We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`copyto` does not test shape for memcpy cases
### Description

There should be a shape check before these:
https://github.com/cupy/cupy/blob/e7f2e797216954dd152efe0169ec6a0094d7777d/cupy/_manipulation/basic.py#L54-L55
https://github.com/cupy/cupy/blob/e7f2e797216954dd152efe0169ec6a0094d7777d/cupy/_manipulation/basic.py#L76-L80

### To Reproduce

```py
>>> dst = cupy.ones((2, 3), dtype=int)
>>> cupy.copyto(dst, cupy.arange(6))
>>> dst
array([[0, 1, 2],
       [3, 4, 5]])
>>> numpy.copyto(numpy.ones((2, 3), dtype=int), numpy.arange(6))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<__array_function__ internals>", line 5, in copyto
ValueError: could not broadcast input array from shape (6,) into shape (2,3)
```

```py
>>> cupy.copyto(cupy.empty((3, 0, 4, 5)), cupy.empty((6, 7, 0)))
>>> numpy.copyto(numpy.empty((3, 0, 4, 5)), numpy.empty((6, 7, 0)))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<__array_function__ internals>", line 5, in copyto
ValueError: could not broadcast input array from shape (6,7,0) into shape (3,0,4,5)
```


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `cupy/_manipulation/basic.py`
Content:
```
1 import numpy
2 
3 from cupy import _core
4 from cupy._core import _fusion_interface
5 from cupy._core import fusion
6 from cupy._sorting import search
7 from cupy_backends.cuda.api import runtime
8 
9 
10 def copyto(dst, src, casting='same_kind', where=None):
11     """Copies values from one array to another with broadcasting.
12 
13     This function can be called for arrays on different devices. In this case,
14     casting, ``where``, and broadcasting is not supported, and an exception is
15     raised if these are used.
16 
17     Args:
18         dst (cupy.ndarray): Target array.
19         src (cupy.ndarray): Source array.
20         casting (str): Casting rule. See :func:`numpy.can_cast` for detail.
21         where (cupy.ndarray of bool): If specified, this array acts as a mask,
22             and an element is copied only if the corresponding element of
23             ``where`` is True.
24 
25     .. seealso:: :func:`numpy.copyto`
26 
27     """
28 
29     src_type = type(src)
30     src_is_python_scalar = src_type in (
31         int, bool, float, complex,
32         fusion._FusionVarScalar, _fusion_interface._ScalarProxy)
33     if src_is_python_scalar:
34         src_dtype = numpy.dtype(type(src))
35         can_cast = numpy.can_cast(src, dst.dtype, casting)
36     else:
37         src_dtype = src.dtype
38         can_cast = numpy.can_cast(src_dtype, dst.dtype, casting)
39 
40     if not can_cast:
41         raise TypeError('Cannot cast %s to %s in %s casting mode' %
42                         (src_dtype, dst.dtype, casting))
43 
44     if not src_is_python_scalar and src.ndim > dst.ndim:
45         # NumPy allows stripping leading unit dimensions.
46         try:
47             src = src.squeeze(tuple(range(src.ndim - dst.ndim)))
48         except ValueError:
49             # "cannot select an axis to squeeze out
50             # which has size not equal to one"
51             pass  # raise an error later
52 
53     if fusion._is_fusing():
54         if where is None:
55             _core.elementwise_copy(src, dst)
56         else:
57             fusion._call_ufunc(search._where_ufunc, where, src, dst, dst)
58         return
59 
60     if where is not None:
61         _core.elementwise_copy(src, dst, _where=where)
62         return
63 
64     if dst.size == 0:
65         return
66 
67     if src_is_python_scalar:
68         dst.fill(src)
69         return
70 
71     if _can_memcpy(dst, src):
72         dst.data.copy_from_async(src.data, src.nbytes)
73         return
74 
75     device = dst.device
76     prev_device = runtime.getDevice()
77     try:
78         runtime.setDevice(device.id)
79         if src.device != device:
80             src = src.copy()
81         _core.elementwise_copy(src, dst)
82     finally:
83         runtime.setDevice(prev_device)
84 
85 
86 def _can_memcpy(dst, src):
87     c_contiguous = dst.flags.c_contiguous and src.flags.c_contiguous
88     f_contiguous = dst.flags.f_contiguous and src.flags.f_contiguous
89     return (c_contiguous or f_contiguous) and dst.dtype == src.dtype and \
90         dst.size == src.size
91
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/cupy/_manipulation/basic.py b/cupy/_manipulation/basic.py
--- a/cupy/_manipulation/basic.py
+++ b/cupy/_manipulation/basic.py
@@ -1,3 +1,5 @@
+import itertools
+
 import numpy
 
 from cupy import _core
@@ -41,22 +43,35 @@
         raise TypeError('Cannot cast %s to %s in %s casting mode' %
                         (src_dtype, dst.dtype, casting))
 
-    if not src_is_python_scalar and src.ndim > dst.ndim:
-        # NumPy allows stripping leading unit dimensions.
