automatic import of python-avro-validator

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+/avro_validator-1.2.1.tar.gz
diff --git a/python-avro-validator.spec b/python-avro-validator.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-avro-validator
+Version:	1.2.1
+Release:	1
+Summary:	Pure python avro schema validator
+License:	MIT License
+URL:		https://github.com/leocalm/avro_validator
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/78/11/96eef6a6d3f8949d9fd21918cb0b023699b0408215c37734334bf83e1d56/avro_validator-1.2.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-pytest
+Requires:	python3-coverage
+Requires:	python3-tox
+Requires:	python3-flake8
+Requires:	python3-hypothesis
+Requires:	python3-pytest-cov
+Requires:	python3-coveralls
+Requires:	python3-sphinx
+Requires:	python3-pallets-sphinx-themes
+Requires:	python3-sphinxcontrib-log-cabinet
+Requires:	python3-sphinx-issues
+
+%description
+[![CI](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml/badge.svg)](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml)
+[![Documentation Status](https://readthedocs.org/projects/avro-validator/badge/?version=latest)](https://avro-validator.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/avro-validator.svg)](https://badge.fury.io/py/avro-validator)
+[![Downloads](https://pepy.tech/badge/avro-validator)](https://pepy.tech/project/avro-validator)
+[![Coverage Status](https://coveralls.io/repos/github/leocalm/avro_validator/badge.svg?branch=main)](https://coveralls.io/github/leocalm/avro_validator?branch=main)
+
+# Avro Validator
+A pure python avro schema validator.
+
+The default avro library for Python provide validation of data against the schema, the problem is that the output of 
+this validation doesn't provide information about the error. 
+All you get is the `the datum is not an example of the schema` error message.
+
+When working with bigger avro schemas, sometimes is not easy to visually find the field that has an issue.
+
+This library provide clearer exceptions when validating data against the avro schema, in order to be easier to 
+identify the field that is not compliant with the schema and the problem with that field.
+
+## Installing
+Install using pip:
+```bash
+$ pip install -U avro_validator
+```
+
+## Validating data against Avro schema
+The validator can be used as a console application. It receives a schema file, and a data file, validating the data
+and returning the error message in case of failure.
+
+The avro_validator can also be used as a library in python code.
+
+### Console usage
+In order to validate the `data_to_validate.json` file against the `schema.avsc` using the `avro_validator` callable, just type:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+OK
+```
+Since the data is valid according to the schema, the return message is `OK`.
+
+#### Error validating the data
+If the data is not valid, the program returns an error message:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+Error validating value for field [data,my_boolean_value]: The value [123] is not from one of the following types: [[NullType, BooleanType]]
+```
+This message indicates that the field `my_boolean_value` inside the `data` dictionary has value `123`, which is not 
+compatible with the `bool` type.
+
+#### Command usage
+It is possible to get information about usage of the `avro_validator` using the help:
+```bash
+$ avro_validator -h
+```
+
+### Library usage
+#### Using schema file
+When using the avr_validator as a library, it is possible to pass the schema as a file:
+```python
+from avro_validator.schema import Schema
+
+schema_file = 'schema.avsc'
+
+schema = Schema(schema_file)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, if the `data_to_validate` is valid according to the schema, then the
+ `parsed_schema.validate(data_to_validate)` call will return `True`.
+
+#### Using a dict as schema
+It is also possible to provide the schema as a json string:
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, the `parsed_schema.validate(data_to_validate)` call will return `True`, since the data is valid according to the schema.
+
+#### Invalid data
+If the data is not valid, the `parsed_schema.validate` will raise a `ValueError`, with the message containing the error description.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'my_name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+The schema defined expects only one field, named `name`, but the data contains only the field `name_2`, 
+making it invalid according to the schema. In this case, the `validate` method will return the following error:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-3-a5e8ce95d21c>", line 23, in <module>
+    parsed_schema.validate(data_to_validate)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 563, in validate
+    raise ValueError(f'The fields from value [{value}] differs from the fields '
+ValueError: The fields from value [{'my_name': 'My Name'}] differs from the fields of the record type [{'name': RecordTypeField <name: name, type: StringType, doc: Field that stores the name, default: None, order: None, aliases: None>}]
+```
+The message detailed enough to enable the developer to pinpoint the error in the data.
+
+#### Invalid schema
+If the schema is not valid according to avro specifications, the `parse` method will also return a `ValueError`.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'invalid_type',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+```
+Since the schema tries to define the `name` field as `invalid_type`, the schema declaration is invalid, 
+thus the following exception will be raised:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-2-7f3f77000f08>", line 18, in <module>
+    parsed_schema = schema.parse()
+  File "/opt/dwh/avro_validator/avro_validator/schema.py", line 28, in parse
+    return RecordType.build(schema)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in build
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in <dictcomp>
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 419, in build
+    field.__type = cls.__build_field_type(json_repr)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 401, in __build_field_type
+    raise ValueError(f'Error parsing the field [{fields}]: {actual_error}')
+ValueError: Error parsing the field [name]: The type [invalid_type] is not recognized by Avro
+```
+The message is clearly indicating that the the `invalid_type` is not recognized by avro.
