()
def status():
<|docstring|>Show status information for all available devices.<|endoftext|> |
0ec8ac587de3e93f8c24706514d968d103c2ff8df68e0d76c6eb5f7260580bb2 | @cli.command()
def maxpi():
'Show the number of available/attached Pis.' | Show the number of available/attached Pis. | projects/clusterctrl/src/python/clusterctrl/__main__.py | maxpi | arrdem/source | 4 | python | @cli.command()
def maxpi():
| @cli.command()
def maxpi():
<|docstring|>Show the number of available/attached Pis.<|endoftext|> |
bb531def3c5caec36fb304731b5ed08ca47608ed5ea5598c2f2d39c286042dbb | @cli.command()
def init():
'Init ClusterHAT' | Init ClusterHAT | projects/clusterctrl/src/python/clusterctrl/__main__.py | init | arrdem/source | 4 | python | @cli.command()
def init():
| @cli.command()
def init():
<|docstring|>Init ClusterHAT<|endoftext|> |
d6fbb6b78a33dc076f9a2ee8798144e08ca31e30bf9d9a4658ccae08785dad2d | def recommend(self, full_graph, K, h_user, h_item):
'\n Return a (n_user, K) matrix of recommended items for each user\n '
graph_slice = full_graph.edge_type_subgraph([self.user_to_item_etype])
n_users = full_graph.number_of_nodes(self.user_ntype)
latest_interactions = dgl.sampling.select_topk(graph_slice, 1, self.timestamp, edge_dir='out')
(user, latest_items) = latest_interactions.all_edges(form='uv', order='srcdst')
assert torch.equal(user, torch.arange(n_users))
recommended_batches = []
user_batches = torch.arange(n_users).split(self.batch_size)
for user_batch in user_batches:
latest_item_batch = latest_items[user_batch].to(device=h_item.device)
dist = (h_item[latest_item_batch] @ h_item.t())
for (i, u) in enumerate(user_batch.tolist()):
interacted_items = full_graph.successors(u, etype=self.user_to_item_etype)
dist[(i, interacted_items)] = (- np.inf)
recommended_batches.append(dist.topk(K, 1)[1])
recommendations = torch.cat(recommended_batches, 0)
return recommendations | Return a (n_user, K) matrix of recommended items for each user | examples/pytorch/pinsage/evaluation.py | recommend | alexpod1000/dgl | 9,516 | python | def recommend(self, full_graph, K, h_user, h_item):
'\n \n '
graph_slice = full_graph.edge_type_subgraph([self.user_to_item_etype])
n_users = full_graph.number_of_nodes(self.user_ntype)
latest_interactions = dgl.sampling.select_topk(graph_slice, 1, self.timestamp, edge_dir='out')
(user, latest_items) = latest_interactions.all_edges(form='uv', order='srcdst')
assert torch.equal(user, torch.arange(n_users))
recommended_batches = []
user_batches = torch.arange(n_users).split(self.batch_size)
for user_batch in user_batches:
latest_item_batch = latest_items[user_batch].to(device=h_item.device)
dist = (h_item[latest_item_batch] @ h_item.t())
for (i, u) in enumerate(user_batch.tolist()):
interacted_items = full_graph.successors(u, etype=self.user_to_item_etype)
dist[(i, interacted_items)] = (- np.inf)
recommended_batches.append(dist.topk(K, 1)[1])
recommendations = torch.cat(recommended_batches, 0)
return recommendations | def recommend(self, full_graph, K, h_user, h_item):
'\n \n '
graph_slice = full_graph.edge_type_subgraph([self.user_to_item_etype])
n_users = full_graph.number_of_nodes(self.user_ntype)
latest_interactions = dgl.sampling.select_topk(graph_slice, 1, self.timestamp, edge_dir='out')
(user, latest_items) = latest_interactions.all_edges(form='uv', order='srcdst')
assert torch.equal(user, torch.arange(n_users))
recommended_batches = []
user_batches = torch.arange(n_users).split(self.batch_size)
for user_batch in user_batches:
latest_item_batch = latest_items[user_batch].to(device=h_item.device)
dist = (h_item[latest_item_batch] @ h_item.t())
for (i, u) in enumerate(user_batch.tolist()):
interacted_items = full_graph.successors(u, etype=self.user_to_item_etype)
dist[(i, interacted_items)] = (- np.inf)
recommended_batches.append(dist.topk(K, 1)[1])
recommendations = torch.cat(recommended_batches, 0)
return recommendations<|docstring|>Return a (n_user, K) matrix of recommended items for each user<|endoftext|> |
69b0b44ef2013d55d20f35b3326cc92ffe4276da98f0ea1310fa7068b49e3e4b | def is_only_embed(maybe_embeds):
'\n Checks whether the given value is a `tuple` or `list` containing only `embed-like`-s.\n \n Parameters\n ----------\n maybe_embeds : (`tuple` or `list`) of `EmbedBase` or `Any`\n The value to check whether is a `tuple` or `list` containing only `embed-like`-s.\n \n Returns\n -------\n is_only_embed : `bool`\n '
if (not isinstance(maybe_embeds, (list, tuple))):
return False
for maybe_embed in maybe_embeds:
if (not isinstance(maybe_embed, EmbedBase)):
return False
return True | Checks whether the given value is a `tuple` or `list` containing only `embed-like`-s.
Parameters
----------
maybe_embeds : (`tuple` or `list`) of `EmbedBase` or `Any`
The value to check whether is a `tuple` or `list` containing only `embed-like`-s.
Returns
-------
is_only_embed : `bool` | hata/ext/slash/responding.py | is_only_embed | asleep-cult/hata | 0 | python | def is_only_embed(maybe_embeds):
'\n Checks whether the given value is a `tuple` or `list` containing only `embed-like`-s.\n \n Parameters\n ----------\n maybe_embeds : (`tuple` or `list`) of `EmbedBase` or `Any`\n The value to check whether is a `tuple` or `list` containing only `embed-like`-s.\n \n Returns\n -------\n is_only_embed : `bool`\n '
if (not isinstance(maybe_embeds, (list, tuple))):
return False
for maybe_embed in maybe_embeds:
if (not isinstance(maybe_embed, EmbedBase)):
return False
return True | def is_only_embed(maybe_embeds):
'\n Checks whether the given value is a `tuple` or `list` containing only `embed-like`-s.\n \n Parameters\n ----------\n maybe_embeds : (`tuple` or `list`) of `EmbedBase` or `Any`\n The value to check whether is a `tuple` or `list` containing only `embed-like`-s.\n \n Returns\n -------\n is_only_embed : `bool`\n '
if (not isinstance(maybe_embeds, (list, tuple))):
return False
for maybe_embed in maybe_embeds:
if (not isinstance(maybe_embed, EmbedBase)):
return False
return True<|docstring|>Checks whether the given value is a `tuple` or `list` containing only `embed-like`-s.
Parameters
----------
maybe_embeds : (`tuple` or `list`) of `EmbedBase` or `Any`
The value to check whether is a `tuple` or `list` containing only `embed-like`-s.
Returns
-------
is_only_embed : `bool`<|endoftext|> |
8026955ea6a04042dfeb0edb9559c9a118a3706c22935a7f70718f072806b055 | async def get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
'\n Gets request coroutine after an output from a command coroutine. Might return `None` if there is nothing to send.\n \n This function is a coroutine generator, which should be ued inside of an async for loop.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n response : `Any`\n Any object yielded or returned by the command coroutine.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
if (response is None):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (isinstance(response, (str, EmbedBase)) or is_only_embed(response)):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
(yield client.interaction_response_message_edit(interaction_event, response))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
(yield client.interaction_followup_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
return
if is_coroutine_generator(response):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, response))
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
(yield request_coro)
return
if isinstance(response, SlashResponse):
for request_coro in response.get_request_coros(client, interaction_event, show_for_invoking_user_only):
(yield request_coro)
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
return | Gets request coroutine after an output from a command coroutine. Might return `None` if there is nothing to send.
This function is a coroutine generator, which should be ued inside of an async for loop.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
response : `Any`
Any object yielded or returned by the command coroutine.
Yields
-------
request_coro : `None` or `coroutine` | hata/ext/slash/responding.py | get_request_coros | asleep-cult/hata | 0 | python | async def get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
'\n Gets request coroutine after an output from a command coroutine. Might return `None` if there is nothing to send.\n \n This function is a coroutine generator, which should be ued inside of an async for loop.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n response : `Any`\n Any object yielded or returned by the command coroutine.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
if (response is None):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (isinstance(response, (str, EmbedBase)) or is_only_embed(response)):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
(yield client.interaction_response_message_edit(interaction_event, response))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
(yield client.interaction_followup_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
return
if is_coroutine_generator(response):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, response))
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
(yield request_coro)
return
if isinstance(response, SlashResponse):
for request_coro in response.get_request_coros(client, interaction_event, show_for_invoking_user_only):
(yield request_coro)
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
return | async def get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
'\n Gets request coroutine after an output from a command coroutine. Might return `None` if there is nothing to send.\n \n This function is a coroutine generator, which should be ued inside of an async for loop.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n response : `Any`\n Any object yielded or returned by the command coroutine.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
if (response is None):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (isinstance(response, (str, EmbedBase)) or is_only_embed(response)):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
(yield client.interaction_response_message_edit(interaction_event, response))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
(yield client.interaction_followup_message_create(interaction_event, response, show_for_invoking_user_only=show_for_invoking_user_only))
return
return
if is_coroutine_generator(response):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, response))
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
(yield request_coro)
return
if isinstance(response, SlashResponse):
for request_coro in response.get_request_coros(client, interaction_event, show_for_invoking_user_only):
(yield request_coro)
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
return
return<|docstring|>Gets request coroutine after an output from a command coroutine. Might return `None` if there is nothing to send.
This function is a coroutine generator, which should be ued inside of an async for loop.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
response : `Any`
Any object yielded or returned by the command coroutine.
Yields
-------
request_coro : `None` or `coroutine`<|endoftext|> |
b7852d26b14371506267a71a59cf8291cf88a10231cb429d1ffca9677fd44d6c | async def process_command_gen(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine generator.\n \n This function os a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `CoroutineGenerator`\n A coroutine generator with will send command response.\n \n Returns\n -------\n response : `Any`\n Returned object by the coroutine generator.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
response_message = None
response_exception = None
while True:
if (response_exception is None):
step = coro.asend(response_message)
else:
step = coro.athrow(response_exception)
try:
response = (await step)
except StopAsyncIteration as err:
if ((response_exception is not None) and (response_exception is not err)):
raise
args = err.args
if args:
response = args[0]
else:
response = None
break
except InteractionAbortedError as err:
response = err.response
break
except BaseException as err:
if ((response_exception is None) or (response_exception is not err)):
raise
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise
else:
response_message = None
response_exception = None
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
response_message = (await request_coro)
except BaseException as err:
response_message = None
response_exception = err
break
return response | Processes a slash command coroutine generator.
This function os a coroutine.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
coro : `CoroutineGenerator`
A coroutine generator with will send command response.
Returns
-------
response : `Any`
Returned object by the coroutine generator.
Raises
------
BaseException
Any exception raised by `coro`. | hata/ext/slash/responding.py | process_command_gen | asleep-cult/hata | 0 | python | async def process_command_gen(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine generator.\n \n This function os a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `CoroutineGenerator`\n A coroutine generator with will send command response.\n \n Returns\n -------\n response : `Any`\n Returned object by the coroutine generator.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
response_message = None
response_exception = None
while True:
if (response_exception is None):
step = coro.asend(response_message)
else:
step = coro.athrow(response_exception)
try:
response = (await step)
except StopAsyncIteration as err:
if ((response_exception is not None) and (response_exception is not err)):
raise
args = err.args
if args:
response = args[0]
else:
response = None
break
except InteractionAbortedError as err:
response = err.response
break
except BaseException as err:
if ((response_exception is None) or (response_exception is not err)):
raise
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise
else:
response_message = None
response_exception = None
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
response_message = (await request_coro)
except BaseException as err:
response_message = None
response_exception = err
break
return response | async def process_command_gen(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine generator.\n \n This function os a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `CoroutineGenerator`\n A coroutine generator with will send command response.\n \n Returns\n -------\n response : `Any`\n Returned object by the coroutine generator.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
response_message = None
response_exception = None
while True:
if (response_exception is None):
step = coro.asend(response_message)
else:
step = coro.athrow(response_exception)
try:
response = (await step)
except StopAsyncIteration as err:
if ((response_exception is not None) and (response_exception is not err)):
raise
args = err.args
if args:
response = args[0]
else:
response = None
break
except InteractionAbortedError as err:
response = err.response
break
except BaseException as err:
if ((response_exception is None) or (response_exception is not err)):
raise
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise
else:
response_message = None
response_exception = None
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
response_message = (await request_coro)
except BaseException as err:
response_message = None
response_exception = err
break
return response<|docstring|>Processes a slash command coroutine generator.
