# SWEGen Resources Server

### Overview
Generates patches or reproduction tests and evaluates them in a sandbox environment.
It gets agent candidate patches/tests and returns a reward. In the case of patch generation, it uses PASS_TO_PASS and FAIL_TO_PASS tests (as defined by SWE-bench) for evaluation. When a reproduction test is generated, it checks whether the test reports `issue reproduced` (exit code 2) before applying the patch and `issue solved` (exit code 0) after applying the patch.

### Input schema
Required fields:
- `responses_create_params`: OpenAI Responses create params
- `instance` (required): Dictionary description of the instance, including entries for instance_id, repo, setup_script, test_script, regression_script, PASS_TO_PASS, FAIL_TO_PASS, patch
- `metadata` (required): Extra information needed for evaluation: relevant_file_contents, remove_repo_name, image
- `mode` (required): use `eval` for patch generation and `repro-gen` for reproduction test generation

Optional fields:
- `dataset_name`: Identifier for the dataset
- `dataset_split`: Split name (e.g., `train`)

### Example dataset row (standard format)
```json
{
    "responses_create_params": {
        "input": [
            {
                "role": "user",
                "content": "You will be provided with a partial code base and an issue statement explaining a problem to resolve.\n<issue>\n...</issue> <code>[start of moto/dynamodb/models/dynamo_type.py]...[end of moto/dynamodb/models/dynamo_type.py]\n</code>\n\nPlease first localize the bug based on the issue statement, and then generate *SEARCH/REPLACE* edits to fix the issue.\n\nEvery *SEARCH/REPLACE* edit must use this format:\n1. ### followed by the file path\n2. The start of search block: <<<<<<< SEARCH\n3. A contiguous chunk of lines to search for in the existing source code\n4. The dividing line: =======\n5. The lines to replace into the source code\n6. The end of the replace block: >>>>>>> REPLACE\n\nHere is an example:\n\n```python\n### mathweb/flask/app.py\n<<<<<<< SEARCH\nfrom flask import Flask\n=======\nimport math\nfrom flask import Flask\n>>>>>>> REPLACE\n```\n    \nImportant Instructions:\n1. Preserve Indentation: The content string must maintain the exact indentation as required by the original code. Each line of the content should be indented to match the indentation level of the surrounding code to ensure proper functionality. For example, if you would like to add the line '        print(x)', you must fully write that out, with all those spaces before the code!\n\n2. Correct Format: Ensure that each line of content maintains proper indentation. For instance, if the code block is inside a function or a loop, the new content should align with that structure.\n\nOutput format requirement: Please put your reasoning tokens in a separate code block, starting with <think> and ending with </think>, and the solution tokens containing the *SEARCH/REPLACE* edits in a separate code block, starting with <solution> and ending with </solution>. \nWrap the *SEARCH/REPLACE* edits in ```python...``` blocks. If you have multiple *SEARCH/REPLACE* edits, use a separate ```python...``` block for each one.\n"
            }
        ]
    },
    "instance": {
        "instance_id": "getmoto__moto-7365",
        "patch": "diff --git a/moto/dynamodb/models/dynamo_type.py b/moto/dynamodb/models/dynamo_type.py\n...",
        "problem_statement": "DynamoDB's `update_item` performs floating-point arithmetic with mock table created via `boto3`\n...\n",
        "repo": "getmoto/moto",
        "PASS_TO_PASS": [
            "tests/test_dynamodb/test_dynamodb_update_expressions.py::test_update_different_map_elements_in_single_request"
        ],
        "FAIL_TO_PASS": [
            "tests/test_dynamodb/test_dynamodb_update_expressions.py::test_update_item_add_float"
        ],
