prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  typing ak.Array for numba.cuda.jit signature
  ### Version of Awkward Array
  
  2.6.2
  
  ### Description and code to reproduce
  
  Hey guys, I followed a hint from the discussion in [#696](https://github.com/scikit-hep/awkward/discussions/696#discussion-2571850) to type `ak.Array` for numba signatures. So I tried something like
  
  ```python
  import awkward as ak
  import numba as nb
  from numba import types
  
  cpu_arr_type = ak.Array([[[0, 1], [2, 3]], [[4, 5]]], backend='cpu').numba_type
  
  @nb.njit(types.void(cpu_arr_type))
  def cpu_kernel(arr):
     do_something_with_arr
  ```
  and this works like a charm.
  
  However, I'm interested in the same case but with a cuda kernel. So I tried what appeared more natural to do:
  ```python
  gpu_arr_type = ak.Array([[[0, 1], [2, 3]], [[4, 5]]], backend='cuda').numba_type
  
  @nb.cuda.jit(types.void(gpu_arr_type), extensions=[ak.numba.cuda])
  def cuda_kernel(arr):
     do_something_with_arr
  ```
  This time, I get the error:
  ```python
  self = <awkward._connect.numba.arrayview_cuda.ArrayViewArgHandler object at 0x784afbc13fa0>
  ty = ak.ArrayView(ak.ListArrayType(array(int64, 1d, C), ak.ListArrayType(array(int64, 1d, C), ak.NumpyArrayType(array(int64, 1d, C), {}), {}), {}), None, ())
  val = <Array [[[4, 1], [2, -1]], [...], [[4, 0]]] type='3 * var * var * int64'>
  stream = 0, retr = []
  
      def prepare_args(self, ty, val, stream, retr):
          if isinstance(val, ak.Array):
              if isinstance(val.layout.backend, CupyBackend):
                  # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value
                  tys = numba.types.UniTuple(numba.types.uint64, 5)
      
  >               start = val._numbaview.start
  E               AttributeError: 'NoneType' object has no attribute 'start'
  
  .../site-packages/awkward/_connect/numba/arrayview_cuda.py:21: AttributeError
  ```
  How should this latter case be correctly treated? Note that, without typing, the thing works as expected:
  ```python
  @nb.cuda.jit(extensions=[ak.numba.cuda])
  def cuda_kernel_no_typing(arr):
     do_something_with_arr
  ```
  However, I'm interested in `ak.Array`s with the 3D layout of integers (as above) and would like to take advantage of numba's eager compilation. I'm passing the `arr` for testing as
  ```python
  backend = 'cpu'  # or 'cuda'
  arr = ak.to_backend(
      ak.Array([
          [[4, 1], [2, -1]],
          [[0, -1], [1, 1], [3, -1]],
          [[4, 0]]
      ]),
      backend
  )
  ```
  Any help is appreciated!
  
