%global _empty_manifest_terminate_build 0
Name:		python-javaobj-py3
Version:	0.4.3
Release:	1
Summary:	Module for serializing and de-serializing Java objects.
License:	Apache License 2.0
URL:		https://github.com/tcalmant/python-javaobj
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/66/d3/0e4fa237161b3a00eea031575f46ac052a79b0908982b52794d113f43813/javaobj-py3-0.4.3.tar.gz
BuildArch:	noarch

Requires:	python3-enum34
Requires:	python3-typing

%description
# javaobj-py3

<p>
    <a href="https://pypi.python.org/pypi/javaobj-py3/">
        <img src="https://img.shields.io/pypi/v/javaobj-py3.svg" alt="Latest Version" />
        <img src="https://img.shields.io/pypi/l/javaobj-py3.svg" alt="License" />
    </a>
    <a href="https://travis-ci.org/tcalmant/python-javaobj">
    <img src="https://travis-ci.org/tcalmant/python-javaobj.svg?branch=master"
        alt="Travis-CI status" />
    </a>
    <a href="https://coveralls.io/r/tcalmant/python-javaobj?branch=master">
        <img src="https://coveralls.io/repos/tcalmant/python-javaobj/badge.svg?branch=master"
            alt="Coveralls status" />
    </a>
</p>

*python-javaobj* is a python library that provides functions for reading and
writing (writing is WIP currently) Java objects serialized or will be
deserialized by `ObjectOutputStream`. This form of object representation is a
standard data interchange format in Java world.

The `javaobj` module exposes an API familiar to users of the standard library
`marshal`, `pickle` and `json` modules.

## About this repository

This project is a fork of *python-javaobj* by Volodymyr Buell, originally from
[Google Code](http://code.google.com/p/python-javaobj/) and now hosted on
[GitHub](https://github.com/vbuell/python-javaobj).

This fork intends to work both on Python 2.7 and Python 3.4+.

## Compatibility Warnings

### New implementation of the parser

| Implementations | Version  |
|-----------------|----------|
| `v1`, `v2`      | `0.4.0+` |

Since version 0.4.0, two implementations of the parser are available:

* `v1`: the *classic* implementation of `javaobj`, with a work in progress
  implementation of a writer.
* `v2`: the *new* implementation, which is a port of the Java project
  [`jdeserialize`](https://github.com/frohoff/jdeserialize/),
  with support of the object transformer (with a new API) and of the `numpy`
  arrays loading.

You can use the `v1` parser to ensure that the behaviour of your scripts
doesn't change and to keep the ability to write down files.

You can use the `v2` parser for new developments
*which won't require marshalling* and as a *fallback* if the `v1`
fails to parse a file.

### Object transformers V1

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.0+` |

As of version 0.2.0, the notion of *object transformer* from the original
project as been replaced by an *object creator*.

The *object creator* is called before the deserialization.
This allows to store the reference of the converted object before deserializing
it, and avoids a mismatch between the referenced object and the transformed one.

### Object transformers V2

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.0+` |

The `v2` implementation provides a new API for the object transformers.
Please look at the *Usage (V2)* section in this file.

### Bytes arrays

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.3+` |

As of version 0.2.3, bytes arrays are loaded as a `bytes` object instead of
an array of integers.

### Custom Transformer

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.2+` |

A new transformer API has been proposed to handle objects written with a custom
Java writer.
You can find a sample usage in the *Custom Transformer* section in this file.

## Features

* Java object instance un-marshalling
* Java classes un-marshalling
* Primitive values un-marshalling
* Automatic conversion of Java Collections to python ones
  (`HashMap` => `dict`, `ArrayList` => `list`, etc.)
* Basic marshalling of simple Java objects (`v1` implementation only)
* Automatically uncompresses GZipped files

## Requirements

* Python >= 2.7 or Python >= 3.4
* `enum34` and `typing` when using Python <= 3.4 (installable with `pip`)
* Maven 2+ (for building test data of serialized objects.
  You can skip it if you do not plan to run `tests.py`)

## Usage (V1 implementation)

Un-marshalling of Java serialised object:

```python
import javaobj

with open("obj5.ser", "rb") as fd:
    jobj = fd.read()

pobj = javaobj.loads(jobj)
print(pobj)
```

Or, you can use `JavaObjectUnmarshaller` object directly:

```python
import javaobj

with open("objCollections.ser", "rb") as fd:
    marshaller = javaobj.JavaObjectUnmarshaller(fd)
    pobj = marshaller.readObject()

    print(pobj.value, "should be", 17)
    print(pobj.next, "should be", True)

    pobj = marshaller.readObject()
```

**Note:** The objects and methods provided by `javaobj` module are shortcuts
to the `javaobj.v1` package, for Compatibility purpose.
It is **recommended** to explicitly import methods and classes from the `v1`
(or `v2`) package when writing new code, in order to be sure that your code
won't need import updates in the future.


## Usage (V2 implementation)

The following methods are provided by the `javaobj.v2` package:

* `load(fd, *transformers, use_numpy_arrays=False)`:
  Parses the content of the given file descriptor, opened in binary mode (`rb`).
  The method accepts a list of custom object transformers. The default object
  transformer is always added to the list.

