добавил вариант решения первой задачи

task1(2).py

@@ -0,0 +1,107 @@
+import random as r
+import bisect as b
+
+def search_binary_diagonal(matrix, K):
+    """
+    Поиск числа K в матрице N x M
+    """
+    if not matrix or not matrix[0]:
+        return False, 0
+    
+    N = len(matrix)
+    M = len(matrix[0])
+    steps = 0
+    
+    left, right = 0, min(N, M) - 1
+    diag_indx = -1
+    
+    while left <= right:
+        steps += 1
+        mid = (left + right) // 2
+        
+        if matrix[mid][mid] == K:
+            return True, 1
+        elif matrix[mid][mid] < K:
+            diag_indx = mid
+            left = mid + 1
+        else:
+            right = mid - 1
+    
+    if diag_indx >= 0 and diag_indx < N:
+        steps += 1
+        row = matrix[diag_indx]
+        pos = b.bisect_left(row, K)
+        if pos < M and row[pos] == K:
+            return True, steps
+    
+    if diag_indx >= 0 and diag_idx < M:
+        steps += 1
+        col_values = [matrix[i][diag_idx] for i in range(N)]
+        pos = b.bisect_left(col_values, K)
+        if pos < N and col_values[pos] == K:
+            return True, steps
+    
+    if diag_indx + 1 < N:
+        steps += 1
+        row = matrix[diag_indx + 1]
+        pos = b.bisect_left(row, K)
+        if pos < M and row[pos] == K:
+            return True, steps
+    
+    if diag_indx + 1 < M:
+        steps += 1
+        col_values = [matrix[i][diag_indx + 1] for i in range(N)]
+        pos = b.bisect_left(col_values, K)
+        if pos < N and col_values[pos] == K:
+            return True, steps
+    
+    return False, steps
+
+
+def generate_matrix(N, M):
+    """
+    Генерирует матрицу N x M со случайными числами
+    """
+    matrix = [[0] * M for g in range(N)]
+    
+   
+    matrix[0][0] = r.randint(1, 10)
+    for j in range(1, M):
+        matrix[0][j] = matrix[0][j-1] + r.randint(1, 8)
+    
+    for i in range(1, N):
+        for j in range(M):
+            min_val = matrix[i-1][j]
+            if j > 0:
+                min_val = max(min_val, matrix[i][j-1])
+            matrix[i][j] = min_val + r.randint(1, 8)
+    
+    return matrix
+
+def print_matrix(matrix):
+    """Вывод матрицы"""
+    for row in matrix:
+        print("\t".join(map(str, row)))
+
+
+def main():
+    N = int(input("Введите количество строк N: "))
+    M = int(input("Введите количество столбцов M: "))
+    
+    matrix = generate_matrix(N, M)
+    
+    print("\nСгенерированная матрица:")
+    print_matrix(matrix)
+    
+    K = int(input("\nВведите число для поиска: "))
+    
+    found, steps = search_binary_diagonal(matrix, K)
+    
+    if found:
+        print(f"\nЧисло {K} найдено \nКоличество шагов: {steps}")
+    else:
+        print(f"\nЧисло {K} не найдено \nКоличество шагов: {steps}")
+
+
+if __name__ == "__main__":
+    main()