[INPUT_SEPARATOR]
First, create a Cython helper module for distance calculations.
```
edit
file: solver.py
lines: 0-0
---
import numpy as np

# Global up and down factors. These might be overridden externally.
up = 1
down = 1

def solve(problem: list) -> list:
    """
    Compute the upfirdn operation for each filter-signal pair in the problem list.

    Each tuple (h, x) is processed by upsampling x by factor 'up', filtering with h, and then downsampling by factor 'down'.

    :param problem: A list of tuples (h, x) where h and x are 1D arrays.
    :return: A list of 1D arrays representing the upfirdn results.
    """
    results = []
    for h, x in problem:
        h_arr = np.asarray(h, dtype=np.float64)
        x_arr = np.asarray(x, dtype=np.float64)
        Lx = x_arr.shape[0]
        # Upsample x: length = (Lx - 1) * up + 1
        upsampled = np.zeros((Lx - 1) * up + 1, dtype=x_arr.dtype)
        upsampled[::up] = x_arr
        # Convolve the upsampled signal with filter h.
        y_full = np.convolve(upsampled, h_arr, mode="full")
        # Downsample the convolved result.
        results.append(y_full[::down])
    return results
---
```
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```
edit
file: solver.py
lines: 1-999
---
from scipy import signal


class Solver:
    def __init__(self):
        pass

    def solve(self, problem: list) -> list:
        """
        Compute the upfirdn operation for each problem definition in the list.

        :param problem: A list of tuples (h, x, up, down).
        :return: A list of 1D arrays representing the upfirdn results.
        """
        results = []
        for h, x, up, down in problem:
            res = signal.upfirdn(h, x, up=up, down=down)
            results.append(res)
        return results
---
```
[INPUT_SEPARATOR]
```
edit
file: solver_pythran.py
lines: 1-999
---
# File: solver_pythran.py
# pythran export solve(list<tuple<float[], float[], int, int>>)
import numpy as np

def solve(problem):
    """
    Pure-NumPy implementation of upfirdn, compiled with Pythran.

    :param problem: list of tuples (h, x, up, down)
                    h, x should be 1D numpy arrays of floats
                    up, down are ints
    :return: list of 1D numpy arrays with upfirdn results
    """
    results = []
    for h, x, up, down in problem:
        # 1) Upsample by inserting up-1 zeros between samples
        n_in = x.shape[0]
        x_up = np.zeros(n_in * up, dtype=x.dtype)
        x_up[::up] = x