  The `use_numpy_arrays` flag indicates that the arrays of primitive type
  elements must be loaded using `numpy` (if available) instead of using the
  standard parsing technic.

* `loads(bytes, *transformers, use_numpy_arrays=False)`:
  This the a shortcut to the `load()` method, providing it the binary data
  using a `BytesIO` object.

**Note:** The V2 parser doesn't have the marshalling capability.

Sample usage:

```python
import javaobj.v2 as javaobj

with open("obj5.ser", "rb") as fd:
    pobj = javaobj.load(fd)

print(pobj.dump())
```

### Object Transformer

An object transformer can be called during the parsing of a Java object
instance or while loading an array.

The Java object instance parsing works in two main steps:

1. The transformer is called to create an instance of a bean that inherits
   `JavaInstance`.
1. The latter bean is then called:

   * When the object is written with a custom block data
   * After the fields and annotations have been parsed, to update the content
   of the Python bean.

Here is an example for a Java `HashMap` object. You can look at the code of
the `javaobj.v2.transformer` module to see the whole implementation.

```python
class JavaMap(dict, javaobj.v2.beans.JavaInstance):
    """
    Inherits from dict for Python usage, JavaInstance for parsing purpose
    """
    def __init__(self):
        # Don't forget to call both constructors
        dict.__init__(self)
        JavaInstance.__init__(self)

    def load_from_blockdata(self, parser, reader, indent=0):
    """
    Reads content stored in a block data.

    This method is called only if the class description has both the
    `SC_EXTERNALIZABLE` and `SC_BLOCK_DATA` flags set.

    The stream parsing will stop and fail if this method returns False.

    :param parser: The JavaStreamParser in use
    :param reader: The underlying data stream reader
    :param indent: Indentation to use in logs
    :return: True on success, False on error
    """
    # This kind of class is not supposed to have the SC_BLOCK_DATA flag set
    return False

    def load_from_instance(self, indent=0):
        # type: (int) -> bool
        """
        Load content from the parsed instance object.

        This method is called after the block data (if any), the fields and
        the annotations have been loaded.

        :param indent: Indentation to use while logging
        :return: True on success (currently ignored)
        """
        # Maps have their content in their annotations
        for cd, annotations in self.annotations.items():
            # Annotations are associated to their definition class
            if cd.name == "java.util.HashMap":
                # We are in the annotation created by the handled class
                # Group annotation elements 2 by 2
                # (storage is: key, value, key, value, ...)
                args = [iter(annotations[1:])] * 2
                for key, value in zip(*args):
                    self[key] = value

                # Job done
                return True

        # Couldn't load the data
        return False

class MapObjectTransformer(javaobj.v2.api.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """
    def create_instance(self, classdesc):
        # type: (JavaClassDesc) -> Optional[JavaInstance]
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name == "java.util.HashMap":
            # We can handle this class description
            return JavaMap()
        else:
            # Return None if the class is not handled
            return None
```

### Custom Object Transformer

The custom transformer is called when the class is not handled by the default
object transformer.
A custom object transformer still inherits from the `ObjectTransformer` class,
but it also implements the `load_custom_writeObject` method.

The sample given here is used in the unit tests.

#### Java sample

On the Java side, we create various classes and write them as we wish:

```java
class CustomClass implements Serializable {

    private static final long serialVersionUID = 1;

    public void start(ObjectOutputStream out) throws Exception {
        this.writeObject(out);
    }

    private void writeObject(ObjectOutputStream out) throws IOException {
        CustomWriter custom = new CustomWriter(42);
        out.writeObject(custom);
        out.flush();
    }
}

class RandomChild extends Random {

    private static final long serialVersionUID = 1;
    private int num = 1;
    private double doub = 4.5;

    RandomChild(int seed) {
        super(seed);
    }
}

class CustomWriter implements Serializable {
    protected RandomChild custom_obj;

    CustomWriter(int seed) {
        custom_obj = new RandomChild(seed);
    }

    private static final long serialVersionUID = 1;
    private static final int CURRENT_SERIAL_VERSION = 0;

    private void writeObject(ObjectOutputStream out) throws IOException {
        out.writeInt(CURRENT_SERIAL_VERSION);
        out.writeObject(custom_obj);
    }
}
```

An here is a sample writing of that kind of object:

```java
ObjectOutputStream oos = new ObjectOutputStream(
    new FileOutputStream("custom_objects.ser"));
CustomClass writer = new CustomClass();
writer.start(oos);
oos.flush();
oos.close();
```

