No project description provided

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for dmontagu from gravatar.com dmontagu Avatar for samuelcolvin from gravatar.com samuelcolvin

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Samuel Colvin
  • Requires: Python >=3.7
Classifiers

Project description

pydantic-core

CI Coverage pypi versions license

This package provides the core functionality for pydantic.

The package is currently a work in progress and subject to significant change.

There is, as yet, no integration with pydantic, so schemas can only be defined via dictionaries.

The plan is for pydantic to adopt pydantic-core in v2 and to generate the schema definition from type hints in pydantic, then create a SchemaValidator upon model creation.

pydantic-core will be a separate package, required by pydantic.

The public interface to pydantic shouldn't change too much as a result of this switch (though I intend to clean up quite a lot in the public API in v2 as well).

Example of usage:

from pydantic_core import SchemaValidator, ValidationError


v = SchemaValidator(
    {
        'type': 'typed-dict',
        'fields': {
            'name': {
                'type': 'typed-dict-field',
                'schema': {
                    'type': 'str',
                },
            },
            'age': {
                'type': 'typed-dict-field',
                'schema': {
                    'type': 'int',
                    'ge': 18,
                },
            },
            'is_developer': {
                'type': 'typed-dict-field',
                'schema': {
                    'type': 'default',
                    'schema': {'type': 'bool'},
                    'default': True,
                },
            },
        },
    }
)

r1 = v.validate_python({'name': 'Samuel', 'age': 35})
assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}

# pydantic-core can also validate JSON directly
r2 = v.validate_json('{"name": "Samuel", "age": 35}')
assert r1 == r2

try:
    v.validate_python({'name': 'Samuel', 'age': 11})
except ValidationError as e:
    print(e)
    """
    1 validation error for model
    age
      Input should be greater than or equal to 18
      [type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]
    """

Pydantic-core is currently around 17x faster than pydantic standard. See tests/benchmarks/ for details.

This relative performance will be less impressive for small models but could be significantly move impressive for deeply nested models.

The improvement will decrease slightly when we have to create a class instance after validation, but shouldn't change more.

The aim is to remain 10x faster than current pydantic for common use cases.

Getting Started

While pydantic-core is not yet released and not designed for direct use, you can still try it.

You'll need rust stable installed, or rust nightly if you want to generate accurate coverage.

With rust and python 3.7+ installed, compiling pydantic-core should be possible with roughly the following:

# clone this repo or your fork
git clone git@github.com:pydantic/pydantic-core.git
cd pydantic-core
# create a new virtual env
python3 -m venv env
source env/bin/activate
# install dependencies and install pydantic-core
make install

That should be it, the example shown above should now run.

You might find it useful to look at pydantic_core/_pydantic_core.pyi and pydantic_core/core_schema.py for more information on the python API, beyond that, tests/ provide a large number of examples of usage.

If you want to contribute to pydantic-core, you'll want to use some other make commands:

  • make build-dev to build the package during development
  • make build-prod to perform an optimised build for benchmarking
  • make test to run the tests
  • make testcov to run the tests and generate a coverage report
  • make lint to run the linter
  • make format to format python and rust code
  • make to run format build-dev lint test

Why not JSONSchema?

Looking at the above schema passed to SchemaValidator it would seem reasonable to ask "why not use JSONSchema?".

And if we could use JSONSchema, why not use an existing rust library to do validation?

In fact, in the very early commits to pydantic-core, I did try to use JSONSchema, however I quickly realized it wouldn't work.

JSONSchema does not match the schema for pydantic that closely:

  • there are lots of extra checks which pydantic wants to do and aren't covered by JSONSchema
  • there are configurations which are possible in JSONSchema but are hard or impossible to imagine in pydantic
  • pydantic has the concept of parsing or coercion at it's core, JSONSchema doesn't - it assumes you either accept or reject the input, never change it
  • There are whole classes of problem pydantic has to deal with (like python class instance validation) which JSONSchema has no idea about since it's dedicated to JSON

Even if we could use JSONSchema, it wouldn't help much since rust JSONSchema validators expect to know the schema at compile time, pydantic-core has no knowledge of the schema until SchemaValidator is initialised.

