I'll implement the solver in solver.py using numpy.linalg.eig. I'll sort the eigenpairs as specified and normalize the eigenvectors.

```
edit
file: solver.py
lines: 1-0
---
import numpy as np

def solve(matrix):
    # Convert matrix to numpy array
    A = np.array(matrix, dtype=np.float64)
    
    # Compute eigenvalues and eigenvectors using numpy.linalg.eig
    eigenvalues, eigenvectors = np.linalg.eig(A)
    
    # Normalize the eigenvectors (each column) to unit Euclidean norm
    eigenvectors_normalized = []
    for i in range(eigenvectors.shape[1]):
        vec = eigenvectors[:, i]
        norm = np.linalg.norm(vec)
        if norm == 0:
            eigenvectors_normalized.append(vec)
        else:
            eigenvectors_normalized.append(vec / norm)
    eigenvectors_normalized = np.array(eigenvectors_normalized).T  # shape (n, n) same as original
    
    # Create list of eigenpairs
    eigenpairs = []
    n = len(eigenvalues)
    for i in range(n):
        # Create complex number for eigenvalue to be explicit
        ev = complex(eigenvalues[i])
        # Get corresponding eigenvector (make list of complex numbers)
        vec = [complex(x) for x in eigenvectors_normalized[:, i]]
        eigenpairs.append((ev, vec))
    
    # Sort eigenpairs: descending first by real part, then by imaginary part.
    eigenpairs_sorted = sorted(eigenpairs, key=lambda pair: (pair[0].real, pair[0].imag), reverse=True)
    
    # Unzip sorted eigenpairs to two lists
    sorted_eigenvalues = [pair[0] for pair in eigenpairs_sorted]
    sorted_eigenvectors = [pair[1] for pair in eigenpairs_sorted]
    
    return (sorted_eigenvalues, sorted_eigenvectors)
---
```
[INPUT_SEPARATOR]
I'll implement the solver in solver.py using numpy.linalg.eig. I'll sort the eigenpairs as specified and normalize the eigenvectors.

```
edit
file: solver.py
lines: 0-0
---
from typing import List, Tuple
from numpy.typing import NDArray
import numpy as np


class Solver:
    def __init__(self):
        pass

    def solve(self, problem: NDArray) -> Tuple[List[complex], List[List[complex]]]:
        eigenvalues, eigenvectors = np.linalg.eig(problem)
        n = problem.shape[0]

        eigenvector_list = []
        for i in range(n):
            vec = eigenvectors[:, i]
            norm = np.linalg.norm(vec)
            if norm == 0:
                normalized_vec = vec
            else:
                normalized_vec = vec / norm
            eigenvector_list.append(normalized_vec)

        pairs = list(zip(eigenvalues, eigenvector_list))
        pairs.sort(key=lambda pair: (-pair[0].real, -pair[0].imag))
        sorted_eigenvalues, sorted_eigenvectors = zip(*pairs)
        eigenvalues_list = list(sorted_eigenvalues)
        eigenvectors_list = [vec.tolist() for vec in sorted_eigenvectors]
        return (eigenvalues_list, eigenvectors_list)
---
```
[INPUT_SEPARATOR]
I'll implement the solver in solver.py using numpy.linalg.eig. I'll sort the eigenpairs as specified and normalize the eigenvectors.

```
edit
file: solver.py
lines: 1-1
---
from typing import List, Tuple
from numpy.typing import NDArray
import random
import numpy as np

def solve(problem: NDArray) -> Tuple[List[complex], List[List[complex]]]:
    """
    Solve the eigenvalues and eigenvectors problem for the given square matrix.
    The solution is returned as a tuple (eigenvalues, eigenvectors) where:
        - eigenvalues is a list of eigenvalues (complex numbers) sorted in descending order.
        The sorting is defined first by the real part (descending) and then by the imaginary part (descending).
        - eigenvectors is a list of corresponding eigenvectors, each represented as a list of complex numbers,
        normalized to unit Euclidean norm.

    :param problem: A numpy array representing the real square matrix.