#### Python sample

On the Python side, the first step is to define the custom transformers.
They are children of the `javaobj.v2.transformers.ObjectTransformer` class.

```python
class BaseTransformer(javaobj.v2.transformers.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """

    def __init__(self, handled_classes=None):
        self.instance = None
        self.handled_classes = handled_classes or {}

    def create_instance(self, classdesc):
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name in self.handled_classes:
            self.instance = self.handled_classes[classdesc.name]()
            return self.instance

        return None

class RandomChildTransformer(BaseTransformer):
    def __init__(self):
        super(RandomChildTransformer, self).__init__(
            {"RandomChild": RandomChildInstance}
        )

class CustomWriterTransformer(BaseTransformer):
    def __init__(self):
        super(CustomWriterTransformer, self).__init__(
            {"CustomWriter": CustomWriterInstance}
        )

class JavaRandomTransformer(BaseTransformer):
    def __init__(self):
        super(JavaRandomTransformer, self).__init__()
        self.name = "java.util.Random"
        self.field_names = ["haveNextNextGaussian", "nextNextGaussian", "seed"]
        self.field_types = [
            javaobj.v2.beans.FieldType.BOOLEAN,
            javaobj.v2.beans.FieldType.DOUBLE,
            javaobj.v2.beans.FieldType.LONG,
        ]

    def load_custom_writeObject(self, parser, reader, name):
        if name != self.name:
            return None

        fields = []
        values = []
        for f_name, f_type in zip(self.field_names, self.field_types):
            values.append(parser._read_field_value(f_type))
            fields.append(javaobj.beans.JavaField(f_type, f_name))

        class_desc = javaobj.beans.JavaClassDesc(
            javaobj.beans.ClassDescType.NORMALCLASS
        )
        class_desc.name = self.name
        class_desc.desc_flags = javaobj.beans.ClassDataType.EXTERNAL_CONTENTS
        class_desc.fields = fields
        class_desc.field_data = values
        return class_desc
```

Second step is defining the representation of the instances, where the real
object loading occurs. Those classes inherit from
`javaobj.v2.beans.JavaInstance`.

```python
class CustomWriterInstance(javaobj.v2.beans.JavaInstance):
    def __init__(self):
        javaobj.v2.beans.JavaInstance.__init__(self)

    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.annotations:
            # Here, we known there is something written before the fields,
            # even if it's not declared in the class description
            fields = ["int_not_in_fields"] + self.classdesc.fields_names
            raw_data = self.annotations[self.classdesc]
            int_not_in_fields = struct.unpack(
                ">i", BytesIO(raw_data[0].data).read(4)
            )[0]
            custom_obj = raw_data[1]
            values = [int_not_in_fields, custom_obj]
            self.field_data = dict(zip(fields, values))
            return True

        return False


class RandomChildInstance(javaobj.v2.beans.JavaInstance):
    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.field_data:
            fields = self.classdesc.fields_names
            values = [
                self.field_data[self.classdesc][self.classdesc.fields[i]]
                for i in range(len(fields))
            ]
            self.field_data = dict(zip(fields, values))
            if (
                self.classdesc.super_class
                and self.classdesc.super_class in self.annotations
            ):
                super_class = self.annotations[self.classdesc.super_class][0]
                self.annotations = dict(
                    zip(super_class.fields_names, super_class.field_data)
                )
            return True

        return False
```

Finally we can use the transformers in the loading process.
Note that even if it is not explicitly given, the `DefaultObjectTransformer`
will be also be used, as it is added automatically by `javaobj` if it is
missing from the given list.

```python
# Load the object using those transformers
transformers = [
    CustomWriterTransformer(),
    RandomChildTransformer(),
    JavaRandomTransformer()
]
pobj = javaobj.loads("custom_objects.ser", *transformers)

# Here we show a field that isn't visible from the class description
# The field belongs to the class but it's not serialized by default because
# it's static. See: https://stackoverflow.com/a/16477421/12621168
print(pobj.field_data["int_not_in_fields"])
```




%package -n python3-javaobj-py3
Summary:	Module for serializing and de-serializing Java objects.
Provides:	python-javaobj-py3
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-javaobj-py3
# javaobj-py3

<p>
    <a href="https://pypi.python.org/pypi/javaobj-py3/">
        <img src="https://img.shields.io/pypi/v/javaobj-py3.svg" alt="Latest Version" />
        <img src="https://img.shields.io/pypi/l/javaobj-py3.svg" alt="License" />
    </a>
    <a href="https://travis-ci.org/tcalmant/python-javaobj">
    <img src="https://travis-ci.org/tcalmant/python-javaobj.svg?branch=master"
        alt="Travis-CI status" />
    </a>
    <a href="https://coveralls.io/r/tcalmant/python-javaobj?branch=master">
        <img src="https://coveralls.io/repos/tcalmant/python-javaobj/badge.svg?branch=master"
            alt="Coveralls status" />
    </a>
</p>

*python-javaobj* is a python library that provides functions for reading and
writing (writing is WIP currently) Java objects serialized or will be
deserialized by `ObjectOutputStream`. This form of object representation is a
standard data interchange format in Java world.

The `javaobj` module exposes an API familiar to users of the standard library
`marshal`, `pickle` and `json` modules.

## About this repository

This project is a fork of *python-javaobj* by Volodymyr Buell, originally from
[Google Code](http://code.google.com/p/python-javaobj/) and now hosted on
[GitHub](https://github.com/vbuell/python-javaobj).

This fork intends to work both on Python 2.7 and Python 3.4+.