+
+
+%package -n python3-avro-validator
+Summary:	Pure python avro schema validator
+Provides:	python-avro-validator
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-avro-validator
+[![CI](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml/badge.svg)](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml)
+[![Documentation Status](https://readthedocs.org/projects/avro-validator/badge/?version=latest)](https://avro-validator.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/avro-validator.svg)](https://badge.fury.io/py/avro-validator)
+[![Downloads](https://pepy.tech/badge/avro-validator)](https://pepy.tech/project/avro-validator)
+[![Coverage Status](https://coveralls.io/repos/github/leocalm/avro_validator/badge.svg?branch=main)](https://coveralls.io/github/leocalm/avro_validator?branch=main)
+
+# Avro Validator
+A pure python avro schema validator.
+
+The default avro library for Python provide validation of data against the schema, the problem is that the output of 
+this validation doesn't provide information about the error. 
+All you get is the `the datum is not an example of the schema` error message.
+
+When working with bigger avro schemas, sometimes is not easy to visually find the field that has an issue.
+
+This library provide clearer exceptions when validating data against the avro schema, in order to be easier to 
+identify the field that is not compliant with the schema and the problem with that field.
+
+## Installing
+Install using pip:
+```bash
+$ pip install -U avro_validator
+```
+
+## Validating data against Avro schema
+The validator can be used as a console application. It receives a schema file, and a data file, validating the data
+and returning the error message in case of failure.
+
+The avro_validator can also be used as a library in python code.
+
+### Console usage
+In order to validate the `data_to_validate.json` file against the `schema.avsc` using the `avro_validator` callable, just type:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+OK
+```
+Since the data is valid according to the schema, the return message is `OK`.
+
+#### Error validating the data
+If the data is not valid, the program returns an error message:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+Error validating value for field [data,my_boolean_value]: The value [123] is not from one of the following types: [[NullType, BooleanType]]
+```
+This message indicates that the field `my_boolean_value` inside the `data` dictionary has value `123`, which is not 
+compatible with the `bool` type.
+
+#### Command usage
+It is possible to get information about usage of the `avro_validator` using the help:
+```bash
+$ avro_validator -h
+```
+
+### Library usage
+#### Using schema file
+When using the avr_validator as a library, it is possible to pass the schema as a file:
+```python
+from avro_validator.schema import Schema
+
+schema_file = 'schema.avsc'
+
+schema = Schema(schema_file)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, if the `data_to_validate` is valid according to the schema, then the
+ `parsed_schema.validate(data_to_validate)` call will return `True`.
+
+#### Using a dict as schema
+It is also possible to provide the schema as a json string:
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, the `parsed_schema.validate(data_to_validate)` call will return `True`, since the data is valid according to the schema.
+
+#### Invalid data
+If the data is not valid, the `parsed_schema.validate` will raise a `ValueError`, with the message containing the error description.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'my_name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+The schema defined expects only one field, named `name`, but the data contains only the field `name_2`, 
+making it invalid according to the schema. In this case, the `validate` method will return the following error:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-3-a5e8ce95d21c>", line 23, in <module>
+    parsed_schema.validate(data_to_validate)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 563, in validate
+    raise ValueError(f'The fields from value [{value}] differs from the fields '
+ValueError: The fields from value [{'my_name': 'My Name'}] differs from the fields of the record type [{'name': RecordTypeField <name: name, type: StringType, doc: Field that stores the name, default: None, order: None, aliases: None>}]
+```
+The message detailed enough to enable the developer to pinpoint the error in the data.
+
+#### Invalid schema
+If the schema is not valid according to avro specifications, the `parse` method will also return a `ValueError`.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'invalid_type',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+```
+Since the schema tries to define the `name` field as `invalid_type`, the schema declaration is invalid, 
+thus the following exception will be raised:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-2-7f3f77000f08>", line 18, in <module>
+    parsed_schema = schema.parse()
+  File "/opt/dwh/avro_validator/avro_validator/schema.py", line 28, in parse
+    return RecordType.build(schema)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in build
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in <dictcomp>
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 419, in build
+    field.__type = cls.__build_field_type(json_repr)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 401, in __build_field_type
+    raise ValueError(f'Error parsing the field [{fields}]: {actual_error}')
+ValueError: Error parsing the field [name]: The type [invalid_type] is not recognized by Avro
+```
+The message is clearly indicating that the the `invalid_type` is not recognized by avro.
+
+
+%package help
+Summary:	Development documents and examples for avro-validator
+Provides:	python3-avro-validator-doc
+%description help
+[![CI](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml/badge.svg)](https://github.com/leocalm/avro_validator/actions/workflows/ci.yaml)
+[![Documentation Status](https://readthedocs.org/projects/avro-validator/badge/?version=latest)](https://avro-validator.readthedocs.io/en/latest/?badge=latest)
+[![PyPI version](https://badge.fury.io/py/avro-validator.svg)](https://badge.fury.io/py/avro-validator)
+[![Downloads](https://pepy.tech/badge/avro-validator)](https://pepy.tech/project/avro-validator)
+[![Coverage Status](https://coveralls.io/repos/github/leocalm/avro_validator/badge.svg?branch=main)](https://coveralls.io/github/leocalm/avro_validator?branch=main)
+
+# Avro Validator
+A pure python avro schema validator.