Still, it wouldn't be that hard to implement a conversion layer (either in python or rust) to convert JSONSchema to "pydantic schema" and thereby achieve partial JSONSchema validation.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for dmontagu from gravatar.com dmontagu Avatar for samuelcolvin from gravatar.com samuelcolvin

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: MIT License (MIT)
  • Author: Samuel Colvin
  • Requires: Python >=3.7
Classifiers

Release history Release notifications | RSS feed

2.27.0

2.26.0

2.25.1

2.25.0

2.24.2

2.24.1

2.24.0

2.23.4

2.23.3

2.23.2

2.23.1

2.23.0

2.22.0

2.21.0

2.20.1

2.20.0

2.19.0

2.18.4

2.18.3

2.18.2

2.18.1

2.18.0

2.17.0

2.16.3

2.16.2

2.16.1

2.16.0

2.15.0

2.14.6

2.14.5

2.14.4

2.14.3

2.14.1

2.14.0

2.13.0

2.12.0

2.11.0

2.10.1

2.10.0

2.9.0

2.8.0

2.7.0

2.6.3

2.6.2

2.6.1

2.6.0

2.5.0

2.4.0

2.3.1

2.3.0

2.2.0

2.1.3

2.1.2

2.0.2

2.0.1

2.0.0

0.42.0

0.41.0

0.40.1

0.40.0

This version

0.39.0

0.38.0

0.37.0

0.36.0

0.35.0

0.34.0

0.33.0

0.32.0

0.31.0

0.30.0

0.29.0

0.28.1

0.28.0

0.27.1

0.27.0

0.26.0

0.25.0

0.24.1

0.24.0

0.23.1

0.23.0

0.22.0

0.21.0

0.20.0

0.19.0

0.18.1

0.18.0

0.17.1

0.16.1

0.16.0

0.15.2

0.15.1

0.15.0

0.14.1

0.14.0

0.13.0

0.12.1

0.12.0

0.11.1

0.11.0

0.10.0

0.9.0

0.8.2

0.8.1

0.7.1

0.7.0

0.6.0

0.5.3

0.5.2

0.5.1

0.5.0

0.4.0

0.3.1

0.3.0

0.1.0

0.0.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_core-0.39.0.tar.gz (302.8 kB view details)

Uploaded Source

Built Distributions

pydantic_core-0.39.0-pp39-pypy39_pp73-win_amd64.whl (1.5 MB view details)

Uploaded PyPy Windows x86-64

pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded PyPy musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded PyPy musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded PyPy macOS 10.7+ x86-64

pydantic_core-0.39.0-pp38-pypy38_pp73-win_amd64.whl (1.5 MB view details)

Uploaded PyPy Windows x86-64

pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded PyPy musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded PyPy musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded PyPy macOS 10.7+ x86-64

pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded PyPy musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded PyPy musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded PyPy manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded PyPy manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded PyPy macOS 10.7+ x86-64

pydantic_core-0.39.0-cp311-none-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.11 Windows x86-64

pydantic_core-0.39.0-cp311-none-win32.whl (1.4 MB view details)

Uploaded CPython 3.11 Windows x86

pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.11 musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_s390x.whl (2.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.24+ s390x

pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_ppc64le.whl (1.6 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.24+ ppc64le

pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_armv7l.whl (1.4 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.24+ ARMv7l

pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-cp311-cp311-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.11 macOS 11.0+ ARM64

pydantic_core-0.39.0-cp311-cp311-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.11 macOS 10.7+ x86-64

pydantic_core-0.39.0-cp310-none-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.10 Windows x86-64

pydantic_core-0.39.0-cp310-none-win32.whl (1.4 MB view details)

Uploaded CPython 3.10 Windows x86

pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_s390x.whl (2.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.24+ s390x

pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_ppc64le.whl (1.6 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.24+ ppc64le

pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_armv7l.whl (1.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.24+ ARMv7l

pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-cp310-cp310-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.10 macOS 11.0+ ARM64

pydantic_core-0.39.0-cp310-cp310-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.10 macOS 10.7+ x86-64

pydantic_core-0.39.0-cp39-none-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

pydantic_core-0.39.0-cp39-none-win32.whl (1.4 MB view details)

Uploaded CPython 3.9 Windows x86

pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_s390x.whl (2.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.24+ s390x

pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_ppc64le.whl (1.6 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.24+ ppc64le

pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_armv7l.whl (1.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.24+ ARMv7l

pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-cp39-cp39-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.9 macOS 11.0+ ARM64

pydantic_core-0.39.0-cp39-cp39-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.9 macOS 10.7+ x86-64

pydantic_core-0.39.0-cp38-none-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.8 Windows x86-64

pydantic_core-0.39.0-cp38-none-win32.whl (1.4 MB view details)

Uploaded CPython 3.8 Windows x86

pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_s390x.whl (2.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.24+ s390x

pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_ppc64le.whl (1.6 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.24+ ppc64le

pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_armv7l.whl (1.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.24+ ARMv7l

pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-cp38-cp38-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.8 macOS 11.0+ ARM64

pydantic_core-0.39.0-cp38-cp38-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.7+ x86-64

pydantic_core-0.39.0-cp37-none-win_amd64.whl (1.5 MB view details)

Uploaded CPython 3.7 Windows x86-64

pydantic_core-0.39.0-cp37-none-win32.whl (1.4 MB view details)

Uploaded CPython 3.7 Windows x86

pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_x86_64.whl (1.7 MB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ x86-64

pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_aarch64.whl (1.6 MB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ ARM64

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_s390x.whl (2.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.24+ s390x

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_ppc64le.whl (1.6 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.24+ ppc64le

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_armv7l.whl (1.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.24+ ARMv7l

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

pydantic_core-0.39.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl (1.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.5+ i686

pydantic_core-0.39.0-cp37-cp37m-macosx_11_0_arm64.whl (1.4 MB view details)

Uploaded CPython 3.7m macOS 11.0+ ARM64

pydantic_core-0.39.0-cp37-cp37m-macosx_10_7_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.7+ x86-64

File details

Details for the file pydantic_core-0.39.0.tar.gz.