## Compatibility Warnings

### New implementation of the parser

| Implementations | Version  |
|-----------------|----------|
| `v1`, `v2`      | `0.4.0+` |

Since version 0.4.0, two implementations of the parser are available:

* `v1`: the *classic* implementation of `javaobj`, with a work in progress
  implementation of a writer.
* `v2`: the *new* implementation, which is a port of the Java project
  [`jdeserialize`](https://github.com/frohoff/jdeserialize/),
  with support of the object transformer (with a new API) and of the `numpy`
  arrays loading.

You can use the `v1` parser to ensure that the behaviour of your scripts
doesn't change and to keep the ability to write down files.

You can use the `v2` parser for new developments
*which won't require marshalling* and as a *fallback* if the `v1`
fails to parse a file.

### Object transformers V1

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.0+` |

As of version 0.2.0, the notion of *object transformer* from the original
project as been replaced by an *object creator*.

The *object creator* is called before the deserialization.
This allows to store the reference of the converted object before deserializing
it, and avoids a mismatch between the referenced object and the transformed one.

### Object transformers V2

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.0+` |

The `v2` implementation provides a new API for the object transformers.
Please look at the *Usage (V2)* section in this file.

### Bytes arrays

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.3+` |

As of version 0.2.3, bytes arrays are loaded as a `bytes` object instead of
an array of integers.

### Custom Transformer

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.2+` |

A new transformer API has been proposed to handle objects written with a custom
Java writer.
You can find a sample usage in the *Custom Transformer* section in this file.

## Features

* Java object instance un-marshalling
* Java classes un-marshalling
* Primitive values un-marshalling
* Automatic conversion of Java Collections to python ones
  (`HashMap` => `dict`, `ArrayList` => `list`, etc.)
* Basic marshalling of simple Java objects (`v1` implementation only)
* Automatically uncompresses GZipped files

## Requirements

* Python >= 2.7 or Python >= 3.4
* `enum34` and `typing` when using Python <= 3.4 (installable with `pip`)
* Maven 2+ (for building test data of serialized objects.
  You can skip it if you do not plan to run `tests.py`)

## Usage (V1 implementation)

Un-marshalling of Java serialised object:

```python
import javaobj

with open("obj5.ser", "rb") as fd:
    jobj = fd.read()

pobj = javaobj.loads(jobj)
print(pobj)
```

Or, you can use `JavaObjectUnmarshaller` object directly:

```python
import javaobj

with open("objCollections.ser", "rb") as fd:
    marshaller = javaobj.JavaObjectUnmarshaller(fd)
    pobj = marshaller.readObject()

    print(pobj.value, "should be", 17)
    print(pobj.next, "should be", True)

    pobj = marshaller.readObject()
```

**Note:** The objects and methods provided by `javaobj` module are shortcuts
to the `javaobj.v1` package, for Compatibility purpose.
It is **recommended** to explicitly import methods and classes from the `v1`
(or `v2`) package when writing new code, in order to be sure that your code
won't need import updates in the future.


## Usage (V2 implementation)

The following methods are provided by the `javaobj.v2` package:

* `load(fd, *transformers, use_numpy_arrays=False)`:
  Parses the content of the given file descriptor, opened in binary mode (`rb`).
  The method accepts a list of custom object transformers. The default object
  transformer is always added to the list.

  The `use_numpy_arrays` flag indicates that the arrays of primitive type
  elements must be loaded using `numpy` (if available) instead of using the
  standard parsing technic.

* `loads(bytes, *transformers, use_numpy_arrays=False)`:
  This the a shortcut to the `load()` method, providing it the binary data
  using a `BytesIO` object.

**Note:** The V2 parser doesn't have the marshalling capability.

Sample usage:

```python
import javaobj.v2 as javaobj

with open("obj5.ser", "rb") as fd:
    pobj = javaobj.load(fd)

print(pobj.dump())
```

### Object Transformer

An object transformer can be called during the parsing of a Java object
instance or while loading an array.

The Java object instance parsing works in two main steps:

1. The transformer is called to create an instance of a bean that inherits
   `JavaInstance`.
1. The latter bean is then called:

   * When the object is written with a custom block data
   * After the fields and annotations have been parsed, to update the content
   of the Python bean.

Here is an example for a Java `HashMap` object. You can look at the code of
the `javaobj.v2.transformer` module to see the whole implementation.

```python
class JavaMap(dict, javaobj.v2.beans.JavaInstance):
    """
    Inherits from dict for Python usage, JavaInstance for parsing purpose
    """
    def __init__(self):
        # Don't forget to call both constructors
        dict.__init__(self)
        JavaInstance.__init__(self)

    def load_from_blockdata(self, parser, reader, indent=0):
    """
    Reads content stored in a block data.

    This method is called only if the class description has both the
    `SC_EXTERNALIZABLE` and `SC_BLOCK_DATA` flags set.

    The stream parsing will stop and fail if this method returns False.

    :param parser: The JavaStreamParser in use
    :param reader: The underlying data stream reader
    :param indent: Indentation to use in logs
    :return: True on success, False on error
    """
    # This kind of class is not supposed to have the SC_BLOCK_DATA flag set
    return False

    def load_from_instance(self, indent=0):
        # type: (int) -> bool
        """
        Load content from the parsed instance object.

        This method is called after the block data (if any), the fields and
        the annotations have been loaded.

        :param indent: Indentation to use while logging
        :return: True on success (currently ignored)
        """
        # Maps have their content in their annotations
        for cd, annotations in self.annotations.items():
            # Annotations are associated to their definition class
            if cd.name == "java.util.HashMap":
                # We are in the annotation created by the handled class
                # Group annotation elements 2 by 2
                # (storage is: key, value, key, value, ...)
                args = [iter(annotations[1:])] * 2
                for key, value in zip(*args):
                    self[key] = value

                # Job done
                return True

        # Couldn't load the data
        return False

class MapObjectTransformer(javaobj.v2.api.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """
    def create_instance(self, classdesc):
        # type: (JavaClassDesc) -> Optional[JavaInstance]
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name == "java.util.HashMap":
            # We can handle this class description
            return JavaMap()
        else:
            # Return None if the class is not handled
            return None
```