  
  </issue>
  <code>
  [start of src/awkward/_connect/numba/arrayview_cuda.py]
  1 # BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE
  2 
  3 from __future__ import annotations
  4 
  5 import numba
  6 from numba.core.errors import NumbaTypeError
  7 
  8 import awkward as ak
  9 from awkward._backends.cupy import CupyBackend
  10 
  11 ########## ArrayView Arguments Handler for CUDA JIT
  12 
  13 
  14 class ArrayViewArgHandler:
  15     def prepare_args(self, ty, val, stream, retr):
  16         if isinstance(val, ak.Array):
  17             if isinstance(val.layout.backend, CupyBackend):
  18                 # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value
  19                 tys = numba.types.UniTuple(numba.types.uint64, 5)
  20 
  21                 start = val._numbaview.start
  22                 stop = val._numbaview.stop
  23                 pos = val._numbaview.pos
  24                 arrayptrs = val._numbaview.lookup.arrayptrs.data.ptr
  25                 pylookup = 0
  26 
  27                 return tys, (pos, start, stop, arrayptrs, pylookup)
  28             else:
  29                 raise NumbaTypeError(
  30                     '`ak.to_backend` should be called with `backend="cuda"` to put '
  31                     "the array on the GPU before using it: "
  32                     'ak.to_backend(array, backend="cuda")'
  33                 )
  34 
  35         else:
  36             return ty, val
  37
  [end of src/awkward/_connect/numba/arrayview_cuda.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/src/awkward/_connect/numba/arrayview_cuda.py b/src/awkward/_connect/numba/arrayview_cuda.py
  --- a/src/awkward/_connect/numba/arrayview_cuda.py
  +++ b/src/awkward/_connect/numba/arrayview_cuda.py
  @@ -15,13 +15,22 @@
       def prepare_args(self, ty, val, stream, retr):
           if isinstance(val, ak.Array):
               if isinstance(val.layout.backend, CupyBackend):
  +                if ty is not val.numba_type:
  +                    raise NumbaTypeError(
  +                        f"the array type: {val.numba_type} does not match "
  +                        f"the kernel signature type: {ty}"
  +                    )
  +
                   # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value
                   tys = numba.types.UniTuple(numba.types.uint64, 5)
   
  -                start = val._numbaview.start
  -                stop = val._numbaview.stop
  -                pos = val._numbaview.pos
  -                arrayptrs = val._numbaview.lookup.arrayptrs.data.ptr
  +                view = val._numbaview
  +                assert view is not None
  +
  +                start = view.start
  +                stop = view.stop
  +                pos = view.pos
  +                arrayptrs = view.lookup.arrayptrs.data.ptr
                   pylookup = 0
   
                   return tys, (pos, start, stop, arrayptrs, pylookup)
  