File metadata

  • Download URL: pydantic_core-0.39.0.tar.gz
  • Upload date:
  • Size: 302.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.11

File hashes

Hashes for pydantic_core-0.39.0.tar.gz
Algorithm Hash digest
SHA256 8368d0510d0020d1bbb4124b31fb663e17e5661b5cc85c48e2b9d9bb0a378d9f
MD5 6aabbdc25c6aa7a2464c872f3f291a68
BLAKE2b-256 4c2949eea949174ad30c23a1f42fb5d105d194d98d7c9b96b1c8fda7d88cc57b

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 7cdcd13b84411d6194de988044b21c2859fcc7c673e0f6dd92e854489c21ae2f
MD5 8550dba66a023b88afd25f3031a71c45
BLAKE2b-256 f268df76ad84b7ed61ef388eeaab89bc9669fb709127c01abafe8009d51329a4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 b2352ef56d0a5157dea6d39f3342980523673ac7f08967e39a68e06c168b94a4
MD5 24d0a1d0b3d4f848d76b669a54d83029
BLAKE2b-256 3fbf0271744df2c60a000ade36ac42ef34aaf9dcde7cfa246f4e20e2f9ba51f3

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 99de1fe8dcb3dc6e853ff6b96da4faa54384bbc4e73dbeb7dc8473f7e9d84243
MD5 31b0008b51b92660160a5c1a887c073f
BLAKE2b-256 022805cf18d0f511ba56e7257027ae24f524d6845ec47c70ad893a165f475b27

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4908e1bbafa91dcbcc8778bfe11ede713ef18dafac4a7f66b7108d348d46e5ea
MD5 f13d040cba789a42ecf1bc499e8b3306
BLAKE2b-256 6e66017c518c8aef90beee755c2236fee4657c9f2004da1b0457454283e1ec6a

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bed383476707e6aeb8d9d6fc83a9b9a6b4f0a00ae440ca3f1cc17686ae3e45ab
MD5 33ef9f30259083ed1f41645ff20e3b9d
BLAKE2b-256 efd611fdc62864ce49bbab385bb06de6b717b2e9447884fe723e1c7f4ff376e2

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 d2c52a9269c302b75265e45f72e1c3a3d4e473c90f11d3c5671ddfa7ba891eae
MD5 71e0917447ff1668551337ef9d72b222
BLAKE2b-256 f8acb53b69b8dc48f26a3c4176960e5d488772c6912cf1bfb998e8ca57498f10

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 96100fb53a7384a99c6121ea90f01aa1cb6100af8dd25f227a5132f38b9199df
MD5 6ff8c284fef30ff8cd798fe784a0c614
BLAKE2b-256 03345bdb0bc053b1c848bbe696cb75007a9b5b1351136de4e7a79d6aea1ae7c6

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-win_amd64.whl
Algorithm Hash digest
SHA256 cf6cff67dcf885f36e9f8a8771225df6b9cd76f308d0c7af66bfd2776f471c7c
MD5 a6bc9032f565eade04360d4d8762fb7c
BLAKE2b-256 e7947400beb368dda61e6f77777f8a708faa001f4aec20a537fe56c4d067dc9d

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 5cf2849e22925411a15d955c03197f71c6507fcdfcaeee110a7202f6e436b5fa
MD5 53466ce6069ef22aebeb42aef72abcf6
BLAKE2b-256 b96e33ce81ab5b6216551c79a0de33d0561870ef8ce7044ad19d1bdcbbf7ac4c

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 711d3a4c9ed8cce84dd55284ff9f4494b825930154a52a37cd02ac4e3a5fd62d
MD5 b57e397b12f4aa366fccbd0b8f679515
BLAKE2b-256 6a2e81ce629273eac5daabcbc47c0e68c2f5e3e7926275726b635b9de50a73c9

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 71e12169625eb4b6939617333dd1aa671c156a44c44880db207c47a2e4c9fe81
MD5 48f6a8e8c4e3e7b2dd9fbc8601cf595e
BLAKE2b-256 c0d0be263343068f7b3add37590ada5aae3e6e9ef9d71fde57bbf5af1c77fd61