### Custom Object Transformer

The custom transformer is called when the class is not handled by the default
object transformer.
A custom object transformer still inherits from the `ObjectTransformer` class,
but it also implements the `load_custom_writeObject` method.

The sample given here is used in the unit tests.

#### Java sample

On the Java side, we create various classes and write them as we wish:

```java
class CustomClass implements Serializable {

    private static final long serialVersionUID = 1;

    public void start(ObjectOutputStream out) throws Exception {
        this.writeObject(out);
    }

    private void writeObject(ObjectOutputStream out) throws IOException {
        CustomWriter custom = new CustomWriter(42);
        out.writeObject(custom);
        out.flush();
    }
}

class RandomChild extends Random {

    private static final long serialVersionUID = 1;
    private int num = 1;
    private double doub = 4.5;

    RandomChild(int seed) {
        super(seed);
    }
}

class CustomWriter implements Serializable {
    protected RandomChild custom_obj;

    CustomWriter(int seed) {
        custom_obj = new RandomChild(seed);
    }

    private static final long serialVersionUID = 1;
    private static final int CURRENT_SERIAL_VERSION = 0;

    private void writeObject(ObjectOutputStream out) throws IOException {
        out.writeInt(CURRENT_SERIAL_VERSION);
        out.writeObject(custom_obj);
    }
}
```

An here is a sample writing of that kind of object:

```java
ObjectOutputStream oos = new ObjectOutputStream(
    new FileOutputStream("custom_objects.ser"));
CustomClass writer = new CustomClass();
writer.start(oos);
oos.flush();
oos.close();
```

#### Python sample

On the Python side, the first step is to define the custom transformers.
They are children of the `javaobj.v2.transformers.ObjectTransformer` class.

```python
class BaseTransformer(javaobj.v2.transformers.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """

    def __init__(self, handled_classes=None):
        self.instance = None
        self.handled_classes = handled_classes or {}

    def create_instance(self, classdesc):
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name in self.handled_classes:
            self.instance = self.handled_classes[classdesc.name]()
            return self.instance

        return None

class RandomChildTransformer(BaseTransformer):
    def __init__(self):
        super(RandomChildTransformer, self).__init__(
            {"RandomChild": RandomChildInstance}
        )

class CustomWriterTransformer(BaseTransformer):
    def __init__(self):
        super(CustomWriterTransformer, self).__init__(
            {"CustomWriter": CustomWriterInstance}
        )

class JavaRandomTransformer(BaseTransformer):
    def __init__(self):
        super(JavaRandomTransformer, self).__init__()
        self.name = "java.util.Random"
        self.field_names = ["haveNextNextGaussian", "nextNextGaussian", "seed"]
        self.field_types = [
            javaobj.v2.beans.FieldType.BOOLEAN,
            javaobj.v2.beans.FieldType.DOUBLE,
            javaobj.v2.beans.FieldType.LONG,
        ]

    def load_custom_writeObject(self, parser, reader, name):
        if name != self.name:
            return None

        fields = []
        values = []
        for f_name, f_type in zip(self.field_names, self.field_types):
            values.append(parser._read_field_value(f_type))
            fields.append(javaobj.beans.JavaField(f_type, f_name))

        class_desc = javaobj.beans.JavaClassDesc(
            javaobj.beans.ClassDescType.NORMALCLASS
        )
        class_desc.name = self.name
        class_desc.desc_flags = javaobj.beans.ClassDataType.EXTERNAL_CONTENTS
        class_desc.fields = fields
        class_desc.field_data = values
        return class_desc
```

