#L0_B_54
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    numring = rdMolDescriptors.CalcNumRings(mol)
    numbridge = rdMolDescriptors.CalcNumBridgeheadAtoms(mol)
    numspiro = rdMolDescriptors.CalcNumSpiroAtoms(mol)
    return [numring, numbridge, numspiro]

Given question below:
------------
Implement a complete python function `solve(smiles)`, which takes a molecule's SMILES as input, calculates the number of rings, bridgehead atoms, and spiral atoms in the molecule respectively, and returns the three numbers in a list in order.
------------

We try to solve it with code:
```python
from rdkit import Chem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if not mol:
        raise ValueError("Invalid SMILES string")

    ring_info = mol.GetRingInfo()
    num_rings = ring_info.NumRings()

    bridgehead_atoms = 0
    atom_ring_counts = [len(ring_info.AtomRings(atomIdx)) for atomIdx in range(mol.GetNumAtoms())]
    bridgehead_atoms = sum(1 for count in atom_ring_counts if count > 1)

    return [num_rings, bridgehead_atoms, spiral_atoms]

```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmp_rzb3o59/tmp3483jnpj.py", line 24, in <module>
    print(solve("C1CCC2CC1CC2"))  # 应该输出包含两个环和至少一个桥头原子的列表
  File "/tmp/tmp_rzb3o59/tmp3483jnpj.py", line 15, in solve
    atom_ring_counts = [len(ring_info.AtomRings(atomIdx)) for atomIdx in range(mol.GetNumAtoms())]
  File "/tmp/tmp_rzb3o59/tmp3483jnpj.py", line 15, in <listcomp>
    atom_ring_counts = [len(ring_info.AtomRings(atomIdx)) for atomIdx in range(mol.GetNumAtoms())]
Boost.Python.ArgumentError: Python argument types in
    RingInfo.AtomRings(RingInfo, int)
did not match C++ signature:
    AtomRings(RDKit::RingInfo const* self)

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve("c1ccccc1") == [1, 0, 0]
    assert solve("C1CCCC12CCC2") == [2, 0, 1]
    assert solve("C1C3CC(CCCC3)CC12CCC2") == [3, 2, 1]

check(solve)


#L0_A_7
from rdkit.Chem import AllChem
from rdkit import Chem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    patt = Chem.MolFromSmarts('[CH3]')
    repl = Chem.MolFromSmiles('[CH2]F')
    rms = AllChem.ReplaceSubstructs(mol, patt, repl, replaceAll=True)
    return Chem.CanonSmiles(Chem.MolToSmiles(rms[0]))

Given question below:
------------
Please write a complete Python function `solve(smiles)`, which takes a SMILES as input, adds a fluorine atom to all terminal carbons, and returns the resulting SMILES.
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem import AllChem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)

    emol = Chem.EditableMol(mol)

    for atom in mol.GetAtoms():
        if atom.GetSymbol() == 'C':
            if len(atom.GetNeighbors()) == 1:
                fluorine_atom = Chem.Atom('F')
                emol.AddAtom(fluorine_atom)
                emol.AddBond(atom.GetIdx(), mol.GetNumAtoms(), Chem.BondType.SINGLE)

    modified_mol = emol.GetMol()

    Chem.SanitizeMol(modified_mol)

    modified_smiles = Chem.MolToSmiles(modified_mol)

    return modified_smiles

test_smiles = "CC(C)C"
result_smiles = solve(test_smiles)
print("Input SMILES:", test_smiles)
print("Modified SMILES:", result_smiles)
```

But some error occurs during execution:
------------
[10:51:14] Explicit valence for atom # 4 F, 3, is greater than permitted
Traceback (most recent call last):
  File "/tmp/tmpqpsv86iw/tmp876jt85a.py", line 35, in <module>
    result_smiles = solve(test_smiles)
  File "/tmp/tmpqpsv86iw/tmp876jt85a.py", line 26, in solve
    Chem.SanitizeMol(modified_mol)
rdkit.Chem.rdchem.AtomValenceException: Explicit valence for atom # 4 F, 3, is greater than permitted

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

Question: Write a piece of python code to achieve the following function.
Find the maximum perfect square number that is less than the nth Fibonacci number.
The function's name is max_square_number, and the input is an integer n.

Answer:
```python
def max_square_number(n):
    if n <= 2:
        return 0  # For the first two numbers in the Fibonacci sequence, no complete square numbers can be used.

    fibs = [0, 1]  # Store the first two numbers of the Fibonacci sequence
    squares = []  # Store the list of perfect square numbers

    max_square = 0
    i = 2

    while i <= n:
        fibs.append(fibs[i-1] + fibs[i-2])  # Calculate the next Fibonacci number
        i += 1

    fib = fibs[-1]
    perfect_squares = [i**2 for i in range(1, int(math.sqrt(fib)) + 1)]
    max_square = max(filter(lambda x: x < fib, perfect_squares))
    return max_square
```

----------------

#<INSERT>

from rdkit import Chem

def check(solve):
    assert Chem.CanonSmiles(solve('CC(C)CO')) == 'OCC(CF)CF'
    assert Chem.CanonSmiles(solve('Cc1ccccc1')) == 'FCc1ccccc1'
    assert Chem.CanonSmiles(solve('CC(C)C')) == 'FCC(CF)CF'

check(solve)


import numpy
from pyscf import gto, dft, lib

def solve(mol):

    mf = dft.RKS(mol)

    HF_X, LDA_X = .6, .08
    B88_X = 1. - HF_X - LDA_X
    LYP_C = .81
    VWN_C = 1. - LYP_C

    mf.xc = f'{HF_X:} * HF + {LDA_X:} * LDA + {B88_X:} * B88, {LYP_C:} * LYP + {VWN_C:} * VWN'
    energy = mf.kernel()
    return energy

Given question below:
------------
Write a Python function `solve(mol)`. The input is a PySCF mole object.
The output is energy calculated by DFT. Please define the exchange functional as a combination
of Hartree-Fock (with a fraction = 0.6), LDA (with a fraction = 0.08) and B88. The correlation functional
is a combination of LYP (fraction = 0.81) and VMN.
------------

We try to solve it with code:
```python
from pyscf import dft

def solve(mol):
    mf = dft.RKS(mol)

    mf.xc = '0.6*HF + 0.08*LDA + B88, 0.81*LYP + VWN'

    energy = mf.kernel()

    return energy

```

But some error occurs during execution:
------------
/root/miniconda3/envs/sandbox-runtime/lib/python3.10/site-packages/pyscf/dft/libxc.py:771: UserWarning: Since PySCF-2.3, B3LYP (and B3P86) are changed to the VWN-RPA variant, corresponding to the original definition by Stephens et al. (issue 1480) and the same as the B3LYP functional in Gaussian. To restore the VWN5 definition, you can put the setting "B3LYP_WITH_VWN5 = True" in pyscf_conf.py
  warnings.warn('Since PySCF-2.3, B3LYP (and B3P86) are changed to the VWN-RPA variant, '
Traceback (most recent call last):
  File "/tmp/tmp6n390qdh/tmp9a84f1o_.py", line 36, in <module>
    check(solve)
  File "/tmp/tmp6n390qdh/tmp9a84f1o_.py", line 34, in check
    assert abs(solve(mol) + 76.331166) < 1e-6
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

from pyscf import gto

def check(solve):
    mol = gto.M(
    verbose = 0,
    atom = '''
    o    0    0.       0.
    h    0    -0.757   0.587
    h    0    0.757    0.587''',
    basis = '6-31g')
    assert abs(solve(mol) + 76.331166) < 1e-6

check(solve)


#L0_A_1
from rdkit import Chem
from rdkit.Chem import AllChem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    patt = Chem.MolFromSmarts('C(O)=O')
    repl = Chem.MolFromSmiles('c1ccccc1')
    rms = AllChem.ReplaceSubstructs(mol, patt, repl, replaceAll=True)
    return Chem.MolToSmiles(rms[0])

Given question below:
------------
Please write a complete Python function `solve(smiles)`. This function should take a molecule's SMILES as input, replace all carboxyl groups in the molecule with a phenyl ring, and return the SMILES of the result. If the molecule's structure does not contain a carboxyl group, then the structure should not be modified.
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem import AllChem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)

    carboxylic_acid_smarts = "[CX3](=O)[OX2H1]"

    carboxylic_acid_pattern = Chem.MolFromSmarts(carboxylic_acid_smarts)

    matches = mol.GetSubstructMatches(carboxylic_acid_pattern)

    for match in reversed(matches):
        carboxyl_carbon_idx = match[0]

        mol.RemoveAtom(match[2])

        phenyl_smiles = "c1ccccc1"
        phenyl_mol = Chem.MolFromSmiles(phenyl_smiles)

        emol = Chem.EditableMol(mol)

        mol = emol.GetMol()

    Chem.SanitizeMol(mol)

    modified_smiles = Chem.MolToSmiles(mol)

    return modified_smiles
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmps68a6iir/tmpqcz7tb08.py", line 52, in <module>
    check(solve)
  File "/tmp/tmps68a6iir/tmpqcz7tb08.py", line 47, in check
    assert solve('O=C(O)Cc1ccccc1') == 'c1ccc(Cc2ccccc2)cc1'
  File "/tmp/tmps68a6iir/tmpqcz7tb08.py", line 23, in solve
    mol.RemoveAtom(match[2])
AttributeError: 'Mol' object has no attribute 'RemoveAtom'

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('O=C(O)Cc1ccccc1') == 'c1ccc(Cc2ccccc2)cc1'
    assert solve('CCC(C)N') == 'CCC(C)N'
    assert solve('Cc1ccc(C(=O)O)c(C)c1') == 'Cc1ccc(-c2ccccc2)c(C)c1'
    assert solve('Cc1cc(C)c(C(=O)O)cc1CC(=O)O') == 'Cc1cc(C)c(-c2ccccc2)cc1Cc1ccccc1'

check(solve)


def is_valid_parentheses(s):
    # 定义括号对应关系和优先级
    bracket_map = {')': '(', ']': '[', '}': '{'}
    priority = {'(': 1, '[': 2, '{': 3}

    stack = []

    for char in s:
        if char in "([{":
            # 如果栈不为空，检查当前括号是否在更大的括号内
            if stack and priority[char] > priority[stack[-1]]:
                return False
            stack.append(char)
        elif char in ")]}":
            # 如果栈为空或栈顶括号不匹配当前闭括号
            if not stack or stack[-1] != bracket_map[char]:
                return False
            stack.pop()

    return not stack

Given question below:
------------
Please write a Python function to validate whether a string containing only parentheses is valid. The function name should be is_valid_parentheses(). For a string to be valid, it needs to meet two conditions: (1) Each type of parenthesis is paired; (2) Round brackets ("(" and ")") must be within square brackets ("[" and "]"), and square brackets must be within curly brackets ("{" and "}"). However, parentheses of the same level can be nested multiple times. The input of the function is a string containing only three types of parentheses, and the output is a Boolean variable.
------------

We try to solve it with code:
```python
def is_valid_parentheses(s: str) -> bool:
    stack = []
    bracket_map = {'(': ')', '[': ']', '{': '}'}
    bracket_level = {'(': 1, '[': 2, '{': 3}

    for char in s:
        if char in bracket_map:
            if stack:
                last_bracket = stack[-1]
                if bracket_level[char] <= bracket_level[last_bracket]:
                    return False
            stack.append(char)
        else:
            if not stack or bracket_map[stack.pop()] != char:
                return False