        "setup_script": "#!/bin/bash\nsource /opt/miniconda3/bin/activate\nconda activate testbed\ncd /testbed\ncd /testbed\nsource /opt/miniconda3/bin/activate\nconda activate testbed\nmake init\ngit checkout 7f6c9cb1deafb280fe7fcc7551c38e397f11a706 tests/test_dynamodb/test_dynamodb_update_expressions.py\ngit apply -v - <<'EOF_114329324912'\ndiff --git a/tests/test_dynamodb/test_dynamodb_update_expressions.py b/tests/test_dynamodb/test_dynamodb_update_expressions.py\n--- a/tests/test_dynamodb/test_dynamodb_update_expressions.py\n+++ b/tests/test_dynamodb/test_dynamodb_update_expressions.py\n@@ -1,3 +1,5 @@\n+from decimal import Decimal\n+\n import boto3\n import pytest\n \n@@ -40,3 +42,50 @@ def test_update_different_map_elements_in_single_request(table_name=None):\n         ExpressionAttributeValues={\":MyCount\": 5},\n     )\n     assert table.get_item(Key={\"pk\": \"example_id\"})[\"Item\"][\"MyTotalCount\"] == 5\n+\n+\n+@pytest.mark.aws_verified\n+@dynamodb_aws_verified()\n+def test_update_item_add_float(table_name=None):\n+    table = boto3.resource(\"dynamodb\", \"us-east-1\").Table(table_name)\n+\n+    # DECIMAL - DECIMAL\n+    table.put_item(Item={\"pk\": \"foo\", \"amount\": Decimal(100), \"nr\": 5})\n+    table.update_item(\n+        Key={\"pk\": \"foo\"},\n+        UpdateExpression=\"ADD amount :delta\",\n+        ExpressionAttributeValues={\":delta\": -Decimal(\"88.3\")},\n+    )\n+    assert table.scan()[\"Items\"][0][\"amount\"] == Decimal(\"11.7\")\n+\n+    # DECIMAL + DECIMAL\n+    table.update_item(\n+        Key={\"pk\": \"foo\"},\n+        UpdateExpression=\"ADD amount :delta\",\n+        ExpressionAttributeValues={\":delta\": Decimal(\"25.41\")},\n+    )\n+    assert table.scan()[\"Items\"][0][\"amount\"] == Decimal(\"37.11\")\n+\n+    # DECIMAL + INT\n+    table.update_item(\n+        Key={\"pk\": \"foo\"},\n+        UpdateExpression=\"ADD amount :delta\",\n+        ExpressionAttributeValues={\":delta\": 6},\n+    )\n+    assert table.scan()[\"Items\"][0][\"amount\"] == Decimal(\"43.11\")\n+\n+    # INT + INT\n+    table.update_item(\n+        Key={\"pk\": \"foo\"},\n+        UpdateExpression=\"ADD nr :delta\",\n+        ExpressionAttributeValues={\":delta\": 1},\n+    )\n+    assert table.scan()[\"Items\"][0][\"nr\"] == Decimal(\"6\")\n+\n+    # INT + DECIMAL\n+    table.update_item(\n+        Key={\"pk\": \"foo\"},\n+        UpdateExpression=\"ADD nr :delta\",\n+        ExpressionAttributeValues={\":delta\": Decimal(\"25.41\")},\n+    )\n+    assert table.scan()[\"Items\"][0][\"nr\"] == Decimal(\"31.41\")\n\nEOF_114329324912",
        "test_script": "#!/bin/bash\nsource /opt/miniconda3/bin/activate\nconda activate testbed\ncd /testbed\npytest -n0 -rA tests/test_dynamodb/test_dynamodb_update_expressions.py",
        "regression_setup_script": "#!/bin/bash\nsource /opt/miniconda3/bin/activate\nconda activate testbed\ncd /testbed\ncd /testbed\nsource /opt/miniconda3/bin/activate\nconda activate testbed\nmake init\ngit checkout 7f6c9cb1deafb280fe7fcc7551c38e397f11a706 tests/test_dynamodb/test_dynamodb_update_expressions.py"
    },
    "mode": "eval",
    "dataset_name": "SWE-Gym/SWE-Gym",
    "dataset_split": "train",
    "partial_similarity": false,
    "metadata": {
        "remove_repo_name": false,
        "image": "/swe-bench-image-dir/xingyaoww_sweb.eval.x86_64.getmoto_s_moto-7365.sif",
        "relevant_file_contents": "{\"moto/dynamodb/models/dynamo_type.py\": \"import base64\\nimport copy\\n...\"}"
    }
}
```


### Example of rollouts and usage
You need to have singularity installed. Image having singularity: 

`/lustre/fsw/portfolios/llmservice/users/asohrabizade/codegen/sqsh/nvidian+nemo+verl_v2_enroot0.8.5.sqsh`

```bash
config_paths="responses_api_agents/simple_agent/configs/simple_agent.yaml,\
responses_api_models/openai_model/configs/openai_model.yaml,\
resources_servers/swerl_gen/configs/swerl_gen.yaml"

ng_run "+config_paths=[$config_paths]" 

ng_collect_rollouts \
    +agent_name=swerl_gen_simple_agent \
    +input_jsonl_fpath=resources_servers/swerl_gen/data/example.jsonl \
    +output_jsonl_fpath=resources_servers/swerl_gen/data/example_rollouts.jsonl \
    +num_repeats=2 \
    +num_samples_in_parallel=4 \
    +responses_create_params.max_output_tokens=4096
```

Rollout example

```json
{
    "responses_create_params": {
        "background": null,
        "include": null,
        "input": [
            {
