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brandon-wadaAuto-format Bot
brandon-wada
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Auto-format Bot
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Update detector (#244)
Adds a series of functions that allow you to update an existing detector --------- Co-authored-by: Auto-format Bot <autoformatbot@groundlight.ai>
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8 files changed

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-36
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‎.pylintrc‎

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@@ -563,5 +563,5 @@ min-public-methods=2
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# Exceptions that will emit a warning when being caught. Defaults to
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# "BaseException, Exception".
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overgeneral-exceptions=BaseException,
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Exception
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overgeneral-exceptions=builtins.BaseException,
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builtins.Exception

‎generated/docs/DetectorRequest.md‎

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# DetectorRequest
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Spec for serializing a detector object in the public API.
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## Properties
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**name** | **str** | A short, descriptive name for the detector. |
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**confidence_threshold** | **float** | If the detector&#39;s prediction is below this confidence threshold, send the image query for human review. | [optional] if omitted the server will use the default value of 0.9
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**patience_time** | **float** | How long Groundlight will attempt to generate a confident prediction | [optional] if omitted the server will use the default value of 30.0
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**status** | **bool, date, datetime, dict, float, int, list, str, none_type** | | [optional]
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**escalation_type** | **bool, date, datetime, dict, float, int, list, str, none_type** | Category that define internal proccess for labeling image queries * &#x60;STANDARD&#x60; - STANDARD * &#x60;NO_HUMAN_LABELING&#x60; - NO_HUMAN_LABELING | [optional]
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**any string name** | **bool, date, datetime, dict, float, int, list, str, none_type** | any string name can be used but the value must be the correct type | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
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‎generated/groundlight_openapi_client/model/detector_request.py‎