    return not stack

```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmp1hak7uxq/tmpib73_nnw.py", line 39, in <module>
    check(is_valid_parentheses)
  File "/tmp/tmp1hak7uxq/tmpib73_nnw.py", line 33, in check
    assert func("{[()]}") == True
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(func):
    assert func("{[()]}") == True
    assert func("{[(])}") == False
    assert func("{[]}") == True
    assert func("({[]})") == False
    assert func("([])") == False

check(is_valid_parentheses)


#L0_B_51
from rdkit import Chem
from rdkit.Chem import Descriptors

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    MW = Descriptors.MolWt(mol)
    HBA = Descriptors.NOCount(mol)
    HBD = Descriptors.NHOHCount(mol)
    LogP = Descriptors.MolLogP(mol)
    conditions = [MW <= 500, HBA <= 10, HBD <= 5, LogP <= 5]
    pass_ro5 = conditions.count(True) >= 3
    return pass_ro5

Given question below:
------------
Implement a complete Python function `solve(smiles)`, which takes a molecule's SMILES as input and analyzes whether the molecule satisfies the Lipinski Rule of 5.
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem import Descriptors, Crippen, rdMolDescriptors as Des

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)

    if mol is None:
        return "Invalid SMILES"

    mol_weight = Des.CalcExactMolWt(mol)
    logP = Crippen.MolLogP(mol)
    h_donors = Descriptors.NumHDonors(mol)
    h_acceptors = Descriptors.NumHAcceptors(mol)

    violations = 0
    if mol_weight >= 500:
        violations += 1
    if logP >= 5:
        violations += 1
    if h_donors > 5:
        violations += 1
    if h_acceptors > 10:
        violations += 1

    if violations == 0:
        return "The molecule satisfies Lipinski's Rule of 5"
    else:
        return f"The molecule violates {violations} of Lipinski's Rule of 5"
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpjxn97apq/tmp5912swsv.py", line 41, in <module>
    check(solve)
  File "/tmp/tmpjxn97apq/tmp5912swsv.py", line 37, in check
    assert not solve('COc1ccc2nc(S(=O)Cc3nc(CC(C)Cc4ccc(C(C)C)cc4)c(C)c(OC)c3C)[nH]c2c1')
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('CC(=O)Nc1ccc(O)cc1')
    assert solve('COc1ccc2nc(S(=O)Cc3ncc(C)c(OC)c3C)[nH]c2c1')
    assert not solve('COc1ccc2nc(S(=O)Cc3nc(CC(C)Cc4ccc(C(C)C)cc4)c(C)c(OC)c3C)[nH]c2c1')
    assert not solve('c1c(C(=O)O)c(C(=O)O)c(CCNCc2c(C(=O)O)cc(C(=O)O)cc2)c(C(=O)O)c1')
    assert not solve('c1(C(N)Cc2c(N)c(N)c(C(=O)O)c(N)c2(CCCN))c(N)c(N)c(CN)c(N)c1')

check(solve)


#L1_A_30
from rdkit import Chem

def solve(core, chains):
    core = Chem.MolFromSmiles(core)
    chainMols = [Chem.MolFromSmiles(chain) for chain in chains]

    product_smi = []
    for chainMol in chainMols:
        product_mol = Chem.ReplaceSubstructs(core,Chem.MolFromSmarts('[#0]'),chainMol)
        product_smi.append(Chem.MolToSmiles(product_mol[0]))
    return set(product_smi)

Given question below:
------------
Please write a complete Python function `solve(cores, chains)`. The input is a fragment SMILES (core) with connection sites marked by "*", and a list containing multiple fragment SMILES (chains). It returns a collection of molecular SMILES obtained by connecting the first atom of each chain fragment to the core fragment.
The connection can be accomplished by replacing the "*" atom with the chain fragment. The SMARTS that matches the "*" atom is "[#0]". The fragment replacement can be carried out through the Chem.ReplaceSubstructs() function.
------------

We try to solve it with code:
```python
from rdkit import Chem

def solve(core, chains):
    core_mol = Chem.MolFromSmiles(core)

    result_smiles = []

    for chain in chains:
        chain_mol = Chem.MolFromSmiles(chain)

        modified_mols = Chem.ReplaceSubstructs(core_mol, dummy_smarts, chain_mol)

        for modified_mol in modified_mols:
            result_smiles.append(Chem.MolToSmiles(modified_mol[0]))

    return result_smiles

cores = "C1=CC=C(*)C=C1"
chains = ["NCC", "OCC", "SCC"]
print(solve(cores, chains))
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpplxgo7qo/tmpz8lhu8ou.py", line 32, in <module>
    print(solve(cores, chains))
  File "/tmp/tmpplxgo7qo/tmpz8lhu8ou.py", line 24, in solve
    result_smiles.append(Chem.MolToSmiles(modified_mol[0]))
TypeError: 'Mol' object is not subscriptable

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('*[n+]1cc[nH]c1', ['C','CC','CCC','CCCN']) == {'CCC[n+]1cc[nH]c1', 'C[n+]1cc[nH]c1', 'CC[n+]1cc[nH]c1', 'NCCC[n+]1cc[nH]c1'}
    assert solve('Nc1cc(*)ccc1', ['C','O','C#N','N#C']) == {'Nc1cccc(O)c1', 'C#Nc1cccc(N)c1', 'N#Cc1cccc(N)c1', 'Cc1cccc(N)c1'}
    assert solve('CCC*', ['Cl','O','C=C']) == {'CCCO', 'CCCCl', 'C=CCCC'}

check(solve)


#L1_A_40
from rdkit import Chem

def solve(smiles, atom_idx1, atom_idx2):
    mol = Chem.MolFromSmiles(smiles)
    path = Chem.GetShortestPath(mol, atom_idx1, atom_idx2)
    return [i for i in path if i not in [atom_idx1, atom_idx2]]

Given question below:
------------
Please write a complete Python function `solve(smiles, atom_idx1, atom_idx2)` that takes a SMILES as input and specifies the indices of two atoms. Return a list of all atom indices on the shortest path between the two specified atoms in this molecule (excluding these two specified atoms). It is known that in RDKit, the shortest path between two atoms can be found by using `Chem.GetShortestPath()`.
------------

We try to solve it with code:
```python
from rdkit import Chem

def solve(smiles, atom_idx1, atom_idx2):
    mol = Chem.MolFromSmiles(smiles)
    if not mol:
        raise ValueError("Invalid SMILES string provided.")

    if atom_idx1 >= mol.GetNumAtoms() or atom_idx2 >= mol.GetNumAtoms() or atom_idx1 < 0 or atom_idx2 < 0:
        raise IndexError("Atom indices are out of range.")

    path = mol.GetShortestPath(atom_idx1, atom_idx2)

    internal_path = path[1:-1] if len(path) > 2 else []

    return list(internal_path)
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpnn7lh9kh/tmpaob8kr57.py", line 39, in <module>
    check(solve)
  File "/tmp/tmpnn7lh9kh/tmpaob8kr57.py", line 35, in check
    assert solve('CC(=O)OC(C(C)N)C(C)CO', 1, 7) == [3, 4, 5]
  File "/tmp/tmpnn7lh9kh/tmpaob8kr57.py", line 27, in solve
    path = mol.GetShortestPath(atom_idx1, atom_idx2)
AttributeError: 'Mol' object has no attribute 'GetShortestPath'

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('CC(=O)OC(C(C)N)C(C)CO', 1, 7) == [3, 4, 5]
    assert solve('c1(C)cc(C)ccc1OCC(CCC)CC', 1, 14) == [0, 7, 8, 9, 10]

check(solve)


Given question below:
------------
Solving Linear Programming Problem

min z = x_1 + x_2 + x_3 + x_4 + x_5 + x_6

s.t.

x_6 + x_1 >= 10
x_3 + x_4 >= 20
x_1 + x_2 >= 15
x_4 + x_5 >= 18
x_2 + x_3 >= 25
x_5 + x_6 >= 12
x_i >= 0, i = 1, ..., 6

Define the function solve() to solve this problem, returning the optimal value z.
------------

We try to solve it with code:
```python
from scipy.optimize import linprog

def solve():

    A = [
        [-1, 0, 0, 0, 0, -1],  # -x6 - x1 <= -10
    ]

    result = linprog(c, A_ub=A, b_ub=b, bounds=x_bounds, method='highs')

    if result.success:
        return result.fun
    else:
        raise Exception("Linear programming did not converge to a solution")

z_min = solve()
print(f"The minimum value of z is {z_min:.2f}")
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpqnduhk21/tmpeu0bkadw.py", line 37, in <module>
    assert 53 == solve()
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


#L0_B_33
from rdkit import Chem
from rdkit.Chem import ChemicalFeatures
from rdkit import RDConfig
import os

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    fdefName = os.path.join(RDConfig.RDDataDir,'BaseFeatures.fdef')
    factory = ChemicalFeatures.BuildFeatureFactory(fdefName)
    feats = factory.GetFeaturesForMol(mol)

    donar_set = set()
    for feat in feats:
        if feat.GetFamily() == 'Donor':
            donar_set.update(feat.GetAtomIds())
    return donar_set

Given question below:
------------
Please write a complete Python function `solve(smiles)`, which takes a SMILES as input, and returns a set of the atomic indices of all hydrogen bond donors in the molecule.
------------

We try to solve it with code:
```python
from rdkit import Chem

def solve(smiles):
    molecule = Chem.MolFromSmiles(smiles)

    donor_indices = set()

    for atom in molecule.GetAtoms():
        if atom.GetSymbol() in {'O', 'N'}:
            for neighbor in atom.GetNeighbors():
                if neighbor.GetSymbol() == 'H':
                    donor_indices.add(atom.GetIdx())

    return donor_indices
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmp1l2gnkxy/tmp_gv_dbyk.py", line 39, in <module>
    check(solve)
  File "/tmp/tmp1l2gnkxy/tmp_gv_dbyk.py", line 34, in check
    assert solve('CC(O)C(\C)=C/c1cc(ccn1)-c1cc(C)ccc1N') == {2, 19}
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('CC(O)C(\C)=C/c1cc(ccn1)-c1cc(C)ccc1N') == {2, 19}
    assert solve('Cc1ccccc1') == set()
    assert solve('c1ccc(C(CCc2cccc3[nH]ccc23)c2cccc3ccccc23)cc1') == {12}

check(solve)


#L1_B_2
from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    scaff = MurckoScaffold.GetScaffoldForMol(mol)
    scaff = MurckoScaffold.MakeScaffoldGeneric(scaff)
    scaf_smi = Chem.MolToSmiles(scaff)
    return scaf_smi

Given question below:
------------
The term 'CSK skeleton' (cyclic skeleton) refers to replacing all non-carbon heavy atoms on a molecular skeleton with carbon atoms and replacing all bonds with single bonds to get the molecular skeleton. Please write a complete Python function `solve(smiles)`, using the MurckoScaffold module to extract the Bemis-Murcko skeleton of the molecule, converting it to CSK, and returning the SMILES of the CSK.
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)

    scaffold = MurckoScaffold.GetScaffoldForMol(mol)

    csk = Chem.RWMol(scaffold)
    for atom in csk.GetAtoms():

    for bond in csk.GetBonds():

    csk_smiles = Chem.MolToSmiles(csk, canonical=True)

    return csk_smiles

example_smiles = 'CC1=CC=C(C=C1)C2=CN=CC=C2'
csk_smiles = solve(example_smiles)
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmph84k_8s8/tmp66rgrnrb.py", line 34, in <module>
    check(solve)
  File "/tmp/tmph84k_8s8/tmp66rgrnrb.py", line 30, in check
    assert solve('c1ccc(Oc2cccnc2)cc1') == 'C1CCC(CC2CCCCC2)CC1'
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert solve('c1ccc(Oc2cccnc2)cc1') == 'C1CCC(CC2CCCCC2)CC1'
    assert solve('Cc1ccc(-c2ccccc2)c(C)c1') == 'C1CCC(C2CCCCC2)CC1'
    assert solve('CC(CC1CCC(O)C1)CC1CCCC(C)N1') == 'C1CCC(CCCC2CCCC2)CC1'

check(solve)


#L0_A_23
from rdkit import Chem
from rdkit.Chem import GetPeriodicTable

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    pt = GetPeriodicTable()
    vdw_radii = []
    for atom in mol.GetAtoms():
        if atom.GetSymbol() == "H":
            continue
        vdw_radii.append(round(pt.GetRvdw(atom.GetSymbol()), 2))
    return vdw_radii

Given question below:
------------
Please write a complete Python function `solve(smiles)`. The function should take a SMILES as input, and use RDKit to get a list of Van der Waals radii of non-hydrogen atoms in the molecule (the order should be the same as the atomic index, and the Van der Waals radius values should be rounded to two decimal places).
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem import AllChem

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)

    van_der_waals_radii = []

    for atom in mol.GetAtoms():
        if atom.GetAtomicNum() != 1:
            vdw_radius = atom.GetVdwRadius()
            van_der_waals_radii.append(round(vdw_radius, 2))

    return van_der_waals_radii

vdw_radii = solve(smiles)
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmp_som4io5/tmp5xo7kyhm.py", line 25, in <module>
    vdw_radii = solve(smiles)
  File "/tmp/tmp_som4io5/tmp5xo7kyhm.py", line 16, in solve
    vdw_radius = atom.GetVdwRadius()
AttributeError: 'Atom' object has no attribute 'GetVdwRadius'

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


import numpy as np
def get_bootstrap_ci(N, df, B):
    bootstrap_estimates = []
    samples = np.random.chisquare(df,N)
    for _ in range(B):
        bs_sample = np.random.choice(samples, size=len(samples), replace=True)
        bootstrap_estimates.append(bs_sample.mean())
    return np.percentile(bootstrap_estimates, [2.5,97.5])

Given question below:
------------
Write a Python function get_bootstrap_ci(N, df, B) to perform the following functions: generate N random numbers with a chi-square distribution of degree of freedom df, estimate the degree of freedom parameter df, and return a numpy array representing the 95% confidence interval for the df estimate through B times bootstrap. You can use numpy.
------------

We try to solve it with code:
```python
import numpy as np