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f29f3197b1b50550c299ad8700918fbe383412df6f864fe02190a958b60a3c29
MD5 79ff7ad1742461996b9812cb8cced60e
BLAKE2b-256 6c55ecbf0c01c8196ca6d3ef37ee03c965a15e4e2fe15748ba4119d102ed8584

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 058ea15957753eeb0547bbcb9aca1dca1f11969f0517a6f988d708110c4717e4
MD5 9294974123072ed7494fa022a747f316
BLAKE2b-256 95378045af516750341f434ea7f414774b37fdf023f3249c5036e1b51a07cc0a

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 e7d2a139d384911ee97ad2e9472ae4e79e9fedf278fa8c31b3e391c0279c0207
MD5 e29f417bd777d2cf0e96369f3ef1c558
BLAKE2b-256 963f7f99a99e3efedddfe04750ab20c38ceb9cf82da8035116367e051143c908

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 45f55076c3844ccf1a9f912d5bef61282baae203b30a7b0158add0b4a2141036
MD5 09901674ee7e9df4c0fe07368d1c1d4d
BLAKE2b-256 eb95ff4b8fdfdc07645854fe141d684ada721eafe182482bcb08d2bac7531ca5

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 a5a377d9bf58a99e55d74167c658cb1f170cc1c008b849f5a8ec31b7b0eb65e7
MD5 ba7a8e569cacf3f9e03eb651c740e4e4
BLAKE2b-256 3831b6d08564f771ce87b5a9dec32746b33b90ae35ddbbc3b06e1cc49e1d6110

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4ee34d942a3ab3d55db4945b42ce000c5ca2fd749ab9bbc3fb4ddab41742cbbd
MD5 c1582f2266eb2b2f8e58392ff6d6a6f6
BLAKE2b-256 a0bb6e4744e06e503aab065cc1a1c30b048e0d7cdc915e7aaec4e90f5dfeb920

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 9d32b45650e5bb8670b73d0f9acdae184197308f5a5ecf8e714849c76fd0a6ed
MD5 282b09539d82c3d474ef9448156bf579
BLAKE2b-256 18a00b22604892e136e7429c17b6bf0d05ef788759b8633c4d94f638740ad779

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 5570c884284a5e69278c4bb259c1694d64a5d9bc9a28674d088134246ad73358
MD5 f946c53663d21e2f8a74f00a923c4fb7
BLAKE2b-256 52e9377b6997e222288a723fc99bcf76d6b68216ef6269f7733a82b408d74e5a

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 d3240340147504a1d6a3c86f43b644469f8c61992e0d88287701ee8f6b1b0b2d
MD5 5c1ded42917197bb9b0f3bcba649f810
BLAKE2b-256 f3d99b7391a49be7ccbd721026c4dd660f1452526a7812b6588d7a41c657c175

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-none-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-none-win_amd64.whl
Algorithm Hash digest
SHA256 c9b4909e971e8745af251cecd92aa55f9239bbc9a9630811dd53a00a2b5285f8
MD5 45621444df272a804588e6b9648b37d5
BLAKE2b-256 825854cda4561c6e099fc41c37ee9cd6752caad6d3cc17a83a1c24b6cd596754

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-none-win32.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-none-win32.whl
Algorithm Hash digest
SHA256 3696a567b7517c09ed7dae942fa5b38cd552af5160d0905b76e9607b87e31d01
MD5 7d9110d0e68940cb77506b1cce4ffb58
BLAKE2b-256 68e14e1553a3ecdd375845f320d386f3149e122ea44c1781e8d923a3186a1dbf

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 7bf7470f621285303a5aa6b3e4fb148c5b69f8ee485a920ee9cc1472fd282d2e
MD5 70eb40187eba98cd9eeb30f72d9a545d
BLAKE2b-256 f86420fd09945907199d0a0f516bd4589e31a04e25fce9775ffc3514016444bf

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 00c3a5ab18f355a0b082019fdc4af41b61c4a2e33d97af44c69b611d818bdfe2
MD5 8ddb375dc8cc97c2ee621aeba3c9d03c
BLAKE2b-256 7aa4e0b1cb44397c99fd3737224bf32af94cf596bef80715c0e26732a8e71a1f

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_s390x.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_s390x.whl
Algorithm Hash digest
SHA256 472049006abb4070750d530c96d76122497aec86b99c618d583e596fe986ad0a
MD5 3880b8e587fe8bd5b94146f6f0dacc7c
BLAKE2b-256 d959b88cfad8e4d286c06eb8b2179b65d81e9f0e8a43b0501a10c5f6e7223059