This function os a coroutine.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
coro : `CoroutineGenerator`
A coroutine generator with will send command response.
Returns
-------
response : `Any`
Returned object by the coroutine generator.
Raises
------
BaseException
Any exception raised by `coro`.<|endoftext|> |
d0af751f5df58ec44f1dffaad1a78d452d9f52fc0162e36ffc55af77a796ca26 | async def process_command_coro(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine.\n \n If the coroutine returns or yields a string or an embed like then sends it to the respective channel.\n \n This function is a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `coroutine`\n A coroutine with will send command response.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
if is_coroutine_generator(coro):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, coro))
else:
try:
response = (await coro)
except InteractionAbortedError as err:
response = err.response
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
(await request_coro)
except BaseException as err:
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise | Processes a slash command coroutine.
If the coroutine returns or yields a string or an embed like then sends it to the respective channel.
This function is a coroutine.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
coro : `coroutine`
A coroutine with will send command response.
Raises
------
BaseException
Any exception raised by `coro`. | hata/ext/slash/responding.py | process_command_coro | asleep-cult/hata | 0 | python | async def process_command_coro(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine.\n \n If the coroutine returns or yields a string or an embed like then sends it to the respective channel.\n \n This function is a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `coroutine`\n A coroutine with will send command response.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
if is_coroutine_generator(coro):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, coro))
else:
try:
response = (await coro)
except InteractionAbortedError as err:
response = err.response
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
(await request_coro)
except BaseException as err:
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise | async def process_command_coro(client, interaction_event, show_for_invoking_user_only, coro):
'\n Processes a slash command coroutine.\n \n If the coroutine returns or yields a string or an embed like then sends it to the respective channel.\n \n This function is a coroutine.\n \n Parameters\n ----------\n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n coro : `coroutine`\n A coroutine with will send command response.\n \n Raises\n ------\n BaseException\n Any exception raised by `coro`.\n '
if is_coroutine_generator(coro):
response = (await process_command_gen(client, interaction_event, show_for_invoking_user_only, coro))
else:
try:
response = (await coro)
except InteractionAbortedError as err:
response = err.response
async for request_coro in get_request_coros(client, interaction_event, show_for_invoking_user_only, response):
try:
(await request_coro)
except BaseException as err:
if isinstance(err, ConnectionError):
return
if isinstance(err, DiscordException):
if (err.code in (ERROR_CODES.unknown_channel, ERROR_CODES.invalid_access, ERROR_CODES.invalid_permissions, ERROR_CODES.cannot_message_user, ERROR_CODES.unknown_interaction)):
return
raise<|docstring|>Processes a slash command coroutine.
If the coroutine returns or yields a string or an embed like then sends it to the respective channel.
This function is a coroutine.
Parameters
----------
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
coro : `coroutine`
A coroutine with will send command response.
Raises
------
BaseException
Any exception raised by `coro`.<|endoftext|> |
a121bc00c1b42f36f4a2bc3fd987c8774abd4f0a71f22efae7d474a2762ee28c | def abort(content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=...):
"\n Aborts the slash response with sending the passed parameters as a response.\n \n The abortion auto detects `show_for_invoking_user_only` if not given. Not follows the command's preference.\n If only a string `content` is given, `show_for_invoking_user_only` will become `True`, else `False`. The reason of\n becoming `False` at that case is, Discord ignores every other field except string content.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user.\n \n If given as `True`, only the message's content will be processed by Discord.\n \n Raises\n ------\n InteractionAbortedError\n The exception which aborts the interaction, then yields the response.\n "
if (show_for_invoking_user_only is ...):
if (embed is not ...):
show_for_invoking_user_only = False
elif (file is not ...):
show_for_invoking_user_only = False
elif (allowed_mentions is not ...):
show_for_invoking_user_only = False
elif (tts is not ...):
show_for_invoking_user_only = False
elif (content is ...):
show_for_invoking_user_only = True
elif is_only_embed(content):
show_for_invoking_user_only = False
else:
show_for_invoking_user_only = True
response = SlashResponse(content, embed=embed, file=file, allowed_mentions=allowed_mentions, tts=tts, show_for_invoking_user_only=show_for_invoking_user_only, force_new_message=(- 1))
raise InteractionAbortedError(response) | Aborts the slash response with sending the passed parameters as a response.
The abortion auto detects `show_for_invoking_user_only` if not given. Not follows the command's preference.
If only a string `content` is given, `show_for_invoking_user_only` will become `True`, else `False`. The reason of
becoming `False` at that case is, Discord ignores every other field except string content.
Parameters
----------
content : `str`, ``EmbedBase``, `Any`, Optional
The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile
if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.
If given as ``EmbedBase`` instance, then is sent as the message's embed.
embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional
The embedded content of the message.
If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.
file : `Any`, Optional
A file to send. Check ``Client._create_file_form`` for details.
allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional
Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.
tts : `bool`, Optional
Whether the message is text-to-speech.
show_for_invoking_user_only : `bool`, Optional
Whether the sent message should only be shown to the invoking user.
If given as `True`, only the message's content will be processed by Discord.
Raises
------
InteractionAbortedError
The exception which aborts the interaction, then yields the response. | hata/ext/slash/responding.py | abort | asleep-cult/hata | 0 | python | def abort(content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=...):
"\n Aborts the slash response with sending the passed parameters as a response.\n \n The abortion auto detects `show_for_invoking_user_only` if not given. Not follows the command's preference.\n If only a string `content` is given, `show_for_invoking_user_only` will become `True`, else `False`. The reason of\n becoming `False` at that case is, Discord ignores every other field except string content.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user.\n \n If given as `True`, only the message's content will be processed by Discord.\n \n Raises\n ------\n InteractionAbortedError\n The exception which aborts the interaction, then yields the response.\n "
if (show_for_invoking_user_only is ...):
if (embed is not ...):
show_for_invoking_user_only = False
elif (file is not ...):
show_for_invoking_user_only = False
elif (allowed_mentions is not ...):
show_for_invoking_user_only = False
elif (tts is not ...):
show_for_invoking_user_only = False
elif (content is ...):
show_for_invoking_user_only = True
elif is_only_embed(content):
show_for_invoking_user_only = False
else:
show_for_invoking_user_only = True
response = SlashResponse(content, embed=embed, file=file, allowed_mentions=allowed_mentions, tts=tts, show_for_invoking_user_only=show_for_invoking_user_only, force_new_message=(- 1))
raise InteractionAbortedError(response) | def abort(content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=...):
"\n Aborts the slash response with sending the passed parameters as a response.\n \n The abortion auto detects `show_for_invoking_user_only` if not given. Not follows the command's preference.\n If only a string `content` is given, `show_for_invoking_user_only` will become `True`, else `False`. The reason of\n becoming `False` at that case is, Discord ignores every other field except string content.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user.\n \n If given as `True`, only the message's content will be processed by Discord.\n \n Raises\n ------\n InteractionAbortedError\n The exception which aborts the interaction, then yields the response.\n "
if (show_for_invoking_user_only is ...):
if (embed is not ...):
show_for_invoking_user_only = False
elif (file is not ...):
show_for_invoking_user_only = False
elif (allowed_mentions is not ...):
show_for_invoking_user_only = False
elif (tts is not ...):
show_for_invoking_user_only = False
elif (content is ...):
show_for_invoking_user_only = True
elif is_only_embed(content):
show_for_invoking_user_only = False
else:
show_for_invoking_user_only = True
response = SlashResponse(content, embed=embed, file=file, allowed_mentions=allowed_mentions, tts=tts, show_for_invoking_user_only=show_for_invoking_user_only, force_new_message=(- 1))
raise InteractionAbortedError(response)<|docstring|>Aborts the slash response with sending the passed parameters as a response.
The abortion auto detects `show_for_invoking_user_only` if not given. Not follows the command's preference.
If only a string `content` is given, `show_for_invoking_user_only` will become `True`, else `False`. The reason of
becoming `False` at that case is, Discord ignores every other field except string content.
Parameters
----------
content : `str`, ``EmbedBase``, `Any`, Optional
The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile
if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.
If given as ``EmbedBase`` instance, then is sent as the message's embed.
embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional
The embedded content of the message.
If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.
file : `Any`, Optional
A file to send. Check ``Client._create_file_form`` for details.
allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional
Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.
tts : `bool`, Optional
Whether the message is text-to-speech.
show_for_invoking_user_only : `bool`, Optional
Whether the sent message should only be shown to the invoking user.
If given as `True`, only the message's content will be processed by Discord.
Raises
------
InteractionAbortedError
The exception which aborts the interaction, then yields the response.<|endoftext|> |
b6cdb732ce13e8beaa293d483cc90b1d38e23d421335269d3a7b12ae0251e588 | def __init__(self, content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=..., force_new_message=False):
"\n Creates a new ``SlashResponse`` instance with the given parameters.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n \n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user. Defaults to the value passed when adding\n the command.\n \n If given as `True` only the message's content will be processed by Discord.\n force_new_message : `int` or `bool`, Optional\n Whether a new message should be forced out from Discord allowing the client to retrieve a new ``Message``\n object as well. Defaults to `False`.\n \n If given as `-1` will only force new message if the event already deferred.\n "
self._force_new_message = force_new_message
self._parameters = parameters = {}
if (content is not ...):
parameters['content'] = content
if (embed is not ...):
parameters['embed'] = embed
if (file is not ...):
parameters['file'] = file
if (allowed_mentions is not ...):
parameters['allowed_mentions'] = allowed_mentions
if (tts is not ...):
parameters['tts'] = tts
if (show_for_invoking_user_only is not ...):
parameters['show_for_invoking_user_only'] = show_for_invoking_user_only | Creates a new ``SlashResponse`` instance with the given parameters.
Parameters
----------
content : `str`, ``EmbedBase``, `Any`, Optional
The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile
if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.
If given as ``EmbedBase`` instance, then is sent as the message's embed.
embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional
The embedded content of the message.
If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.
file : `Any`, Optional
A file to send. Check ``Client._create_file_form`` for details.
allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional
Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.
tts : `bool`, Optional
Whether the message is text-to-speech.
show_for_invoking_user_only : `bool`, Optional
Whether the sent message should only be shown to the invoking user. Defaults to the value passed when adding
the command.
If given as `True` only the message's content will be processed by Discord.
force_new_message : `int` or `bool`, Optional
Whether a new message should be forced out from Discord allowing the client to retrieve a new ``Message``
object as well. Defaults to `False`.