Second step is defining the representation of the instances, where the real
object loading occurs. Those classes inherit from
`javaobj.v2.beans.JavaInstance`.

```python
class CustomWriterInstance(javaobj.v2.beans.JavaInstance):
    def __init__(self):
        javaobj.v2.beans.JavaInstance.__init__(self)

    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.annotations:
            # Here, we known there is something written before the fields,
            # even if it's not declared in the class description
            fields = ["int_not_in_fields"] + self.classdesc.fields_names
            raw_data = self.annotations[self.classdesc]
            int_not_in_fields = struct.unpack(
                ">i", BytesIO(raw_data[0].data).read(4)
            )[0]
            custom_obj = raw_data[1]
            values = [int_not_in_fields, custom_obj]
            self.field_data = dict(zip(fields, values))
            return True

        return False


class RandomChildInstance(javaobj.v2.beans.JavaInstance):
    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.field_data:
            fields = self.classdesc.fields_names
            values = [
                self.field_data[self.classdesc][self.classdesc.fields[i]]
                for i in range(len(fields))
            ]
            self.field_data = dict(zip(fields, values))
            if (
                self.classdesc.super_class
                and self.classdesc.super_class in self.annotations
            ):
                super_class = self.annotations[self.classdesc.super_class][0]
                self.annotations = dict(
                    zip(super_class.fields_names, super_class.field_data)
                )
            return True

        return False
```

Finally we can use the transformers in the loading process.
Note that even if it is not explicitly given, the `DefaultObjectTransformer`
will be also be used, as it is added automatically by `javaobj` if it is
missing from the given list.

```python
# Load the object using those transformers
transformers = [
    CustomWriterTransformer(),
    RandomChildTransformer(),
    JavaRandomTransformer()
]
pobj = javaobj.loads("custom_objects.ser", *transformers)

# Here we show a field that isn't visible from the class description
# The field belongs to the class but it's not serialized by default because
# it's static. See: https://stackoverflow.com/a/16477421/12621168
print(pobj.field_data["int_not_in_fields"])
```




%package help
Summary:	Development documents and examples for javaobj-py3
Provides:	python3-javaobj-py3-doc
%description help
# javaobj-py3

<p>
    <a href="https://pypi.python.org/pypi/javaobj-py3/">
        <img src="https://img.shields.io/pypi/v/javaobj-py3.svg" alt="Latest Version" />
        <img src="https://img.shields.io/pypi/l/javaobj-py3.svg" alt="License" />
    </a>
    <a href="https://travis-ci.org/tcalmant/python-javaobj">
    <img src="https://travis-ci.org/tcalmant/python-javaobj.svg?branch=master"
        alt="Travis-CI status" />
    </a>
    <a href="https://coveralls.io/r/tcalmant/python-javaobj?branch=master">
        <img src="https://coveralls.io/repos/tcalmant/python-javaobj/badge.svg?branch=master"
            alt="Coveralls status" />
    </a>
</p>

*python-javaobj* is a python library that provides functions for reading and
writing (writing is WIP currently) Java objects serialized or will be
deserialized by `ObjectOutputStream`. This form of object representation is a
standard data interchange format in Java world.

The `javaobj` module exposes an API familiar to users of the standard library
`marshal`, `pickle` and `json` modules.

## About this repository

This project is a fork of *python-javaobj* by Volodymyr Buell, originally from
[Google Code](http://code.google.com/p/python-javaobj/) and now hosted on
[GitHub](https://github.com/vbuell/python-javaobj).

This fork intends to work both on Python 2.7 and Python 3.4+.

## Compatibility Warnings

### New implementation of the parser

| Implementations | Version  |
|-----------------|----------|
| `v1`, `v2`      | `0.4.0+` |

Since version 0.4.0, two implementations of the parser are available:

* `v1`: the *classic* implementation of `javaobj`, with a work in progress
  implementation of a writer.
* `v2`: the *new* implementation, which is a port of the Java project
  [`jdeserialize`](https://github.com/frohoff/jdeserialize/),
  with support of the object transformer (with a new API) and of the `numpy`
  arrays loading.

You can use the `v1` parser to ensure that the behaviour of your scripts
doesn't change and to keep the ability to write down files.

You can use the `v2` parser for new developments
*which won't require marshalling* and as a *fallback* if the `v1`
fails to parse a file.

### Object transformers V1

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.0+` |

As of version 0.2.0, the notion of *object transformer* from the original
project as been replaced by an *object creator*.

The *object creator* is called before the deserialization.
This allows to store the reference of the converted object before deserializing
it, and avoids a mismatch between the referenced object and the transformed one.

### Object transformers V2

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.0+` |

The `v2` implementation provides a new API for the object transformers.
Please look at the *Usage (V2)* section in this file.

### Bytes arrays

| Implementations | Version  |
|-----------------|----------|
| `v1`            | `0.2.3+` |

As of version 0.2.3, bytes arrays are loaded as a `bytes` object instead of
an array of integers.

### Custom Transformer

| Implementations | Version  |
|-----------------|----------|
| `v2`            | `0.4.2+` |

A new transformer API has been proposed to handle objects written with a custom
Java writer.
You can find a sample usage in the *Custom Transformer* section in this file.