                "content": "You are an expert Python coder and are given:\n- An issue description from a code repository.  \n- (Optional) Relevant file contents or snippets that may need adjustments.  \n\nYour task is to generate a complete test that can be used to both reproduce the issue and check whether the issue is resolved. \n\nThe complete test should contain the following:\n1. Necessary imports\n2. Code to reproduce the issue described in the issue text\n- If your test script determines that the issue is NOT YET SOLVED, it should return an exit code of 2. This should happen when running your test on the original codebase (before any edits are applied).\n- If your test script determines that the issue is SOLVED, it should return an exit code of 0. This should only happen when running your test on an edited codebase that fixes the issue.\n- If your test script crashes or something unexpected happens, it should return an exit code of 1. \n\nHere is an example:\n\n```python\nimport sys\n\ndef test_issue():\n    try:\n        # Setup: Import necessary modules and initialize test conditions\n        import some_module  # Replace with actual module\n        from some_module import function_to_test  # Replace with actual function\n\n        # Step 1: Define the input that triggers the issue\n        input_data = \"some input that causes the bug\"  # Replace with actual problematic input\n\n        # Step 2: Compute the actual output\n        actual_output = function_to_test(input_data)\n\n        # Step 3: Define the expected correct output\n        expected_output = \"expected correct result\"  # Replace with correct expected output\n\n        # Step 4: Compare results\n        if actual_output == expected_output:\n            sys.exit(0)  # Issue is fixed\n        else:\n            print(f\"Issue still exists. Actual output: {actual_output} != Expected output: {expected_output}\")\n            sys.exit(2)  # Issue still exists\n\n    except Exception as e:\n        print(f\"Unexpected error occurred: {e}\")\n        sys.exit(1)  # Unexpected error occurred\n\nif __name__ == \"__main__\":\n    test_issue()\n```\n\nPlease ensure the generated test reflects the issue described in the provided issue text. \nSince you are writing the test script before the issue is resolved, your test should fail and return an exit code of 2. I will run your script without any modifications, so do not leave any placeholders that I need to fill in.\n\nOutput format requirement: Please put your reasoning tokens in a separate code block, starting with <think> and ending with </think>, and the solution tokens containing the complete test in a separate code block, starting with <solution> and ending with </solution>. \nWrap the complete test in ```python...``` blocks.\n\n<issue>\nModeling's `separability_matrix` does not compute separability correctly for nested CompoundModels\nConsider the following model:\r\n\r\n```python\r\nfrom astropy.modeling import models as m\r\nfrom astropy.modeling.separable import separability_matrix\r\n\r\ncm = m.Linear1D(10) & m.Linear1D(5)\r\n```\r\n\r\nIt's separability matrix as you might expect is a diagonal:\r\n\r\n```python\r\n>>> separability_matrix(cm)\r\narray([[ True, False],\r\n       [False,  True]])\r\n```\r\n\r\nIf I make the model more complex:\r\n```python\r\n>>> separability_matrix(m.Pix2Sky_TAN() & m.Linear1D(10) & m.Linear1D(5))\r\narray([[ True,  True, False, False],\r\n       [ True,  True, False, False],\r\n       [False, False,  True, False],\r\n       [False, False, False,  True]])\r\n```\r\n\r\nThe output matrix is again, as expected, the outputs and inputs to the linear models are separable and independent of each other.\r\n\r\nIf however, I nest these compound models:\r\n```python\r\n>>> separability_matrix(m.Pix2Sky_TAN() & cm)\r\narray([[ True,  True, False, False],\r\n       [ True,  True, False, False],\r\n       [False, False,  True,  True],\r\n       [False, False,  True,  True]])\r\n```\r\nSuddenly the inputs and outputs are no longer separable?\r\n\r\nThis feels like a bug to me, but I might be missing something?\n\n</issue>\n<relevant_files>\n[start of astropy/modeling/separable.py]\n# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\n\"\"\"\nFunctions to determine if a model is separable, i.e.\nif the model outputs are independent.\n\nIt analyzes ``n_inputs``, ``n_outputs`` and the operators\nin a compound model by stepping through the transforms\nand creating a ``coord_matrix`` of shape (``n_outputs``, ``n_inputs``).\n\n\nEach modeling operator is represented by a function which\ntakes two simple models (or two ``coord_matrix`` arrays) and\nreturns an array of shape (``n_outputs``, ``n_inputs``).\n\n\"\"\"\n\nimport numpy as np\n\nfrom .core import Model, ModelDefinitionError, CompoundModel\nfrom .mappings import Mapping\n\n\n__all__ = [\"is_separable\", \"separability_matrix\"]\n\n\ndef is_separable(transform):\n    \"\"\"\n    A separability test for the outputs of a transform.