If given as `-1` will only force new message if the event already deferred. | hata/ext/slash/responding.py | __init__ | asleep-cult/hata | 0 | python | def __init__(self, content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=..., force_new_message=False):
"\n Creates a new ``SlashResponse`` instance with the given parameters.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n \n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user. Defaults to the value passed when adding\n the command.\n \n If given as `True` only the message's content will be processed by Discord.\n force_new_message : `int` or `bool`, Optional\n Whether a new message should be forced out from Discord allowing the client to retrieve a new ``Message``\n object as well. Defaults to `False`.\n \n If given as `-1` will only force new message if the event already deferred.\n "
self._force_new_message = force_new_message
self._parameters = parameters = {}
if (content is not ...):
parameters['content'] = content
if (embed is not ...):
parameters['embed'] = embed
if (file is not ...):
parameters['file'] = file
if (allowed_mentions is not ...):
parameters['allowed_mentions'] = allowed_mentions
if (tts is not ...):
parameters['tts'] = tts
if (show_for_invoking_user_only is not ...):
parameters['show_for_invoking_user_only'] = show_for_invoking_user_only | def __init__(self, content=..., *, embed=..., file=..., allowed_mentions=..., tts=..., show_for_invoking_user_only=..., force_new_message=False):
"\n Creates a new ``SlashResponse`` instance with the given parameters.\n \n Parameters\n ----------\n content : `str`, ``EmbedBase``, `Any`, Optional\n The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile\n if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.\n \n If given as ``EmbedBase`` instance, then is sent as the message's embed.\n \n embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional\n The embedded content of the message.\n \n If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.\n file : `Any`, Optional\n A file to send. Check ``Client._create_file_form`` for details.\n allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional\n Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.\n tts : `bool`, Optional\n Whether the message is text-to-speech.\n show_for_invoking_user_only : `bool`, Optional\n Whether the sent message should only be shown to the invoking user. Defaults to the value passed when adding\n the command.\n \n If given as `True` only the message's content will be processed by Discord.\n force_new_message : `int` or `bool`, Optional\n Whether a new message should be forced out from Discord allowing the client to retrieve a new ``Message``\n object as well. Defaults to `False`.\n \n If given as `-1` will only force new message if the event already deferred.\n "
self._force_new_message = force_new_message
self._parameters = parameters = {}
if (content is not ...):
parameters['content'] = content
if (embed is not ...):
parameters['embed'] = embed
if (file is not ...):
parameters['file'] = file
if (allowed_mentions is not ...):
parameters['allowed_mentions'] = allowed_mentions
if (tts is not ...):
parameters['tts'] = tts
if (show_for_invoking_user_only is not ...):
parameters['show_for_invoking_user_only'] = show_for_invoking_user_only<|docstring|>Creates a new ``SlashResponse`` instance with the given parameters.
Parameters
----------
content : `str`, ``EmbedBase``, `Any`, Optional
The message's content if given. If given as `str` or empty string, then no content will be sent, meanwhile
if any other non `str` or ``EmbedBase`` instance is given, then will be casted to string.
If given as ``EmbedBase`` instance, then is sent as the message's embed.
embed : ``EmbedBase`` instance or `list` of ``EmbedBase`` instances, Optional
The embedded content of the message.
If `embed` and `content` parameters are both given as ``EmbedBase`` instance, then `TypeError` is raised.
file : `Any`, Optional
A file to send. Check ``Client._create_file_form`` for details.
allowed_mentions : `None`, `str`, ``UserBase``, ``Role``, `list` of (`str`, ``UserBase``, ``Role`` ), Optional
Which user or role can the message ping (or everyone). Check ``Client._parse_allowed_mentions`` for details.
tts : `bool`, Optional
Whether the message is text-to-speech.
show_for_invoking_user_only : `bool`, Optional
Whether the sent message should only be shown to the invoking user. Defaults to the value passed when adding
the command.
If given as `True` only the message's content will be processed by Discord.
force_new_message : `int` or `bool`, Optional
Whether a new message should be forced out from Discord allowing the client to retrieve a new ``Message``
object as well. Defaults to `False`.
If given as `-1` will only force new message if the event already deferred.<|endoftext|> |
818aca27430876f05a36fe73805c3a9f17dba19cf32d5f0d576bde09fd070516 | def _get_response_parameters(self, allowed_parameters):
'\n Gets response parameters to pass to a ``Client`` method.\n \n Parameters\n ----------\n allowed_parameters : `tuple` of `str`\n Allowed parameters to be passed to the respective client method.\n \n Returns\n -------\n response_parameters : `dict` of (`str`, `Any`) items\n Parameters to pass the the respective client method.\n '
parameters = self._parameters
response_parameters = {}
for key in allowed_parameters:
try:
value = parameters[key]
except KeyError:
continue
response_parameters[key] = value
return response_parameters | Gets response parameters to pass to a ``Client`` method.
Parameters
----------
allowed_parameters : `tuple` of `str`
Allowed parameters to be passed to the respective client method.
Returns
-------
response_parameters : `dict` of (`str`, `Any`) items
Parameters to pass the the respective client method. | hata/ext/slash/responding.py | _get_response_parameters | asleep-cult/hata | 0 | python | def _get_response_parameters(self, allowed_parameters):
'\n Gets response parameters to pass to a ``Client`` method.\n \n Parameters\n ----------\n allowed_parameters : `tuple` of `str`\n Allowed parameters to be passed to the respective client method.\n \n Returns\n -------\n response_parameters : `dict` of (`str`, `Any`) items\n Parameters to pass the the respective client method.\n '
parameters = self._parameters
response_parameters = {}
for key in allowed_parameters:
try:
value = parameters[key]
except KeyError:
continue
response_parameters[key] = value
return response_parameters | def _get_response_parameters(self, allowed_parameters):
'\n Gets response parameters to pass to a ``Client`` method.\n \n Parameters\n ----------\n allowed_parameters : `tuple` of `str`\n Allowed parameters to be passed to the respective client method.\n \n Returns\n -------\n response_parameters : `dict` of (`str`, `Any`) items\n Parameters to pass the the respective client method.\n '
parameters = self._parameters
response_parameters = {}
for key in allowed_parameters:
try:
value = parameters[key]
except KeyError:
continue
response_parameters[key] = value
return response_parameters<|docstring|>Gets response parameters to pass to a ``Client`` method.
Parameters
----------
allowed_parameters : `tuple` of `str`
Allowed parameters to be passed to the respective client method.
Returns
-------
response_parameters : `dict` of (`str`, `Any`) items
Parameters to pass the the respective client method.<|endoftext|> |
d6809d4ffc276cde9eff8bdf3c626fece8e60d7c2503954370f90329c667101f | def get_request_coros(self, client, interaction_event, show_for_invoking_user_only):
'\n Gets request coroutine buildable from the ``SlashResponse``.\n \n This method is a generator, which should be used inside of a `for` loop.\n \n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
force_new_message = self._force_new_message
if force_new_message:
if (force_new_message > 0):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embedfile'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return | Gets request coroutine buildable from the ``SlashResponse``.
This method is a generator, which should be used inside of a `for` loop.
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
Yields
-------
request_coro : `None` or `coroutine` | hata/ext/slash/responding.py | get_request_coros | asleep-cult/hata | 0 | python | def get_request_coros(self, client, interaction_event, show_for_invoking_user_only):
'\n Gets request coroutine buildable from the ``SlashResponse``.\n \n This method is a generator, which should be used inside of a `for` loop.\n \n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
force_new_message = self._force_new_message
if force_new_message:
if (force_new_message > 0):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embedfile'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return | def get_request_coros(self, client, interaction_event, show_for_invoking_user_only):
'\n Gets request coroutine buildable from the ``SlashResponse``.\n \n This method is a generator, which should be used inside of a `for` loop.\n \n client : ``Client``\n The client who will send the responses if applicable.\n interaction_event : ``InteractionEvent``\n The respective event to respond on.\n show_for_invoking_user_only : `bool`\n Whether the response message should only be shown for the invoking user.\n \n Yields\n -------\n request_coro : `None` or `coroutine`\n '
response_state = interaction_event._response_state
force_new_message = self._force_new_message
if force_new_message:
if (force_new_message > 0):
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
(yield client.interaction_response_message_create(interaction_event))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return
else:
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_NONE):
if ('file' in self._parameters):
show_for_invoking_user_only = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, show_for_invoking_user_only=show_for_invoking_user_only))
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
else:
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_response_message_create(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_DEFERRED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embedfile'))
(yield client.interaction_response_message_edit(interaction_event, **response_parameters))
return
if (response_state == INTERACTION_EVENT_RESPONSE_STATE_RESPONDED):
response_parameters = self._get_response_parameters(('allowed_mentions', 'content', 'embed', 'file', 'tts'))
response_parameters['show_for_invoking_user_only'] = self._parameters.get('show_for_invoking_user_only', show_for_invoking_user_only)
(yield client.interaction_followup_message_create(interaction_event, **response_parameters))
return<|docstring|>Gets request coroutine buildable from the ``SlashResponse``.
This method is a generator, which should be used inside of a `for` loop.
client : ``Client``
The client who will send the responses if applicable.
interaction_event : ``InteractionEvent``
The respective event to respond on.
show_for_invoking_user_only : `bool`
Whether the response message should only be shown for the invoking user.
Yields
-------
request_coro : `None` or `coroutine`<|endoftext|> |
8f18684ad28fd31b242696a04f7b4b1b5e9b1059cfee0e0169fbc220cd76f35e | def __repr__(self):
"Returns the slash response's representation."
result = ['<', self.__class__.__name__, ' ']
if self._force_new_message:
result.append('(force new message) ')
parameters = self._parameters
if parameters:
for (key, value) in parameters.items():
result.append(key)
result.append('=')
result.append(repr(value))
result.append(', ')
result[(- 1)] = '>'
else:
result.append('>')
return ''.join(result) | Returns the slash response's representation. | hata/ext/slash/responding.py | __repr__ | asleep-cult/hata | 0 | python | def __repr__(self):
result = ['<', self.__class__.__name__, ' ']
if self._force_new_message:
result.append('(force new message) ')
parameters = self._parameters
if parameters:
for (key, value) in parameters.items():
result.append(key)
result.append('=')
result.append(repr(value))
result.append(', ')
result[(- 1)] = '>'
else:
result.append('>')
return .join(result) | def __repr__(self):
result = ['<', self.__class__.__name__, ' ']
if self._force_new_message:
result.append('(force new message) ')
parameters = self._parameters
if parameters:
for (key, value) in parameters.items():
result.append(key)
result.append('=')
result.append(repr(value))
result.append(', ')
result[(- 1)] = '>'
else:
result.append('>')
return .join(result)<|docstring|>Returns the slash response's representation.<|endoftext|> |
51855473bed46c50be89308644d4f0bb093b925895ce2f58ab32e488b87a741c | def __init__(self, response):
'\n Creates a new ``InteractionAbortedError`` instance with the given response.\n \n Parameters\n ----------\n response : ``SlashResponse``\n The response to send.\n '
self.response = response
BaseException.__init__(self, response) | Creates a new ``InteractionAbortedError`` instance with the given response.
Parameters
----------
response : ``SlashResponse``
The response to send. | hata/ext/slash/responding.py | __init__ | asleep-cult/hata | 0 | python | def __init__(self, response):
'\n Creates a new ``InteractionAbortedError`` instance with the given response.\n \n Parameters\n ----------\n response : ``SlashResponse``\n The response to send.\n '
self.response = response
BaseException.__init__(self, response) | def __init__(self, response):
'\n Creates a new ``InteractionAbortedError`` instance with the given response.\n \n Parameters\n ----------\n response : ``SlashResponse``\n The response to send.\n '
self.response = response
BaseException.__init__(self, response)<|docstring|>Creates a new ``InteractionAbortedError`` instance with the given response.
