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        "author": "Jaumo GmbH",
        "author_email": "nicola.bova@jaumo.com",
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        "description": "# pyavro-gen\n\nStandard Avro implementation for Python is typeless and operates on `dict`s.\nWhile this is convenient for small projects, larger ones, often with hundreds of schemas, \ncertainly benefit from the ability to enforce schemas during record construction.   \n\nThis library allows Python Avro users to employ Specific Records.\n\n## Usage:\n\n```bash\npip install pyavro-gen\n```\n\nFor now, install this branch of `confluent_kafka`\n```bash\npip install -e git+https://github.com/confluentinc/confluent-kafka-python.git@serde#egg=confluent_kafka\n```\n\n```bash\npyavrogen.py -v \\\n    -i my_schemas_dir \\\n    -o jaumoavro \\\n    -ie 'avsc' \\\n    -b com.jaumo.schema \\\n    -r com.jaumo.schema.rpc \\\n    -t com.jaumo.schema.type\n```\n\nNow you can import your classes like\n\n```python\nfrom jaumoavro.com.jaumo.schema.domain.user import Updated\n\nu = Updated(...)\n```\n\nFor a full stack test with Kafka Avro Serdes and `fastavro`, run\n```bash\npyavrogen_test.py -m jaumoavro -d com.jaumo.schema.domain -n 100 -v\n```\n\n## Generation and test programmatically\n\n```python\nfrom avro_preprocessor.avro_paths import AvroPaths\nfrom pyavro_gen.generator import AvroGenerator\nfrom pyavro_gen.pyavrogen_test import do_test_generated_classes\n\ngenerator = AvroGenerator(\n    AvroPaths(\n        input_path='myschemas/',\n        output_path='avroclasses/',\n        input_schema_file_extension='avsc',\n        base_namespace='com.jaumo.schema',\n        rpc_namespace='com.jaumo.schema.rpc',\n        types_namespace='com.jaumo.schema.type',\n    )\n)\ngenerator.process()\n\n# Optionally, test the classes using fastavro:\ndo_test_generated_classes(\n    module='avroclasses/',\n    domain_namespace='com.jaumo.schema.domain',\n    number_of_cycles=100\n)\n```\n\n### Generation using custom classes:\n\n```python\nfrom pyavro_gen.generation_classes import GENERATION_CLASSES, GenerationClassesType\nfrom pyavro_gen.codewriters.base import Decorator, ClassWriter, Extension\nfrom typing import Optional\n\n# First define two custom ClassWriters\n\nclass RpcWriter(ClassWriter):\n    def __init__(self,\n                 fully_qualified_name: str,\n                 doc: Optional[str] = None,\n                 prefix: Optional[str] = None):\n        super().__init__(fully_qualified_name, doc, prefix)\n\n        self.extensions = [\n            Extension('abc.ABC')\n        ]\n\n\nclass UndictifiableClassWriter(ClassWriter):\n    def __init__(self,\n                 fully_qualified_name: str,\n                 doc: Optional[str] = None,\n                 prefix: Optional[str] = None):\n        super().__init__(fully_qualified_name, doc, prefix)\n\n        self.decorators = [\n            Decorator('@type_checked_constructor()',\n                      ClassWriter('undictify.type_checked_constructor')),\n            Decorator('@dataclass', ClassWriter('dataclasses.dataclass'))\n        ]\n\n\n# Then, register them in the GENERATION_CLASSES variable\n\nGENERATION_CLASSES[GenerationClassesType.RECORD_CLASS] = UndictifiableClassWriter\nGENERATION_CLASSES[GenerationClassesType.RPC_CLASS] = RpcWriter\n\n# Then, generate classes in module `avroclasses` as shown above.\n```\n\n\n",
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