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"""
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Groundlight API
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Groundlight makes it simple to understand images. You can easily create computer vision detectors just by describing what you want to know using natural language. # noqa: E501
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The version of the OpenAPI document: 0.15.3
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Contact: support@groundlight.ai
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Generated by: https://openapi-generator.tech
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"""
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import re # noqa: F401
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import sys # noqa: F401
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from groundlight_openapi_client.model_utils import ( # noqa: F401
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ApiTypeError,
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ModelComposed,
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ModelNormal,
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ModelSimple,
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cached_property,
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change_keys_js_to_python,
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convert_js_args_to_python_args,
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date,
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datetime,
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file_type,
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none_type,
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validate_get_composed_info,
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OpenApiModel,
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)
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from groundlight_openapi_client.exceptions import ApiAttributeError
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def lazy_import():
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from groundlight_openapi_client.model.blank_enum import BlankEnum
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from groundlight_openapi_client.model.escalation_type_enum import EscalationTypeEnum
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from groundlight_openapi_client.model.status_enum import StatusEnum
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globals()["BlankEnum"] = BlankEnum
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globals()["EscalationTypeEnum"] = EscalationTypeEnum
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globals()["StatusEnum"] = StatusEnum
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class DetectorRequest(ModelNormal):
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"""NOTE: This class is auto generated by OpenAPI Generator.
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Ref: https://openapi-generator.tech
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Do not edit the class manually.
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Attributes:
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allowed_values (dict): The key is the tuple path to the attribute
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and the for var_name this is (var_name,). The value is a dict
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with a capitalized key describing the allowed value and an allowed
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value. These dicts store the allowed enum values.
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attribute_map (dict): The key is attribute name
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and the value is json key in definition.
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discriminator_value_class_map (dict): A dict to go from the discriminator
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variable value to the discriminator class name.
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validations (dict): The key is the tuple path to the attribute
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and the for var_name this is (var_name,). The value is a dict
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that stores validations for max_length, min_length, max_items,
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min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
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inclusive_minimum, and regex.
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additional_properties_type (tuple): A tuple of classes accepted
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as additional properties values.
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"""
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allowed_values = {}
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validations = {
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("name",): {
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"max_length": 200,
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"min_length": 1,
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},
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("confidence_threshold",): {
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"inclusive_maximum": 1.0,
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"inclusive_minimum": 0.0,
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},
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("patience_time",): {
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"inclusive_maximum": 3600,
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"inclusive_minimum": 0,
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},
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}
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@cached_property
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def additional_properties_type():
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"""
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This must be a method because a model may have properties that are
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of type self, this must run after the class is loaded
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"""
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lazy_import()
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return (
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bool,
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date,
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datetime,
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dict,
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float,
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int,
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list,
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str,
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none_type,
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) # noqa: E501
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_nullable = False
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@cached_property
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def openapi_types():
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"""
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This must be a method because a model may have properties that are
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of type self, this must run after the class is loaded
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Returns
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openapi_types (dict): The key is attribute name
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and the value is attribute type.
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"""
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lazy_import()
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return {
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"name": (str,), # noqa: E501
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"confidence_threshold": (float,), # noqa: E501
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"patience_time": (float,), # noqa: E501
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"status": (
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bool,
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date,
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datetime,
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dict,
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float,
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int,
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list,
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str,
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none_type,
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), # noqa: E501
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"escalation_type": (
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bool,
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date,
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datetime,
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dict,
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float,
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int,
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list,
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str,
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none_type,
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), # noqa: E501
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}
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@cached_property
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def discriminator():
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return None
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attribute_map = {
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"name": "name", # noqa: E501
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"confidence_threshold": "confidence_threshold", # noqa: E501
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"patience_time": "patience_time", # noqa: E501
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"status": "status", # noqa: E501
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"escalation_type": "escalation_type", # noqa: E501
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}
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read_only_vars = {}
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_composed_schemas = {}
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@classmethod
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@convert_js_args_to_python_args
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def _from_openapi_data(cls, name, *args, **kwargs): # noqa: E501
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"""DetectorRequest - a model defined in OpenAPI
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Args:
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name (str): A short, descriptive name for the detector.
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Keyword Args:
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_check_type (bool): if True, values for parameters in openapi_types
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will be type checked and a TypeError will be
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raised if the wrong type is input.
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Defaults to True
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_path_to_item (tuple/list): This is a list of keys or values to
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drill down to the model in received_data
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when deserializing a response
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_spec_property_naming (bool): True if the variable names in the input data
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are serialized names, as specified in the OpenAPI document.
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False if the variable names in the input data
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are pythonic names, e.g. snake case (default)
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_configuration (Configuration): the instance to use when
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deserializing a file_type parameter.
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If passed, type conversion is attempted
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If omitted no type conversion is done.
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_visited_composed_classes (tuple): This stores a tuple of
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classes that we have traveled through so that
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if we see that class again we will not use its
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discriminator again.
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When traveling through a discriminator, the
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composed schema that is
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is traveled through is added to this set.
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For example if Animal has a discriminator
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petType and we pass in "Dog", and the class Dog
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allOf includes Animal, we move through Animal
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once using the discriminator, and pick Dog.
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Then in Dog, we will make an instance of the
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Animal class but this time we won't travel
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through its discriminator because we passed in
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_visited_composed_classes = (Animal,)
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confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501
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patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501
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status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501
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escalation_type (bool, date, datetime, dict, float, int, list, str, none_type): Category that define internal proccess for labeling image queries * `STANDARD` - STANDARD * `NO_HUMAN_LABELING` - NO_HUMAN_LABELING. [optional] # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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_configuration = kwargs.pop("_configuration", None)
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_visited_composed_classes = kwargs.pop("_visited_composed_classes", ())
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self = super(OpenApiModel, cls).__new__(cls)
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if args:
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raise ApiTypeError(
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"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments."
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% (
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args,
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self.__class__.__name__,
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),
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path_to_item=_path_to_item,
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valid_classes=(self.__class__,),
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)
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self._data_store = {}
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item
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self._configuration = _configuration
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self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
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self.name = name
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for var_name, var_value in kwargs.items():
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if (
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var_name not in self.attribute_map
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and self._configuration is not None
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and self._configuration.discard_unknown_keys
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and self.additional_properties_type is None
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):
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# discard variable.
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continue
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setattr(self, var_name, var_value)
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return self
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required_properties = set([
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"_data_store",
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"_check_type",
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"_spec_property_naming",
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"_path_to_item",
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"_configuration",
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"_visited_composed_classes",
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])
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@convert_js_args_to_python_args
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def __init__(self, name, *args, **kwargs): # noqa: E501
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"""DetectorRequest - a model defined in OpenAPI
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Args:
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name (str): A short, descriptive name for the detector.
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Keyword Args:
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_check_type (bool): if True, values for parameters in openapi_types
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will be type checked and a TypeError will be
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raised if the wrong type is input.
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Defaults to True
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_path_to_item (tuple/list): This is a list of keys or values to
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drill down to the model in received_data
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when deserializing a response
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_spec_property_naming (bool): True if the variable names in the input data
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are serialized names, as specified in the OpenAPI document.
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False if the variable names in the input data
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are pythonic names, e.g. snake case (default)
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_configuration (Configuration): the instance to use when
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deserializing a file_type parameter.
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If passed, type conversion is attempted
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If omitted no type conversion is done.
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_visited_composed_classes (tuple): This stores a tuple of
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classes that we have traveled through so that
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if we see that class again we will not use its
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discriminator again.
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When traveling through a discriminator, the
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composed schema that is
281+
is traveled through is added to this set.
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For example if Animal has a discriminator
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petType and we pass in "Dog", and the class Dog
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allOf includes Animal, we move through Animal
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once using the discriminator, and pick Dog.
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Then in Dog, we will make an instance of the
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Animal class but this time we won't travel
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through its discriminator because we passed in
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_visited_composed_classes = (Animal,)
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confidence_threshold (float): If the detector's prediction is below this confidence threshold, send the image query for human review.. [optional] if omitted the server will use the default value of 0.9 # noqa: E501
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patience_time (float): How long Groundlight will attempt to generate a confident prediction. [optional] if omitted the server will use the default value of 30.0 # noqa: E501
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status (bool, date, datetime, dict, float, int, list, str, none_type): [optional] # noqa: E501
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escalation_type (bool, date, datetime, dict, float, int, list, str, none_type): Category that define internal proccess for labeling image queries * `STANDARD` - STANDARD * `NO_HUMAN_LABELING` - NO_HUMAN_LABELING. [optional] # noqa: E501
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"""
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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_configuration = kwargs.pop("_configuration", None)
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_visited_composed_classes = kwargs.pop("_visited_composed_classes", ())
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if args:
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raise ApiTypeError(
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"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments."
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% (
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args,
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self.__class__.__name__,
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),
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path_to_item=_path_to_item,
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valid_classes=(self.__class__,),
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)
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self._data_store = {}
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item
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self._configuration = _configuration
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self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
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self.name = name
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for var_name, var_value in kwargs.items():
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if (
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var_name not in self.attribute_map
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and self._configuration is not None
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and self._configuration.discard_unknown_keys
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and self.additional_properties_type is None
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):
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# discard variable.
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continue
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setattr(self, var_name, var_value)
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if var_name in self.read_only_vars:
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raise ApiAttributeError(
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f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
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"class with read only attributes."
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)

‎generated/model.py‎

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# generated by datamodel-codegen:
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# filename: public-api.yaml
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# timestamp: 2024-10-30T23:38:30+00:00
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# timestamp: 2024-11-21T00:58:02+00:00
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from __future__ import annotations
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