Parameters
----------
response : ``SlashResponse``
The response to send.<|endoftext|> |
df21c598289e9404ea4abded861b3300f2c1f6fb2bc43579bcedd92a59816026 | def __repr__(self):
"Returns the exception's representation."
return f'{self.__class__.__name__}({self.response!r})' | Returns the exception's representation. | hata/ext/slash/responding.py | __repr__ | asleep-cult/hata | 0 | python | def __repr__(self):
return f'{self.__class__.__name__}({self.response!r})' | def __repr__(self):
return f'{self.__class__.__name__}({self.response!r})'<|docstring|>Returns the exception's representation.<|endoftext|> |
ebd30a773a9ee97aef335333e13e05bf269a4b2c35870ffb6dd02c98e5011f44 | def test_write_jpeg():
'See if Pillow can write JPEG (tests linkage against mozjpeg)'
im = Image.new('RGB', (10, 10))
buffer = BytesIO()
im.save(buffer, format='JPEG')
if (sys.version_info[0] == 2):
buffer.seek(0)
size = len(buffer.read())
else:
size = buffer.getbuffer().nbytes
if (size != 375):
logger.error('JPEG optimization is not working as expected! size=%s', size) | See if Pillow can write JPEG (tests linkage against mozjpeg) | src/tests/test_jpeg.py | test_write_jpeg | edoburu/demo.django-fluent.org | 24 | python | def test_write_jpeg():
im = Image.new('RGB', (10, 10))
buffer = BytesIO()
im.save(buffer, format='JPEG')
if (sys.version_info[0] == 2):
buffer.seek(0)
size = len(buffer.read())
else:
size = buffer.getbuffer().nbytes
if (size != 375):
logger.error('JPEG optimization is not working as expected! size=%s', size) | def test_write_jpeg():
im = Image.new('RGB', (10, 10))
buffer = BytesIO()
im.save(buffer, format='JPEG')
if (sys.version_info[0] == 2):
buffer.seek(0)
size = len(buffer.read())
else:
size = buffer.getbuffer().nbytes
if (size != 375):
logger.error('JPEG optimization is not working as expected! size=%s', size)<|docstring|>See if Pillow can write JPEG (tests linkage against mozjpeg)<|endoftext|> |
70664de1827930684106fc23b9e6950944a1ff3266b656b51c8b348d4a5de8e2 | def _astroid_interface_for_visitor(visitor_function):
'Turn codewatch visitors into astroid-compatible transform functions\n\n codewatch visitors can make use of 3 args, the node, stats, and the\n relative file path you were visited for\n\n astroid transforms must take only the node\n\n By annotating the node with stats and relative file path, we can make our\n codewatch visitors compatible with astroid transform functions.\n '
@wraps(visitor_function)
def call_visitor(annotated_node, *args, **kwargs):
return visitor_function(annotated_node, annotated_node._codewatch.stats, annotated_node._codewatch.rel_file_path, *args, **kwargs)
return call_visitor | Turn codewatch visitors into astroid-compatible transform functions
codewatch visitors can make use of 3 args, the node, stats, and the
relative file path you were visited for
astroid transforms must take only the node
By annotating the node with stats and relative file path, we can make our
codewatch visitors compatible with astroid transform functions. | codewatch/node_visitor.py | _astroid_interface_for_visitor | kazu9su/codewatch | 39 | python | def _astroid_interface_for_visitor(visitor_function):
'Turn codewatch visitors into astroid-compatible transform functions\n\n codewatch visitors can make use of 3 args, the node, stats, and the\n relative file path you were visited for\n\n astroid transforms must take only the node\n\n By annotating the node with stats and relative file path, we can make our\n codewatch visitors compatible with astroid transform functions.\n '
@wraps(visitor_function)
def call_visitor(annotated_node, *args, **kwargs):
return visitor_function(annotated_node, annotated_node._codewatch.stats, annotated_node._codewatch.rel_file_path, *args, **kwargs)
return call_visitor | def _astroid_interface_for_visitor(visitor_function):
'Turn codewatch visitors into astroid-compatible transform functions\n\n codewatch visitors can make use of 3 args, the node, stats, and the\n relative file path you were visited for\n\n astroid transforms must take only the node\n\n By annotating the node with stats and relative file path, we can make our\n codewatch visitors compatible with astroid transform functions.\n '
@wraps(visitor_function)
def call_visitor(annotated_node, *args, **kwargs):
return visitor_function(annotated_node, annotated_node._codewatch.stats, annotated_node._codewatch.rel_file_path, *args, **kwargs)
return call_visitor<|docstring|>Turn codewatch visitors into astroid-compatible transform functions
codewatch visitors can make use of 3 args, the node, stats, and the
relative file path you were visited for
astroid transforms must take only the node
By annotating the node with stats and relative file path, we can make our
codewatch visitors compatible with astroid transform functions.<|endoftext|> |
35512da6b5b991b297cc54f739d2189e1c8d83e68a8e83aae139ccfb7143886c | def __init__(self, type=None, is_active=None, percentage=None, fixed_cost=None, field=None, projects=None, portfolio=None, valid_values=None, _configuration=None):
'Constraint - a model defined in Swagger'
if (_configuration is None):
_configuration = Configuration()
self._configuration = _configuration
self._type = None
self._is_active = None
self._percentage = None
self._fixed_cost = None
self._field = None
self._projects = None
self._portfolio = None
self._valid_values = None
self.discriminator = None
if (type is not None):
self.type = type
if (is_active is not None):
self.is_active = is_active
if (percentage is not None):
self.percentage = percentage
if (fixed_cost is not None):
self.fixed_cost = fixed_cost
if (field is not None):
self.field = field
if (projects is not None):
self.projects = projects
if (portfolio is not None):
self.portfolio = portfolio
if (valid_values is not None):
self.valid_values = valid_values | Constraint - a model defined in Swagger | python/dlxapi/models/constraint.py | __init__ | dlens/dlxapi | 0 | python | def __init__(self, type=None, is_active=None, percentage=None, fixed_cost=None, field=None, projects=None, portfolio=None, valid_values=None, _configuration=None):
if (_configuration is None):
_configuration = Configuration()
self._configuration = _configuration
self._type = None
self._is_active = None
self._percentage = None
self._fixed_cost = None
self._field = None
self._projects = None
self._portfolio = None
self._valid_values = None
self.discriminator = None
if (type is not None):
self.type = type
if (is_active is not None):
self.is_active = is_active
if (percentage is not None):
self.percentage = percentage
if (fixed_cost is not None):
self.fixed_cost = fixed_cost
if (field is not None):
self.field = field
if (projects is not None):
self.projects = projects
if (portfolio is not None):
self.portfolio = portfolio
if (valid_values is not None):
self.valid_values = valid_values | def __init__(self, type=None, is_active=None, percentage=None, fixed_cost=None, field=None, projects=None, portfolio=None, valid_values=None, _configuration=None):
if (_configuration is None):
_configuration = Configuration()
self._configuration = _configuration
self._type = None
self._is_active = None
self._percentage = None
self._fixed_cost = None
self._field = None
self._projects = None
self._portfolio = None
self._valid_values = None
self.discriminator = None
if (type is not None):
self.type = type
if (is_active is not None):
self.is_active = is_active
if (percentage is not None):
self.percentage = percentage
if (fixed_cost is not None):
self.fixed_cost = fixed_cost
if (field is not None):
self.field = field
if (projects is not None):
self.projects = projects
if (portfolio is not None):
self.portfolio = portfolio
if (valid_values is not None):
self.valid_values = valid_values<|docstring|>Constraint - a model defined in Swagger<|endoftext|> |
fc807628e93915f20cae05e4317c83b52fbf6ec812bcbcdea7004e2158311d62 | @property
def type(self):
'Gets the type of this Constraint. # noqa: E501\n\n\n :return: The type of this Constraint. # noqa: E501\n :rtype: ConstraintType\n '
return self._type | Gets the type of this Constraint. # noqa: E501
:return: The type of this Constraint. # noqa: E501
:rtype: ConstraintType | python/dlxapi/models/constraint.py | type | dlens/dlxapi | 0 | python | @property
def type(self):
'Gets the type of this Constraint. # noqa: E501\n\n\n :return: The type of this Constraint. # noqa: E501\n :rtype: ConstraintType\n '
return self._type | @property
def type(self):
'Gets the type of this Constraint. # noqa: E501\n\n\n :return: The type of this Constraint. # noqa: E501\n :rtype: ConstraintType\n '
return self._type<|docstring|>Gets the type of this Constraint. # noqa: E501
:return: The type of this Constraint. # noqa: E501
:rtype: ConstraintType<|endoftext|> |
9133ab96c666415eaaf3f3ee071f844352968f295f58fdc0caa2b2e3cdfc7715 | @type.setter
def type(self, type):
'Sets the type of this Constraint.\n\n\n :param type: The type of this Constraint. # noqa: E501\n :type: ConstraintType\n '
self._type = type | Sets the type of this Constraint.
:param type: The type of this Constraint. # noqa: E501
:type: ConstraintType | python/dlxapi/models/constraint.py | type | dlens/dlxapi | 0 | python | @type.setter
def type(self, type):
'Sets the type of this Constraint.\n\n\n :param type: The type of this Constraint. # noqa: E501\n :type: ConstraintType\n '
self._type = type | @type.setter
def type(self, type):
'Sets the type of this Constraint.\n\n\n :param type: The type of this Constraint. # noqa: E501\n :type: ConstraintType\n '
self._type = type<|docstring|>Sets the type of this Constraint.
:param type: The type of this Constraint. # noqa: E501
:type: ConstraintType<|endoftext|> |
fa2489278b76094bd36939f95741e13528ceb54c839bc8e21dc0b9306e7d43e1 | @property
def is_active(self):
'Gets the is_active of this Constraint. # noqa: E501\n\n\n :return: The is_active of this Constraint. # noqa: E501\n :rtype: bool\n '
return self._is_active | Gets the is_active of this Constraint. # noqa: E501
:return: The is_active of this Constraint. # noqa: E501
:rtype: bool | python/dlxapi/models/constraint.py | is_active | dlens/dlxapi | 0 | python | @property
def is_active(self):
'Gets the is_active of this Constraint. # noqa: E501\n\n\n :return: The is_active of this Constraint. # noqa: E501\n :rtype: bool\n '
return self._is_active | @property
def is_active(self):
'Gets the is_active of this Constraint. # noqa: E501\n\n\n :return: The is_active of this Constraint. # noqa: E501\n :rtype: bool\n '
return self._is_active<|docstring|>Gets the is_active of this Constraint. # noqa: E501
:return: The is_active of this Constraint. # noqa: E501
:rtype: bool<|endoftext|> |
06e5879e277b90abd0189af7574ed864b5ead9c140e7fd1cd27a9493b551eb60 | @is_active.setter
def is_active(self, is_active):
'Sets the is_active of this Constraint.\n\n\n :param is_active: The is_active of this Constraint. # noqa: E501\n :type: bool\n '
self._is_active = is_active | Sets the is_active of this Constraint.
:param is_active: The is_active of this Constraint. # noqa: E501
:type: bool | python/dlxapi/models/constraint.py | is_active | dlens/dlxapi | 0 | python | @is_active.setter
def is_active(self, is_active):
'Sets the is_active of this Constraint.\n\n\n :param is_active: The is_active of this Constraint. # noqa: E501\n :type: bool\n '
self._is_active = is_active | @is_active.setter
def is_active(self, is_active):
'Sets the is_active of this Constraint.\n\n\n :param is_active: The is_active of this Constraint. # noqa: E501\n :type: bool\n '
self._is_active = is_active<|docstring|>Sets the is_active of this Constraint.
:param is_active: The is_active of this Constraint. # noqa: E501
:type: bool<|endoftext|> |
84e71e6d4423f2083e04084512574609ec165c5265f30f2c5180473ff802c7b4 | @property
def percentage(self):
'Gets the percentage of this Constraint. # noqa: E501\n\n\n :return: The percentage of this Constraint. # noqa: E501\n :rtype: float\n '
return self._percentage | Gets the percentage of this Constraint. # noqa: E501
:return: The percentage of this Constraint. # noqa: E501
:rtype: float | python/dlxapi/models/constraint.py | percentage | dlens/dlxapi | 0 | python | @property
def percentage(self):
'Gets the percentage of this Constraint. # noqa: E501\n\n\n :return: The percentage of this Constraint. # noqa: E501\n :rtype: float\n '
return self._percentage | @property
def percentage(self):
'Gets the percentage of this Constraint. # noqa: E501\n\n\n :return: The percentage of this Constraint. # noqa: E501\n :rtype: float\n '
return self._percentage<|docstring|>Gets the percentage of this Constraint. # noqa: E501
:return: The percentage of this Constraint. # noqa: E501
:rtype: float<|endoftext|> |
6be4da2b3ccdc09c3e68e49af5956ee04ecc3b79a7a9b71a40a67ca91835c5b1 | @percentage.setter
def percentage(self, percentage):
'Sets the percentage of this Constraint.\n\n\n :param percentage: The percentage of this Constraint. # noqa: E501\n :type: float\n '
self._percentage = percentage | Sets the percentage of this Constraint.
:param percentage: The percentage of this Constraint. # noqa: E501
:type: float | python/dlxapi/models/constraint.py | percentage | dlens/dlxapi | 0 | python | @percentage.setter
def percentage(self, percentage):
'Sets the percentage of this Constraint.\n\n\n :param percentage: The percentage of this Constraint. # noqa: E501\n :type: float\n '
self._percentage = percentage | @percentage.setter
def percentage(self, percentage):
'Sets the percentage of this Constraint.\n\n\n :param percentage: The percentage of this Constraint. # noqa: E501\n :type: float\n '
self._percentage = percentage<|docstring|>Sets the percentage of this Constraint.