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_ppc64le.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_ppc64le.whl
Algorithm Hash digest
SHA256 87e4d828c0cc28e8ec1abad2e83110e0cca175b3799fc3eeab22e615cefc551f
MD5 5255b713a3800f8e5d7829275b5aa812
BLAKE2b-256 88598feb158be87ba69547bf471709a84acd8e3b671729cabcb691697bcfb1b4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_armv7l.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_24_armv7l.whl
Algorithm Hash digest
SHA256 d3a8d0734b1e82df4a725fa39a1b78625c407b8cf31ae1652382a2f4c8c757b4
MD5 5813157143ba137e4c7134d02cd9ed3b
BLAKE2b-256 9cdf2140080b89404fe58c49cd13b87ff9fea0006c375ee6db526f665c6e1ed9

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 126db921ac7c72cb5d8d1b46540d594d559c25e243b3515aed040a0cd0eafac8
MD5 10c14000579440ff738858e634e11cf0
BLAKE2b-256 91c4ecae440c8c627ae810544257294b78a52f5eaff2c6c1b2005fda386e0d3f

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f8178dc88a246c60fb8264a8422587abc209cac1d9160329af8a6f5f89797cb3
MD5 d9360c693eceb16564f12b2274aa3f2a
BLAKE2b-256 0a5e5c089f8b5633c304b82cfbde3fee9c4e6d0897fa9ab0c5a09e01469d24dc

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 9595444868ece2e67b5e64a583eab10756491d9bf4e6d3876410299297d4adb3
MD5 9939476a59b8a772634ab8d72f195d2b
BLAKE2b-256 8a7c55f6824b2966b5a13fdddc8cf67ed2a830ab2a3d1e428c3f1b84c868f52d

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e7ad056aeb02a474957fa950ff169549e214cec5f0b5c18dae683b0d21227def
MD5 a122d801eb23d4524709dcc4bbad01f6
BLAKE2b-256 77b7ee023ab701dc89b9509a1f435d73316b9ef6789396a7655601a7e3816710

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp311-cp311-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp311-cp311-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 9f1ddc997865f29eee3fea397885595d361a76df80356fec8c47f45b987b7490
MD5 daaacdc2bc7805cab30c6dc3b21aa4eb
BLAKE2b-256 630381dce913aef3ff28a5c1c3938590d9ad3336d4bb35e39ffffd7f5450dbbe

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-none-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-none-win_amd64.whl
Algorithm Hash digest
SHA256 ddf0177f2324200cf9ea3100d4232be275c8986a4cb9b1892e1f53d2584f6b17
MD5 479b98f21f2d9fba3f7451b756fa1a5b
BLAKE2b-256 788bd1bfbd837d46dc150e7f90cbf8129077c9464f755aa78d92356cc298c1bf

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-none-win32.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-none-win32.whl
Algorithm Hash digest
SHA256 dc12b16753591143bdd0c117d74fbe46fda84b4a8eb0383931811819d8679c89
MD5 43fa2be6d2709f0981defe75d73bfd63
BLAKE2b-256 ba5d8da971a0738ba60e4b5fa4017edfc8b2ea7c33b497ff36c47579aaf8b2f7

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 8e86833a13a4f00f264d04f3f36048ce3fea0a92a730db6c9c9c0bf33e277bc2
MD5 625c28e136f2231b0000ebd774f8cb6d
BLAKE2b-256 ff891c2c8c2f9631dcce456e7f116e3af5c049795336ae6bcd8631154ead29d0

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 30ad66243a64818e8afb584596cc9f7f8738406ffe1b4ab0be00e23375753b7c
MD5 29d8a78f9112de6674264b47082f9bf6
BLAKE2b-256 2e3987420354aaedf26df93f26a6c6a36fb6e77a32d97a608fa2ee3bb086bf45

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_s390x.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_s390x.whl
Algorithm Hash digest
SHA256 f8992a135442959269d2c013bd9913c837d5ea215b0e480cda1820c7310df55b
MD5 fb0f0b71a655d877739bf7cce780e977
BLAKE2b-256 865dc1d7ad0f076292d5a473585c36e30eb51229d72749ab1306fa0a4ec33b76

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_ppc64le.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_ppc64le.whl
Algorithm Hash digest
SHA256 8ac4ba34ac3977fa5c6cef5921564133e1295b9d202fed2d6e62187f61651f3c
MD5 652be136e1e06cbf6843dafd4434cc65
BLAKE2b-256 af13a2263078a389ae865416ebd11b7e1ea6857ba17a3fe824247443dc8094dc

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_armv7l.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_24_armv7l.whl
Algorithm Hash digest
SHA256 ac8756c26cbfc57e89ff52817a2802e97376948de002ffe39adb238fbb12c8db
MD5 dc025b3cfbea150d7644b9e28ce39d27
BLAKE2b-256 cb72aa3d34b34ce6efe00fd1f3cf91e26d5df3fe17fec6233d411117f1fd1afc