\n\n    Parameters\n    ----------\n    transform : `~astropy.modeling.core.Model`\n        A (compound) model.\n\n    Returns\n    -------\n    is_separable : ndarray\n        A boolean array with size ``transform.n_outputs`` where\n        each element indicates whether the output is independent\n        and the result of a separable transform.\n\n    Examples\n    --------\n    >>> from astropy.modeling.models import Shift, Scale, Rotation2D, Polynomial2D\n    >>> is_separable(Shift(1) & Shift(2) | Scale(1) & Scale(2))\n        array([ True,  True]...)\n    >>> is_separable(Shift(1) & Shift(2) | Rotation2D(2))\n        array([False, False]...)\n    >>> is_separable(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]) | \\\n        Polynomial2D(1) & Polynomial2D(2))\n        array([False, False]...)\n    >>> is_separable(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]))\n        array([ True,  True,  True,  True]...)\n\n    \"\"\"\n    if transform.n_inputs == 1 and transform.n_outputs > 1:\n        is_separable = np.array([False] * transform.n_outputs).T\n        return is_separable\n    separable_matrix = _separable(transform)\n    is_separable = separable_matrix.sum(1)\n    is_separable = np.where(is_separable != 1, False, True)\n    return is_separable\n\n\ndef separability_matrix(transform):\n    \"\"\"\n    Compute the correlation between outputs and inputs.\n\n    Parameters\n    ----------\n    transform : `~astropy.modeling.core.Model`\n        A (compound) model.\n\n    Returns\n    -------\n    separable_matrix : ndarray\n        A boolean correlation matrix of shape (n_outputs, n_inputs).\n        Indicates the dependence of outputs on inputs. For completely\n        independent outputs, the diagonal elements are True and\n        off-diagonal elements are False.\n\n    Examples\n    --------\n    >>> from astropy.modeling.models import Shift, Scale, Rotation2D, Polynomial2D\n    >>> separability_matrix(Shift(1) & Shift(2) | Scale(1) & Scale(2))\n        array([[ True, False], [False,  True]]...)\n    >>> separability_matrix(Shift(1) & Shift(2) | Rotation2D(2))\n        array([[ True,  True], [ True,  True]]...)\n    >>> separability_matrix(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]) | \\\n        Polynomial2D(1) & Polynomial2D(2))\n        array([[ True,  True], [ True,  True]]...)\n    >>> separability_matrix(Shift(1) & Shift(2) | Mapping([0, 1, 0, 1]))\n        array([[ True, False], [False,  True], [ True, False], [False,  True]]...)\n\n    \"\"\"\n    if transform.n_inputs == 1 and transform.n_outputs > 1:\n        return np.ones((transform.n_outputs, transform.n_inputs),\n                       dtype=np.bool_)\n    separable_matrix = _separable(transform)\n    separable_matrix = np.where(separable_matrix != 0, True, False)\n    return separable_matrix\n\n\ndef _compute_n_outputs(left, right):\n    \"\"\"\n    Compute the number of outputs of two models.\n\n    The two models are the left and right model to an operation in\n    the expression tree of a compound model.\n\n    Parameters\n    ----------\n    left, right : `astropy.modeling.Model` or ndarray\n        If input is of an array, it is the output of `coord_matrix`.\n\n    \"\"\"\n    if isinstance(left, Model):\n        lnout = left.n_outputs\n    else:\n        lnout = left.shape[0]\n    if isinstance(right, Model):\n        rnout = right.n_outputs\n    else:\n        rnout = right.shape[0]\n    noutp = lnout + rnout\n    return noutp\n\n\ndef _arith_oper(left, right):\n    \"\"\"\n    Function corresponding to one of the arithmetic operators\n    ['+', '-'. '*', '/', '**'].\n\n    This always returns a nonseparable output.\n\n\n    Parameters\n    ----------\n    left, right : `astropy.modeling.Model` or ndarray\n        If input is of an array, it is the output of `coord_matrix`.\n\n    Returns\n    -------\n    result : ndarray\n        Result from this operation.\n    \"\"\"\n    # models have the same number of inputs and outputs\n    def _n_inputs_outputs(input):\n        if isinstance(input, Model):\n            n_outputs, n_inputs = input.n_outputs, input.n_inputs\n        else:\n            n_outputs, n_inputs = input.shape\n        return n_inputs, n_outputs\n\n    left_inputs, left_outputs = _n_inputs_outputs(left)\n    right_inputs, right_outputs = _n_inputs_outputs(right)\n\n    if left_inputs != right_inputs or left_outputs != right_outputs:\n        raise ModelDefinitionError(\n            \"Unsupported operands for arithmetic operator: left (n_inputs={}, \"\n            \"n_outputs={}) and right (n_inputs={}, n_outputs={}); \"\n            \"models must have the same n_inputs and the same \"\n            \"n_outputs for this operator.