def get_bootstrap_ci(N, df, B):
    data = np.random.chisquare(df, N)

    df_estimate = np.mean(data)

    bootstrap_estimates = np.zeros(B)

    for i in range(B):
        sample = np.random.choice(data, size=N, replace=True)
        bootstrap_estimates[i] = np.mean(sample)

    lower_bound = np.percentile(bootstrap_estimates, 2.5)
    upper_bound = np.percentile(bootstrap_estimates, 97.5)

    return np.array([lower_bound, upper_bound])

N = 1000
df = 5
B = 10000
ci = get_bootstrap_ci(N, df, B)
print(f"Estimated 95% confidence interval for df: {ci}")
```

But some error occurs during execution:
------------
/bin/bash: 1: Syntax error: end of file unexpected

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

N=1000
df=10
B=500
np.random.seed(42)
bootstrap_ci = get_bootstrap_ci(N,df, B)
assert bootstrap_ci[0] < df < bootstrap_ci[1]


import numpy as np
from scipy.optimize import milp, LinearConstraint

def solve():
    c = -np.array([5, 8], dtype=np.int32)
    A = np.array([[1, 1], [5, 9]], dtype=np.int32)
    b_u = np.array([6, 45], dtype=np.int32)
    b_l = np.full_like(b_u, -np.inf)
    cons = LinearConstraint(A, b_l, b_u)
    integrality = np.ones_like(c)
    res = milp(c = c, constraints=cons, integrality=integrality)
    return -1 * res.fun

Given question below:
------------
max z = 5x + 8y
s.t.
x + y <= 6
5x + 9y <= 45
x, y are integers

Define a python function solve() to solve this problem and return the maximum value of z.
------------

We try to solve it with code:
```python
from scipy.optimize import linprog
import numpy as np

def solve():

    A = [[1, 1], [5, 9]]
    b = [6, 45]

    x_bounds = (0, None)
    y_bounds = (0, None)

    res = linprog(c, A_ub=A, b_ub=b, bounds=[x_bounds, y_bounds], method='highs')

    x, y = res.x

    x_int = int(round(x))
    y_int = int(round(y))

    max_z = 5 * x_int + 8 * y_int

    return max_z

print(solve())
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpw928wlon/tmp18xtmd4g.py", line 34, in <module>
    assert 40 == solve()
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


from sympy import integrate, oo, Symbol

def limit_of_an():
    # Defining the continuous variable and function for approximation
    x = Symbol('x')
    f = 1 / (1 + x**2)

    # Integrating the function over the interval [0, 1]
    integral_result = integrate(f, (x, 0, 1))
    return integral_result.evalf()

Given question below:
------------
Please write a Python function limit_of_an() to calculate the limit of the sequence \{a_n\}_{n=1}^{\infinity}, where a_n is defined as a_n = \sum_{k=1}^n n/(n^2+k^2).
What is needed to be calculated is, when n tends to infinity, what is the limit value of an?
------------

We try to solve it with code:
```python
import numpy as np

def limit_of_an(epsilon=1e-7, max_iter=100000):
    n = 1
    prev_an = 0
    while n < max_iter:
        k = np.arange(1, n+1)
        terms = n / (n**2 + k**2)
        an = np.sum(terms)

        if abs(an - prev_an) < epsilon:
            return an

        prev_an = an
        n += 1

    return None

limit = limit_of_an()
print(f"The estimated limit of the sequence is approximately: {limit}")
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpxlhw22vg/tmpdytn6b0a.py", line 31, in <module>
    assert (abs(limit_of_an() - ans) < tolerance)
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

import math
ans = math.pi / 4
tolerance = 1e-6

assert (abs(limit_of_an() - ans) < tolerance)


#L0_B_53
from rdkit import Chem
from rdkit.Chem.SaltRemover import SaltRemover
from rdkit.Chem import Descriptors

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    remover = SaltRemover()
    res = remover.StripMol(mol)
    logp = Descriptors.MolLogP(res)
    return round(logp, 3)

Given question below:
------------
Implement a complete Python function `solve(smiles)`. The input is the SMILES of a molecule. The function removes the salts from the molecule and calculates the logP of the product. The result should be reported with three decimal places.
------------

We try to solve it with code:
```python
from rdkit import Chem
from rdkit.Chem import Descriptors, SaltRemover

def solve(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        raise ValueError("Invalid SMILES string")

    remover = SaltRemover.SaltRemover()

    mol_no_salts = remover.StripMol(mol)

    logP = Descriptors.MolLogP(mol_no_salts)

    return f"{logP:.3f}"

```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpcd9sw6rb/tmpjds3uv3_.py", line 30, in <module>
    check(solve)
  File "/tmp/tmpcd9sw6rb/tmpjds3uv3_.py", line 26, in check
    assert abs(solve("Cl.NCCC1(O)CCC2(CC1)OCCO2") - 0.3834) < 1e-3
TypeError: unsupported operand type(s) for -: 'str' and 'float'

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

def check(solve):
    assert abs(solve("Cl.NCCC1(O)CCC2(CC1)OCCO2") - 0.3834) < 1e-3
    assert abs(solve("Cl.NCC(C)C.Cl.Br") - 0.6011) < 1e-3
    assert abs(solve("c1ccccc1") - 1.6866) < 1e-3

check(solve)


def single_num(nums):
    seen_once = seen_twice = 0

    for num in nums:
        seen_once = ~seen_twice & (seen_once ^ num)
        seen_twice = ~seen_once & (seen_twice ^ num)

    return seen_once

Given question below:
------------
Given a non-empty array of integers, every element appears three times except for one element, which only appears once. Find the element that only appears once.
We define a function in Python called single_num to solve this problem. It accepts a non-empty array of integers and returns the element that only appears once.
In this problem, you can solve this issue by traversing the list and performing an XOR operation on each number. The main steps are as follows:

Iterate through the list, using XOR operation to determine whether the current element is a repeat. If it is not a repeated appearance, record the current element. If it is repeated, record its bit pattern, to indicate multiple appearances of the element.
------------

We try to solve it with code:
```python
def single_num(nums):
    seen_once = 0
    seen_twice = 0
    seen_thrice = 0

    for num in nums:
        seen_once = ~seen_twice & (seen_once ^ num)
        seen_twice = ~seen_once & (seen_twice ^ num)
        seen_thrice = ~seen_twice & (seen_thrice ^ num)

        seen_once &= ~seen_thrice
        seen_twice &= ~seen_thrice

    return seen_once

nums = [2, 2, 3, 2]
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpcphx8hem/tmphakvhpq0.py", line 23, in <module>
    assert(1 == single_num([1, 2, 2, 2, 3, 3, 3]))
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


def bytes_to_int32(bytes_obj):
    if len(bytes_obj) != 4:
        raise ValueError("32-bit integer must be 4 bytes in length")
    return int.from_bytes(bytes_obj, byteorder='big', signed=False)

Given question below:
------------
I have obtained some bytes from a remote computer and need to convert them to an unsigned int32 type using Python.
For example, 4 bytes \x00\x00\x01\x01 will be converted to 257, \x01\x02\x01\x01 will be converted to 16908545.
However, there are constant issues with my code below. Help me find the reason and fix it, and present the corrected code.
def bytes_to_int32(bytes_obj):
    if len(bytes_obj) != 4:
        raise ValueError("32-bit integer must be 4 bytes in length")
    return int.from_bytes(bytes_obj, byteorder='little', signed=True)
------------

We try to solve it with code:
```python
def bytes_to_int32(bytes_obj):
    if len(bytes_obj) != 4:
        raise ValueError("32-bit integer must be 4 bytes in length")
    return int.from_bytes(bytes_obj, byteorder='little', signed=False)
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmp40gdlnj0/tmpcvxvyg3o.py", line 6, in <module>
    assert bytes_to_int32(b'\x00\x00\x01\x01') == 257
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


from git import Repo
import datetime

def create_local_tag(repo_path):
    repo = Repo(repo_path)
    tag_name = datetime.datetime.now().strftime("tag-%Y%m%d-%H%M%S")
    new_tag = repo.create_tag(tag_name)
    print(f"Tag created: {new_tag.name}")
    return new_tag.name

Given question below:
------------
Given a path, it is required to create a new tag in the local repository using the git python sdk, and return the name of the created tag. The name of the new tag should be based on the current date and time. The function name is defined as create_local_tag.
------------

We try to solve it with code:
```python
import git
import datetime

def create_local_tag(repo_path):
    repo = git.Repo(repo_path)

    now = datetime.datetime.now()
    tag_name = f"tag_{now.strftime('%Y%m%d_%H%M%S')}"

    new_tag = repo.create_tag(tag_name)

    return new_tag.name
```

But some error occurs during execution:
------------
/bin/bash: 1: Syntax error: end of file unexpected

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)

#<INSERT>

from git import Repo
def check():
    repo_path = "./git_python_test"
    def init(repo_path):
        import os, shutil
        from git import Repo, Actor, Commit
        if os.path.exists(repo_path):
            shutil.rmtree(repo_path)
        os.makedirs(repo_path)
        repo = Repo.init(repo_path)
        index = repo.index
        index.add('*')
        author = Actor('author', 'author@xxx.com')
        index.commit('test', author=author)
        return
    init(repo_path)

    tag_created = create_local_tag(repo_path)
    assert tag_created
    repo = Repo(repo_path)
    tags = [tag.name for tag in repo.tags]
    assert tag_created in tags

check()


from io import StringIO
import pandas as pd
from sklearn.cluster import KMeans

def kmeans_iris(csv_str):
    df = pd.read_csv(StringIO(csv_str), sep=',')
    X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']].to_numpy()
    kmeans = KMeans(n_clusters = 3, random_state=0, n_init="auto").fit(X)
    df = df.assign(cluster_id=kmeans.labels_)
    return df

The iris dataset is a data set for iris flower classification, recording 4 numerical features for each type of iris: sepal_length, sepal_width, petal_length, petal_width

A csv file stores this dataset, an example looks like this:

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa

Implement a python function kmeans_iris(csv_str), where the input is the content string csv_str of the csv file, cluster the iris according to 4 features into 3 categories (0, 1, 2), finally add a column in the dataframe to record the cluster id each row of data belongs to, and name the column as cluster_id, finally return the dataframe after clustering.


from sympy import symbols, Eq, solve

def f():
    total_parts = symbols('total_parts')

    equation = Eq(total_parts, (2/7) * total_parts + 1600 + 1/2 * ((2/7) * total_parts + 1600))

    solution = solve(equation, total_parts)

    return int(solution[0])

Given question below:
------------
There are three teams in a workshop making a batch of parts. The first team made 2/7 of the total number, the second team made 1600 parts, and the third team made half of the total parts made by the first two teams. How many parts are there in total? Write the Python code to solve this problem, naming the function `f()`, and it should return an integer.
------------

We try to solve it with code:
```python
def f():
    second_group = 1600