        # 2) FIR filter via convolution
        y = np.convolve(x_up, h)

        # 3) Downsample by taking every down-th sample
        y_down = y[::down]

        results.append(y_down)
    return results
---
```
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```
edit
file: solver_pythran.py
lines: 1-999
---
# ──────────────────────────────────────────
# File: solver_pythran.py
# ──────────────────────────────────────────
# pythran export solve_upfirdn(float64[], float64[], int, int)
import numpy as np

def solve_upfirdn(h, x, up, down):
    """Pure-NumPy upfirdn (upsample-filter-downsample) kernel.

    Parameters
    ----------
    h : 1-D float64 array
        FIR filter coefficients.
    x : 1-D float64 array
        Input signal.
    up, down : int
        Integer up- and down-sampling factors.

    Returns
    -------
    y_down : 1-D float64 array
        Output signal after upsample → FIR → downsample.
    """
    # ---- 1) Upsample ----------------------------------------------------------
    n_in = x.shape[0]
    x_up = np.zeros(n_in * up, dtype=np.float64)
    for i in range(n_in):                 # (simple loop = Pythran-friendly)
        x_up[i * up] = x[i]

    # ---- 2) Convolve with h ---------------------------------------------------
    m = h.shape[0]
    y_len = x_up.shape[0] + m - 1
    y = np.zeros(y_len, dtype=np.float64)

    for i in range(x_up.shape[0]):        # manual convolution
        xi = x_up[i]
        for j in range(m):
            y[i + j] += xi * h[j]

    # ---- 3) Downsample --------------------------------------------------------
    out_len = (y_len + down - 1) // down
    y_down = np.empty(out_len, dtype=np.float64)
    for k in range(out_len):
        y_down[k] = y[k * down]

    return y_down
---
```
[INPUT_SEPARATOR]
```
edit
file: solver.py
lines: 1-999
---
# -------------------------
# File: solver.py
# -------------------------
from solver_pythran import solve_upfirdn
from typing import List, Tuple
import numpy as np

class Solver:
    def solve(self, problem: List[Tuple[np.ndarray, np.ndarray, int, int]]) -> List[np.ndarray]:
        """
        :param problem: list of (h, x, up, down)
        :return: list of upfirdn outputs
        """
        return [solve_upfirdn(h, x, up, down) for (h, x, up, down) in problem]
---
```
[INPUT_SEPARATOR]
```
edit
file: solver_dace.py
lines: 1-999
---
import dace
import numpy as np

# ────────────────── symbolic dimensions ──────────────────
H   = dace.symbol("H")      # filter length
X   = dace.symbol("X")      # input length
OUT = dace.symbol("OUT")    # output length
# ──────────────────────────────────────────────────────────


@dace.program
def upfirdn_dace(
    h: dace.float64[H],
    x: dace.float64[X],
    up: dace.int32,
    down: dace.int32,
    y: dace.float64[OUT],
):
    """Direct up-firdn into pre-allocated y."""
    n_up = X * up

    for k in dace.map[0:OUT]:
        y_idx = k * down
        acc   = 0.0
        for j in dace.map[0:H]:
            up_idx = y_idx - j
            if (up_idx >= 0) and (up_idx < n_up) and ((up_idx % up) == 0):
                ix = up_idx // up
                if ix < X:
                    acc += x[ix] * h[j]
        y[k] = acc


# Compile once; DaCe will still specialise on the first call
compiled_upfirdn = upfirdn_dace.compile()


def solve_upfirdn(
    h: np.ndarray,
    x: np.ndarray,
    up: int,
    down: int,
) -> np.ndarray:
    """
    NumPy-friendly wrapper around the DaCe kernel.

    Parameters
    ----------
    h, x : 1-D float64 arrays
    up, down : positive ints

    Returns
    -------
    y : 1-D float64 array
        The upsample-filter-downsample result.
    """
    n_up    = x.shape[0] * up
    y_len   = n_up + h.shape[0] - 1
    out_len = (y_len + down - 1) // down

    y = np.zeros(out_len, dtype=np.float64)

    # ↘ bind the symbolic sizes explicitly ↙
    compiled_upfirdn(
        h,
        x,
        np.int32(up),
        np.int32(down),
        y,
        H=h.shape[0],
        X=x.shape[0],
        OUT=y.shape[0],
    )
    return y
---
```
[INPUT_SEPARATOR]
```edit
file: solver.py
lines: 1-999
---
"""
High-level wrapper around the DaCe-powered `upfirdn` kernel.

`solver_dace.py` must be in the same directory (or importable on PYTHONPATH)
and must expose the function `solve_upfirdn(h, x, up, down) -> np.ndarray`.
"""

from __future__ import annotations

import numpy as np
from typing import Iterable, List, Sequence, Tuple

# Import the compiled wrapper from the SDFG module
from solver_dace import solve_upfirdn


class Solver:
    """
    Batch interface for computing upfirdn on many inputs.

    Each problem is a tuple (h, x, up, down):
        h    : 1-D NumPy array        – FIR filter taps
        x    : 1-D NumPy array        – input signal
        up   : int                    – up-sampling factor (>= 1)
        down : int                    – down-sampling factor (>= 1)
    """

    def __init__(self) -> None:
        # Nothing to initialise yet, but you could cache data here.