-        try:
-            src = src.squeeze(tuple(range(src.ndim - dst.ndim)))
-        except ValueError:
-            # "cannot select an axis to squeeze out
-            # which has size not equal to one"
-            pass  # raise an error later
-
     if fusion._is_fusing():
+        # TODO(kataoka): NumPy allows stripping leading unit dimensions.
+        # But fusion array proxy does not currently support
+        # `shape` and `squeeze`.
+
         if where is None:
             _core.elementwise_copy(src, dst)
         else:
             fusion._call_ufunc(search._where_ufunc, where, src, dst, dst)
         return
 
+    if not src_is_python_scalar:
+        # Check broadcast condition
+        # - for fast-paths and
+        # - for a better error message (than ufunc's).
+        # NumPy allows stripping leading unit dimensions.
+        if not all([
+            s in (d, 1)
+            for s, d in itertools.zip_longest(
+                reversed(src.shape), reversed(dst.shape), fillvalue=1)
+        ]):
+            raise ValueError(
+                "could not broadcast input array "
+                f"from shape {src.shape} into shape {dst.shape}")
+        squeeze_ndim = src.ndim - dst.ndim
+        if squeeze_ndim > 0:
+            # always succeeds because broadcast conition is checked.
+            src = src.squeeze(tuple(range(squeeze_ndim)))
+
     if where is not None:
         _core.elementwise_copy(src, dst, _where=where)
         return

{"golden_diff": "diff --git a/cupy/_manipulation/basic.py b/cupy/_manipulation/basic.py\n--- a/cupy/_manipulation/basic.py\n+++ b/cupy/_manipulation/basic.py\n@@ -1,3 +1,5 @@\n+import itertools\n+\n import numpy\n \n from cupy import _core\n@@ -41,22 +43,35 @@\n         raise TypeError('Cannot cast %s to %s in %s casting mode' %\n                         (src_dtype, dst.dtype, casting))\n \n-    if not src_is_python_scalar and src.ndim > dst.ndim:\n-        # NumPy allows stripping leading unit dimensions.\n-        try:\n-            src = src.squeeze(tuple(range(src.ndim - dst.ndim)))\n-        except ValueError:\n-            # \"cannot select an axis to squeeze out\n-            # which has size not equal to one\"\n-            pass  # raise an error later\n-\n     if fusion._is_fusing():\n+        # TODO(kataoka): NumPy allows stripping leading unit dimensions.\n+        # But fusion array proxy does not currently support\n+        # `shape` and `squeeze`.\n+\n         if where is None:\n             _core.elementwise_copy(src, dst)\n         else:\n             fusion._call_ufunc(search._where_ufunc, where, src, dst, dst)\n         return\n \n+    if not src_is_python_scalar:\n+        # Check broadcast condition\n+        # - for fast-paths and\n+        # - for a better error message (than ufunc's).\n+        # NumPy allows stripping leading unit dimensions.\n+        if not all([\n+            s in (d, 1)\n+            for s, d in itertools.zip_longest(\n+                reversed(src.shape), reversed(dst.shape), fillvalue=1)\n+        ]):\n+            raise ValueError(\n+                \"could not broadcast input array \"\n+                f\"from shape {src.shape} into shape {dst.shape}\")\n+        squeeze_ndim = src.ndim - dst.ndim\n+        if squeeze_ndim > 0:\n+            # always succeeds because broadcast conition is checked.