    # (5/14) * total = 2400

    total = 6720
    return total

f()
```

But some error occurs during execution:
------------
Traceback (most recent call last):
  File "/tmp/tmpu9r0sybb/tmp_kupvrw4.py", line 28, in <module>
    assert(f() == 4200)
AssertionError

------------

Please try to analyze the cause of the error and give the full code after the modification (with markdown code block format)


from io import StringIO
import pandas as pd

def corr_sepal_petal(csv_str):
    df = pd.read_csv(StringIO(csv_str), sep=',')
    return df['sepal_length'].corr(df['petal_length'], method='pearson'), df['sepal_width'].corr(df['petal_width'], method='pearson')

The iris dataset is a dataset about iris flower classification, which records 4 numerical features for each type of iris: sepal_length, sepal_width, petal_length, petal_width.

There is a csv file that stores this dataset, an example is as follows:

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa

Implement a python function corr_sepal_petal(csv_str), which takes the content string of the csv file csv_str as input, calculates the Pearson correlation coefficient of sepal_length and petal_length, as well as sepal_width and petal_width, and returns the 2 correlation coefficients.


from io import StringIO
import pandas as pd

def solve(csv_str, n):
    df = pd.read_csv(StringIO(csv_str))
    top_n = df.nlargest(n, 'sepal_length')
    species_counts = top_n['species'].value_counts().to_dict()
    return species_counts

The iris dataset is a dataset for iris flower classification, where 4 numeric features of each type of iris are recorded: sepal_length, sepal_width, petal_length, petal_width.

A csv file stores this dataset, with the sample as follows:

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa

Implement a python function solve(csv_str, n), inputting the content string csv_str of the csv file and the integer n, calculate the count of different types of iris in the top n data with the highest average sepal_length, and return a dict.


from io import StringIO
import pandas as pd

def solve(csv_str):
    df = pd.read_csv(StringIO(csv_str), sep=',')
    df = df.groupby('species').mean()
    return df[df['sepal_length'] > df['sepal_width'] * 2].index.to_list()

The iris dataset is a dataset about the classification of iris flowers, which records 4 numeric characteristics for each type of iris:
sepal_length, sepal_width, petal_length, petal_width

A csv file stores this dataset, with a sample as follows:

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa

The python function solve(csv_str) is implemented, which takes the content string csv_str of a csv file as an input, calculates which kind of iris has an average sepal_length greater than twice the sepal_width and returns a list of irises that meet this condition.


from io import StringIO
import pandas as pd

def species_with_min_petal_width(csv_str):
    df = pd.read_csv(StringIO(csv_str), sep=',')
    return df.groupby('species').mean().sort_values('petal_width').iloc[0].name

The Iris dataset is a dataset for iris flower classification, which records 4 numerical features for each type of iris flower:
sepal_length, sepal_width, petal_length, petal_width

There is a csv file that stores this dataset, and the sample is as follows:

sepal_length,sepal_width,petal_length,petal_width,species
5.1,3.5,1.4,0.2,setosa
4.9,3.0,1.4,0.2,setosa
4.7,3.2,1.3,0.2,setosa
4.6,3.1,1.5,0.2,setosa
5.0,3.6,1.4,0.2,setosa

Implement a python function species_with_min_petal_width(csv_str), take the content string csv_str of the csv file as input, calculate and return the species of iris with the smallest average petal_width.


import ipaddress

def summarise_ip(ip_list):
    total_ips = len(ip_list)
    valid_ipv4 = 0
    valid_ipv6 = 0
    private_ips = 0
    public_ips = 0
    special_ips = 0

    for ip in ip_list:
        try:
            parsed_ip = ipaddress.ip_address(ip)
            if isinstance(parsed_ip, ipaddress.IPv4Address):
                valid_ipv4 += 1
                if parsed_ip.is_private:
                    private_ips += 1
                elif parsed_ip.is_global:
                    public_ips += 1
                elif parsed_ip.is_loopback or parsed_ip.is_link_local:
                    special_ips += 1
            elif isinstance(parsed_ip, ipaddress.IPv6Address):
                valid_ipv6 += 1
                if parsed_ip.is_private:
                    private_ips += 1
                elif parsed_ip.is_global:
                    public_ips += 1
                elif parsed_ip.is_loopback or parsed_ip.is_link_local:
                    special_ips += 1
        except ValueError:
            # 如果解析失败，跳过该地址
            continue

    return [total_ips, valid_ipv4, valid_ipv6, private_ips, public_ips, special_ips]

Write a Python function summarise_ip to process and analyze a series of IP addresses. The function's parameter is a list containing strings, each string is an IP address. The function returns a list, where the elements are respectively the total number of IP addresses, the number of valid IPv4 addresses, the number of valid IPv6 addresses, the number of private IPs, the number of public IPs, and the number of special IPs.


def get_most_active_users(json_data, number):
    # Dictionary to keep track of user activity scores
    user_scores = {}

    # Process each post in the json data
    for post in json_data:
        # Count the post for the author
        author = post['author']
        user_scores[author] = user_scores.get(author, 0) + 1

        # Count likes (0.1 points each)
        for user in post['likes']:
            user_scores[user] = user_scores.get(user, 0) + 0.1

        # Count comments (0.5 points each)
        for comment in post['comments']:
            commenter = comment['author']
            user_scores[commenter] = user_scores.get(commenter, 0) + 0.5

    # Sort the users by their scores in descending order and get the top 'number' users
    most_active_users = sorted(user_scores, key=user_scores.get, reverse=True)[:number]
    return most_active_users

You have obtained the original data from a social media website, formatted as a python dict parsed from JSON. These data include the posts of users, the number of likes and comments of the posts. Your task is to write a Python program to analyze these data and find out the most active N users.

The format of the input data is as follows:

[
    {
        'title': 'Post title',
        'content': 'Post content',
        'likes': [
            'user ID 1',
            'user ID 2',
            ...
            'user ID N'
        ]
        'comments': [
            {
                'content': 'comment content',
                'author': 'comment user ID'
            }
            ...
        ],
        'author': 'author ID'
    }
]

Please write the get_most_active_users(json_data, number) function, return a list, containing the IDs of the most active number of users. Where json_data is the original data in dict format. The activity level is the sum of the number of posts, the number of likes (with a coefficient of 0.1) and the number of comments (with a coefficient of 0.5).

#<INSERT>

test_data = [
    {
        "title": "Post 1",
        "content": "Content of post 1",
        "likes": ["user1", "user5"],
        "comments": [
            {"content": "Great post!", "author": "user2"},
            {"content": "I agree!", "author": "user4"}
        ],
        "author": "user1"
    },
    {
        "title": "Post 2",
        "content": "Content of post 2",
        "likes": ["user4"],
        "comments": [
            {"content": "Interesting perspective.", "author": "user1"},
            {"content": "Thanks for sharing.", "author": "user3"}
        ],
        "author": "user2"
    },
    {
        "title": "Post 3",
        "content": "Content of post 3",
        "likes": ["user1", "user3", "user4"],
        "comments": [
            {"content": "Nice post!", "author": "user5"}
        ],
        "author": "user3"
    }
]

assert set(get_most_active_users(test_data, 1)) == {'user1'}
assert set(get_most_active_users(test_data, 2)) == {'user1', 'user3'}
assert set(get_most_active_users(test_data, 3)) == {'user1', 'user2', 'user3'}
assert set(get_most_active_users(test_data, 4)) == {'user1', 'user2', 'user3', 'user4'}
assert set(get_most_active_users(test_data, 5)) == {'user1', 'user2', 'user3', 'user4', 'user5'}


export interface Imcard {
    bg_url: string;
    cover_url: string;
    title: string;
    subtitle: string;
    schema: string;
}

export interface Reflow {
    schema: string;
}

export interface Data {
    min_version: number;
    imcard: Imcard;
    reflow: Reflow;
}

{
    "min_version": 2901,
    "imcard": {
      "bg_url": "https://picsum.photos/200/300",
      "cover_url": "https://picsum.photos/200/300",
      "title": "Red Packet Rain is coming",
      "subtitle": "Click me to grab the red packet",
      "schema": "aweme://lynxview_popup/..."
    },
    "reflow": {
      "schema": "aweme://lynxview_popup/..."
    }
}

Now, let's convert it into typescript definition. The type name is Data.
Please wrap the answering code with markdown code block format, e.g.
```typescript
[code]
```

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('Data Type Test', () => {
  const mockData: Data = {
    "min_version": 2901,
    "imcard": {
      "bg_url": "https://picsum.photos/200/300",
      "cover_url": "https://picsum.photos/200/300",
      "title": "红包雨来袭",
      "subtitle": "点我抢红包",
      "schema": "aweme://lynxview_popup/?surl=https%3A%2F%2Flf-normal-gr-sourcecdn.bytegecko.com%2Fobj%2Fbyte-gurd-source-gr%2Flife%2Fmarketing%2Flynx%2Flife_marketing_lynx_red_rain_base%2Freflow%2Ftemplate.js&hide_nav_bar=1&trans_status_bar=1&use_bdx=1&close_by_mask=0&mask_color=00000000&width_percent=100&popup_enter_type=center&enable_prefetch=1&is_hidden=1&hide_loading=1&share_config=1"
    },
    "reflow": {
      "schema": "aweme://lynxview_popup/?trans_status_bar=1&surl=https%3A%2F%2Flf-normal-gr-sourcecdn.bytegecko.com%2Fobj%2Fbyte-gurd-source-gr%2Flife%2Fmarketing%2Flynx%2Flife_marketing_lynx_red_rain_base%2Fred%2Ftemplate.js&hide_nav_bar=1&popup_enter_type=center&height_percent=100&width_percent=100&dynamic=1&hide_loading=1&enable_canvas=1&enable_canvas_optimize=1&enable_dynamic_v8=1&hide_loading=1&delay_open=1&block_back_press=1"
    }
  };

  test('should match the Data type structure', () => {
    expect(mockData).toHaveProperty('min_version');
    expect(mockData).toHaveProperty('imcard');
    expect(mockData).toHaveProperty('reflow');
    expect(mockData.imcard).toHaveProperty('bg_url');
    expect(mockData.imcard).toHaveProperty('cover_url');
    expect(mockData.imcard).toHaveProperty('title');
    expect(mockData.imcard).toHaveProperty('subtitle');
    expect(mockData.imcard).toHaveProperty('schema');
    expect(mockData.reflow).toHaveProperty('schema');
  });

  // More detailed tests about the structure can be added
});


import random
import os

def write_by_line(path, data, mod="w", encode="utf-8"):
    """write the data"""
    with open(path, mod, encoding=encode) as outfile:
        [outfile.write(d + "\n") for d in data]

def dataset_process(data, output_folder, max_len=20):
    # 去掉内容长度超过长度的数据
    data = [(content, label) for content, label in data if len(content) <= max_len]
    # 按内容去重
    data = list(set(tuple(d) for d in data))

    # 随机打乱数据
    random.shuffle(data)

    # 划分数据集
    total_samples = len(data)
    train_samples = int(total_samples * 0.8)
    valid_samples = int(total_samples * 0.1)
    test_samples = total_samples - train_samples - valid_samples

    train_data = ["\t".join(d) for d in data[:train_samples]]
    valid_data = ["\t".join(d) for d in data[train_samples: train_samples + valid_samples]]
    test_data = ["\t".join(d) for d in data[train_samples + valid_samples:]]

    # 保存数据集到指定目录下
    os.makedirs(output_folder, exist_ok=True)
    write_by_line(os.path.join(output_folder, 'train.txt'), train_data)
    write_by_line(os.path.join(output_folder, 'valid.txt'), valid_data)
    write_by_line(os.path.join(output_folder, 'test.txt'), test_data)

Implement the dataset processing method dataset_process(data, output_folder, max_len=20) using python, which processes the input data (the data format is [[content, label], ...]) and saves it to the specified folder.
Processing requirements:
1. De-duplicate data based on content
2. Remove content that exceeds 20 in length
3. Randomly shuffle data
4. Divide train, valid, test into 8:1:1, and save them to train.txt, valid.txt, test.txt in the specified directory

#<INSERT>

def check():
    def del_file(file_path):
        if os.path.exists(file_path):
            os.remove(file_path)

    import os
    datas = [["周杰伦身高多少", "需求"],
            ["周杰身高多少", "需求"],
            ["你好", "非需求"],
            ["你好", "非需求"],
            ["谢娜身高多少", "需求"],
            ["谢娜身高多少", "需求"],
            ["周的身高多少", "需求"],
            ["周的xx身高多少", "需求"],
            ["周的ewr身高多少", "需求"],
            ["你好啊", "非需求"],
            ["你好啊", "非需求"],
            ["周的earthyr身高多少", "需求"],
            ["周的enter身高多少", "需求"],
            ["周的en32435身高多少", "需求"],
            ["周的en32435身高3456789876543223456多少", "需求"],
            ["周的en32435身高34567223456多少", "需求"]]
    file_dir = "dataset_test_1"
    del_file(os.path.join(file_dir, "train.txt"))
    del_file(os.path.join(file_dir, "valid.txt"))
    del_file(os.path.join(file_dir, "test.txt"))

    dataset_process(datas, file_dir)
    assert os.path.exists(os.path.join(file_dir, "train.txt"))
    assert os.path.exists(os.path.join(file_dir, "valid.txt"))
    assert os.path.exists(os.path.join(file_dir, "test.txt"))

check()


from typing import List, Tuple, Optional, Dict, Any

class CategoryNode:
    def __init__(self, name: str):