\".format(\n                left_inputs, left_outputs, right_inputs, right_outputs))\n\n    result = np.ones((left_outputs, left_inputs))\n    return result\n\n\ndef _coord_matrix(model, pos, noutp):\n    \"\"\"\n    Create an array representing inputs and outputs of a simple model.\n\n    The array has a shape (noutp, model.n_inputs).\n\n    Parameters\n    ----------\n    model : `astropy.modeling.Model`\n        model\n    pos : str\n        Position of this model in the expression tree.\n        One of ['left', 'right'].\n    noutp : int\n        Number of outputs of the compound model of which the input model\n        is a left or right child.\n\n    \"\"\"\n    if isinstance(model, Mapping):\n        axes = []\n        for i in model.mapping:\n            axis = np.zeros((model.n_inputs,))\n            axis[i] = 1\n            axes.append(axis)\n        m = np.vstack(axes)\n        mat = np.zeros((noutp, model.n_inputs))\n        if pos == 'left':\n            mat[: model.n_outputs, :model.n_inputs] = m\n        else:\n            mat[-model.n_outputs:, -model.n_inputs:] = m\n        return mat\n    if not model.separable:\n        # this does not work for more than 2 coordinates\n        mat = np.zeros((noutp, model.n_inputs))\n        if pos == 'left':\n            mat[:model.n_outputs, : model.n_inputs] = 1\n        else:\n            mat[-model.n_outputs:, -model.n_inputs:] = 1\n    else:\n        mat = np.zeros((noutp, model.n_inputs))\n\n        for i in range(model.n_inputs):\n            mat[i, i] = 1\n        if pos == 'right':\n            mat = np.roll(mat, (noutp - model.n_outputs))\n    return mat\n\n\ndef _cstack(left, right):\n    \"\"\"\n    Function corresponding to '&' operation.\n\n    Parameters\n    ----------\n    left, right : `astropy.modeling.Model` or ndarray\n        If input is of an array, it is the output of `coord_matrix`.\n\n    Returns\n    -------\n    result : ndarray\n        Result from this operation.\n\n    \"\"\"\n    noutp = _compute_n_outputs(left, right)\n\n    if isinstance(left, Model):\n        cleft = _coord_matrix(left, 'left', noutp)\n    else:\n        cleft = np.zeros((noutp, left.shape[1]))\n        cleft[: left.shape[0], : left.shape[1]] = left\n    if isinstance(right, Model):\n        cright = _coord_matrix(right, 'right', noutp)\n    else:\n        cright = np.zeros((noutp, right.shape[1]))\n        cright[-right.shape[0]:, -right.shape[1]:] = 1\n\n    return np.hstack([cleft, cright])\n\n\ndef _cdot(left, right):\n    \"\"\"\n    Function corresponding to \"|\" operation.\n\n    Parameters\n    ----------\n    left, right : `astropy.modeling.Model` or ndarray\n        If input is of an array, it is the output of `coord_matrix`.\n\n    Returns\n    -------\n    result : ndarray\n        Result from this operation.\n    \"\"\"\n\n    left, right = right, left\n\n    def _n_inputs_outputs(input, position):\n        \"\"\"\n        Return ``n_inputs``, ``n_outputs`` for a model or coord_matrix.\n        \"\"\"\n        if isinstance(input, Model):\n            coords = _coord_matrix(input, position, input.n_outputs)\n        else:\n            coords = input\n        return coords\n\n    cleft = _n_inputs_outputs(left, 'left')\n    cright = _n_inputs_outputs(right, 'right')\n\n    try:\n        result = np.dot(cleft, cright)\n    except ValueError:\n        raise ModelDefinitionError(\n            'Models cannot be combined with the \"|\" operator; '\n            'left coord_matrix is {}, right coord_matrix is {}'.format(\n                cright, cleft))\n    return result\n\n\ndef _separable(transform):\n    \"\"\"\n    Calculate the separability of outputs.\n\n    Parameters\n    ----------\n    transform : `astropy.modeling.Model`\n        A transform (usually a compound model).\n\n    Returns :\n    is_separable : ndarray of dtype np.bool\n        An array of shape (transform.n_outputs,) of boolean type\n        Each element represents the separablity of the corresponding output.\n    \"\"\"\n    if (transform_matrix := transform._calculate_separability_matrix()) is not NotImplemented:\n        return transform_matrix\n    elif isinstance(transform, CompoundModel):\n        sepleft = _separable(transform.left)\n        sepright = _separable(transform.right)\n        return _operators[transform.op](sepleft, sepright)\n    elif isinstance(transform, Model):\n        return _coord_matrix(transform, 'left', transform.n_outputs)\n\n\n# Maps modeling operators to a function computing and represents the\n# relationship of axes as an array of 0-es and 1-s\n_operators = {'&': _cstack, '|': _cdot, '+': _arith_oper, '-': _arith_oper,\n              '*': _arith_oper, '/': _arith_oper, '**': _arith_oper}\n[end of astropy/modeling/separable.py]\n</relevant_files>\n<think>\n",