verification_info: {"golden_diff": "diff --git a/src/awkward/_connect/numba/arrayview_cuda.py b/src/awkward/_connect/numba/arrayview_cuda.py\n--- a/src/awkward/_connect/numba/arrayview_cuda.py\n+++ b/src/awkward/_connect/numba/arrayview_cuda.py\n@@ -15,13 +15,22 @@\n     def prepare_args(self, ty, val, stream, retr):\n         if isinstance(val, ak.Array):\n             if isinstance(val.layout.backend, CupyBackend):\n+                if ty is not val.numba_type:\n+                    raise NumbaTypeError(\n+                        f\"the array type: {val.numba_type} does not match \"\n+                        f\"the kernel signature type: {ty}\"\n+                    )\n+\n                 # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value\n                 tys = numba.types.UniTuple(numba.types.uint64, 5)\n \n-                start = val._numbaview.start\n-                stop = val._numbaview.stop\n-                pos = val._numbaview.pos\n-                arrayptrs = val._numbaview.lookup.arrayptrs.data.ptr\n+                view = val._numbaview\n+                assert view is not None\n+\n+                start = view.start\n+                stop = view.stop\n+                pos = view.pos\n+                arrayptrs = view.lookup.arrayptrs.data.ptr\n                 pylookup = 0\n \n                 return tys, (pos, start, stop, arrayptrs, pylookup)\n", "issue": "typing ak.Array for numba.cuda.jit signature\n### Version of Awkward Array\n\n2.6.2\n\n### Description and code to reproduce\n\nHey guys, I followed a hint from the discussion in [#696](https://github.com/scikit-hep/awkward/discussions/696#discussion-2571850) to type `ak.Array` for numba signatures. So I tried something like\r\n\r\n```python\r\nimport awkward as ak\r\nimport numba as nb\r\nfrom numba import types\r\n\r\ncpu_arr_type = ak.Array([[[0, 1], [2, 3]], [[4, 5]]], backend='cpu').numba_type\r\n\r\n@nb.njit(types.void(cpu_arr_type))\r\ndef cpu_kernel(arr):\r\n   do_something_with_arr\r\n```\r\nand this works like a charm.\r\n\r\nHowever, I'm interested in the same case but with a cuda kernel. So I tried what appeared more natural to do:\r\n```python\r\ngpu_arr_type = ak.Array([[[0, 1], [2, 3]], [[4, 5]]], backend='cuda').numba_type\r\n\r\n@nb.cuda.jit(types.void(gpu_arr_type), extensions=[ak.numba.cuda])\r\ndef cuda_kernel(arr):\r\n   do_something_with_arr\r\n```\r\nThis time, I get the error:\r\n```python\r\nself = <awkward._connect.numba.arrayview_cuda.ArrayViewArgHandler object at 0x784afbc13fa0>\r\nty = ak.ArrayView(ak.ListArrayType(array(int64, 1d, C), ak.ListArrayType(array(int64, 1d, C), ak.NumpyArrayType(array(int64, 1d, C), {}), {}), {}), None, ())\r\nval = <Array [[[4, 1], [2, -1]], [...], [[4, 0]]] type='3 * var * var * int64'>\r\nstream = 0, retr = []\r\n\r\n    def prepare_args(self, ty, val, stream, retr):\r\n        if isinstance(val, ak.Array):\r\n            if isinstance(val.layout.backend, CupyBackend):\r\n                # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value\r\n                tys = numba.types.UniTuple(numba.types.uint64, 5)\r\n    \r\n>               start = val._numbaview.start\r\nE               AttributeError: 'NoneType' object has no attribute 'start'\r\n\r\n.../site-packages/awkward/_connect/numba/arrayview_cuda.py:21: AttributeError\r\n```\r\nHow should this latter case be correctly treated? Note that, without typing, the thing works as expected:\r\n```python\r\n@nb.cuda.jit(extensions=[ak.numba.cuda])\r\ndef cuda_kernel_no_typing(arr):\r\n   do_something_with_arr\r\n```\r\nHowever, I'm interested in `ak.Array`s with the 3D layout of integers (as above) and would like to take advantage of numba's eager compilation. I'm passing the `arr` for testing as\r\n```python\r\nbackend = 'cpu'  # or 'cuda'\r\narr = ak.to_backend(\r\n    ak.Array([\r\n        [[4, 1], [2, -1]],\r\n        [[0, -1], [1, 1], [3, -1]],\r\n        [[4, 0]]\r\n    ]),\r\n    backend\r\n)\r\n```\r\nAny help is appreciated!\r\n\n", "before_files": [{"content": "# BSD 3-Clause License; see https://github.com/scikit-hep/awkward/blob/main/LICENSE\n\nfrom __future__ import annotations\n\nimport numba\nfrom numba.core.errors import NumbaTypeError\n\nimport awkward as ak\nfrom awkward._backends.cupy import CupyBackend\n\n########## ArrayView Arguments Handler for CUDA JIT\n\n\nclass ArrayViewArgHandler:\n    def prepare_args(self, ty, val, stream, retr):\n        if isinstance(val, ak.Array):\n            if isinstance(val.layout.backend, CupyBackend):\n                # Use uint64 for pos, start, stop, the array pointers values, and the pylookup value\n                tys = numba.types.UniTuple(numba.types.uint64, 5)\n\n                start = val._numbaview.start\n                stop = val._numbaview.stop\n                pos = val._numbaview.pos\n                arrayptrs = val._numbaview.lookup.arrayptrs.data.ptr\n                pylookup = 0\n\n                return tys, (pos, start, stop, arrayptrs, pylookup)\n            else:\n                raise NumbaTypeError(\n                    '`ak.to_backend` should be called with `backend=\"cuda\"` to put '\n                    \"the array on the GPU before using it: \"\n                    'ak.to_backend(array, backend=\"cuda\")'\n                )\n\n        else:\n            return ty, val\n", "path": "src/awkward/_connect/numba/arrayview_cuda.py"}]}