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f9e18e91b5f4172ea5ebc8fe0461acf6f2de73d4b85b47aee26924dcd304141a
MD5 2fdb854d9f0c9cc723f7ff28eca87c41
BLAKE2b-256 101582a7866bd5660dd4251c87ca23b01b8ba495ea0b7013bb8ee884dfce0311

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 73f785920e974f86d2e37a7fc44a5fdd3e9b6d40fba94137da7a1e02c4dd7c11
MD5 23635cf2ebbc701694f5001fe8c8ec6a
BLAKE2b-256 b6cf7e4c78ea6dd20fae6fcc40d080ea0cc0d243c455bd7f2876f4358083e734

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 3cb0cdc1c5c036aa7caccf6a28e9a2ce3058ae18aceb143870a6a379235b075d
MD5 70215c67905cf0f688c2792223f7c25f
BLAKE2b-256 61e348b6fd9589f9d04efd16604b384aa6cf662513041794155f30ca2dc76ec2

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c9c16fc80e2612b0121eb425890e2e29f03e3038bbd53e5b637cd72aad2c5339
MD5 15aab13feab7ad1ac9646e8138c5586d
BLAKE2b-256 1013f6b741c1118001be9fbfd469eeee2b415d004fa6ddafbfd355e4ff9db634

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp310-cp310-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp310-cp310-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 361135e456636aecad3c15ceabc206fe93b4f86778ca24eefd707952954b7e70
MD5 df1795c2b9103ab57cad6062c8394454
BLAKE2b-256 2e4a71cd253c40cd57fafe76d3701c4deab358799e6f72a63bac4356d8cba806

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-none-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-none-win_amd64.whl
Algorithm Hash digest
SHA256 3a4d4d87dc988d9c52d4e7e31b77d6036b4cab36109826b57cd293ae3179440d
MD5 b9f837fac26bb52bc190a0afb4bf597c
BLAKE2b-256 9df149aa2eed0fb5c4593ff7e6b682b55227ccff446e5968de4244858aa153a4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-none-win32.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-none-win32.whl
Algorithm Hash digest
SHA256 579c69e5a7bf2fd3dedd3c51e91b1beb0c89782ea6c0d1ffd8e48259b70f9d61
MD5 5d5a6a5b8b41997333bf4ec95067803a
BLAKE2b-256 83fc3e212b137c5f53564ed8228509700dde0c8dd17f110a097225bd988af0eb

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 d0ef384f3b4aa5ef4c66d47f4446ca9dc8adbfbc7bf74d1b31b19cc2e2bdf9e8
MD5 94ea8c427deb78f18aa52e838df6aa34
BLAKE2b-256 c13bdfbf0b7528a391cc9f5f0ece71384d80a462a3ea29629d04d0077c60e2a6

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 2c7b5e09e5e6c7cf2ed0ccb5a1ddf1f6d12131315ff70d100fc197711df4a37e
MD5 82e577c228c4cf53d0de11401cd13bd8
BLAKE2b-256 28accda6b041201ccb695f2eded45bf0b834eaa60beebe616973f1f3f1e782c2

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_s390x.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_s390x.whl
Algorithm Hash digest
SHA256 5438ffb320ad430de40de405078b0770beb1eec00e54de18a63c842d114d86b9
MD5 3456b0efd4d3e09f3f9f5afd76a5ac52
BLAKE2b-256 5b0e6b3aaf8e73eab3846e0f0898dc9c5611e3c30c80fa1f567f1160881738ef

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_ppc64le.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_ppc64le.whl
Algorithm Hash digest
SHA256 17a08e9af181097759362182bffa5614cd8c1278f12bab529e6e3bdc4b2d3860
MD5 22a55257bb246d016caf6e311a2477e9
BLAKE2b-256 cfaf2c3098c684b769aa23e8cd52aeb6f9f1f57bd29001de4d504bffd971d061

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_armv7l.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_24_armv7l.whl
Algorithm Hash digest
SHA256 bb4cea3c63defa0fe891af206ad04c8b167852feb6bfba7c1cc4f26a4a2eb39e
MD5 aa978bbeff6323815a35c87189957e40
BLAKE2b-256 965b09027349536a371da3bdc413e129824778ea25fe036d8270ed038bb17cde

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0203bc58c551d018bff3ce3bc863cd2c88504dbb96f79357fdaa22955db64ffe
MD5 0c644542edb8ca573fb5bacd69b26ec0
BLAKE2b-256 4cd15254c030dadeb88a063ac7135f443ccd50a1033681980016a3976a655d0f

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c3b42892739cd3e27c2efbab6682fa91ec07c76381db1ca66da2d889c21e1c8a
MD5 9c2869f74bcd8510652fbd15c20154ab
BLAKE2b-256 8d1c119f34be34bd9f2078f5e6225cb4dd9af4748612becb46a8541c4ee8f05c