        self.name = name
        self.children: List[CategoryNode] = []

    def add_child(self, child: 'CategoryNode') -> None:
        self.children.append(child)

def convert_category_tree(categories: List[Tuple[str, str]]) -> Optional[CategoryNode]:
    # 创建一个字典来存储所有的节点，方便后续查找
    nodes: Dict[str, CategoryNode] = {}
    # 创建一个字典来存储所有节点的父节点名称
    parent_names: Dict[str, str] = {}

    # 首先创建所有的节点
    for category, parent in categories:
        nodes[category] = CategoryNode(category)
        parent_names[category] = parent

    # 定义根节点
    root: Optional[CategoryNode] = None

    # 找到根节点
    for _, parent in categories:
        if parent in nodes:
            continue
        if root:
            return False
        else:
            root = CategoryNode(parent)
            nodes[parent] = root

    # 构建树结构
    for category, parent in categories:
        nodes[parent].add_child(nodes[category])

    # 检查是否所有节点都正确连接到树上
    for category, parent in parent_names.items():
        if category != parent and nodes[category] not in nodes[parent].children:
            return False

    return root

Please implement a function in Python that converts the e-commerce product classification system queried into a tree structure representation.
Requirements:
1. Implement a method called convert_category_tree that converts the product classification system stored in a list into a tree structure.
2. The input is a list where each entity is a tuple containing the product category name and its parent category name.
3. There should only be one product system. If there are multiple systems, the function should return False
4. Please add type annotations in the Python 3 writing style.

#<INSERT>

def check():
    categories_1 = [
        ("电子产品", "root"),
        ("手机", "电子产品"),
        ("电子产品", ""),
        ("笔记本电脑", "电子产品"),
        ("耳机", "手机配件"),
        ("手机配件", "电子产品"),
        ("充电器", "手机配件"),
        ("连衣裙", "服饰")
    ]

    categories_2 = [
        ("电子产品", "root"),
        ("服饰", "root"),
        ("清洁用品", "root"),
        ("手机", "电子产品"),
        ("笔记本电脑", "电子产品"),
        ("耳机", "手机配件"),
        ("手机配件", "电子产品"),
        ("充电器", "手机配件"),
        ("连衣裙", "服饰"),
        ("内衣裤", "服饰"),
        ("羽绒服", "服饰")
    ]
    assert not convert_category_tree(categories_1)
    assert convert_category_tree(categories_2)

check()


def print_dir_tree(dir_structure):
    def build_tree(structure, prefix=''):
        tree = []
        # First, process directories
        for directory in sorted(structure.get('dirs', {})):
            tree.append(f"{prefix}├── {directory}")
            subdir_structure = structure['dirs'][directory]
            sub_tree = build_tree(subdir_structure, prefix + "│   ")
            tree.extend(sub_tree)

        # Then, process files
        for file in sorted(structure.get('files', [])):
            tree.append(f"{prefix}├── {file}")

        # Replace last "├──" with "└──" for the last element
        if tree:
            tree[-1] = tree[-1].replace("├──", "└──")

        return tree

    return '\n'.join(build_tree(dir_structure))

Write a print_dir_tree(dir_structure) function that prints the structure of a folder in a tree structure, in the same level folder, first show all subfolders, then show all subfiles, files and subfolders are arranged in dictionary order.

The parameter dir_structure is a dict data in the following format:

{
    "files: [
        "file1.txt",
        "file2.txt"
    ],
    "dirs": [
        "dir1": {
            "files": [
                "pic.png",
                "photos": [
                    "1.jpg"
                ]
            ]
        }
    ]
}

The tree structure of the above folder corresponds to the following format:

├── dir1
│   ├── photos
│   │   └── 1.jpg
│   └── pic.png
├── file1.txt
├── file2.txt

The output of the function is a string representing the above tree-structured folder structure.

#<INSERT>

dir_structure = {
    "dirs": {
        "eval": {
            "dirs": {
                "test": {
                    "files": ["test.txt"]
                },
                "test2": {
                    "files": ["1.txt"]
                }
            },
            "files": [
                "eval.py"
            ]
        }
    },
    "files": [
        "README.md",
        "LICENSE"
    ]
}

# Print the directory tree
assert print_dir_tree(dir_structure).strip() == """
├── eval
│   ├── test
│   │   └── test.txt
│   ├── test2
│   │   └── 1.txt
│   └── eval.py
├── LICENSE
└── README.md
""".strip()


import json
import csv
from collections import defaultdict

def process_data(json_path, csv_path):
    # 读取产品信息
    with open(json_path, 'r') as json_file:
        products = json.load(json_file)

    # 将产品信息存储在字典中，方便查找
    product_info = {product['product_id']: product for product in products}

    # 读取订单信息
    sales = defaultdict(int)  # 用于存储每个产品的销售额
    with open(csv_path, newline='') as csv_file:
        reader = csv.DictReader(csv_file)
        for row in reader:
            product_id = row['product_id']
            quantity = int(row['quantity'])
            price = float(product_info[product_id]['price'])
            sales[product_id] += price * quantity

    # 找出销售额最高的产品ID
    max_sales = max(sales.values())
    top_selling_products = [product_id for product_id, total in sales.items() if total == max_sales]

    return top_selling_products

Assume you are dealing with data from an e-commerce company. Your task is to write a function process_data(json_path, csv_path) to accomplish the following tasks:

Read a JSON file from json_path: This file contains information for multiple products. Each product has the following fields: product_id (unique product ID), name (product name), category (product category), price (product price).

Read a CSV file from csv_path: This file contains a series of order information. Each order includes the following fields: order_id (unique order ID), product_id (product ID), quantity (quantity purchased).

Data processing:

Return the product IDs with the highest sales (product price times quantity purchased) in the form of a list.

#<INSERT>

assert process_data("structured_io_test_data/sales_data_1.json", "structured_io_test_data/sales_data_1.csv") == ['202']
assert process_data("structured_io_test_data/sales_data_1.json", "structured_io_test_data/sales_data_2.csv") == ['201', '202', '203']


import argparse

def get_parser():
    # 创建 ArgumentParser 对象
    parser = argparse.ArgumentParser(description="PDF file assistant program")

    # 创建子命令的解析器
    subparsers = parser.add_subparsers(dest="command", help="Subcommands")

    # 创建 merge 子命令的解析器
    merge_parser = subparsers.add_parser("merge", help="Merge multiple PDF files into one")
    merge_parser.add_argument("files", nargs='+', help="PDF files to merge")
    merge_parser.add_argument("--output", required=True, help="Output file name for merged PDF")

    # 创建 split 子命令的解析器
    split_parser = subparsers.add_parser("split", help="Split a PDF file into multiple files")
    split_parser.add_argument("file", help="PDF file to split")

    return parser

Help me write a get_parser() function, which returns an ArgumentParser object, parsing command line arguments for a PDF file helper program.

This program supports two sub-commands: merge and split.

The merge command supports multiple filenames and merges them into one PDF file, with the new filename being specified by the --output parameter. Such as merge --output merged.pdf file1.pdf file2.pdf file3.pdf etc., the number of input files is unlimited and exist as a list in the variable files.

The split command supports one PDF filename, splitting it into multiple PDF files, this sub-command only accepts one argument, that is the PDF filename to be split. As in split file.pdf, the filename exists in the variable file.

#<INSERT>

parser = get_parser()

command_line = "merge 1.pdf 2.pdf 3.pdf 4.pdf --output merged.pdf"
arguments = parser.parse_args(command_line.split())
assert arguments.files == ['1.pdf', '2.pdf', '3.pdf', '4.pdf']
assert arguments.output == 'merged.pdf'
assert "file" not in arguments

command_line = "split input.pdf"
arguments = parser.parse_args(command_line.split())
assert "files" not in arguments
assert "output" not in arguments
assert arguments.file == "input.pdf"


from typing import List
import math

def doc_cosine_similarity(doc1: List[str], doc2: List[str]) -> float:
    # 创建词频字典
    word_freq1 = {}
    word_freq2 = {}
    # 计算第一篇文章的词频
    for word in doc1:
        word_freq1[word] = word_freq1.get(word, 0) + 1

    # 计算第二篇文章的词频
    for word in doc2:
        word_freq2[word] = word_freq2.get(word, 0) + 1

    # 计算词频向量的点积
    dot_product = 0
    for word, freq in word_freq1.items():
        if word in word_freq2:
            dot_product += freq * word_freq2[word]

    # 计算两个词频向量的模
    magnitude1 = math.sqrt(sum([freq ** 2 for freq in word_freq1.values()]))
    magnitude2 = math.sqrt(sum([freq ** 2 for freq in word_freq2.values()]))

    # 避免除以零
    if magnitude1 == 0 or magnitude2 == 0:
        return 0.0

    # 计算余弦相似度
    cosine_similarity = dot_product / (magnitude1 * magnitude2)

    return cosine_similarity

Please implement the calculation of cosine similarity between two articles using Python.
Requirements:
1. Include a function for calculating the cosine similarity of articles named `doc_cosine_similarity`,
2. Implement efficiently using a hash table, avoiding the use of other existing methods,
3. Each article should be word-segmented and input as a list,
4. Please add type annotations according to Python 3 syntax.

#<INSERT>

def check():
    doc1 = ["我", "喜欢", "看", "电影", "因为", "电影", "质量", "高"]
    doc2 = ["电影", "是", "我", "的", "爱好"]
    doc3 = []

    assert round(doc_cosine_similarity(doc1, doc2), 5) == 0.42426
    assert round(doc_cosine_similarity(doc1, doc1), 5) == 1.0
    assert doc_cosine_similarity(doc1, doc3) == 0.0

check()


function deepCopy<T>(obj: T): T {
    if (typeof obj === 'object' && obj !== null) {
        if (Array.isArray(obj)) {
            return obj.map(deepCopy) as unknown as T;
        } else if (obj instanceof Date) {
            return new Date(obj.getTime()) as unknown as T;
        } else if (obj instanceof Map) {
            return new Map(Array.from(obj.entries()).map(([k, v]) => [k, deepCopy(v)])) as unknown as T;
        } else if (obj instanceof Set) {
            return new Set(Array.from(obj.values()).map(deepCopy)) as unknown as T;
        } else {
            const copiedObj = Object.create(Object.getPrototypeOf(obj));
            for (const key in obj) {
                if (obj.hasOwnProperty(key)) {
                    copiedObj[key] = deepCopy(obj[key]);
                }
            }
            return copiedObj;
        }
    } else {
        return obj;
    }
}

Deep copy of TypeScript objects, the method name is deepCopy. Please wrap the answer in Markdown.
Please wrap the answering code with markdown code block format, e.g.
```typescript
[code]
```

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('deepCopy', () => {
    test('should create a deep copy of a simple object', () => {
        const obj = { a: 1, b: 'test' };
        const copiedObj = deepCopy(obj);
        expect(copiedObj).toEqual(obj);
        expect(copiedObj).not.toBe(obj);
    });

    test('should create a deep copy of a nested object', () => {
        const nestedObj = { a: { b: { c: { d: 1 } } } };
        const copiedNestedObj = deepCopy(nestedObj);
        expect(copiedNestedObj).toEqual(nestedObj);
        expect(copiedNestedObj.a).not.toBe(nestedObj.a);
    });

    test('should create a deep copy of an array', () => {
        const array = [1, 2, 3, { a: 4 }];
        const copiedArray = deepCopy(array);
        expect(copiedArray).toEqual(array);
        expect(copiedArray[3]).not.toBe(array[3]);
    });

    test('should create a deep copy of an object with a Date', () => {
        const dateObj = { now: new Date() };
        const copiedDateObj = deepCopy(dateObj);
        expect(copiedDateObj.now).toEqual(dateObj.now);
        expect(copiedDateObj.now).not.toBe(dateObj.now);
    });

    // Add more tests for Map, Set, etc., if your deepCopy function supports those.
});


function heapify(arr: number[], n: number, i: number): void {
    let largest = i;
    const left = 2 * i + 1;
    const right = 2 * i + 2;

    if (left < n && arr[left] > arr[largest]) {
        largest = left;
    }

    if (right < n && arr[right] > arr[largest]) {
        largest = right;
    }

    if (largest !== i) {
        [arr[i], arr[largest]] = [arr[largest], arr[i]];
        heapify(arr, n, largest);
    }
}

export function heapSort(arr: number[]): number[] {
    const n = arr.length;

    // Build max heap
    for (let i = Math.floor(n / 2) - 1; i >= 0; i--) {
        heapify(arr, n, i);
    }

    // Extract elements from the heap
    for (let i = n - 1; i > 0; i--) {
        [arr[0], arr[i]] = [arr[i], arr[0]]; // Move current root to end
        heapify(arr, i, 0); // call max heapify on the reduced heap
    }

    return arr;
}

Tell me about the implementation of heap sort in TypeScript, the method name is heapSort. Wrap the answer in markdown.
Please wrap the answering code with markdown code block format, e.g.
```typescript
[code]
```

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('heapSort', () => {
    test('sorts an array in ascending order', () => {
        expect(heapSort([3, 0, 2, 5, -1, 4, 1])).toEqual([-1, 0, 1, 2, 3, 4, 5]);
        expect(heapSort([])).toEqual([]);
        expect(heapSort([1])).toEqual([1]);
        expect(heapSort([2, 1])).toEqual([1, 2]);
    });
});


import matplotlib.pyplot as plt

def plot_bar_chart(data, title="chart", xlabel="X-axis", ylabel="Y-axis"):
    quarters = list(data.keys())
    categories = list(data[quarters[0]].keys())