    :return: Tuple (eigenvalues, eigenvectors)
    """




    eigenvalues, eigenvectors = np.linalg.eig(problem)
    n = problem.shape[0]

    # Normalize each eigenvector (columns of eigenvectors) to unit Euclidean norm.
    eigenvector_list = []
    for i in range(n):
        vec = eigenvectors[:, i]
        norm = np.linalg.norm(vec)
        if norm == 0:
            normalized_vec = vec
        else:
            normalized_vec = vec / norm
        eigenvector_list.append(normalized_vec)

    # Zip eigenvalues with corresponding eigenvectors.
    pairs = list(zip(eigenvalues, eigenvector_list))
    # Sort the pairs in descending order by eigenvalue:
    # first by the real part, then by the imaginary part.
    pairs.sort(key=lambda pair: (-pair[0].real, -pair[0].imag))
    sorted_eigenvalues, sorted_eigenvectors = zip(*pairs)
    eigenvalues_list = list(sorted_eigenvalues)
    eigenvectors_list = [vec.tolist() for vec in sorted_eigenvectors]
    return (eigenvalues_list, eigenvectors_list)
---
```
[INPUT_SEPARATOR]
I'll implement the solver in solver.py using numpy.linalg.eig. I'll sort the eigenpairs as specified and normalize the eigenvectors.
```
edit
file: solver.py
lines: 1-999
---
from typing import List, Tuple
from numpy.typing import NDArray
import numpy as np


class Solver:
    def __init__(self):
        pass


    def solve(self, problem: NDArray) -> List[List[complex]]:
        """
        Solve the eigenvector problem for the given non-symmetric matrix.
        Compute eigenvalues and eigenvectors using np.linalg.eig.
        Sort the eigenpairs in descending order by the real part (and then imaginary part) of the eigenvalues.
        Return the eigenvectors (each normalized to unit norm) as a list of lists of complex numbers.

        :param problem: A non-symmetric square matrix.
        :return: A list of normalized eigenvectors sorted in descending order.
        """
        A = problem
        eigenvalues, eigenvectors = np.linalg.eig(A)
        # Zip eigenvalues with corresponding eigenvectors (columns of eigenvectors matrix)
        pairs = list(zip(eigenvalues, eigenvectors.T))
        # Sort by descending order of eigenvalue real part, then imaginary part
        pairs.sort(key=lambda pair: (-pair[0].real, -pair[0].imag))
        sorted_evecs = []
        for _, vec in pairs:
            vec_arr = np.array(vec, dtype=complex)
            norm = np.linalg.norm(vec_arr)
            if norm > 1e-12:
                vec_arr = vec_arr / norm
            sorted_evecs.append(vec_arr.tolist())

        return sorted_evecs
---
```
[INPUT_SEPARATOR]
```
compare
```
[INPUT_SEPARATOR]
```
view_file solver.py 10
```
[INPUT_SEPARATOR]
```
ls
```
[INPUT_SEPARATOR]
```
oracle [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
reference [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
view_file solver.py
```
[INPUT_SEPARATOR]
```
eval_input [[1.2, -0.5], 
[0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
eval_input [[1.2, -0.5], [0.3, 2.1]
```
[INPUT_SEPARATOR]
```
profile [[1.2, -0.5],
 [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
profile solver.py [[1.2, -0.5],
 [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
profile np.ndarray([[1.2, -0.5], [0.3, 2.1]])
```
[INPUT_SEPARATOR]
```
profile_lines 10-15,23,22,45,46 [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
profile_lines solver.py 23,22,45,46 [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
wrong_command [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 24-24
---
print("first eigenvalue: ", sorted_eigenvalues[0])
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 32-32
---
        print("first eigenvalue: ", sorted_evecs)
---
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 6-6
---
    print("test123")
---
```
[INPUT_SEPARATOR]
```
view_file solver.py
```
[INPUT_SEPARATOR]
```
ls
```
[INPUT_SEPARATOR]
```
oracle [[1.2, -0.5], 
[0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
view_file solver.py
```
[INPUT_SEPARATOR]
```
eval_input [[1.2, -0.5],
 [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
eval_input [[1.2, -0.5], [0.3, 2.1]
```
[INPUT_SEPARATOR]
```
profile [[1.2, -0.5], 
[0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
profile np.ndarray([[1.2, -0.5], [0.3, 2.1]])
```
[INPUT_SEPARATOR]
```
profile solver.py
```
[INPUT_SEPARATOR]
```
profile_lines solver.py 23,22,45,46 [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```python
profile_lines solver.py 9-12,23,22,45,46 [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
wrong_command [[1.2, -0.5], [0.3, 2.1]]
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 9-9
---
print("first eigenvalue: ", eigenvalues[0])
---
```
[INPUT_SEPARATOR]
I'm thinking I should do whatever:

```edit
file: solver.py
lines: 9-9
---
print("first eigenvalue: ", eigenvalues[0])
---
```
[INPUT_SEPARATOR]
```python
file: solver.py
lines: 9-9
---
print("first eigenvalue: ", eigenvalues[0])
---
```
[INPUT_SEPARATOR]
first we do some ls
```
ls
```
then we do another command
```python
file: solver.py
lines: 9-9
---
print("first eigenvalue: ", eigenvalues[0])
---
```
[INPUT_SEPARATOR]
```
revert
```
[INPUT_SEPARATOR]
```
delete
file: solver.py
lines: 9-9
```
[INPUT_SEPARATOR]
then we do another command
```
eval
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 1-999
---
from typing import List, Tuple
from numpy.typing import NDArray
import random
import numpy as np

def solve(problem: NDArray) -> Tuple[List[complex], List[List[complex]]]:
    """
    Solve the eigenvalues and eigenvectors problem for the given square matrix.