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 bf55dc04bb037c06d256e22254401b10fbc3a478835837987435e3c77ec929f9
MD5 1cc36b337e34c95e1ccb0f4106ac8cdc
BLAKE2b-256 1bfb760b5481281b9615e55f4c7a3363feaef05f8441cbc93925a4621148f70a

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 103179d734dafb5da5f74065d1a18274709684a9dc01c61f119c5e39dea4d2df
MD5 ced148109c4520744b0d4c80263660ff
BLAKE2b-256 d15709cf964c2fc35b47d720328e0cfc6db9541a253ff6cc252caea948dbbd9f

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp39-cp39-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp39-cp39-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 c2fb59f98fd0f9f3ee1b77b2c44f282ed8b1cc52d332c2d2a34b6364f63dbfa1
MD5 73ac07547b0499a9afc7f4ea3f687440
BLAKE2b-256 3248df3cd4a5256f3832b5ce0bc45e8d97081db80860870d3522c5d524e27fe4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-none-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-none-win_amd64.whl
Algorithm Hash digest
SHA256 dc8234bd4325341eb660a86dba3688a5287789309a84261b3dbe76d4dd0472e8
MD5 374d42c32a33acb5265a0b8900b14142
BLAKE2b-256 3db28d38bd801be8244e0c97d6a83ac898fae172e918dc63afd016cb67e958f8

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-none-win32.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-none-win32.whl
Algorithm Hash digest
SHA256 3a5795a0521ce88adbffd16796e85cdd06546805ff869fed0cac1a9c297d9445
MD5 0f17359f0e087a579ad6d808eb606a7b
BLAKE2b-256 30ad86af97225b000bdaae55672d1bae9cc2c8dc9149c57cd37e2116ed8699a5

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 5ccb4a21713a831f888c0bf22be4888df475650f699cc35a675cb91cc55e780b
MD5 6e8448f3f7ad2030e75d3addca23f23e
BLAKE2b-256 7ccd128d387f0acc2e008eb1d1869648c0358ac89637d067fffde77440ef9548

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 9602b61c85d9c12b0e66792eebc85152b565c24431a69c47f3cba811b1507389
MD5 afbd294bbbcbc8fe15b57dfa5927833f
BLAKE2b-256 105b3ae9f4f1e5299c9c2ba7a578b852f0a40c54f9c1a83f3de5d08b90826281

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_s390x.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_s390x.whl
Algorithm Hash digest
SHA256 e77a053384a29c4ffdad2a717809e347b864c582c407ab88eb9ff3fa3bce22a7
MD5 d8dd824300b4a61a6107eb302efa1d99
BLAKE2b-256 1e823b5a71e50b570b041e402469ae21b8242081504e4c50ba6bc40a51440308

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_ppc64le.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_ppc64le.whl
Algorithm Hash digest
SHA256 749c0a2038ca7fb3a29ff7f9ce7d3248d8bb9f42d2ef609b4e4c158e74368145
MD5 f9712e29cc80149c724a930b3f959551
BLAKE2b-256 49d406ca9f1732fe981f3ae7c749a0ceb3b9e0848896eac610afeaf413ae7d38

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_armv7l.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_24_armv7l.whl
Algorithm Hash digest
SHA256 b749a22e311e235974516a4bca00afdf4f2e67dd7773d3850c1b17f1da1131b1
MD5 fe2cb1cbfb8a28b195dd0ee638507b3a
BLAKE2b-256 07b49c10f031fa975fde14f97d4be8c58c3be669b0cd2ff20242b405312c86a8

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4060e765690727095969f7038e8e1c7251d4bd518e151174b5cb54dae0163c68
MD5 ff313585dfd046c91a9b1ccd0a19df2f
BLAKE2b-256 fde462a993fd668c679373487a9f4eb89bcda1d44a7d75c532872cf549cabc93

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d1f187d852ca78b9e23a0155be5f759f65f5e5c7dc4b29b7ea7aa5df48537ba1
MD5 c841100a4459cce9712868532407d89d
BLAKE2b-256 4ac9e3aede8a6e8e2b552cd12a7a8b5488686d6a6534060ff7d6da2867022fa3

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 ef212fafe222ae7fdf38ba16b5c7bb7eddf1b44cc901bc3056569eefcf54da94
MD5 d0fbaf1a4fb6447c64523c8b473f2e2f
BLAKE2b-256 e1050e5fc693d422d8fc385af604dedbbf7989fd5ec0d48b18f2eb8c5cd215a3

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a5ca573f261ea525c5bb10e03d41dd9ce76aea93b75f9ca2dc5dc4ef147ea2a3
MD5 57910af388457b778f2a9edccebdf295
BLAKE2b-256 bd10eae179a0e14dafdb99d8daad2214fbd4ce37ec930d5f4d6d5a76c53fb9b4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp38-cp38-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp38-cp38-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 abbcbc69868ad7f642caa319a7b5f829e6f9b6db5194b6b82b7a9561ac992663
MD5 541010b065d4fad3b08725eaf8326b70
BLAKE2b-256 30be207b81da280ba0c4111d12bc5dc9667a584280a8f7c94c79b6a0ec485652