    num_categories = len(categories)
    fig, ax = plt.subplots()
    bar_width = 0.1
    positions = [i * bar_width for i in range(num_categories)]
    for i, quarter in enumerate(quarters):
        values = [data[quarter][category] for category in categories]
        ax.bar([p + i * bar_width for p in positions], values, bar_width, label=quarter)

    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_title(title)

    ax.set_xticks([p + (num_categories - 1) * bar_width / 2 for p in positions])
    ax.set_xticklabels(categories)
    ax.legend()
    return plt

Implement a histogram in Python to compare economic data for different quarters with plot_bar_chart(data, title="chart", xlabel="X-axis", ylabel="Y-axis") . The data format is {"2023Q1": {"a": 0.1, "b": 0.2}, ...}. Please use the matplotlib library to implement this method. The title, x-axis, and y-axis of the chart are all specified by parameters, and the method returns the corresponding chart object.


import json

def find_common_friends(json_data, user_id1, user_id2):
    # 将字符串形式的JSON数据解析为字典
    data = json.loads(json_data)

    # 初始化两个用户的好友列表
    friends_of_user1 = []
    friends_of_user2 = []

    # 遍历用户数据，找到两个用户的好友列表
    for user in data["users"]:
        if user["id"] == user_id1:
            friends_of_user1 = user["friends"]
        elif user["id"] == user_id2:
            friends_of_user2 = user["friends"]

    # 计算共同好友
    common_friends = list(set(friends_of_user1) & set(friends_of_user2))

    return common_friends

Assume you have a JSON data file for a social network, which includes user information and their friend relationships. Each user has a unique ID, and a list of friends, which are the IDs of their friends.

Example of the data structure:
```json
{
    "users": [
        {"id": "user1", "friends": ["user2", "user5", "user6"]},
        {"id": "user2", "friends": ["user1", "user3"]},
        {"id": "user3", "friends": ["user2", "user4"]},
        // More users...
    ]
}
```

Create a function called find_common_friends. The first parameter of the function is a json data string. The second and third parameters are the IDs of two users respectively. The function should return the list of common friends of these two users.

#<INSERT>

json_data = '''
{
    "users": [
        {"id": "user1", "friends": ["user2", "user3", "user6"]},
        {"id": "user2", "friends": ["user1", "user3"]},
        {"id": "user3", "friends": ["user2", "user4", "user1"]}
    ]
}
'''

assert find_common_friends(json_data, "user1", "user2") == ['user3']


import math
from collections import Counter

def cal_tf_idf(documents):
    # 计算tf值
    tf_docs = []
    for doc in documents:
        word_counts = Counter(doc)
        total_words = len(doc)
        tf_doc = {word: count/total_words for word, count in word_counts.items()}
        tf_docs.append(tf_doc)

    # Step 2: 计算idf
    all_words = set(word for doc in documents for word in doc)
    idf = {}
    num_docs = len(documents)
    for word in all_words:
        num_docs_with_word = sum(word in doc for doc in documents)
        idf[word] = math.log(num_docs / (1 + num_docs_with_word))

    # Step 3: 计算tf-idf
    tf_idf_docs = []
    for tf_doc in tf_docs:
        tf_idf_doc = {word: tf * idf[word] for word, tf in tf_doc.items()}
        tf_idf_docs.append(tf_idf_doc)

    return tf_idf_docs

Implement the method cal_tf_idf(documents) in Python to calculate tf-idf. The input is a list of documents, with each document being a list of words. The function returns a dictionary where the keys are the words in the documents and the values are their tf-idf scores.

#<INSERT>

def check():
    documents = [
        ["apple", "banana", "apple", "orange", "orange", "hello"],
        ["banana", "orange", "grape", "hello"],
        ["apple", "grape", "kiwi", "banana", "apple"],
        ["orange", "kiwi", "kiwi", "new", "abstract"]
    ]
    tf_idf_documents = cal_tf_idf(documents)
    assert len(tf_idf_documents) == 4
    assert round(tf_idf_documents[2]["kiwi"], 4) == 0.0575

check()


import json

def fix_json_data(file_path, new_path):
    # 读取JSON文件
    data = []
    with open(file_path, 'r') as file:
        for line in file:
            data.append(json.loads(line.strip()))

    # 确保数据是列表形式
    if not isinstance(data, list):
        raise ValueError("JSON data must be a list of objects")

    # 计算所有数据的cost的平均值
    total_cost = sum(item['cost'] for item in data)
    average_cost = total_cost / len(data)

    # 更新数据，每个项目的cost减去平均值
    for item in data:
        item['cost_diff'] = item['cost'] - average_cost

    # 写入修改后的数据到新的JSON文件
    with open(new_path, 'w') as file:
        json.dump(data, file, indent=4, ensure_ascii=False)

The Python function fix_json_data(file_path, new_path) operates on JSON files. This function reads jsonl files (one JSON per line) based on file_path, calculates the average cost of all data, subtracts the average cost from the cost of all JSON data and saves it to the cost_diff attribute, and then writes the new JSON into new_path.

#<INSERT>

def check():
    import os
    save_path = "file_test_4/data_new.json"
    if os.path.exists(save_path):
        os.remove(save_path)
    path = "file_test_4/data.json"
    fix_json_data(path, save_path)

check()


function MinSubArrayLen(target: number, nums: number[]): number {
    let n = nums.length;
    let minLength = Infinity; // 初始化为极大值
    let sum = 0; // 存储子数组的和
    let start = 0; // 子数组的起始索引

    for (let end = 0; end < n; end++) {
        sum += nums[end]; // 将当前元素加到和中

        while (sum >= target) {
            minLength = Math.min(minLength, end - start + 1); // 更新最小长度
            sum -= nums[start]; // 移动起始索引，并减去这个值
            start++;
        }
    }

    return minLength === Infinity ? 0 : minLength; // 如果长度未被更新过，则返回0，表示没有找到
}

Given an array of n positive integers and a positive integer target,
find the minimal length of a contiguous subarray [numsl, numsl+1, ..., numsr-1, numsr] of which the sum is greater than or equal to target, and return its length. If there is no such subarray, return 0.
Please answer in TypeScript and implement the function "function MinSubArrayLen(s: number, nums: number[]): number".
Please wrap the answer code in a markdown code block format, like
```typescript
[code]
```

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('MinSubArrayLen', () => {
    test('finds minimum length of a subarray with sum >= target', () => {
        expect(MinSubArrayLen(7, [2, 3, 1, 2, 4, 3])).toBe(2);
        expect(MinSubArrayLen(15, [1, 2, 3, 4, 5])).toBe(5);
        expect(MinSubArrayLen(4, [1, 4, 4])).toBe(1);
        expect(MinSubArrayLen(11, [1, 1, 1, 1, 1, 1, 1, 1])).toBe(0);
    });

    test('returns 0 if no such subarray exists', () => {
        expect(MinSubArrayLen(100, [1, 2, 3, 4, 5])).toBe(0);
        expect(MinSubArrayLen(7, [2, 1])).toBe(0);
    });

    test('works with single element array', () => {
        expect(MinSubArrayLen(3, [3])).toBe(1);
        expect(MinSubArrayLen(3, [1])).toBe(0);
    });

    test('works with a large array', () => {
        // 这个测试用例是用于验证算法在处理大数据量时的性能
        const largeArray = Array(10000).fill(1);
        expect(MinSubArrayLen(9999, largeArray)).toBe(9999);
    });
});


import requests

def download_txt(url, filename):
    response = requests.get(url)
    with open(filename, 'w') as file:
        file.write(response.text)

import csv

def txt_to_csv(txt_filename, csv_filename):
    with open(txt_filename, 'r') as in_file, open(csv_filename, 'w', newline='') as out_file:
        lines = in_file.read().splitlines()
        writer = csv.writer(out_file)
        for line in lines:
            writer.writerow(line.split(','))

def download_txt_and_convert_to_csv(url, txt_filename, csv_filename):
    download_txt(url, txt_filename)
    txt_to_csv(txt_filename, csv_filename)

Using Python, write a function to download a web page txt file and finally organize the file into a csv file output. The txt contains content separated by commas.
1. The function to download the txt is download_txt, which takes url and filename as inputs
2. The conversion function is txt_to_csv, which takes txt_filename and csv_filename as inputs
3. Combine the calls to these two functions into one function: download_txt_and_convert_to_csv

#pyqt_test_32

#<INSERT>

def check():
    import os
    url = 'https://m-selig.ae.illinois.edu/ads/coord_updates/ag23.dat'
    txt_filename = 'example.txt'
    csv_filename = 'example.csv'
    download_txt_and_convert_to_csv(url, txt_filename, csv_filename)
    assert os.path.exists(csv_filename)
    if os.path.exists(txt_filename):  # 清理测试文件
        os.remove(txt_filename)
    if os.path.exists(csv_filename):  # 清理测试文件
        os.remove(csv_filename)

check()


import json

def read_json(key_path, json_str):
    try:
        # 将 JSON 字符串转换为字典
        data = json.loads(json_str)

        # 分割键路径
        keys = key_path.split('.')

        # 遍历键路径
        for key in keys:
            if '[' in key and ']' in key:
                # 提取数组下标
                index = int(key[key.find('[') + 1:key.find(']')])
                # 提取键名
                key = key[:key.find('[')]
                # 获取数组元素
                data = data[key][index]
            else:
                # 获取键值
                data = data[key]

        return data
    except (KeyError, IndexError, ValueError, TypeError):
        # 如果路径不正确或键不存在，返回 None
        return None

Write a read_json function, where the parameters are the path of the key and a string containing json data. The path of the key is separated by dots for child keys, and square brackets are used to indicate the index. For example, "key1.key2[0]". The function returns the key-value, and returns None when the key name does not exist.

#<INSERT>

json_data = '{"key1": {"key2": ["value1", "value2"]}, "key2": "value2"}'
key_path = "key1.key2[0]"
assert read_json(key_path, json_data) == "value1"
key_path = "key2"
assert read_json(key_path, json_data) == "value2"


import argparse

def get_parser():
    parser = argparse.ArgumentParser(description="Process command line arguments.")

    # 添加 files 参数，用于指定要处理的文件列表
    parser.add_argument("--files", "-f", nargs=2, required=True, help="Specify two files to process")

    # 添加 output 参数，用于指定输出文件
    parser.add_argument("--output", "-o", type=str, required=True, help="Specify the output file")

    # 添加 optimiser 参数，可以被多次调用
    parser.add_argument("--optimiser", action="append", help="Specify the name of the optimiser, can be called multiple times")

    return parser

Help me create a get_parser() function, which should return an ArgumentParser object that can be used to parse command line arguments, to support the processing of the following command line commands:

--files, -f Two consecutive strings, specify the list of files to be processed
--output, -o String, specify the output file
--optimiser String, specify the name of the optimiser, can be called multiple times, for example --optimiser=adam --optimiser=sgd, with the results saved in a list.

#<INSERT>

parser = get_parser()

command_line = "--files 1.txt 2.txt --output 3.txt --optimiser a --optimiser b"
arguments = parser.parse_args(command_line.split())
assert arguments.files == ['1.txt', '2.txt']
assert arguments.output == '3.txt'
assert arguments.optimiser == ['a', 'b']

command_line = "-f 1.txt 2.txt -o 3.txt --optimiser a --optimiser b"
arguments = parser.parse_args(command_line.split())
assert arguments.files == ['1.txt', '2.txt']
assert arguments.output == '3.txt'
assert arguments.optimiser == ['a', 'b']


import json
import csv

def save_to_csv(json_str, csv_filename):
    # 解析 JSON 字符串
    data = json.loads(json_str)

    # 打开 CSV 文件进行写入
    with open(csv_filename, 'w', newline='', encoding='utf-8') as csvfile:
        # 如果列表不为空，提取键名作为列名
        if data:
            fieldnames = data[0].keys()

            # 创建一个 csv 写入器对象
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

            # 写入列名（键名）
            writer.writeheader()

            # 写入数据行
            for row in data:
                writer.writerow(row)
        else:
            print("JSON data is empty. No data written to CSV.")

Write a function named save_to_csv, which takes a JSON string and a desired output CSV filename as arguments. The function's task is to parse the JSON string into a JSON object. The JSON format should be a list composed of dictionaries. Then, the function should save the JSON object into a CSV file. The first line of the CSV file should be the key names extracted from the dictionary, used as column names.

#<INSERT>

import csv
import os
json_str = '[{"name": "Alice", "age": 30}, {"name": "Bob", "age": 25}]'
csv_filename = 'output.csv'
save_to_csv(json_str, csv_filename)
with open(csv_filename, 'r') as f:
    csv_reader = csv.reader(f)
    header = next(csv_reader)
    assert header == ['name', 'age']
    rows = list(csv_reader)
    assert rows == [['Alice', '30'], ['Bob', '25']]
os.remove(csv_filename)


def split(n, k):
    d,r = divmod(n, k)
    return [d+1]*r + [d]*(k-r)

def divideNumber(n, k):
    return [2 * x for x in split(n // 2, k)]

Dividing an even number into N parts each part being a multiple of 2

Let's assume I have the number 100 which I need to divide into N parts each of which shouldn't exceed 30 initially. So the initial grouping would be (30,30,30). The remainder (which is 10) is to be distributed among these three groups by adding 2 to each group in succession, thus ensuring that each group is a multiple of 2. The desired output should therefore look like (34,34,32).

Note: The original number is always even.

I tried solving this in Python and this is what I came up with. Clearly it's not working in the way I thought it would. It distributes the remainder by adding 1 (and not 2, as desired) iteratively to each group.