:param percentage: The percentage of this Constraint. # noqa: E501
:type: float<|endoftext|> |
43ebdf82c14f12601a76d8018c4aa78ddc5e84ce17ec1d353fc415a628287d12 | @property
def fixed_cost(self):
'Gets the fixed_cost of this Constraint. # noqa: E501\n\n\n :return: The fixed_cost of this Constraint. # noqa: E501\n :rtype: float\n '
return self._fixed_cost | Gets the fixed_cost of this Constraint. # noqa: E501
:return: The fixed_cost of this Constraint. # noqa: E501
:rtype: float | python/dlxapi/models/constraint.py | fixed_cost | dlens/dlxapi | 0 | python | @property
def fixed_cost(self):
'Gets the fixed_cost of this Constraint. # noqa: E501\n\n\n :return: The fixed_cost of this Constraint. # noqa: E501\n :rtype: float\n '
return self._fixed_cost | @property
def fixed_cost(self):
'Gets the fixed_cost of this Constraint. # noqa: E501\n\n\n :return: The fixed_cost of this Constraint. # noqa: E501\n :rtype: float\n '
return self._fixed_cost<|docstring|>Gets the fixed_cost of this Constraint. # noqa: E501
:return: The fixed_cost of this Constraint. # noqa: E501
:rtype: float<|endoftext|> |
74fb38c7a55112950132de3e40ddd27580d3f39caaaff052ad8c540491f546d5 | @fixed_cost.setter
def fixed_cost(self, fixed_cost):
'Sets the fixed_cost of this Constraint.\n\n\n :param fixed_cost: The fixed_cost of this Constraint. # noqa: E501\n :type: float\n '
self._fixed_cost = fixed_cost | Sets the fixed_cost of this Constraint.
:param fixed_cost: The fixed_cost of this Constraint. # noqa: E501
:type: float | python/dlxapi/models/constraint.py | fixed_cost | dlens/dlxapi | 0 | python | @fixed_cost.setter
def fixed_cost(self, fixed_cost):
'Sets the fixed_cost of this Constraint.\n\n\n :param fixed_cost: The fixed_cost of this Constraint. # noqa: E501\n :type: float\n '
self._fixed_cost = fixed_cost | @fixed_cost.setter
def fixed_cost(self, fixed_cost):
'Sets the fixed_cost of this Constraint.\n\n\n :param fixed_cost: The fixed_cost of this Constraint. # noqa: E501\n :type: float\n '
self._fixed_cost = fixed_cost<|docstring|>Sets the fixed_cost of this Constraint.
:param fixed_cost: The fixed_cost of this Constraint. # noqa: E501
:type: float<|endoftext|> |
ffe86f296d59c908803c4f6c6f1b10464ac407c7a6f37f38f654905e8385ac0d | @property
def field(self):
'Gets the field of this Constraint. # noqa: E501\n\n\n :return: The field of this Constraint. # noqa: E501\n :rtype: Field\n '
return self._field | Gets the field of this Constraint. # noqa: E501
:return: The field of this Constraint. # noqa: E501
:rtype: Field | python/dlxapi/models/constraint.py | field | dlens/dlxapi | 0 | python | @property
def field(self):
'Gets the field of this Constraint. # noqa: E501\n\n\n :return: The field of this Constraint. # noqa: E501\n :rtype: Field\n '
return self._field | @property
def field(self):
'Gets the field of this Constraint. # noqa: E501\n\n\n :return: The field of this Constraint. # noqa: E501\n :rtype: Field\n '
return self._field<|docstring|>Gets the field of this Constraint. # noqa: E501
:return: The field of this Constraint. # noqa: E501
:rtype: Field<|endoftext|> |
452f22a911818c19f61ae2b852d8a50f18fc4ac9c07cf22825bf32fb77354cee | @field.setter
def field(self, field):
'Sets the field of this Constraint.\n\n\n :param field: The field of this Constraint. # noqa: E501\n :type: Field\n '
self._field = field | Sets the field of this Constraint.
:param field: The field of this Constraint. # noqa: E501
:type: Field | python/dlxapi/models/constraint.py | field | dlens/dlxapi | 0 | python | @field.setter
def field(self, field):
'Sets the field of this Constraint.\n\n\n :param field: The field of this Constraint. # noqa: E501\n :type: Field\n '
self._field = field | @field.setter
def field(self, field):
'Sets the field of this Constraint.\n\n\n :param field: The field of this Constraint. # noqa: E501\n :type: Field\n '
self._field = field<|docstring|>Sets the field of this Constraint.
:param field: The field of this Constraint. # noqa: E501
:type: Field<|endoftext|> |
6efe63fafc41c16a29d1858364af93ce26dd9bc029b6c6c876d565d4b1de7435 | @property
def projects(self):
'Gets the projects of this Constraint. # noqa: E501\n\n\n :return: The projects of this Constraint. # noqa: E501\n :rtype: Projects\n '
return self._projects | Gets the projects of this Constraint. # noqa: E501
:return: The projects of this Constraint. # noqa: E501
:rtype: Projects | python/dlxapi/models/constraint.py | projects | dlens/dlxapi | 0 | python | @property
def projects(self):
'Gets the projects of this Constraint. # noqa: E501\n\n\n :return: The projects of this Constraint. # noqa: E501\n :rtype: Projects\n '
return self._projects | @property
def projects(self):
'Gets the projects of this Constraint. # noqa: E501\n\n\n :return: The projects of this Constraint. # noqa: E501\n :rtype: Projects\n '
return self._projects<|docstring|>Gets the projects of this Constraint. # noqa: E501
:return: The projects of this Constraint. # noqa: E501
:rtype: Projects<|endoftext|> |
a110b31b915c6ed70eb6de1056824c8266262a40c4a98944317abb48ccac6fda | @projects.setter
def projects(self, projects):
'Sets the projects of this Constraint.\n\n\n :param projects: The projects of this Constraint. # noqa: E501\n :type: Projects\n '
self._projects = projects | Sets the projects of this Constraint.
:param projects: The projects of this Constraint. # noqa: E501
:type: Projects | python/dlxapi/models/constraint.py | projects | dlens/dlxapi | 0 | python | @projects.setter
def projects(self, projects):
'Sets the projects of this Constraint.\n\n\n :param projects: The projects of this Constraint. # noqa: E501\n :type: Projects\n '
self._projects = projects | @projects.setter
def projects(self, projects):
'Sets the projects of this Constraint.\n\n\n :param projects: The projects of this Constraint. # noqa: E501\n :type: Projects\n '
self._projects = projects<|docstring|>Sets the projects of this Constraint.
:param projects: The projects of this Constraint. # noqa: E501
:type: Projects<|endoftext|> |
13b2128957c2283404a0a5f7ba60d91782c9e7fdf1c1cb816b0fb0c10efc9551 | @property
def portfolio(self):
'Gets the portfolio of this Constraint. # noqa: E501\n\n\n :return: The portfolio of this Constraint. # noqa: E501\n :rtype: Portfolio\n '
return self._portfolio | Gets the portfolio of this Constraint. # noqa: E501
:return: The portfolio of this Constraint. # noqa: E501
:rtype: Portfolio | python/dlxapi/models/constraint.py | portfolio | dlens/dlxapi | 0 | python | @property
def portfolio(self):
'Gets the portfolio of this Constraint. # noqa: E501\n\n\n :return: The portfolio of this Constraint. # noqa: E501\n :rtype: Portfolio\n '
return self._portfolio | @property
def portfolio(self):
'Gets the portfolio of this Constraint. # noqa: E501\n\n\n :return: The portfolio of this Constraint. # noqa: E501\n :rtype: Portfolio\n '
return self._portfolio<|docstring|>Gets the portfolio of this Constraint. # noqa: E501
:return: The portfolio of this Constraint. # noqa: E501
:rtype: Portfolio<|endoftext|> |
0c0691230bb3ec87226e45039c7f66e4d0463b0764140081d96dbc1dcccb927f | @portfolio.setter
def portfolio(self, portfolio):
'Sets the portfolio of this Constraint.\n\n\n :param portfolio: The portfolio of this Constraint. # noqa: E501\n :type: Portfolio\n '
self._portfolio = portfolio | Sets the portfolio of this Constraint.
:param portfolio: The portfolio of this Constraint. # noqa: E501
:type: Portfolio | python/dlxapi/models/constraint.py | portfolio | dlens/dlxapi | 0 | python | @portfolio.setter
def portfolio(self, portfolio):
'Sets the portfolio of this Constraint.\n\n\n :param portfolio: The portfolio of this Constraint. # noqa: E501\n :type: Portfolio\n '
self._portfolio = portfolio | @portfolio.setter
def portfolio(self, portfolio):
'Sets the portfolio of this Constraint.\n\n\n :param portfolio: The portfolio of this Constraint. # noqa: E501\n :type: Portfolio\n '
self._portfolio = portfolio<|docstring|>Sets the portfolio of this Constraint.
:param portfolio: The portfolio of this Constraint. # noqa: E501
:type: Portfolio<|endoftext|> |
1ba93649db21123f3ae18e33f1da8b610196240903d8faca241fdf277e946d72 | @property
def valid_values(self):
'Gets the valid_values of this Constraint. # noqa: E501\n\n\n :return: The valid_values of this Constraint. # noqa: E501\n :rtype: list[ValidConstraintValue]\n '
return self._valid_values | Gets the valid_values of this Constraint. # noqa: E501
:return: The valid_values of this Constraint. # noqa: E501
:rtype: list[ValidConstraintValue] | python/dlxapi/models/constraint.py | valid_values | dlens/dlxapi | 0 | python | @property
def valid_values(self):
'Gets the valid_values of this Constraint. # noqa: E501\n\n\n :return: The valid_values of this Constraint. # noqa: E501\n :rtype: list[ValidConstraintValue]\n '
return self._valid_values | @property
def valid_values(self):
'Gets the valid_values of this Constraint. # noqa: E501\n\n\n :return: The valid_values of this Constraint. # noqa: E501\n :rtype: list[ValidConstraintValue]\n '
return self._valid_values<|docstring|>Gets the valid_values of this Constraint. # noqa: E501
:return: The valid_values of this Constraint. # noqa: E501
:rtype: list[ValidConstraintValue]<|endoftext|> |
21b62786d7e3fb8f1f3ce841d3f729bd64f50556d05bc77e5945bebc0a2028fc | @valid_values.setter
def valid_values(self, valid_values):
'Sets the valid_values of this Constraint.\n\n\n :param valid_values: The valid_values of this Constraint. # noqa: E501\n :type: list[ValidConstraintValue]\n '
self._valid_values = valid_values | Sets the valid_values of this Constraint.
:param valid_values: The valid_values of this Constraint. # noqa: E501
:type: list[ValidConstraintValue] | python/dlxapi/models/constraint.py | valid_values | dlens/dlxapi | 0 | python | @valid_values.setter
def valid_values(self, valid_values):
'Sets the valid_values of this Constraint.\n\n\n :param valid_values: The valid_values of this Constraint. # noqa: E501\n :type: list[ValidConstraintValue]\n '
self._valid_values = valid_values | @valid_values.setter
def valid_values(self, valid_values):
'Sets the valid_values of this Constraint.\n\n\n :param valid_values: The valid_values of this Constraint. # noqa: E501\n :type: list[ValidConstraintValue]\n '
self._valid_values = valid_values<|docstring|>Sets the valid_values of this Constraint.