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-none-win_amd64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 84675545b74ce4ea53f0e2413f182db1501b1684d6359f7c2cb4d37d24a9afec
MD5 1c9a70d523221de075d4f18f0dca4ff1
BLAKE2b-256 d149665fa0ab78fb9d64737f9244611112ed2dd62294da07abbec5ff621e78cb

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-none-win32.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-none-win32.whl
Algorithm Hash digest
SHA256 2bfe35d886d93c7911b4d56ab3a6725a7866b35f09aceaf3d0d64babed701b73
MD5 e7e0bd1503cc27253eb72cb4473baa5d
BLAKE2b-256 035fe72f736d303d695be4d45b00cd9a26063575058d57b49c2962cfae75a43c

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 1f89064cf78b57bf2930a14b41aa4b4240bd2993a57efb56b5ad8be02f4048b3
MD5 79bb292c36fc30fd16f11ea50e4a360a
BLAKE2b-256 1b7bb9ccbcd5392b02270ee14e1121672bc102b903bb3eb2d528347c08ee6fa8

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-musllinux_1_1_aarch64.whl
Algorithm Hash digest
SHA256 6e9f98a9df2cea76b1a8bd70d00b4ee3b7597887281c2e85c8ad690ed881ef34
MD5 e0fbfbf3d9c0c967c8bb20d81396cffc
BLAKE2b-256 6dc4a7e33420c947fb90e84686db7e156d212931405c453f71d6237489eb78d6

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_s390x.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_s390x.whl
Algorithm Hash digest
SHA256 a0f7d1454f86865cd2ec47e36ebe2e20cca5be71eaa5c2e422adf1e0355ae705
MD5 daf60fd42f5fd28034121c4014ca4817
BLAKE2b-256 a8a01a8775109cb96cf2814b2e4672ce8077639ea8fea9a763855961f962a08a

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_ppc64le.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_ppc64le.whl
Algorithm Hash digest
SHA256 549a0940b7164b23d52df4d6bd73637e989688d28dbeb9e24d87a27da48f23bc
MD5 3d11e7769114248f6fb5c31cf01c0a0b
BLAKE2b-256 622f090ba715ed3e6211f9fd5e8a0c3ff29ebd0e0c01eb69971cec46493923e4

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_armv7l.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_24_armv7l.whl
Algorithm Hash digest
SHA256 45926ab2adef2deff4fc89e12bf5ba015ad297efefcd0f84b8689123a6c3b126
MD5 0daa0a7f9f80b7d9d508d734be2e21a6
BLAKE2b-256 65cba5fb2d0d9ddedb109af8104445843059173d036e53be720e257fa51c035f

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 38ac25daeb3ea45ecd4e8580f7375e6434daf02c6ca185f246f2e682de554ab5
MD5 3703532665765468506717731eb9a380
BLAKE2b-256 e3d203bfd28dbe7b88c0c3dc6d972eaa1893174802c11375ebafff3b5047374e

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 351a34da43f44a52bbd52380322bfb364f2efe423275c4917c8c26100c1b8ced
MD5 edffbcb5b43d7191bb060215c3498062
BLAKE2b-256 06594117e5ac138bca096879d07f446fd24a9b198755d344e0f640fb1f2b23bf

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
Algorithm Hash digest
SHA256 8849794ae4da5d244d215ee582e83e169120c475a7e05f1adf227035b9edbde3
MD5 79b744ee6eccb489bf88c8d7f5270824
BLAKE2b-256 e922a393b654f9e74c37f5ace2739d521430ce8944c2ceb5eec9c1b0576c47f1

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 bd5f3e98d112c6eab8c41cf2baf3dab8006c3223d5f2c9695b1cba7ab87bbfb5
MD5 fc25182945d54a30df04dd16e9ed0b0d
BLAKE2b-256 02e6fdf028114408f727b7767106509664a4d560198c40abe5eb4a7e7d6650ba

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_core-0.39.0-cp37-cp37m-macosx_10_7_x86_64.whl.

File metadata

File hashes

Hashes for pydantic_core-0.39.0-cp37-cp37m-macosx_10_7_x86_64.whl
Algorithm Hash digest
SHA256 9aba06c25f356c0fb11f1dd2d0317a5599f223a3b283ebbc1aa3a1100a6920f7
MD5 eb880ff0d3a9057c4686bb9691dcd9e7
BLAKE2b-256 21157cc4530aaa6800fa91ed70464a79593865159f629ce22675d1f7e5a90e7b

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page