```python
num = 100
parts = num//30  #Number of parts into which 'num' is to be divided

def split(a, b):
  result = ([a//b + 1] * (a%b) + [a//b] * (b - a%b))
  return(result)

print(split(num, parts))
```

Output:

```
[34, 33, 33]
```

Desired output:

```
[34, 34, 32]
```

Please write a function `divideNumber` in Python, which takes `num` and `n` as arguments and returns the dividing results (divide `num` into `n` parts) in List according to my above requirements.

Function signature is: `divideNumber(num, n)`

#<INSERT>

def split(n, k):
    d,r = divmod(n, k)
    return [d+1]*r + [d]*(k-r)

def split_even(n, k):
    return [2 * x for x in split(n // 2, k)]

assert split_even(100, 3) == divideNumber(100, 3)
assert split_even(1000, 3) == divideNumber(1000, 3)
assert split_even(10000, 5) == divideNumber(10000, 5)
assert split_even(666, 7) == divideNumber(666, 7)


def mu(n):
    if n == 1:
        return 1
    prime_factors = {}
    original_n = n
    factor = 2
    # 素因数分解
    while factor * factor <= n:
        while (n % factor) == 0:
            if factor in prime_factors:
                prime_factors[factor] += 1
            else:
                prime_factors[factor] = 1
            n //= factor
        factor += 1
    if n > 1:  # 这是最后的素因子，如果有的话
        prime_factors[n] = 1

    # 检查是否含有平方因子
    for count in prime_factors.values():
        if count > 1:
            return 0  # n 是平方数的倍数

    # 根据素因子的个数决定返回值
    if len(prime_factors) % 2 == 0:
        return 1
    else:
        return -1

Python implements the function mu(n) to return the value of the Möbius function for n, given a positive integer n


import shlex
def process_command(command):
    # 分割命令字符串为单独的部分
    parts = shlex.split(command)

    # 处理主命令：第一个元素，并以绿色显示
    parts[0] = f'{parts[0]}'

    for i in range(1, len(parts)):
        if parts[i].startswith("-") and i + 1< len(parts) and not parts[i + 1].startswith("-"):
            parts[i + 1] = f'{parts[i + 1]}'
            i += 1

    # 将处理后的部分用空格连接并返回
    return ' '.join(parts)

Write a process_command(command) function, where the input is a command string, and it returns an HTML string with style.

Take 'ls -la *.txt' as an example, the requirements are as follows:

The main command name is displayed in green: 'ls' is represented as ls.
The argument name remains unchanged, such as -la.
The value of the argument is underlined, such as *.txt is represented as *.txt.

Therefore, the complete return value for the above example will be ls -la *.txt.

#<INSERT>

# 示例
command = "ls -la *.txt"
assert process_command(command).strip() == "ls -la *.txt"

command = "gcc main.cpp -O3 -o main.o -c"
print(process_command(command).strip())
assert process_command(command).strip() == "gcc main.cpp -O3 -o main.o -c"


import configparser

def read_conf(key, ini_content):
    # 读取 INI 内容
    config = configparser.ConfigParser()
    config.read_string(ini_content)

    # 分割键名和子键名
    parts = key.split('.')

    # 确保键名格式正确
    if len(parts) != 2:
        raise ValueError("Key format is incorrect. Expected format: 'section.key'")

    section, option = parts

    # 尝试获取值
    try:
        value = config.get(section, option)
        return value
    except (configparser.NoSectionError, configparser.NoOptionError):
        # 如果指定的节或选项不存在
        return None

Write a function named read_conf. The parameters of this function are a key name (sub-key names are separated by '.', such as "key1.key2") and the content of a configuration file in ini format. The function returns the value of the key.

#<INSERT>

ini_content = """
[section1]
key1 = value1
key2 = value2
"""

key = 'section1.key1'
assert read_conf(key, ini_content) == "value1"

key = 'section1.key3'  # 不存在的键
assert read_conf(key, ini_content) is None


def text_wrap(text):
    wrapped_text = ""
    for line in text.splitlines():
        while len(line) > 80:
            # 找到最后一个空格的位置，用于拆分行
            split_index = line.rfind(' ', 0, 80)
            if split_index == -1:  # 如果找不到空格，直接在80个字符处截断
                split_index = 80

            # 添加新行并更新当前行
            wrapped_text += line[:split_index] + '\n'
            line = line[split_index:].lstrip()  # 去除左边的空格

        wrapped_text += line + '\n'

    return wrapped_text

Write a text_wrap(text) function, which takes a string of text as input. It should truncate each line that exceeds 80 characters and split it into several lines, ensuring each line does not exceed 80 characters. If truncating a line would split a word, move the entire word to the next line. The function should return the processed string.
If a single word exceeds 80 characters, you can truncate the word.

#<INSERT>

# 正常长度的行
assert text_wrap("This is a normal line.") == "This is a normal line.\n"

# 超长行无单词
long_text_no_space = "a" * 85
expected_wrapped_no_space = "a" * 80 + "\n" + "a" * 5 + "\n"
assert text_wrap(long_text_no_space) == expected_wrapped_no_space

# 超长行有单词
long_text_with_space = "This line has some words and then a very long sequence of characters: " + "a" * 60
expected_wrapped_with_space = "This line has some words and then a very long sequence of characters:" + "\n" +"a" * 60 + "\n"
assert text_wrap(long_text_with_space).strip() == expected_wrapped_with_space.strip()

# 多行混合
mixed_lines = "Short line\n" + "a" * 85 + "\nAnother short line that is actually quite long and needs to be wrapped properly."
expected_mixed = "Short line\n" + "a" * 80 + "\n" + "a" * 5 + "\nAnother short line that is actually quite long and needs to be wrapped properly.\n"
assert text_wrap(mixed_lines).strip() == expected_mixed.strip()

# 空字符串
assert text_wrap("").strip() == ""

print("All test cases passed!")


function isPrime(num: number): boolean {
    if (num <= 1) return false;
    if (num <= 3) return true;

    if (num % 2 === 0 || num % 3 === 0) return false;

    for (let i = 5; i * i <= num; i += 6) {
        if (num % i === 0 || num % (i + 2) === 0) return false;
    }

    return true;
}

function findFirstPrimeInRange(start: number, end: number): number | undefined {
    for (let i = start; i <= end; i++) {
        if (isPrime(i)) {
            return i;
        }
    }
}

Implement a findFirstPrimeInRange method using ts to find the first prime number between 100 and 1000, and wrap the answer in markdown.
Please wrap the answering code with markdown code block format, e.g.
```typescript
[code]
```

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('prime numbers tests', () => {

    test('test if number is prime', () => {
        // 一些已知的素数情况
        expect(isPrime(2)).toBe(true);
        expect(isPrime(3)).toBe(true);
        expect(isPrime(5)).toBe(true);
        expect(isPrime(7)).toBe(true);
        // 非素数情况
        expect(isPrime(1)).toBe(false);
        expect(isPrime(4)).toBe(false);
        expect(isPrime(6)).toBe(false);
        expect(isPrime(8)).toBe(false);
        expect(isPrime(9)).toBe(false);
    });

    test('find first prime number between 100 and 1000', () => {
        const firstPrime = findFirstPrimeInRange(100, 1000);
        expect(firstPrime).toBeDefined();
        expect(isPrime(firstPrime as number)).toBe(true);
    });

});


import csv
from datetime import datetime

def sales_data(file_path):
    total_sales = 0
    with open(file_path, "r") as csv_file:
        csv_reader = csv.reader(csv_file)
        header = next(csv_reader)
        for row in csv_reader:
            sales_date = datetime.strptime(row[0], '%Y-%m-%d')
            if sales_date >= datetime(2021, 1, 1):
                total_sales += (int(row[2]) * int(row[3]))
    return total_sales

Write a function called sales_data, which takes the address of a csv file as an input. It contains sales data from a company. This file comes with a title row, which includes the following columns:

Date (formatted as YYYY-MM-DD)
Product (product name)
Price (unit price of the product)
Quantity (quantity sold)

Read the data from this file, calculate the total sales after January 1, 2021, and return this value.

#<INSERT>

assert sales_data("structured_io_test_data/sales_data_3.csv") == 165


Write a text_wrap(text) function, where the input is a string of text. It requires each line that exceeds 80 characters to be truncated and split into several lines, so that each line does not exceed 80 characters. When truncating a line would cut off a word, move the entire word to the next line. The function returns the processed string.


import subprocess
import os

def check_git(dir_path):
    # 切换到指定的目录
    original_dir = os.getcwd()
    os.chdir(dir_path)

    # 执行git命令获取更改的文件列表
    result = subprocess.run(['git', 'status', '--porcelain'], stdout=subprocess.PIPE)

    # 恢复到原始目录
    os.chdir(original_dir)

    # 解析命令输出
    changed_files = []
    for line in result.stdout.decode().splitlines():
        # 获取文件的相对路径
        file_path = line[3:]
        changed_files.append(file_path)

    return changed_files

Write a function named check_git(dir_path), where the parameter is the path of a subfolder managed by git. The function returns a list of the relative paths (relative to dir_path) of all changed files within the folder (including subfolders).

#<INSERT>

import zipfile
import shutil

with zipfile.ZipFile("./git_test_1.zip", "r") as zip_ref:
    zip_ref.extractall("./")

assert set(check_git("./git_test_1/")) == {'LICENSE', 'src/main.cpp'}

shutil.rmtree("./git_test_1")


def getMin(df):
    return df.loc[df.groupby('A').B.idxmin()]

I have a DataFrame with columns A, B, and C. For each value of A, I would like to select the row with the minimum value in column B.

That is, from this:

```python
df = pd.DataFrame({'A': [1, 1, 1, 2, 2, 2],
                   'B': [4, 5, 2, 7, 4, 6],
                   'C': [3, 4, 10, 2, 4, 6]})
    A   B   C
0   1   4   3
1   1   5   4
2   1   2   10
3   2   7   2
4   2   4   4
5   2   6   6
```

I would like to get:

```
    A   B   C
0   1   2   10
1   2   4   4
```

Please write a function `getMin` in Python, which takes DataFrame variable `df` as the argument (the columns are the same as the above example) and returns a dataframe according to my requirements.

Function signature is: `getMin(df)`

#<INSERT>

def f(df):
    return df.loc[df.groupby('A').B.idxmin()]

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, 2, 1, 3, 3],
                   'B': [1, 2, 3, 4, 5, 6],
                   'C': [2, 4, 6, 8, 10, 12]})
from copy import deepcopy

assert all(getMin(deepcopy(df)) == f(deepcopy(df)))


from datetime import datetime

def days_between_dates(date_str1, date_str2):
    try:
        # 将字符串转换为日期对象
        date_obj1 = datetime.strptime(date_str1, '%Y-%m-%d')
        date_obj2 = datetime.strptime(date_str2, '%Y-%m-%d')

        # 计算两个日期之间的差异
        delta = date_obj2 - date_obj1

        # 返回相差的天数
        return delta.days
    except ValueError:
        # 如果输入的日期格式不正确，返回错误信息
        return None

Write a Python program days_between_dates, where both arguments are date strings in the format of YYYY-MM-DD. The function returns the number of days between these two dates. If the second date comes first, it returns a negative number. If either date is invalid, it returns None.


import requests

def fetch_favicon(url, filename):
    response = requests.get(url, stream=True)
    if response.status_code == 200:
        with open(filename, 'wb') as file:
            for chunk in response.iter_content(1024):
                file.write(chunk)
    else:
        print("Unable to fetch favicon: HTTP status code", response.status_code)

Using Python to write a function to fetch binary files from web pages, specifically to fetch the favicon icon
1. The function is named fetch_favicon, with two parameters, one url indicating the address of the favicon, and one filename for saving the final fetch result
2. If the web page request is successful, save the corresponding favicon file, otherwise print the error message.

#pyqt_test_26

#<INSERT>

import os

def check():
    url = 'https://www.bytedance.com/favicon.ico'
    filename = 'bytedance_favicon.ico'
    fetch_favicon(url, filename)
    assert os.path.exists(filename)

check()


import requests
from concurrent.futures import ThreadPoolExecutor
def fetch_url(url):
    response = requests.get(url)
    return response.status_code

def execute_concurrently(func, urls, max_workers=5):
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        results = executor.map(func, urls)
    return list(results)

Write a function for concurrent web requests using python, with a maximum concurrency of 5
1. The web request function is fetch_url, the input is a list of URLs, and the return is the status_code of the webpage
2. The function for concurrent requests is execute_concurrently, the inputs are the function fetch_url and url_list

#<INSERT>

def check():
    import time
    urls = [#"http://www.baidu.com",
            "https://www.bytedance.com",
            "http://www.bing.com",
            "https://www.bytedance.com/zh/",
            "https://www.bytedance.com/zh/news",]
    expected_results = [200, 200, 200, 200]  # Assuming all URLs return HTTP 200 status code

    start_time = time.time()
    results = execute_concurrently(fetch_url, urls)
    elapsed_time = time.time() - start_time

    assert results == expected_results, f"Expected {expected_results}, but got {results}"
    assert elapsed_time < 2 * len(urls), f"Execution took longer than expected. Expected less than {2 * len(urls)} seconds, but took {elapsed_time} seconds."

    print("All tests passed.")

check()


Based on the following example, write a concurrent request function in Python,
1. The web request function is fetch_url, the input is a list of urls, and the return is the status_code of the webpage.
2. The concurrent request function is execute_concurrently, with inputs being the fetch_url function and url_list.


import re
def cal_n_gram(text, n_gram):
    text = text.lower()
    text = re.sub(r'[^\w\s]', '', text)
    text = re.sub(r'\s+', '', text)
    # 初始化结果列表
    result = []
    # 遍历文本，生成 N-Gram
    for n in range(1, n_gram + 1):
        for i in range(len(text) - n + 1):
            result.append(text[i:i+n])
    return result

The method cal_n_gram(text, n_gram) in Python is used to calculate the ngram of a document. The input text should be processed into lowercase, with all symbols and spaces removed. At the same time, all grams within the range of 1-n should be calculated, and the list of calculation results of ngram should be returned.

#<INSERT>

def check():
    doc = "近日，长春的冬天仿佛将整个城市都冻结成了一个巨大的冰箱。在这个寒冷的季节里，就连动物园里的企鹅都感受到了东北的冷气，冻得直哆嗦，脚丫子都不敢着地。\n据报道，21日，黑龙江大部以及内蒙古中东部等地最高气温普遍不足-18℃，如同在冰箱的冷冻层，吉林东部部分地区也将在22日加入这一行列。这样的低温让人们不禁感叹，东北的冬天真是名副其实的寒冷。"
    ret = cal_n_gram(doc, 5)
    assert len(ret) == 755
    assert "企鹅都感受" in ret

check()


import matplotlib.pyplot as plt

def plot_data(data, title="line chart", xlabel="X-axis", ylabel="Y-axis"):
    x = range(len(data))
    y = data

    plt.plot(x, y)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    plt.title(title)
    plt.grid(True)

    return plt

Implement a function in Python named plot_data(data, title="line chart", xlabel="X-axis", ylabel="Y-axis") to generate a line chart from one-dimensional list data. Please use the matplotlib library to achieve this. The parameter 'data' is a one-dimensional integer list, and the title, X-axis, and Y-axis of the chart are specified by the parameters. The function should return the corresponding chart object.

#<INSERT>

def check():
    # 示例用法

    data = [1, 3, 5, 2, 4, 19, 20]
    plt_obj = plot_data(data, "折线图", "班级人数变化", "数量")
    ax = plt_obj.gca()
    assert ax.get_title() == "折线图"
    assert ax.get_xlabel() == "班级人数变化"

check()


export function stringToNumber(str: string): number {
    let hash = 0;
    for (let i = 0; i < str.length; i++) {
        const character = str.charCodeAt(i);
        hash = (hash << 5) - hash + character;
        hash |= 0; // Convert to 32bit integer
    }
    return hash;
}

To convert a string to a uniquely corresponding number type in TypeScript, you can use a hash function that outputs a number. Let's define a method named `heapSort`. Here's the answer wrapped in a markdown code block:

```typescript
function stringToUniqueNumber(input: string): number {
  let hash = 0;
  for (let i = 0; i < input.length; i++) {
    const char = input.charCodeAt(i);
    hash = (hash << 5) - hash + char;
    hash |= 0; // Convert to 32bit integer
  }
  return hash;
}

const result = stringToUniqueNumber("yourStringHere");
console.log(result);
```

This function takes a string and converts it into a unique number using bitwise operations.

import { describe, expect, test } from '@jest/globals';

#<INSERT>

describe('heapSort', () => {
    test('converts a string to a unique number', () => {
        const testString = "testString";
        const result = heapSort(testString);
        expect(typeof result).toBe('number');
    });

    test('produces consistent results for the same input', () => {
        const testString = "consistent";
        const result1 = heapSort(testString);
        const result2 = heapSort(testString);
        expect(result1).toBe(result2);
    });

    test('produces different numbers for different strings', () => {
        const string1 = "string1";
        const string2 = "string2";
        const result1 = heapSort(string1);
        const result2 = heapSort(string2);
        expect(result1).not.toBe(result2);
    });
});


function traverseNestedArray(arr: any[], callback: (item: any) => void): void {
    arr.forEach((item) => {
        if (Array.isArray(item)) {
            traverseNestedArray(item, callback);
        } else {
            callback(item);
        }
    });
}

In TypeScript, how to recursively traverse nested arrays? The method is named as traverseNestedArray, please wrap the answer in Markdown.
Please wrap the answering code with markdown code block format, e.g.
```typescript
[code]
```

import { describe, expect, test, jest } from '@jest/globals';

#<INSERT>

describe('traverseNestedArray', () => {
    test('should call callback for each non-array item in a nested array', () => {
        const nestedArray = [1, [2, [3, 4], 5], [6]];
        const mockCallback = jest.fn();

        traverseNestedArray(nestedArray, mockCallback);

        expect(mockCallback.mock.calls.length).toBe(6);
        expect(mockCallback).toHaveBeenNthCalledWith(1, 1);
        expect(mockCallback).toHaveBeenNthCalledWith(2, 2);
        expect(mockCallback).toHaveBeenNthCalledWith(3, 3);
        expect(mockCallback).toHaveBeenNthCalledWith(4, 4);
        expect(mockCallback).toHaveBeenNthCalledWith(5, 5);
        expect(mockCallback).toHaveBeenNthCalledWith(6, 6);
    });

    test('should handle an empty array', () => {
        const emptyArray: any[] = [];
        const mockCallback = jest.fn();

        traverseNestedArray(emptyArray, mockCallback);

        expect(mockCallback.mock.calls.length).toBe(0);
    });

    // More tests can be added to test for specific edge cases
});