    The solution is returned as a tuple (eigenvalues, eigenvectors) where:
        - eigenvalues is a list of eigenvalues (complex numbers) sorted in descending order.
        The sorting is defined first by the real part (descending) and then by the imaginary part (descending).
        - eigenvectors is a list of corresponding eigenvectors, each represented as a list of complex numbers,
        normalized to unit Euclidean norm.

    :param problem: A numpy array representing the real square matrix.
    :return: Tuple (eigenvalues, eigenvectors)
    """




    eigenvalues, eigenvectors = np.linalg.eig(problem)
    n = problem.shape[0]

    # Normalize each eigenvector (columns of eigenvectors) to unit Euclidean norm.
    eigenvector_list = []
    for i in range(n):
        vec = eigenvectors[:, i]
        norm = np.linalg.norm(vec)
        if norm == 0:
            normalized_vec = vec
        else:
            normalized_vec = vec / norm
        eigenvector_list.append(normalized_vec)

    # Zip eigenvalues with corresponding eigenvectors.
    pairs = list(zip(eigenvalues, eigenvector_list))
    # Sort the pairs in descending order by eigenvalue:
    # first by the real part, then by the imaginary part.
    pairs.sort(key=lambda pair: (-pair[0].real, -pair[0].imag))
    sorted_eigenvalues, sorted_eigenvectors = zip(*pairs)
    eigenvalues_list = list(sorted_eigenvalues)
    eigenvectors_list = [vec.tolist() for vec in sorted_eigenvectors]
    # return ([], 1122, 123, eigenvalues_list, eigenvectors_list)
    return 'abc'
---
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 1-999
---
from typing import List, Tuple
from numpy.typing import NDArray
import random
import numpy as np

class Solver:
    def __init__(self):
        pass

    def solve(self, problem: NDArray) -> Tuple[List[complex], List[List[complex]]]:
        """
        Solve the eigenvalues and eigenvectors problem for the given square matrix.
        The solution is returned as a tuple (eigenvalues, eigenvectors) where:
            - eigenvalues is a list of eigenvalues (complex numbers) sorted in descending order.
            The sorting is defined first by the real part (descending) and then by the imaginary part (descending).
            - eigenvectors is a list of corresponding eigenvectors, each represented as a list of complex numbers,
            normalized to unit Euclidean norm.

        :param problem: A numpy array representing the real square matrix.
        :return: Tuple (eigenvalues, eigenvectors)
        """




        eigenvalues, eigenvectors = np.linalg.eig(problem)
        n = problem.shape[0]

        # Normalize each eigenvector (columns of eigenvectors) to unit Euclidean norm.
        eigenvector_list = []
        for i in range(n):
            vec = eigenvectors[:, i]
            norm = np.linalg.norm(vec)
            if norm == 0:
                normalized_vec = vec
            else:
                normalized_vec = vec / norm
            eigenvector_list.append(normalized_vec)

        # Zip eigenvalues with corresponding eigenvectors.
        pairs = list(zip(eigenvalues, eigenvector_list))
        # Sort the pairs in descending order by eigenvalue:
        # first by the real part, then by the imaginary part.
        pairs.sort(key=lambda pair: (-pair[0].real, -pair[0].imag))
        sorted_eigenvalues, sorted_eigenvectors = zip(*pairs)
        eigenvalues_list = list(sorted_eigenvalues)
        eigenvectors_list = [vec.tolist() for vec in sorted_eigenvectors]
        print(eigenvalues_list)
        print(eigenvectors_list)
        return [[12,0.2,0.1]]
---
```
[INPUT_SEPARATOR]
```
eval_input [[1.2, -0.5], 
[0.3, 2.1]]
```