Bump rapidfuzz from 0.12.2 to 1.2.1

[dependabot-preview]

Bumps rapidfuzz from 0.12.2 to 1.2.1.

Release notes

Sourced from rapidfuzz's releases.

Release 1.2.1

Performance

• further improve bitparallel implementation of uniform Levenshtein distance for strings with a length > 64 (in many cases more than 50% faster)

Release 1.2.0

Changed

• add more benchmarks to documentation

Performance

• add bitparallel implementation to InDel Distance (Levenshtein with the weights 1,1,2) for strings with a length > 64
• improve bitparallel implementation of uniform Levenshtein distance for strings with a length > 64
• use the InDel Distance and uniform Levenshtein distance in more cases instead of the generic implementation
• Directly use the Levenshtein implementation in C++ instead of using it through Python in process.*

Release 1.1.2

Fixed

• Fix reference counting in process.extract (see #81)

Release 1.1.1

Fixed

• Fix result conversion in process.extract (see #79)

Release 1.1.0

Changed

• string_metric.normalized_levenshtein supports now all weights
• when different weights are used for Insertion and Deletion the strings are not swapped inside the Levenshtein implementation anymore. So different weights for Insertion and Deletion are now supported.
• replace C++ implementation with a Cython implementation. This has the following advantages:
  • The implementation is less error prone, since a lot of the complex things are done by Cython
  • slighly faster than the current implementation (up to 10% for some parts)
  • about 33% smaller binary size
  • reduced compile time
• Added **kwargs argument to process.extract/extractOne/extract_iter that is passed to the scorer
• Add max argument to hamming distance
• Add support for whole Unicode range to utils.default_process

Performance

• replaced Wagner Fischer usage in the normal Levenshtein distance with a bitparallel implementation

Release 1.0.2

Fixed

• The bitparallel LCS algorithm in fuzz.partial_ratio did not find the longest common substring properly in some cases. The old algorithm is used again until this bug is fixed.

Release 1.0.1

Changed

• string_metric.normalized_levenshtein supports now the weights (1, 1, N) with N >= 1

Performance Improvements

• The Levenshtein distance with the weights (1, 1, >2) do now use the same implementation as the weight (1, 1, 2), since Substitution > Insertion + Deletion has no effect

... (truncated)

Commits

• e124f4f improve performance of Levenshtein distance
• 2d80120 add more benchmarks to documentation
• 53b8e3b update build mechanism
• 779e26b add some benchmarks to Levenshtein distance
• 83b9fd8 implement cached Levenshtein
• c6eebb7 fix incorrect ref counting
• 74e41ad replace macro usage with templates
• e8102a4 Fix result conversion process.extract
• 97119cd fix formula in docstring
• 5383d28 Release v1.1.0 (#75)
• Additional commits viewable in compare view

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Superseded by #57.