## Features

* Java object instance un-marshalling
* Java classes un-marshalling
* Primitive values un-marshalling
* Automatic conversion of Java Collections to python ones
  (`HashMap` => `dict`, `ArrayList` => `list`, etc.)
* Basic marshalling of simple Java objects (`v1` implementation only)
* Automatically uncompresses GZipped files

## Requirements

* Python >= 2.7 or Python >= 3.4
* `enum34` and `typing` when using Python <= 3.4 (installable with `pip`)
* Maven 2+ (for building test data of serialized objects.
  You can skip it if you do not plan to run `tests.py`)

## Usage (V1 implementation)

Un-marshalling of Java serialised object:

```python
import javaobj

with open("obj5.ser", "rb") as fd:
    jobj = fd.read()

pobj = javaobj.loads(jobj)
print(pobj)
```

Or, you can use `JavaObjectUnmarshaller` object directly:

```python
import javaobj

with open("objCollections.ser", "rb") as fd:
    marshaller = javaobj.JavaObjectUnmarshaller(fd)
    pobj = marshaller.readObject()

    print(pobj.value, "should be", 17)
    print(pobj.next, "should be", True)

    pobj = marshaller.readObject()
```

**Note:** The objects and methods provided by `javaobj` module are shortcuts
to the `javaobj.v1` package, for Compatibility purpose.
It is **recommended** to explicitly import methods and classes from the `v1`
(or `v2`) package when writing new code, in order to be sure that your code
won't need import updates in the future.


## Usage (V2 implementation)

The following methods are provided by the `javaobj.v2` package:

* `load(fd, *transformers, use_numpy_arrays=False)`:
  Parses the content of the given file descriptor, opened in binary mode (`rb`).
  The method accepts a list of custom object transformers. The default object
  transformer is always added to the list.

  The `use_numpy_arrays` flag indicates that the arrays of primitive type
  elements must be loaded using `numpy` (if available) instead of using the
  standard parsing technic.

* `loads(bytes, *transformers, use_numpy_arrays=False)`:
  This the a shortcut to the `load()` method, providing it the binary data
  using a `BytesIO` object.

**Note:** The V2 parser doesn't have the marshalling capability.

Sample usage:

```python
import javaobj.v2 as javaobj

with open("obj5.ser", "rb") as fd:
    pobj = javaobj.load(fd)

print(pobj.dump())
```

### Object Transformer

An object transformer can be called during the parsing of a Java object
instance or while loading an array.

The Java object instance parsing works in two main steps:

1. The transformer is called to create an instance of a bean that inherits
   `JavaInstance`.
1. The latter bean is then called:

   * When the object is written with a custom block data
   * After the fields and annotations have been parsed, to update the content
   of the Python bean.

Here is an example for a Java `HashMap` object. You can look at the code of
the `javaobj.v2.transformer` module to see the whole implementation.

```python
class JavaMap(dict, javaobj.v2.beans.JavaInstance):
    """
    Inherits from dict for Python usage, JavaInstance for parsing purpose
    """
    def __init__(self):
        # Don't forget to call both constructors
        dict.__init__(self)
        JavaInstance.__init__(self)

    def load_from_blockdata(self, parser, reader, indent=0):
    """
    Reads content stored in a block data.

    This method is called only if the class description has both the
    `SC_EXTERNALIZABLE` and `SC_BLOCK_DATA` flags set.

    The stream parsing will stop and fail if this method returns False.

    :param parser: The JavaStreamParser in use
    :param reader: The underlying data stream reader
    :param indent: Indentation to use in logs
    :return: True on success, False on error
    """
    # This kind of class is not supposed to have the SC_BLOCK_DATA flag set
    return False

    def load_from_instance(self, indent=0):
        # type: (int) -> bool
        """
        Load content from the parsed instance object.

        This method is called after the block data (if any), the fields and
        the annotations have been loaded.

        :param indent: Indentation to use while logging
        :return: True on success (currently ignored)
        """
        # Maps have their content in their annotations
        for cd, annotations in self.annotations.items():
            # Annotations are associated to their definition class
            if cd.name == "java.util.HashMap":
                # We are in the annotation created by the handled class
                # Group annotation elements 2 by 2
                # (storage is: key, value, key, value, ...)
                args = [iter(annotations[1:])] * 2
                for key, value in zip(*args):
                    self[key] = value

                # Job done
                return True

        # Couldn't load the data
        return False

class MapObjectTransformer(javaobj.v2.api.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """
    def create_instance(self, classdesc):
        # type: (JavaClassDesc) -> Optional[JavaInstance]
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name == "java.util.HashMap":
            # We can handle this class description
            return JavaMap()
        else:
            # Return None if the class is not handled
            return None
```

### Custom Object Transformer

The custom transformer is called when the class is not handled by the default
object transformer.
A custom object transformer still inherits from the `ObjectTransformer` class,
but it also implements the `load_custom_writeObject` method.

The sample given here is used in the unit tests.