:param valid_values: The valid_values of this Constraint. # noqa: E501
:type: list[ValidConstraintValue]<|endoftext|> |
c01b5d5247cf6cce880ba7f715edc961da5cf04c0d485b9a39ba333b391d1da7 | def to_dict(self):
'Returns the model properties as a dict'
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(Constraint, dict):
for (key, value) in self.items():
result[key] = value
return result | Returns the model properties as a dict | python/dlxapi/models/constraint.py | to_dict | dlens/dlxapi | 0 | python | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(Constraint, dict):
for (key, value) in self.items():
result[key] = value
return result | def to_dict(self):
result = {}
for (attr, _) in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map((lambda x: (x.to_dict() if hasattr(x, 'to_dict') else x)), value))
elif hasattr(value, 'to_dict'):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map((lambda item: ((item[0], item[1].to_dict()) if hasattr(item[1], 'to_dict') else item)), value.items()))
else:
result[attr] = value
if issubclass(Constraint, dict):
for (key, value) in self.items():
result[key] = value
return result<|docstring|>Returns the model properties as a dict<|endoftext|> |
cbb19eaa2fc8a113d9e32f924ef280a7e97563f8915f94f65dab438997af2e99 | def to_str(self):
'Returns the string representation of the model'
return pprint.pformat(self.to_dict()) | Returns the string representation of the model | python/dlxapi/models/constraint.py | to_str | dlens/dlxapi | 0 | python | def to_str(self):
return pprint.pformat(self.to_dict()) | def to_str(self):
return pprint.pformat(self.to_dict())<|docstring|>Returns the string representation of the model<|endoftext|> |
772243a2c2b3261a9b954d07aaf295e3c1242a579a495e2d6a5679c677861703 | def __repr__(self):
'For `print` and `pprint`'
return self.to_str() | For `print` and `pprint` | python/dlxapi/models/constraint.py | __repr__ | dlens/dlxapi | 0 | python | def __repr__(self):
return self.to_str() | def __repr__(self):
return self.to_str()<|docstring|>For `print` and `pprint`<|endoftext|> |
c76e1b60ca72c2286bd4f6c997206257a140b9c7e2561a29b704fdc25747898f | def __eq__(self, other):
'Returns true if both objects are equal'
if (not isinstance(other, Constraint)):
return False
return (self.to_dict() == other.to_dict()) | Returns true if both objects are equal | python/dlxapi/models/constraint.py | __eq__ | dlens/dlxapi | 0 | python | def __eq__(self, other):
if (not isinstance(other, Constraint)):
return False
return (self.to_dict() == other.to_dict()) | def __eq__(self, other):
if (not isinstance(other, Constraint)):
return False
return (self.to_dict() == other.to_dict())<|docstring|>Returns true if both objects are equal<|endoftext|> |
779b09800607cc29a60dc6a61f6a4267875a6bf47b755c2afb89b4abe12b628e | def __ne__(self, other):
'Returns true if both objects are not equal'
if (not isinstance(other, Constraint)):
return True
return (self.to_dict() != other.to_dict()) | Returns true if both objects are not equal | python/dlxapi/models/constraint.py | __ne__ | dlens/dlxapi | 0 | python | def __ne__(self, other):
if (not isinstance(other, Constraint)):
return True
return (self.to_dict() != other.to_dict()) | def __ne__(self, other):
if (not isinstance(other, Constraint)):
return True
return (self.to_dict() != other.to_dict())<|docstring|>Returns true if both objects are not equal<|endoftext|> |
11d719f9fca2ac8a775c85ef6cde28ca44820d7db0919b49b3bac0f9e62fdded | def repeat(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices) | Timing utility for measuring time spent by both CPU and GPU.
This function is a very convenient helper for setting up a timing test. The
GPU time is properly recorded by synchronizing internal streams. As a
result, to time a multi-GPU function all participating devices must be
passed as the ``devices`` argument so that this helper knows which devices
to record. A simple example is given as follows:
.. code-block:: py
import cupy as cp
from cupyx.time import repeat
def f(a, b):
return 3 * cp.sin(-a) * b
a = 0.5 - cp.random.random((100,))
b = cp.random.random((100,))
print(repeat(f, (a, b), n_repeat=1000))
Args:
func (callable): a callable object to be timed.
args (tuple): positional argumens to be passed to the callable.
kwargs (dict): keyword arguments to be passed to the callable.
n_repeat (int): number of times the callable is called. Increasing
this value would improve the collected statistics at the cost
of longer test time.
name (str): the function name to be reported. If not given, the
callable's ``__name__`` attribute is used.
n_warmup (int): number of times the callable is called. The warm-up
runs are not timed.
max_duration (float): the maximum time (in seconds) that the entire
test can use. If the taken time is longer than this limit, the test
is stopped and the statistics collected up to the breakpoint is
reported.
devices (tuple): a tuple of device IDs (int) that will be timed during
the timing test. If not given, the current device is used.
Returns:
:class:`_PerfCaseResult`: an object collecting all test results.
.. warning::
This API is currently experimental and subject to change in future
releases. | demo/_time_gpu.py | repeat | grlee77/uskimage-demo | 0 | python | def repeat(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices) | def repeat(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices)<|docstring|>Timing utility for measuring time spent by both CPU and GPU.
This function is a very convenient helper for setting up a timing test. The
GPU time is properly recorded by synchronizing internal streams. As a
result, to time a multi-GPU function all participating devices must be
passed as the ``devices`` argument so that this helper knows which devices
to record. A simple example is given as follows:
.. code-block:: py
import cupy as cp
from cupyx.time import repeat
def f(a, b):
return 3 * cp.sin(-a) * b
a = 0.5 - cp.random.random((100,))
b = cp.random.random((100,))
print(repeat(f, (a, b), n_repeat=1000))
Args:
func (callable): a callable object to be timed.
args (tuple): positional argumens to be passed to the callable.
kwargs (dict): keyword arguments to be passed to the callable.
n_repeat (int): number of times the callable is called. Increasing
this value would improve the collected statistics at the cost
of longer test time.
name (str): the function name to be reported. If not given, the
callable's ``__name__`` attribute is used.
n_warmup (int): number of times the callable is called. The warm-up
runs are not timed.
max_duration (float): the maximum time (in seconds) that the entire
test can use. If the taken time is longer than this limit, the test
is stopped and the statistics collected up to the breakpoint is
reported.
devices (tuple): a tuple of device IDs (int) that will be timed during
the timing test. If not given, the current device is used.
Returns:
:class:`_PerfCaseResult`: an object collecting all test results.
.. warning::
This API is currently experimental and subject to change in future
releases.<|endoftext|> |
3a514804a8ae59a3c3566f458afb1107d8df247ee1e2069bd7bc1ab4e8a6796c | def repeat_dask(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat_dask(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices) | Timing utility for measuring time spent by both CPU and GPU.
This function is a very convenient helper for setting up a timing test. The
GPU time is properly recorded by synchronizing internal streams. As a
result, to time a multi-GPU function all participating devices must be
passed as the ``devices`` argument so that this helper knows which devices
to record. A simple example is given as follows:
.. code-block:: py
import cupy as cp
from cupyx.time import repeat
def f(a, b):
return 3 * cp.sin(-a) * b
a = 0.5 - cp.random.random((100,))
b = cp.random.random((100,))
print(repeat(f, (a, b), n_repeat=1000))
Args:
func (callable): a callable object to be timed.
args (tuple): positional argumens to be passed to the callable.
kwargs (dict): keyword arguments to be passed to the callable.
n_repeat (int): number of times the callable is called. Increasing
this value would improve the collected statistics at the cost
of longer test time.
name (str): the function name to be reported. If not given, the
callable's ``__name__`` attribute is used.
n_warmup (int): number of times the callable is called. The warm-up
runs are not timed.
max_duration (float): the maximum time (in seconds) that the entire
test can use. If the taken time is longer than this limit, the test
is stopped and the statistics collected up to the breakpoint is
reported.
devices (tuple): a tuple of device IDs (int) that will be timed during
the timing test. If not given, the current device is used.
Returns:
:class:`_PerfCaseResult`: an object collecting all test results.
.. warning::
This API is currently experimental and subject to change in future
releases. | demo/_time_gpu.py | repeat_dask | grlee77/uskimage-demo | 0 | python | def repeat_dask(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat_dask(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices) | def repeat_dask(func, args=(), kwargs={}, n_repeat=10000, *, name=None, n_warmup=10, max_duration=_math.inf, devices=None):
" Timing utility for measuring time spent by both CPU and GPU.\n\n This function is a very convenient helper for setting up a timing test. The\n GPU time is properly recorded by synchronizing internal streams. As a\n result, to time a multi-GPU function all participating devices must be\n passed as the ``devices`` argument so that this helper knows which devices\n to record. A simple example is given as follows:\n\n .. code-block:: py\n\n import cupy as cp\n from cupyx.time import repeat\n\n def f(a, b):\n return 3 * cp.sin(-a) * b\n\n a = 0.5 - cp.random.random((100,))\n b = cp.random.random((100,))\n print(repeat(f, (a, b), n_repeat=1000))\n\n\n Args:\n func (callable): a callable object to be timed.\n args (tuple): positional argumens to be passed to the callable.\n kwargs (dict): keyword arguments to be passed to the callable.\n n_repeat (int): number of times the callable is called. Increasing\n this value would improve the collected statistics at the cost\n of longer test time.\n name (str): the function name to be reported. If not given, the\n callable's ``__name__`` attribute is used.\n n_warmup (int): number of times the callable is called. The warm-up\n runs are not timed.\n max_duration (float): the maximum time (in seconds) that the entire\n test can use. If the taken time is longer than this limit, the test\n is stopped and the statistics collected up to the breakpoint is\n reported.\n devices (tuple): a tuple of device IDs (int) that will be timed during\n the timing test. If not given, the current device is used.\n\n Returns:\n :class:`_PerfCaseResult`: an object collecting all test results.\n\n .. warning::\n This API is currently experimental and subject to change in future\n releases.\n\n "
if (name is None):
name = func.__name__
if (devices is None):
devices = (_cupy.cuda.get_device_id(),)
if (not callable(func)):
raise ValueError('`func` should be a callable object.')
if (not isinstance(args, tuple)):
raise ValueError('`args` should be of tuple type.')
if (not isinstance(kwargs, dict)):
raise ValueError('`kwargs` should be of dict type.')
if (not isinstance(n_repeat, int)):
raise ValueError('`n_repeat` should be an integer.')
if (not isinstance(name, str)):
raise ValueError('`name` should be a string.')
if (not isinstance(n_warmup, int)):
raise ValueError('`n_warmup` should be an integer.')
if (not _numpy.isreal(max_duration)):
raise ValueError('`max_duration` should be given in seconds')
if (not isinstance(devices, tuple)):
raise ValueError('`devices` should be of tuple type')
return _repeat_dask(func, args, kwargs, n_repeat, name, n_warmup, max_duration, devices)<|docstring|>Timing utility for measuring time spent by both CPU and GPU.
This function is a very convenient helper for setting up a timing test. The
GPU time is properly recorded by synchronizing internal streams. As a
result, to time a multi-GPU function all participating devices must be
passed as the ``devices`` argument so that this helper knows which devices
to record. A simple example is given as follows:
.. code-block:: py
import cupy as cp
from cupyx.time import repeat
def f(a, b):
return 3 * cp.sin(-a) * b
a = 0.5 - cp.random.random((100,))
b = cp.random.random((100,))
print(repeat(f, (a, b), n_repeat=1000))
Args:
func (callable): a callable object to be timed.
args (tuple): positional argumens to be passed to the callable.
kwargs (dict): keyword arguments to be passed to the callable.
n_repeat (int): number of times the callable is called. Increasing
this value would improve the collected statistics at the cost
of longer test time.
name (str): the function name to be reported. If not given, the
callable's ``__name__`` attribute is used.
n_warmup (int): number of times the callable is called. The warm-up
runs are not timed.
max_duration (float): the maximum time (in seconds) that the entire
test can use. If the taken time is longer than this limit, the test
is stopped and the statistics collected up to the breakpoint is
reported.
devices (tuple): a tuple of device IDs (int) that will be timed during
the timing test. If not given, the current device is used.
Returns:
:class:`_PerfCaseResult`: an object collecting all test results.
