pre-commit: Upgrade psf/black for stable style 2023 (TheAlgorithms#8110)

* pre-commit: Upgrade psf/black for stable style 2023

Updating https://github.com/psf/black ... updating 22.12.0 -> 23.1.0 for their `2023 stable style`.
* https://github.com/psf/black/blob/main/CHANGES.md#2310

> This is the first [psf/black] release of 2023, and following our stability policy, it comes with a number of improvements to our stable style…

Also, add https://github.com/tox-dev/pyproject-fmt and https://github.com/abravalheri/validate-pyproject to pre-commit.

I only modified `.pre-commit-config.yaml` and all other files were modified by pre-commit.ci and psf/black.

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: cclauss

.pre-commit-config.yaml

@@ -15,7 +15,7 @@ repos:
     -   id: auto-walrus
 
   - repo: https://github.com/psf/black
-    rev: 22.12.0
+    rev: 23.1.0
     hooks:
       - id: black
 
@@ -26,6 +26,16 @@ repos:
         args:
           - --profile=black
 
+  - repo: https://github.com/tox-dev/pyproject-fmt
+    rev: "0.6.0"
+    hooks:
+      - id: pyproject-fmt
+
+  - repo: https://github.com/abravalheri/validate-pyproject
+    rev: v0.12.1
+    hooks:
+      - id: validate-pyproject
+
   - repo: https://github.com/asottile/pyupgrade
     rev: v3.3.1
     hooks:

arithmetic_analysis/newton_raphson_new.py

@@ -59,7 +59,6 @@ def newton_raphson(
 
 # Let's Execute
 if __name__ == "__main__":
-
     # Find root of trigonometric function
     # Find value of pi
     print(f"The root of sin(x) = 0 is {newton_raphson('sin(x)', 2)}")

backtracking/n_queens_math.py

@@ -107,7 +107,6 @@ def depth_first_search(
 
     # We iterate each column in the row to find all possible results in each row
     for col in range(n):
-
         # We apply that we learned previously. First we check that in the current board
         # (possible_board) there are not other same value because if there is it means
         # that there are a collision in vertical. Then we apply the two formulas we

blockchain/chinese_remainder_theorem.py

@@ -53,6 +53,7 @@ def chinese_remainder_theorem(n1: int, r1: int, n2: int, r2: int) -> int:
 
 # ----------SAME SOLUTION USING InvertModulo instead ExtendedEuclid----------------
 
+
 # This function find the inverses of a i.e., a^(-1)
 def invert_modulo(a: int, n: int) -> int:
     """

ciphers/enigma_machine2.py

@@ -230,7 +230,6 @@ def enigma(
     # encryption/decryption process --------------------------
     for symbol in text:
         if symbol in abc:
-
             # 1st plugboard --------------------------
             if symbol in plugboard:
                 symbol = plugboard[symbol]

ciphers/playfair_cipher.py

@@ -39,7 +39,6 @@ def prepare_input(dirty: str) -> str:
 
 
 def generate_table(key: str) -> list[str]:
-
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     alphabet = "ABCDEFGHIKLMNOPQRSTUVWXYZ"

ciphers/polybius.py

@@ -19,7 +19,6 @@
 
 class PolybiusCipher:
     def __init__(self) -> None:
-
         self.SQUARE = np.array(SQUARE)
 
     def letter_to_numbers(self, letter: str) -> np.ndarray:

ciphers/xor_cipher.py

@@ -130,7 +130,6 @@ def encrypt_file(self, file: str, key: int = 0) -> bool:
         try:
             with open(file) as fin:
                 with open("encrypt.out", "w+") as fout:
-
                     # actual encrypt-process
                     for line in fin:
                         fout.write(self.encrypt_string(line, key))
@@ -155,7 +154,6 @@ def decrypt_file(self, file: str, key: int) -> bool:
         try:
             with open(file) as fin:
                 with open("decrypt.out", "w+") as fout:
-
                     # actual encrypt-process
                     for line in fin:
                         fout.write(self.decrypt_string(line, key))

compression/lz77.py

@@ -89,7 +89,6 @@ def compress(self, text: str) -> list[Token]:
 
         # while there are still characters in text to compress
         while text:
-
             # find the next encoding phrase
             # - triplet with offset, length, indicator (the next encoding character)
             token = self._find_encoding_token(text, search_buffer)

computer_vision/cnn_classification.py

@@ -28,7 +28,6 @@
 from tensorflow.keras import layers, models
 
 if __name__ == "__main__":
-
     # Initialising the CNN
     # (Sequential- Building the model layer by layer)
     classifier = models.Sequential()