Initially this was my attempt to use stanza, which is an nlp engine by
Stanford.  But generally, it's an update to allow for one to add NLP
engines and custom recognizers more easily.  Specifically, I
standardized the format of the recognizers, removed use of global
variables when possible, and removed a lot of hard-coding of defaults.

I am thinking of using presidio for several non-english projects at work
and these are several of the changes that I made.

Below is a list of the changes in list form:

* make spacy and/or stanza optional
  * remove requirement of en_core_web_lg from install
* allow predefined recognizers to take parameters
  * this allows for easily using these as non-english recognizers
* create config files for different NLP engines
* create tests for stanza
* make all spacy and stanza tests optional
* create a Dockerfile for an anaconda-based image
  * pytorch is built with MKL and is much faster on cpu from conda
* completely rewrote the IBAN recognizer
  * the current version only recognizes IBANs if they are the entirety
    of the string.  This version will find IBANs in sentences.
* fixed some tests
* created a `run.sh` file, so just run dockers without rebuilding them

"Breaking" Changes:

* I would like to use [black](https://github.com/psf/black), but it's
  not super friendly with pylint.  My suggestion is to drop pylint and
  use black instead.
* Default spacy model is `en` rather than `en_core_web_lg` and no spacy
  models are downloaded by default.  The idea is to let the user choose
  which models they want.  For non-english users, it saves a lot of time
  at installation because you don't need to install the large spacy
  model that you aren't using.

Signed-off-by: David Pollack <d.pollack@solvemate.com>

* made spacy required
* using spacy-stanza for stanza models
* refactor tests to use pytest
* make one test reliant on big model optional

All tests have been refactored to use pytest.  Previously, there was a
mix of unittest, pytest and miscellaneous global initializations.  This
commit moves everything to pytest.  There is now extensive use of
fixtures instead of global variables and parametrized tests instead of
duplicated code for each test.  The major difference is that
parametrized tests are not individually named.

this installs the big spacy model by default in the Docker and the Azure
pipeline.

* add documentation and doc strings
* change yaml field names to be more logical

…e_langs

* fix merge conflicts with documentation