.. warning::
This API is currently experimental and subject to change in future
releases.<|endoftext|> |
d12cebf3f46a863a2376eeb58f6bbee0119c84c82d6ace347911985249553cc0 | @property
def cpu_times(self):
' Returns an array of CPU times of size ``n_repeat``. '
return self._ts[0] | Returns an array of CPU times of size ``n_repeat``. | demo/_time_gpu.py | cpu_times | grlee77/uskimage-demo | 0 | python | @property
def cpu_times(self):
' '
return self._ts[0] | @property
def cpu_times(self):
' '
return self._ts[0]<|docstring|>Returns an array of CPU times of size ``n_repeat``.<|endoftext|> |
635d2531ed6622d7a6ec5b6531c7d5a1be431687dac47522fdb21c1a0d6ca47e | @property
def gpu_times(self):
' Returns an array of GPU times of size ``n_repeat``. '
return self._ts[1:] | Returns an array of GPU times of size ``n_repeat``. | demo/_time_gpu.py | gpu_times | grlee77/uskimage-demo | 0 | python | @property
def gpu_times(self):
' '
return self._ts[1:] | @property
def gpu_times(self):
' '
return self._ts[1:]<|docstring|>Returns an array of GPU times of size ``n_repeat``.<|endoftext|> |
7fe0425551e51f12beee55e0897829ef58cab9f85df2128a737b7cd2ffd52f78 | def infix_to_postfix(infix_expr):
'\n 中缀表达式 -> 后缀表达式\n :param infix_expr: 这里的中缀表达式是一个由空格分隔的标记字符串\n '
prec = {}
prec['*'] = 3
prec['/'] = 3
prec['+'] = 2
prec['-'] = 2
prec['('] = 1
tokens = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
op_stack = Stack()
postfix_list = []
infix_list = infix_expr.split()
for token in infix_list:
if (token in tokens):
postfix_list.append(token)
elif (token == '('):
op_stack.push(token)
elif (token == ')'):
top_token = op_stack.pop()
while (top_token != '('):
postfix_list.append(top_token)
top_token = op_stack.pop()
else:
while ((not op_stack.is_empty()) and (prec[op_stack.peek()] >= prec[token])):
postfix_list.append(op_stack.pop())
op_stack.push(token)
while (not op_stack.is_empty()):
postfix_list.append(op_stack.pop())
return ''.join(postfix_list) | 中缀表达式 -> 后缀表达式
:param infix_expr: 这里的中缀表达式是一个由空格分隔的标记字符串 | chapter_3/py_3_9_postfix_expressions.py | infix_to_postfix | kfrime/algo-in-python | 0 | python | def infix_to_postfix(infix_expr):
'\n 中缀表达式 -> 后缀表达式\n :param infix_expr: 这里的中缀表达式是一个由空格分隔的标记字符串\n '
prec = {}
prec['*'] = 3
prec['/'] = 3
prec['+'] = 2
prec['-'] = 2
prec['('] = 1
tokens = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
op_stack = Stack()
postfix_list = []
infix_list = infix_expr.split()
for token in infix_list:
if (token in tokens):
postfix_list.append(token)
elif (token == '('):
op_stack.push(token)
elif (token == ')'):
top_token = op_stack.pop()
while (top_token != '('):
postfix_list.append(top_token)
top_token = op_stack.pop()
else:
while ((not op_stack.is_empty()) and (prec[op_stack.peek()] >= prec[token])):
postfix_list.append(op_stack.pop())
op_stack.push(token)
while (not op_stack.is_empty()):
postfix_list.append(op_stack.pop())
return .join(postfix_list) | def infix_to_postfix(infix_expr):
'\n 中缀表达式 -> 后缀表达式\n :param infix_expr: 这里的中缀表达式是一个由空格分隔的标记字符串\n '
prec = {}
prec['*'] = 3
prec['/'] = 3
prec['+'] = 2
prec['-'] = 2
prec['('] = 1
tokens = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
op_stack = Stack()
postfix_list = []
infix_list = infix_expr.split()
for token in infix_list:
if (token in tokens):
postfix_list.append(token)
elif (token == '('):
op_stack.push(token)
elif (token == ')'):
top_token = op_stack.pop()
while (top_token != '('):
postfix_list.append(top_token)
top_token = op_stack.pop()
else:
while ((not op_stack.is_empty()) and (prec[op_stack.peek()] >= prec[token])):
postfix_list.append(op_stack.pop())
op_stack.push(token)
while (not op_stack.is_empty()):
postfix_list.append(op_stack.pop())
return .join(postfix_list)<|docstring|>中缀表达式 -> 后缀表达式
:param infix_expr: 这里的中缀表达式是一个由空格分隔的标记字符串<|endoftext|> |
af33f857f39b994cda6cfc1460e26eecd283dbe86ac04c831a98a4b11f7f1659 | def do_math(op, op1, op2):
'\n 执行数学运算\n :param op: 操作符\n :param op1: 操作数1\n :param op2: 操作数2\n '
if (op == '+'):
return (op1 + op2)
elif (op == '-'):
return (op1 - op2)
elif (op == '*'):
return (op1 * op2)
elif (op == '/'):
return (op1 / op2) | 执行数学运算
:param op: 操作符
:param op1: 操作数1
:param op2: 操作数2 | chapter_3/py_3_9_postfix_expressions.py | do_math | kfrime/algo-in-python | 0 | python | def do_math(op, op1, op2):
'\n 执行数学运算\n :param op: 操作符\n :param op1: 操作数1\n :param op2: 操作数2\n '
if (op == '+'):
return (op1 + op2)
elif (op == '-'):
return (op1 - op2)
elif (op == '*'):
return (op1 * op2)
elif (op == '/'):
return (op1 / op2) | def do_math(op, op1, op2):
'\n 执行数学运算\n :param op: 操作符\n :param op1: 操作数1\n :param op2: 操作数2\n '
if (op == '+'):
return (op1 + op2)
elif (op == '-'):
return (op1 - op2)
elif (op == '*'):
return (op1 * op2)
elif (op == '/'):
return (op1 / op2)<|docstring|>执行数学运算
:param op: 操作符
:param op1: 操作数1
:param op2: 操作数2<|endoftext|> |
e9f1478b095c8a80b40462cd4fd62dffd091ff5fb9e28ca1fe5ba4e2adf4ab4d | def postfix_eval(postfix_expr):
'\n 后缀表达式求值\n\n 如果 token 是操作数，将其从字符串转换为整数，并将值压到operandStack。\n\n 如果 token 是运算符*，/，+或-，它将需要两个操作数。弹出operandStack 两次。\n 第一个弹出的是第二个操作数，第二个弹出的是第一个操作数。执行算术运算后，\n 将结果压到操作数栈中。\n\n :param postfix_expr: 这里的后缀表达式是一个由空格分隔的标记(token)字符串\n '
operand_stack = Stack()
token_list = postfix_expr.split()
for token in token_list:
if (token in '0123456789'):
operand_stack.push(int(token))
else:
op2 = operand_stack.pop()
op1 = operand_stack.pop()
result = do_math(token, op1, op2)
operand_stack.push(result)
return operand_stack.pop() | 后缀表达式求值
如果 token 是操作数，将其从字符串转换为整数，并将值压到operandStack。
如果 token 是运算符*，/，+或-，它将需要两个操作数。弹出operandStack 两次。
第一个弹出的是第二个操作数，第二个弹出的是第一个操作数。执行算术运算后，
将结果压到操作数栈中。
:param postfix_expr: 这里的后缀表达式是一个由空格分隔的标记(token)字符串 | chapter_3/py_3_9_postfix_expressions.py | postfix_eval | kfrime/algo-in-python | 0 | python | def postfix_eval(postfix_expr):
'\n 后缀表达式求值\n\n 如果 token 是操作数，将其从字符串转换为整数，并将值压到operandStack。\n\n 如果 token 是运算符*，/，+或-，它将需要两个操作数。弹出operandStack 两次。\n 第一个弹出的是第二个操作数，第二个弹出的是第一个操作数。执行算术运算后，\n 将结果压到操作数栈中。\n\n :param postfix_expr: 这里的后缀表达式是一个由空格分隔的标记(token)字符串\n '
operand_stack = Stack()
token_list = postfix_expr.split()
for token in token_list:
if (token in '0123456789'):
operand_stack.push(int(token))
else:
op2 = operand_stack.pop()
op1 = operand_stack.pop()
result = do_math(token, op1, op2)
operand_stack.push(result)
return operand_stack.pop() | def postfix_eval(postfix_expr):
'\n 后缀表达式求值\n\n 如果 token 是操作数，将其从字符串转换为整数，并将值压到operandStack。\n\n 如果 token 是运算符*，/，+或-，它将需要两个操作数。弹出operandStack 两次。\n 第一个弹出的是第二个操作数，第二个弹出的是第一个操作数。执行算术运算后，\n 将结果压到操作数栈中。\n\n :param postfix_expr: 这里的后缀表达式是一个由空格分隔的标记(token)字符串\n '
operand_stack = Stack()
token_list = postfix_expr.split()
for token in token_list:
if (token in '0123456789'):
operand_stack.push(int(token))
else:
op2 = operand_stack.pop()
op1 = operand_stack.pop()
result = do_math(token, op1, op2)
operand_stack.push(result)
return operand_stack.pop()<|docstring|>后缀表达式求值
如果 token 是操作数，将其从字符串转换为整数，并将值压到operandStack。
如果 token 是运算符*，/，+或-，它将需要两个操作数。弹出operandStack 两次。
第一个弹出的是第二个操作数，第二个弹出的是第一个操作数。执行算术运算后，
将结果压到操作数栈中。
:param postfix_expr: 这里的后缀表达式是一个由空格分隔的标记(token)字符串<|endoftext|> |
2d02ef1e499ab61e92b078dcead5c0d1fdb4eb576d734ea89375ecd1bf4b3847 | def get_regnet(channels_init, channels_slope, channels_mult, depth, groups, use_se=False, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
"\n Create RegNet model with specific parameters.\n\n Parameters:\n ----------\n channels_init : float\n Initial value for channels/widths.\n channels_slope : float\n Slope value for channels/widths.\n width_mult : float\n Width multiplier value.\n groups : int\n Number of groups.\n depth : int\n Depth value.\n use_se : bool, default False\n Whether to use SE-module.\n model_name : str or None, default None\n Model name for loading pretrained model.\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
divisor = 8
assert ((channels_slope >= 0) and (channels_init > 0) and (channels_mult > 1) and ((channels_init % divisor) == 0))
channels_cont = ((np.arange(depth) * channels_slope) + channels_init)
channels_exps = np.round((np.log((channels_cont / channels_init)) / np.log(channels_mult)))
channels = (channels_init * np.power(channels_mult, channels_exps))
channels = (np.round((channels / divisor)) * divisor).astype(np.int)
(channels_per_stage, layers) = np.unique(channels, return_counts=True)
groups_per_stage = [min(groups, c) for c in channels_per_stage]
channels_per_stage = [int((round((c / g)) * g)) for (c, g) in zip(channels_per_stage, groups_per_stage)]
channels = [([ci] * li) for (ci, li) in zip(channels_per_stage, layers)]
init_block_channels = 32
net = RegNet(channels=channels, init_block_channels=init_block_channels, groups=groups_per_stage, use_se=use_se, **kwargs)
if pretrained:
if ((model_name is None) or (not model_name)):
raise ValueError('Parameter `model_name` should be properly initialized for loading pretrained model.')
from .model_store import download_model
download_model(net=net, model_name=model_name, local_model_store_dir_path=root)
return net | Create RegNet model with specific parameters.