                "role": "user",
                "type": "message"
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        "instructions": null,
        "max_output_tokens": 4096,
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                "id": "msg_aa662ac7aa140767",
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                    {
                        "annotations": [],
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    },
    "verification_time": 108.05575942993164,
    "model_patch": "diff --git a/astropy/modeling/separable.py b/astropy/modeling/separable.py\n--- a/astropy/modeling/separable.py\n+++ b/astropy/modeling/separable.py\n@@ -242,7 +242,7 @@ def _cstack(left, right):\n         cright = _coord_matrix(right, 'right', noutp)\n     else:\n         cright = np.zeros((noutp, right.shape[1]))\n-        cright[-right.shape[0]:, -right.shape[1]:] = 1\n+        cright[-right.shape[0]:, -right.shape[1]:] = right\n \n     return np.hstack([cleft, cright])\n \n",
    "repro_test_info_base64": "...",
    "model_output": "<think>\nOkay, let's tackle this issue. The user is reporting that the separability_matrix function in Astropy's modeling module isn't working correctly for nested CompoundModels. Let me understand the problem.\n\nThe example given is when you have a compound model made by combining two Linear1D models with the '&' operator. The separability matrix should be diagonal, indicating each output is independent. But when nesting these models, like combining a Pix2Sky_TAN model with the compound model, the matrix isn't as expected. Specifically, the outputs seem to be incorrectly marked as non-separable.\n\nLooking at the relevant code in separable.py, the _separable function is recursive. It checks if the transform is a CompoundModel and then applies the operator function. The operators are mapped to functions like _cstack for '&' and _cdot for '|'.\n\nWait, the _cstack function for '&' combines left and right models by stacking their coordinate matrices horizontally. For the '&' operator, each model's outputs are considered independent. But when models are nested, maybe the operator functions aren't handling the nesting correctly.\n\nIn the example where Pix2Sky_TAN is combined with cm (which is Linear1D & Linear1D), the separability matrix shows that the outputs of the linear models are no longer separable. That suggests that the operator function for '|' (the '|' operator is used in the nested case) is combining the coordinate matrices in a way that mixes the inputs.\n\nLooking at the _cdot function for the '|' operator, it's using matrix multiplication. For example, when combining models, the coordinate matrix of the left model is multiplied by the right model's matrix. But if the left model is a compound model, its coordinate matrix might have overlapping inputs, leading to incorrect separability.\n\nWait, the user's example shows that when combining Pix2Sky_TAN & cm, the separability matrix has the linear models' outputs marked as non-separable. That's because the '|' operator is combining the models, and the matrix multiplication is causing dependencies between the inputs.\n\nBut in the original case where cm is a compound model, the separability matrix is diagonal. That suggests that the '&' operator correctly separates the models. However, when nesting, the '|' operator might be causing the issue.\n\nLet me think about the _cdot function. When combining models with '|', it's using matrix multiplication. For example, if the left model is Pix2Sky_TAN and the right is cm (which is Linear1D & Linear1D), then the coordinate matrices are multiplied. The Pix2Sky_TAN model's coordinate matrix might have outputs that depend on the inputs of the Linear models, leading to the separability matrix entries being True where they shouldn't be.