thus in TimestampWithTimezone.from_dict(). This is needed to help consuming
existing (invalid) messages from kafka.

Warning: tests added in this revision do not cover the whole
normalize_timestamp() function.

This is a step forward having model objects, declared as frozen, immutable.

This requires attrs_strict >= 0.0.7.

the visit attribute is expected to be strictly positive.

this attribute is not an intrinsic property of a content object, so it
should not be used when comparing or hashing.

Simply add a BaseModel.anonymize() method. Default implementation returns
None, meaning the object is not anonymizable.

For Person, the method returns a Person whith hashed fullname (and unset
name and email).

For Revision and Release, the method returns an anonymized version of
the object, i.e. with instance of Person replaced by anonymized ones.

Production should only use aware datetimes.

Make it possible to generate Content and SkippedContent under different
object types (namely "content" and "skipped_content").

Default to False to keep backward compat.

so one can choose not to generate some of the object types.

Blacklist "origin_visit_status" by default to prevent breaking dependent
packages' tests.

and user required=True to check that, as it is the preferred way

This also adapts the hypothesis strategies, using the plural form
origin_visit_statuses. That plural form is acceptable because in our context,
the statuses are countable.

Related to T2310

- blackify all the python files,
- enable black in pre-commit,
- add a black tox environment.

A release may have an empty message, for instance those derived from
a Mercurial repository.

So make that field optional to avoid type validation errors.

Fix keyword parameters transmission to snapshots_d strategy.

Ensure max_size constraint is respected when fixing snapshot aliases.

With support for str representation of date.
Mostly for testing purpose.

instead of a reference to an Origin entity.

for each entity model `Model`, provide a `models_d` strategy that
produces dicts suitable for using as argument for the `Model.from_dict`
factory method, and reimplement the `models` generator using this
former hypothesis generator.

This is needed to help writing low level tests for model entities.

- add a validator for negative_utc (can be True iff offset is 0),
- update the timestamps_with_timezone hypothesis strategy,
- add low-level tests for it.

This ensures all instanciated model entities have valid types for attributes.

Related to T2308.

(pairing with @vlorentz)

Related to T2310

These are expected to be bytes, not str.

this does not work in the general case since there is no (recursive)
convertion of objects used as model object initialization.

We can only check when using the from_dict() factory.

for better clarity on the code author's intention.

it was still here for bw-compat but should not be necessary any more.

this function does not need to be a local function of the to_dict namespace.

This will allow deduplicating code across loaders.

This generates more realistic contents and avoids spurious HashCollisions when
generating a set of objects using these hypothesis strategies, at the cost of
slightly worse "boundary checking" (i.e. we won't check contents with a length >
4096 bytes).

This lets us generate Content objects directly from a bytestring, with the
proper set of hashes auto-generated from the contents.

Required by loaders, when they can't parse the fullname.