Parameters:
----------
channels_init : float
Initial value for channels/widths.
channels_slope : float
Slope value for channels/widths.
width_mult : float
Width multiplier value.
groups : int
Number of groups.
depth : int
Depth value.
use_se : bool, default False
Whether to use SE-module.
model_name : str or None, default None
Model name for loading pretrained model.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | get_regnet | JacobARose/imgclsmob | 2,649 | python | def get_regnet(channels_init, channels_slope, channels_mult, depth, groups, use_se=False, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
"\n Create RegNet model with specific parameters.\n\n Parameters:\n ----------\n channels_init : float\n Initial value for channels/widths.\n channels_slope : float\n Slope value for channels/widths.\n width_mult : float\n Width multiplier value.\n groups : int\n Number of groups.\n depth : int\n Depth value.\n use_se : bool, default False\n Whether to use SE-module.\n model_name : str or None, default None\n Model name for loading pretrained model.\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
divisor = 8
assert ((channels_slope >= 0) and (channels_init > 0) and (channels_mult > 1) and ((channels_init % divisor) == 0))
channels_cont = ((np.arange(depth) * channels_slope) + channels_init)
channels_exps = np.round((np.log((channels_cont / channels_init)) / np.log(channels_mult)))
channels = (channels_init * np.power(channels_mult, channels_exps))
channels = (np.round((channels / divisor)) * divisor).astype(np.int)
(channels_per_stage, layers) = np.unique(channels, return_counts=True)
groups_per_stage = [min(groups, c) for c in channels_per_stage]
channels_per_stage = [int((round((c / g)) * g)) for (c, g) in zip(channels_per_stage, groups_per_stage)]
channels = [([ci] * li) for (ci, li) in zip(channels_per_stage, layers)]
init_block_channels = 32
net = RegNet(channels=channels, init_block_channels=init_block_channels, groups=groups_per_stage, use_se=use_se, **kwargs)
if pretrained:
if ((model_name is None) or (not model_name)):
raise ValueError('Parameter `model_name` should be properly initialized for loading pretrained model.')
from .model_store import download_model
download_model(net=net, model_name=model_name, local_model_store_dir_path=root)
return net | def get_regnet(channels_init, channels_slope, channels_mult, depth, groups, use_se=False, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
"\n Create RegNet model with specific parameters.\n\n Parameters:\n ----------\n channels_init : float\n Initial value for channels/widths.\n channels_slope : float\n Slope value for channels/widths.\n width_mult : float\n Width multiplier value.\n groups : int\n Number of groups.\n depth : int\n Depth value.\n use_se : bool, default False\n Whether to use SE-module.\n model_name : str or None, default None\n Model name for loading pretrained model.\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
divisor = 8
assert ((channels_slope >= 0) and (channels_init > 0) and (channels_mult > 1) and ((channels_init % divisor) == 0))
channels_cont = ((np.arange(depth) * channels_slope) + channels_init)
channels_exps = np.round((np.log((channels_cont / channels_init)) / np.log(channels_mult)))
channels = (channels_init * np.power(channels_mult, channels_exps))
channels = (np.round((channels / divisor)) * divisor).astype(np.int)
(channels_per_stage, layers) = np.unique(channels, return_counts=True)
groups_per_stage = [min(groups, c) for c in channels_per_stage]
channels_per_stage = [int((round((c / g)) * g)) for (c, g) in zip(channels_per_stage, groups_per_stage)]
channels = [([ci] * li) for (ci, li) in zip(channels_per_stage, layers)]
init_block_channels = 32
net = RegNet(channels=channels, init_block_channels=init_block_channels, groups=groups_per_stage, use_se=use_se, **kwargs)
if pretrained:
if ((model_name is None) or (not model_name)):
raise ValueError('Parameter `model_name` should be properly initialized for loading pretrained model.')
from .model_store import download_model
download_model(net=net, model_name=model_name, local_model_store_dir_path=root)
return net<|docstring|>Create RegNet model with specific parameters.
Parameters:
----------
channels_init : float
Initial value for channels/widths.
channels_slope : float
Slope value for channels/widths.
width_mult : float
Width multiplier value.
groups : int
Number of groups.
depth : int
Depth value.
use_se : bool, default False
Whether to use SE-module.
model_name : str or None, default None
Model name for loading pretrained model.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
7207c26b47d35375182b60f1c4e798376c12fe0380095df05373b3857d6eb546 | def regnetx002(**kwargs):
"\n RegNetX-200MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=36.44, channels_mult=2.49, depth=13, groups=8, model_name='regnetx002', **kwargs) | RegNetX-200MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx002 | JacobARose/imgclsmob | 2,649 | python | def regnetx002(**kwargs):
"\n RegNetX-200MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=36.44, channels_mult=2.49, depth=13, groups=8, model_name='regnetx002', **kwargs) | def regnetx002(**kwargs):
"\n RegNetX-200MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=36.44, channels_mult=2.49, depth=13, groups=8, model_name='regnetx002', **kwargs)<|docstring|>RegNetX-200MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
6b631ac0da3798e38dd79e44d9bf53d3d2fda3f22e6fc041d44562bdd3c26cf3 | def regnetx004(**kwargs):
"\n RegNetX-400MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=24.48, channels_mult=2.54, depth=22, groups=16, model_name='regnetx004', **kwargs) | RegNetX-400MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx004 | JacobARose/imgclsmob | 2,649 | python | def regnetx004(**kwargs):
"\n RegNetX-400MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=24.48, channels_mult=2.54, depth=22, groups=16, model_name='regnetx004', **kwargs) | def regnetx004(**kwargs):
"\n RegNetX-400MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=24, channels_slope=24.48, channels_mult=2.54, depth=22, groups=16, model_name='regnetx004', **kwargs)<|docstring|>RegNetX-400MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
3ca93c2816f268d505433fcc4e406736cb37c1075b0c014dfb0488629cf9e1cf | def regnetx006(**kwargs):
"\n RegNetX-600MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=48, channels_slope=36.97, channels_mult=2.24, depth=16, groups=24, model_name='regnetx006', **kwargs) | RegNetX-600MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx006 | JacobARose/imgclsmob | 2,649 | python | def regnetx006(**kwargs):
"\n RegNetX-600MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=48, channels_slope=36.97, channels_mult=2.24, depth=16, groups=24, model_name='regnetx006', **kwargs) | def regnetx006(**kwargs):
"\n RegNetX-600MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=48, channels_slope=36.97, channels_mult=2.24, depth=16, groups=24, model_name='regnetx006', **kwargs)<|docstring|>RegNetX-600MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
8623831404d6f757658d5f5e8ed650b081d7aaea1615ebbd13eba15956808f2f | def regnetx008(**kwargs):
"\n RegNetX-800MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=56, channels_slope=35.73, channels_mult=2.28, depth=16, groups=16, model_name='regnetx008', **kwargs) | RegNetX-800MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx008 | JacobARose/imgclsmob | 2,649 | python | def regnetx008(**kwargs):
"\n RegNetX-800MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=56, channels_slope=35.73, channels_mult=2.28, depth=16, groups=16, model_name='regnetx008', **kwargs) | def regnetx008(**kwargs):
"\n RegNetX-800MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=56, channels_slope=35.73, channels_mult=2.28, depth=16, groups=16, model_name='regnetx008', **kwargs)<|docstring|>RegNetX-800MF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
688d2de6dddfda89a4be29da91bd9168f4d743a3708f3ace803af63e91563a4b | def regnetx016(**kwargs):
"\n RegNetX-1.6GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=34.01, channels_mult=2.25, depth=18, groups=24, model_name='regnetx016', **kwargs) | RegNetX-1.6GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx016 | JacobARose/imgclsmob | 2,649 | python | def regnetx016(**kwargs):
"\n RegNetX-1.6GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=34.01, channels_mult=2.25, depth=18, groups=24, model_name='regnetx016', **kwargs) | def regnetx016(**kwargs):
"\n RegNetX-1.6GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=34.01, channels_mult=2.25, depth=18, groups=24, model_name='regnetx016', **kwargs)<|docstring|>RegNetX-1.6GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
3f59319a5e1eeee45b18e4f72e295fb8d71e17b98e17551f5f34cfcbfad4bbf9 | def regnetx032(**kwargs):
"\n RegNetX-3.2GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=88, channels_slope=26.31, channels_mult=2.25, depth=25, groups=48, model_name='regnetx032', **kwargs) | RegNetX-3.2GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx032 | JacobARose/imgclsmob | 2,649 | python | def regnetx032(**kwargs):
"\n RegNetX-3.2GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=88, channels_slope=26.31, channels_mult=2.25, depth=25, groups=48, model_name='regnetx032', **kwargs) | def regnetx032(**kwargs):
"\n RegNetX-3.2GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=88, channels_slope=26.31, channels_mult=2.25, depth=25, groups=48, model_name='regnetx032', **kwargs)<|docstring|>RegNetX-3.2GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
26c4105e9b585b7103ebbea8b2c49df29417a9f91f477749a84a71a79c9ffebe | def regnetx040(**kwargs):
"\n RegNetX-4.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=96, channels_slope=38.65, channels_mult=2.43, depth=23, groups=40, model_name='regnetx040', **kwargs) | RegNetX-4.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx040 | JacobARose/imgclsmob | 2,649 | python | def regnetx040(**kwargs):
"\n RegNetX-4.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=96, channels_slope=38.65, channels_mult=2.43, depth=23, groups=40, model_name='regnetx040', **kwargs) | def regnetx040(**kwargs):
"\n RegNetX-4.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=96, channels_slope=38.65, channels_mult=2.43, depth=23, groups=40, model_name='regnetx040', **kwargs)<|docstring|>RegNetX-4.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
5bcd82bd068f0e2913a6a6cf42a44fac009bb8273c89b033a93221aecac576c1 | def regnetx064(**kwargs):
"\n RegNetX-6.4GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=184, channels_slope=60.83, channels_mult=2.07, depth=17, groups=56, model_name='regnetx064', **kwargs) | RegNetX-6.4GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx064 | JacobARose/imgclsmob | 2,649 | python | def regnetx064(**kwargs):
"\n RegNetX-6.4GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=184, channels_slope=60.83, channels_mult=2.07, depth=17, groups=56, model_name='regnetx064', **kwargs) | def regnetx064(**kwargs):
"\n RegNetX-6.4GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=184, channels_slope=60.83, channels_mult=2.07, depth=17, groups=56, model_name='regnetx064', **kwargs)<|docstring|>RegNetX-6.4GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
42e229899fb67e2d8567717bc8c538ee6e760c9891c9ef217d5fc57de46ec807 | def regnetx080(**kwargs):
"\n RegNetX-8.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=49.56, channels_mult=2.88, depth=23, groups=120, model_name='regnetx080', **kwargs) | RegNetX-8.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx080 | JacobARose/imgclsmob | 2,649 | python | def regnetx080(**kwargs):
"\n RegNetX-8.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=49.56, channels_mult=2.88, depth=23, groups=120, model_name='regnetx080', **kwargs) | def regnetx080(**kwargs):
"\n RegNetX-8.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=80, channels_slope=49.56, channels_mult=2.88, depth=23, groups=120, model_name='regnetx080', **kwargs)<|docstring|>RegNetX-8.0GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
3b7e26d0c0acf7fb26fd638abff430674530e8fcc35dfb70cdc5883ff50ab38e | def regnetx120(**kwargs):
"\n RegNetX-12GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=168, channels_slope=73.36, channels_mult=2.37, depth=19, groups=112, model_name='regnetx120', **kwargs) | RegNetX-12GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx120 | JacobARose/imgclsmob | 2,649 | python | def regnetx120(**kwargs):
"\n RegNetX-12GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=168, channels_slope=73.36, channels_mult=2.37, depth=19, groups=112, model_name='regnetx120', **kwargs) | def regnetx120(**kwargs):
"\n RegNetX-12GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=168, channels_slope=73.36, channels_mult=2.37, depth=19, groups=112, model_name='regnetx120', **kwargs)<|docstring|>RegNetX-12GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
1709961543514d99ec238ec35d5579ae72e684989fe9ef55e8bd8c89894b919d | def regnetx160(**kwargs):
"\n RegNetX-16GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=216, channels_slope=55.59, channels_mult=2.1, depth=22, groups=128, model_name='regnetx160', **kwargs) | RegNetX-16GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx160 | JacobARose/imgclsmob | 2,649 | python | def regnetx160(**kwargs):
"\n RegNetX-16GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=216, channels_slope=55.59, channels_mult=2.1, depth=22, groups=128, model_name='regnetx160', **kwargs) | def regnetx160(**kwargs):
"\n RegNetX-16GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=216, channels_slope=55.59, channels_mult=2.1, depth=22, groups=128, model_name='regnetx160', **kwargs)<|docstring|>RegNetX-16GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
2f43aaa30f0215fea1e9dd59e4c4e39558f4c5cd821acac812adc1e84b70519a | def regnetx320(**kwargs):
"\n RegNetX-32GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=320, channels_slope=69.86, channels_mult=2.0, depth=23, groups=168, model_name='regnetx320', **kwargs) | RegNetX-32GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | pytorch/pytorchcv/models/regnet.py | regnetx320 | JacobARose/imgclsmob | 2,649 | python | def regnetx320(**kwargs):
"\n RegNetX-32GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=320, channels_slope=69.86, channels_mult=2.0, depth=23, groups=168, model_name='regnetx320', **kwargs) | def regnetx320(**kwargs):
"\n RegNetX-32GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.\n\n Parameters:\n ----------\n pretrained : bool, default False\n Whether to load the pretrained weights for model.\n root : str, default '~/.torch/models'\n Location for keeping the model parameters.\n "
return get_regnet(channels_init=320, channels_slope=69.86, channels_mult=2.0, depth=23, groups=168, model_name='regnetx320', **kwargs)<|docstring|>RegNetX-32GF model from 'Designing Network Design Spaces,' https://arxiv.org/abs/2003.13678.
Parameters:
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters.<|endoftext|> |
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