\n\nSo the problem is in how the '|' operator is combining the coordinate matrices. The matrix multiplication might be causing dependencies that aren't actually there. For example, if the left model's outputs depend on the right model's inputs, but in reality, the models are independent.\n\nWait, but in the example, the Pix2Sky_TAN is combined with cm. The Pix2Sky_TAN model's outputs are independent of the cm's inputs. But the way the '|' operator is implemented might be causing the matrix multiplication to mix the inputs and outputs, leading to incorrect separability.\n\nSo the test needs to check that when a model is nested (like Pix2Sky_TAN & cm), the separability matrix correctly identifies the separable outputs. The current code might be incorrectly combining the coordinate matrices, leading to the wrong matrix.\n\nThe test should create the models as described, compute the separability matrix, and check if the result matches the expected output. For the nested case, the expected matrix should have the Pix2Sky_TAN's outputs as separable, and the Linear models' outputs as separable but not overlapping with each other.\n\nSo the test script should import the necessary modules, create the models, compute the separability matrix, and compare it to the expected result. If the matrix is not as expected, the test returns 2, indicating the issue is not fixed.\n\nNow, writing the test. The code should:\n\n1. Import models and separability_matrix.\n2. Create the compound model cm as Linear1D(10) & Linear1D(5).\n3. Create a more complex model by combining Pix2Sky_TAN with cm.\n4. Compute the separability matrix for the nested model.\n5. Check if the matrix matches the expected structure.\n\nThe expected matrix for the nested case should have the first two outputs (from Pix2Sky_TAN) as separable, and the next two (from the Linear models) also separable but not overlapping. However, the user's example shows that the current code produces a matrix where the Linear outputs are marked as non-separable, which is incorrect.\n\nWait, the user's example shows that when combining Pix2Sky_TAN & cm, the separability matrix is:\n\narray([[ True,  True, False, False],\n       [ True,  True, False, False],\n       [False, False,  True,  True],\n       [False, False,  True,  True]])\n\nBut according to the user's expectation, the outputs of the linear models should be separable. However, the matrix shows that the third and fourth outputs are separable, but the first two are also marked as True. That suggests that the code is incorrectly marking the Pix2Sky_TAN's outputs as dependent on the Linear models' inputs, which they shouldn't be.\n\nSo the test should check that the matrix for the nested model has the correct structure. The expected matrix should have the first two outputs (Pix2Sky_TAN) as separable, and the next two (Linear models) as separable, but the Pix2Sky_TAN's outputs should not depend on the Linear models' inputs.\n\nTherefore, the test should create the nested model, compute the matrix, and check if the matrix has True in the diagonal positions for each model's outputs and inputs, and False elsewhere.\n\nSo the test code would look like this:\n\nImport the necessary modules, create the models, compute the matrix, and compare against the expected result. If the matrix is not as expected, return 2.\n\nNow, coding this. The test will use the models from astropy.modeling, create the compound models, compute the matrix, and check if it's correct.\n\nThe expected matrix for the nested case should be:\n\narray([[ True, False, False, False],\n       [False, False, False, False],\n       [False, False,  True, False],\n       [False, False, False,  True]])\n\nWait, no. Let me think again. The original example for Pix2Sky_TAN & cm (which is Linear1D & Linear1D) gives:\n\narray([[ True,  True, False, False],\n       [ True,  True, False, False],\n       [False, False,  True,  True],\n       [False, False,  True,  True]])\n\nBut according to the user's expectation, the outputs of the linear models should be independent. However, in the current code, the matrix shows that the first two outputs (from Pix2Sky_TAN) are dependent on the first two inputs (from Pix2Sky_TAN), and the next two outputs (from the Linear models) are dependent on the next two inputs. But the matrix shows that the first two outputs are also dependent on the Linear models' inputs. That's incorrect.