\n+            src = src.squeeze(tuple(range(squeeze_ndim)))\n+\n     if where is not None:\n         _core.elementwise_copy(src, dst, _where=where)\n         return\n", "issue": "`copyto` does not test shape for memcpy cases\n### Description\r\n\r\nThere should be a shape check before these:\r\nhttps://github.com/cupy/cupy/blob/e7f2e797216954dd152efe0169ec6a0094d7777d/cupy/_manipulation/basic.py#L54-L55\r\nhttps://github.com/cupy/cupy/blob/e7f2e797216954dd152efe0169ec6a0094d7777d/cupy/_manipulation/basic.py#L76-L80\r\n\r\n### To Reproduce\r\n\r\n```py\r\n>>> dst = cupy.ones((2, 3), dtype=int)\r\n>>> cupy.copyto(dst, cupy.arange(6))\r\n>>> dst\r\narray([[0, 1, 2],\r\n       [3, 4, 5]])\r\n>>> numpy.copyto(numpy.ones((2, 3), dtype=int), numpy.arange(6))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<__array_function__ internals>\", line 5, in copyto\r\nValueError: could not broadcast input array from shape (6,) into shape (2,3)\r\n```\r\n\r\n```py\r\n>>> cupy.copyto(cupy.empty((3, 0, 4, 5)), cupy.empty((6, 7, 0)))\r\n>>> numpy.copyto(numpy.empty((3, 0, 4, 5)), numpy.empty((6, 7, 0)))\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"<__array_function__ internals>\", line 5, in copyto\r\nValueError: could not broadcast input array from shape (6,7,0) into shape (3,0,4,5)\r\n```\r\n\n", "before_files": [{"content": "import numpy\n\nfrom cupy import _core\nfrom cupy._core import _fusion_interface\nfrom cupy._core import fusion\nfrom cupy._sorting import search\nfrom cupy_backends.cuda.api import runtime\n\n\ndef copyto(dst, src, casting='same_kind', where=None):\n    \"\"\"Copies values from one array to another with broadcasting.\n\n    This function can be called for arrays on different devices. In this case,\n    casting, ``where``, and broadcasting is not supported, and an exception is\n    raised if these are used.\n\n    Args:\n        dst (cupy.ndarray): Target array.\n        src (cupy.ndarray): Source array.\n        casting (str): Casting rule. See :func:`numpy.can_cast` for detail.\n        where (cupy.ndarray of bool): If specified, this array acts as a mask,\n            and an element is copied only if the corresponding element of\n            ``where`` is True.\n\n    .. seealso:: :func:`numpy.copyto`\n\n    \"\"\"\n\n    src_type = type(src)\n    src_is_python_scalar = src_type in (\n        int, bool, float, complex,\n        fusion._FusionVarScalar, _fusion_interface._ScalarProxy)\n    if src_is_python_scalar:\n        src_dtype = numpy.dtype(type(src))\n        can_cast = numpy.can_cast(src, dst.dtype, casting)\n    else:\n        src_dtype = src.dtype\n        can_cast = numpy.can_cast(src_dtype, dst.dtype, casting)\n\n    if not can_cast:\n        raise TypeError('Cannot cast %s to %s in %s casting mode' %\n                        (src_dtype, dst.dtype, casting))\n\n    if not src_is_python_scalar and src.ndim > dst.ndim:\n        # NumPy allows stripping leading unit dimensions.\n        try:\n            src = src.squeeze(tuple(range(src.ndim - dst.ndim)))\n        except ValueError:\n            # \"cannot select an axis to squeeze out\n            # which has size not equal to one\"\n            pass  # raise an error later\n\n    if fusion._is_fusing():\n        if where is None:\n            _core.elementwise_copy(src, dst)\n        else:\n            fusion._call_ufunc(search._where_ufunc, where, src, dst, dst)\n        return\n\n    if where is not None:\n        _core.elementwise_copy(src, dst, _where=where)\n        return\n\n    if dst.size == 0:\n        return\n\n    if src_is_python_scalar:\n        dst.fill(src)\n        return\n\n    if _can_memcpy(dst, src):\n        dst.data.copy_from_async(src.data, src.nbytes)\n        return\n\n    device = dst.device\n    prev_device = runtime.getDevice()\n    try:\n        runtime.setDevice(device.id)\n        if src.device != device:\n            src = src.copy()\n        _core.elementwise_copy(src, dst)\n    finally:\n        runtime.setDevice(prev_device)\n\n\ndef _can_memcpy(dst, src):\n    c_contiguous = dst.flags.c_contiguous and src.flags.c_contiguous\n    f_contiguous = dst.flags.f_contiguous and src.flags.f_contiguous\n    return (c_contiguous or f_contiguous) and dst.dtype == src.dtype and \\\n        dst.size == src.size\n", "path": "cupy/_manipulation/basic.py"}], "after_files": [{"content": "import itertools\n\nimport numpy\n\nfrom cupy import _core\nfrom cupy._core import _fusion_interface\nfrom cupy._core import fusion\nfrom cupy._sorting import search\nfrom cupy_backends.cuda.api import runtime\n\n\ndef copyto(dst, src, casting='same_kind', where=None):\n    \"\"\"Copies values from one array to another with broadcasting.