        pass

    # ------------------------------------------------------------------ #
    # Helper utilities
    # ------------------------------------------------------------------ #

    @staticmethod
    def _to_ndarray(arr: Sequence[float] | np.ndarray) -> np.ndarray:
        """
        Return a *contiguous, owned* 1-D float64 NumPy array.

        DaCe forbids passing array *views* (non-owning slices).  We therefore:
        1.  Make the data contiguous (`np.ascontiguousarray`).
        2.  If it is still a view (`arr_np.base is not None`), copy to force
            ownership.
        """
        arr_np = np.ascontiguousarray(arr, dtype=np.float64)
        if arr_np.ndim != 1:
            raise ValueError("h and x must be 1-D arrays")
        if arr_np.base is not None:           # still a view  →  copy
            arr_np = arr_np.copy()
        return arr_np

    # ------------------------------------------------------------------ #
    # Public API
    # ------------------------------------------------------------------ #

    def solve(
        self,
        problems: Iterable[
            Tuple[
                Sequence[float] | np.ndarray,
                Sequence[float] | np.ndarray,
                int,
                int,
            ]
        ],
    ) -> List[np.ndarray]:
        """
        Run `upfirdn` for every problem in *problems*.

        Parameters
        ----------
        problems
            Iterable of tuples (h, x, up, down).

        Returns
        -------
        results
            List of NumPy arrays with the filtered / resampled signals.
        """
        results: List[np.ndarray] = []

        for h, x, up, down in problems:
            h_arr = self._to_ndarray(h)
            x_arr = self._to_ndarray(x)

            if up < 1 or down < 1:
                raise ValueError("up and down factors must be positive integers")

            res = solve_upfirdn(h_arr, x_arr, int(up), int(down))
            results.append(res)

        return results
--- 
```
[INPUT_SEPARATOR]
```python
edit
file: solver.py
lines: 1-999
---
"""
High-level wrapper around the DaCe-powered `upfirdn` kernel.

`solver_dace.py` must be in the same directory (or importable on PYTHONPATH)
and must expose the function `solve_upfirdn(h, x, up, down) -> np.ndarray`.
"""

from __future__ import annotations

import numpy as np
from typing import Iterable, List, Sequence, Tuple

# Import the compiled wrapper from the SDFG module
from solver_dace import solve_upfirdn


class Solver:
    """
    Batch interface for computing upfirdn on many inputs.

    Each problem is a tuple (h, x, up, down):
        h    : 1-D NumPy array        – FIR filter taps
        x    : 1-D NumPy array        – input signal
        up   : int                    – up-sampling factor (>= 1)
        down : int                    – down-sampling factor (>= 1)
    """

    def __init__(self) -> None:
        # Nothing to initialise yet, but you could cache data here.
        pass

    # ------------------------------------------------------------------ #
    # Helper utilities
    # ------------------------------------------------------------------ #

    @staticmethod
    def _to_ndarray(arr: Sequence[float] | np.ndarray) -> np.ndarray:
        """
        Return a *contiguous, owned* 1-D float64 NumPy array.

        DaCe forbids passing array *views* (non-owning slices).  We therefore:
        1.  Make the data contiguous (`np.ascontiguousarray`).
        2.  If it is still a view (`arr_np.base is not None`), copy to force
            ownership.
        """
        arr_np = np.ascontiguousarray(arr, dtype=np.float64)
        if arr_np.ndim != 1:
            raise ValueError("h and x must be 1-D arrays")
        if arr_np.base is not None:           # still a view  →  copy
            arr_np = arr_np.copy()
        return arr_np

    # ------------------------------------------------------------------ #
    # Public API
    # ------------------------------------------------------------------ #

    def solve(
        self,
        problems: Iterable[
            Tuple[
                Sequence[float] | np.ndarray,
                Sequence[float] | np.ndarray,
                int,
                int,
            ]
        ],
    ) -> List[np.ndarray]:
        """
        Run `upfirdn` for every problem in *problems*.

        Parameters
        ----------
        problems
            Iterable of tuples (h, x, up, down).

        Returns
        -------
        results
            List of NumPy arrays with the filtered / resampled signals.
        """
        results: List[np.ndarray] = []

        for h, x, up, down in problems:
            h_arr = self._to_ndarray(h)
            x_arr = self._to_ndarray(x)

            if up < 1 or down < 1:
                raise ValueError("up and down factors must be positive integers")

            res = solve_upfirdn(h_arr, x_arr, int(up), int(down))
            results.append(res)

        return results
--- 
```