#### Java sample

On the Java side, we create various classes and write them as we wish:

```java
class CustomClass implements Serializable {

    private static final long serialVersionUID = 1;

    public void start(ObjectOutputStream out) throws Exception {
        this.writeObject(out);
    }

    private void writeObject(ObjectOutputStream out) throws IOException {
        CustomWriter custom = new CustomWriter(42);
        out.writeObject(custom);
        out.flush();
    }
}

class RandomChild extends Random {

    private static final long serialVersionUID = 1;
    private int num = 1;
    private double doub = 4.5;

    RandomChild(int seed) {
        super(seed);
    }
}

class CustomWriter implements Serializable {
    protected RandomChild custom_obj;

    CustomWriter(int seed) {
        custom_obj = new RandomChild(seed);
    }

    private static final long serialVersionUID = 1;
    private static final int CURRENT_SERIAL_VERSION = 0;

    private void writeObject(ObjectOutputStream out) throws IOException {
        out.writeInt(CURRENT_SERIAL_VERSION);
        out.writeObject(custom_obj);
    }
}
```

An here is a sample writing of that kind of object:

```java
ObjectOutputStream oos = new ObjectOutputStream(
    new FileOutputStream("custom_objects.ser"));
CustomClass writer = new CustomClass();
writer.start(oos);
oos.flush();
oos.close();
```

#### Python sample

On the Python side, the first step is to define the custom transformers.
They are children of the `javaobj.v2.transformers.ObjectTransformer` class.

```python
class BaseTransformer(javaobj.v2.transformers.ObjectTransformer):
    """
    Creates a JavaInstance object with custom loading methods for the
    classes it can handle
    """

    def __init__(self, handled_classes=None):
        self.instance = None
        self.handled_classes = handled_classes or {}

    def create_instance(self, classdesc):
        """
        Transforms a parsed Java object into a Python object

        :param classdesc: The description of a Java class
        :return: The Python form of the object, or the original JavaObject
        """
        if classdesc.name in self.handled_classes:
            self.instance = self.handled_classes[classdesc.name]()
            return self.instance

        return None

class RandomChildTransformer(BaseTransformer):
    def __init__(self):
        super(RandomChildTransformer, self).__init__(
            {"RandomChild": RandomChildInstance}
        )

class CustomWriterTransformer(BaseTransformer):
    def __init__(self):
        super(CustomWriterTransformer, self).__init__(
            {"CustomWriter": CustomWriterInstance}
        )

class JavaRandomTransformer(BaseTransformer):
    def __init__(self):
        super(JavaRandomTransformer, self).__init__()
        self.name = "java.util.Random"
        self.field_names = ["haveNextNextGaussian", "nextNextGaussian", "seed"]
        self.field_types = [
            javaobj.v2.beans.FieldType.BOOLEAN,
            javaobj.v2.beans.FieldType.DOUBLE,
            javaobj.v2.beans.FieldType.LONG,
        ]

    def load_custom_writeObject(self, parser, reader, name):
        if name != self.name:
            return None

        fields = []
        values = []
        for f_name, f_type in zip(self.field_names, self.field_types):
            values.append(parser._read_field_value(f_type))
            fields.append(javaobj.beans.JavaField(f_type, f_name))

        class_desc = javaobj.beans.JavaClassDesc(
            javaobj.beans.ClassDescType.NORMALCLASS
        )
        class_desc.name = self.name
        class_desc.desc_flags = javaobj.beans.ClassDataType.EXTERNAL_CONTENTS
        class_desc.fields = fields
        class_desc.field_data = values
        return class_desc
```

Second step is defining the representation of the instances, where the real
object loading occurs. Those classes inherit from
`javaobj.v2.beans.JavaInstance`.

```python
class CustomWriterInstance(javaobj.v2.beans.JavaInstance):
    def __init__(self):
        javaobj.v2.beans.JavaInstance.__init__(self)

    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.annotations:
            # Here, we known there is something written before the fields,
            # even if it's not declared in the class description
            fields = ["int_not_in_fields"] + self.classdesc.fields_names
            raw_data = self.annotations[self.classdesc]
            int_not_in_fields = struct.unpack(
                ">i", BytesIO(raw_data[0].data).read(4)
            )[0]
            custom_obj = raw_data[1]
            values = [int_not_in_fields, custom_obj]
            self.field_data = dict(zip(fields, values))
            return True

        return False


class RandomChildInstance(javaobj.v2.beans.JavaInstance):
    def load_from_instance(self):
        """
        Updates the content of this instance
        from its parsed fields and annotations
        :return: True on success, False on error
        """
        if self.classdesc and self.classdesc in self.field_data:
            fields = self.classdesc.fields_names
            values = [
                self.field_data[self.classdesc][self.classdesc.fields[i]]
                for i in range(len(fields))
            ]
            self.field_data = dict(zip(fields, values))
            if (
                self.classdesc.super_class
                and self.classdesc.super_class in self.annotations
            ):
                super_class = self.annotations[self.classdesc.super_class][0]
                self.annotations = dict(
                    zip(super_class.fields_names, super_class.field_data)
                )
            return True

        return False
```

Finally we can use the transformers in the loading process.
Note that even if it is not explicitly given, the `DefaultObjectTransformer`
will be also be used, as it is added automatically by `javaobj` if it is
missing from the given list.

```python
# Load the object using those transformers
transformers = [
    CustomWriterTransformer(),
    RandomChildTransformer(),
    JavaRandomTransformer()
]
pobj = javaobj.loads("custom_objects.ser", *transformers)

# Here we show a field that isn't visible from the class description
# The field belongs to the class but it's not serialized by default because
# it's static. See: https://stackoverflow.com/a/16477421/12621168
print(pobj.field_data["int_not_in_fields"])
```




%prep
%autosetup -n javaobj-py3-0.4.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-javaobj-py3 -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.3-1
- Package Spec generated