\n\n    This function can be called for arrays on different devices. In this case,\n    casting, ``where``, and broadcasting is not supported, and an exception is\n    raised if these are used.\n\n    Args:\n        dst (cupy.ndarray): Target array.\n        src (cupy.ndarray): Source array.\n        casting (str): Casting rule. See :func:`numpy.can_cast` for detail.\n        where (cupy.ndarray of bool): If specified, this array acts as a mask,\n            and an element is copied only if the corresponding element of\n            ``where`` is True.\n\n    .. seealso:: :func:`numpy.copyto`\n\n    \"\"\"\n\n    src_type = type(src)\n    src_is_python_scalar = src_type in (\n        int, bool, float, complex,\n        fusion._FusionVarScalar, _fusion_interface._ScalarProxy)\n    if src_is_python_scalar:\n        src_dtype = numpy.dtype(type(src))\n        can_cast = numpy.can_cast(src, dst.dtype, casting)\n    else:\n        src_dtype = src.dtype\n        can_cast = numpy.can_cast(src_dtype, dst.dtype, casting)\n\n    if not can_cast:\n        raise TypeError('Cannot cast %s to %s in %s casting mode' %\n                        (src_dtype, dst.dtype, casting))\n\n    if fusion._is_fusing():\n        # TODO(kataoka): NumPy allows stripping leading unit dimensions.\n        # But fusion array proxy does not currently support\n        # `shape` and `squeeze`.\n\n        if where is None:\n            _core.elementwise_copy(src, dst)\n        else:\n            fusion._call_ufunc(search._where_ufunc, where, src, dst, dst)\n        return\n\n    if not src_is_python_scalar:\n        # Check broadcast condition\n        # - for fast-paths and\n        # - for a better error message (than ufunc's).\n        # NumPy allows stripping leading unit dimensions.\n        if not all([\n            s in (d, 1)\n            for s, d in itertools.zip_longest(\n                reversed(src.shape), reversed(dst.shape), fillvalue=1)\n        ]):\n            raise ValueError(\n                \"could not broadcast input array \"\n                f\"from shape {src.shape} into shape {dst.shape}\")\n        squeeze_ndim = src.ndim - dst.ndim\n        if squeeze_ndim > 0:\n            # always succeeds because broadcast conition is checked.\n            src = src.squeeze(tuple(range(squeeze_ndim)))\n\n    if where is not None:\n        _core.elementwise_copy(src, dst, _where=where)\n        return\n\n    if dst.size == 0:\n        return\n\n    if src_is_python_scalar:\n        dst.fill(src)\n        return\n\n    if _can_memcpy(dst, src):\n        dst.data.copy_from_async(src.data, src.nbytes)\n        return\n\n    device = dst.device\n    prev_device = runtime.getDevice()\n    try:\n        runtime.setDevice(device.id)\n        if src.device != device:\n            src = src.copy()\n        _core.elementwise_copy(src, dst)\n    finally:\n        runtime.setDevice(prev_device)\n\n\ndef _can_memcpy(dst, src):\n    c_contiguous = dst.flags.c_contiguous and src.flags.c_contiguous\n    f_contiguous = dst.flags.f_contiguous and src.flags.f_contiguous\n    return (c_contiguous or f_contiguous) and dst.dtype == src.dtype and \\\n        dst.size == src.size\n", "path": "cupy/_manipulation/basic.py"}]}