Coverage for skema/gromet/metadata/text_extraction_metadata.py: 45%

1# coding: utf-8 

2 

3""" 

4 GroMEt Metadata spec 

5 

6 Grounded Model Exchange (GroMEt) Metadata schema specification __Using Swagger to Generate Class Structure__ To automatically generate Python or Java models corresponding to this document, you can use [swagger-codegen](https://swagger.io/tools/swagger-codegen/). We can use this to generate client code based off of this spec that will also generate the class structure. 1. Install via the method described for your operating system [here](https://github.com/swagger-api/swagger-codegen#Prerequisites). Make sure to install a version after 3.0 that will support openapi 3. 2. Run swagger-codegen with the options in the example below. The URL references where the yaml for this documentation is stored on github. Make sure to replace CURRENT_VERSION with the correct version. (The current version is `0.1.4`.) To generate Java classes rather, change the `-l python` to `-l java`. Change the value to the `-o` option to the desired output location. ``` swagger-codegen generate -l python -o ./client -i https://raw.githubusercontent.com/ml4ai/automates-v2/master/docs/source/gromet_metadata_v{CURRENT_VERSION}.yaml ``` 3. Once it executes, the client code will be generated at your specified location. For python, the classes will be located in `$OUTPUT_PATH/swagger_client/models/`. For java, they will be located in `$OUTPUT_PATH/src/main/java/io/swagger/client/model/` If generating GroMEt Metadata schema data model classes in SKEMA (AutoMATES), then after generating the above, follow the instructions here: ``` <automates>/automates/model_assembly/gromet/metadata/README.md ``` # noqa: E501 

7 

8 OpenAPI spec version: 0.1.8 

9 Contact: claytonm@arizona.edu 

10 Generated by: https://github.com/swagger-api/swagger-codegen.git 

11""" 

12 

13import pprint 

14import re # noqa: F401 

15 

16import six 

17from skema.gromet.metadata.metadata import Metadata # noqa: F401,E501 

18 

19class TextExtractionMetadata(Metadata): 

20 """NOTE: This class is auto generated by the swagger code generator program. 

21 

22 Do not edit the class manually. 

23 """ 

24 """ 

25 Attributes: 

26 swagger_types (dict): The key is attribute name 

27 and the value is attribute type. 

28 attribute_map (dict): The key is attribute name 

29 and the value is json key in definition. 

30 """ 

31 swagger_types = { 

32 'gromet_type': 'str', 

33 'grounding': 'list[TextGrounding]' 

34 } 

35 if hasattr(Metadata, "swagger_types"): 

36 swagger_types.update(Metadata.swagger_types) 

37 

38 attribute_map = { 

39 'gromet_type': 'gromet_type', 

40 'grounding': 'grounding' 

41 } 

42 if hasattr(Metadata, "attribute_map"): 

43 attribute_map.update(Metadata.attribute_map) 

44 

45 def __init__(self, gromet_type='TextExtractionMetadata', grounding=None, *args, **kwargs): # noqa: E501 

46 """TextExtractionMetadata - a model defined in Swagger""" # noqa: E501 

47 self._gromet_type = None 

48 self._grounding = None 

49 self.discriminator = None 

50 if gromet_type is not None: 

51 self.gromet_type = gromet_type 

52 if grounding is not None: 

53 self.grounding = grounding 

54 Metadata.__init__(self, *args, **kwargs) 

55 

56 @property 

57 def gromet_type(self): 

58 """Gets the gromet_type of this TextExtractionMetadata. # noqa: E501 

59 

60 

61 :return: The gromet_type of this TextExtractionMetadata. # noqa: E501 

62 :rtype: str 

63 """ 

64 return self._gromet_type 

65 

66 @gromet_type.setter 

67 def gromet_type(self, gromet_type): 

68 """Sets the gromet_type of this TextExtractionMetadata. 

69 

70 

71 :param gromet_type: The gromet_type of this TextExtractionMetadata. # noqa: E501 

72 :type: str 

73 """ 

74 

75 self._gromet_type = gromet_type 

76 

77 @property 

78 def grounding(self): 

79 """Gets the grounding of this TextExtractionMetadata. # noqa: E501 

80 

81 Array of grounding hypotheses # noqa: E501 

82 

83 :return: The grounding of this TextExtractionMetadata. # noqa: E501 

84 :rtype: list[TextGrounding] 

85 """ 

86 return self._grounding 

87 

88 @grounding.setter 

89 def grounding(self, grounding): 

90 """Sets the grounding of this TextExtractionMetadata. 

91 

92 Array of grounding hypotheses # noqa: E501 

93 

94 :param grounding: The grounding of this TextExtractionMetadata. # noqa: E501 

95 :type: list[TextGrounding] 

96 """ 

97 

98 self._grounding = grounding 

99 

100 def to_dict(self): 

101 """Returns the model properties as a dict""" 

102 result = {} 

103 

104 for attr, _ in six.iteritems(self.swagger_types): 

105 value = getattr(self, attr) 

106 if isinstance(value, list): 

107 result[attr] = list(map( 

108 lambda x: x.to_dict() if hasattr(x, "to_dict") else x, 

109 value 

110 )) 

111 elif hasattr(value, "to_dict"): 

112 result[attr] = value.to_dict() 

113 elif isinstance(value, dict): 

114 result[attr] = dict(map( 

115 lambda item: (item[0], item[1].to_dict()) 

116 if hasattr(item[1], "to_dict") else item, 

117 value.items() 

118 )) 

119 else: 

120 result[attr] = value 

121 if issubclass(TextExtractionMetadata, dict): 

122 for key, value in self.items(): 

123 result[key] = value 

124 

125 return result 

126 

127 def to_str(self): 

128 """Returns the string representation of the model""" 

129 return pprint.pformat(self.to_dict()) 

130 

131 def __repr__(self): 

132 """For `print` and `pprint`""" 

133 return self.to_str() 

134 

135 def __eq__(self, other): 

136 """Returns true if both objects are equal""" 

137 if not isinstance(other, TextExtractionMetadata): 

138 return False 

139 

140 return self.__dict__ == other.__dict__ 

141 

142 def __ne__(self, other): 

143 """Returns true if both objects are not equal""" 

144 return not self == other