+
+The default avro library for Python provide validation of data against the schema, the problem is that the output of 
+this validation doesn't provide information about the error. 
+All you get is the `the datum is not an example of the schema` error message.
+
+When working with bigger avro schemas, sometimes is not easy to visually find the field that has an issue.
+
+This library provide clearer exceptions when validating data against the avro schema, in order to be easier to 
+identify the field that is not compliant with the schema and the problem with that field.
+
+## Installing
+Install using pip:
+```bash
+$ pip install -U avro_validator
+```
+
+## Validating data against Avro schema
+The validator can be used as a console application. It receives a schema file, and a data file, validating the data
+and returning the error message in case of failure.
+
+The avro_validator can also be used as a library in python code.
+
+### Console usage
+In order to validate the `data_to_validate.json` file against the `schema.avsc` using the `avro_validator` callable, just type:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+OK
+```
+Since the data is valid according to the schema, the return message is `OK`.
+
+#### Error validating the data
+If the data is not valid, the program returns an error message:
+```bash
+$ avro_validator schema.avsc data_to_valdate.json
+Error validating value for field [data,my_boolean_value]: The value [123] is not from one of the following types: [[NullType, BooleanType]]
+```
+This message indicates that the field `my_boolean_value` inside the `data` dictionary has value `123`, which is not 
+compatible with the `bool` type.
+
+#### Command usage
+It is possible to get information about usage of the `avro_validator` using the help:
+```bash
+$ avro_validator -h
+```
+
+### Library usage
+#### Using schema file
+When using the avr_validator as a library, it is possible to pass the schema as a file:
+```python
+from avro_validator.schema import Schema
+
+schema_file = 'schema.avsc'
+
+schema = Schema(schema_file)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, if the `data_to_validate` is valid according to the schema, then the
+ `parsed_schema.validate(data_to_validate)` call will return `True`.
+
+#### Using a dict as schema
+It is also possible to provide the schema as a json string:
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+In this example, the `parsed_schema.validate(data_to_validate)` call will return `True`, since the data is valid according to the schema.
+
+#### Invalid data
+If the data is not valid, the `parsed_schema.validate` will raise a `ValueError`, with the message containing the error description.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'string',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+
+data_to_validate = {
+    'my_name': 'My Name'
+}
+
+parsed_schema.validate(data_to_validate)
+```
+The schema defined expects only one field, named `name`, but the data contains only the field `name_2`, 
+making it invalid according to the schema. In this case, the `validate` method will return the following error:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-3-a5e8ce95d21c>", line 23, in <module>
+    parsed_schema.validate(data_to_validate)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 563, in validate
+    raise ValueError(f'The fields from value [{value}] differs from the fields '
+ValueError: The fields from value [{'my_name': 'My Name'}] differs from the fields of the record type [{'name': RecordTypeField <name: name, type: StringType, doc: Field that stores the name, default: None, order: None, aliases: None>}]
+```
+The message detailed enough to enable the developer to pinpoint the error in the data.
+
+#### Invalid schema
+If the schema is not valid according to avro specifications, the `parse` method will also return a `ValueError`.
+```python
+import json
+from avro_validator.schema import Schema
+
+schema = json.dumps({
+    'name': 'test schema',
+    'type': 'record',
+    'doc': 'schema for testing avro_validator',
+    'fields': [
+        {
+            'name': 'name',
+            'type': 'invalid_type',
+            'doc': 'Field that stores the name'
+        }
+    ]
+})
+
+schema = Schema(schema)
+parsed_schema = schema.parse()
+```
+Since the schema tries to define the `name` field as `invalid_type`, the schema declaration is invalid, 
+thus the following exception will be raised:
+```
+Traceback (most recent call last):
+  File "/Users/leonardo.almeida/.pyenv/versions/avro_validator_venv/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3326, in run_code
+    exec(code_obj, self.user_global_ns, self.user_ns)
+  File "<ipython-input-2-7f3f77000f08>", line 18, in <module>
+    parsed_schema = schema.parse()
+  File "/opt/dwh/avro_validator/avro_validator/schema.py", line 28, in parse
+    return RecordType.build(schema)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in build
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 588, in <dictcomp>
+    record_type.__fields = {field['name']: RecordTypeField.build(field) for field in json_repr['fields']}
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 419, in build
+    field.__type = cls.__build_field_type(json_repr)
+  File "/opt/dwh/avro_validator/avro_validator/avro_types.py", line 401, in __build_field_type
+    raise ValueError(f'Error parsing the field [{fields}]: {actual_error}')
+ValueError: Error parsing the field [name]: The type [invalid_type] is not recognized by Avro
+```
+The message is clearly indicating that the the `invalid_type` is not recognized by avro.
+
+
+%prep
+%autosetup -n avro-validator-1.2.1
+
+%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-avro-validator -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..fe6fbb0
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+89533801ce60ef8e5a6a34d7f0bc9004  avro_validator-1.2.1.tar.gz