\n\nSo the test should check that the matrix for the nested model has the correct structure. The expected matrix should have True only on the diagonal for each model's outputs and inputs.\n\nSo the correct matrix for the nested case would have:\n\n- The first two outputs (from Pix2Sky_TAN) are separable and depend on the first two inputs (from Pix2Sky_TAN).\n- The next two outputs (from the Linear models) are separable and depend on the next two inputs (from the Linear models).\n\nBut in the current code, the matrix for the nested model shows that the first two outputs are dependent on all inputs, which is incorrect.\n\nTherefore, the test should create the nested model, compute the matrix, and compare it to the expected matrix. If it doesn't match, the test returns 2.\n\nNow, writing the test code. The code will import the necessary modules, create the models, compute the matrix, and check if it's correct.\n\nSo the test code would be:\n\nimport sys\nfrom astropy.modeling.models import Linear1D, Pix2Sky_TAN\nfrom astropy.modeling.separable import separability_matrix\n\ndef test_issue():\n    try:\n        # Create the compound model cm\n        cm = Linear1D(10) & Linear1D(5)\n        # Create the nested model\n        nested_model = Pix2Sky_TAN() & cm\n        # Compute the separability matrix\n        matrix = separability_matrix(nested_model)\n        # Expected matrix\n        expected = np.array([\n            [True, False, False, False],\n            [False, False, False, False],\n            [False, False, True, False],\n            [False, False, False, True]\n        ])\n        # Check if the matrix matches the expected\n        if np.array_equal(matrix, expected):\n            sys.exit(0)\n        else:\n            print(f\"Issue still exists. Actual matrix:\\n{matrix}\\nExpected matrix:\\n{expected}\")\n            sys.exit(2)\n    except Exception as e:\n        print(f\"Unexpected error occurred: {e}\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    test_issue()\n\nWait, but the user's example shows that the current code produces a matrix where the first two outputs are True for the first two inputs. So the expected matrix might be different. Let me recheck the user's example.\n\nIn the user's example, when they do:\n\nseparability_matrix(m.Pix2Sky_TAN() & cm)\n\nThe output is:\n\narray([[ True,  True, False, False],\n       [ True,  True, False, False],\n       [False, False,  True,  True],\n       [False, False,  True,  True]])\n\nBut according to the user's expectation, this is incorrect. The first two outputs (from Pix2Sky_TAN) should be separable and depend on their own inputs, and the next two outputs (from the Linear models) should be separable and depend on their own inputs. But in the current code, the matrix shows that the first two outputs depend on all inputs, which is wrong.\n\nSo the expected matrix should have True only on the diagonal for each model's outputs and inputs. For example, the first two outputs (Pix2Sky_TAN) should have True only for their own inputs, and the next two outputs (Linear models) should have True for their own inputs.\n\nSo the expected matrix should be:\n\narray([\n    [True, False, False, False],\n    [False, True, False, False],\n    [False, False, True, False],\n    [False, False, False, True]\n])\n\nWait, but the Pix2Sky_TAN model has 2 outputs and 2 inputs. The Linear models have 2 outputs and 2 inputs each. When combined with '&' operator, the total inputs are 2 (from Pix2Sky_TAN) + 2 (from cm) = 4. The outputs are 2 (Pix2Sky_TAN) + 2 (cm) = 4.\n\nSo the correct matrix should have True for the first two outputs (Pix2Sky_TAN) depending on the first two inputs, and the next two outputs (cm's outputs) depending on the next two inputs.\n\nSo the expected matrix should be:\n\narray([\n    [True, False, False, False],\n    [False, True, False, False],\n    [False, False, True, False],\n    [False, False, False, True]\n])\n\nBut the current code produces:\n\narray([[ True,  True, False, False],\n       [ True,  True, False, False],\n       [False, False,  True,  True],\n       [False, False,  True,  True]])\n\nWhich indicates that the first two outputs are dependent on all inputs, which is incorrect.\n\nSo the test should check if the matrix matches the expected one. If not, return 2.\n\nSo the test code would create the nested model, compute the matrix, and compare to the expected one.\n\nThus, the test code should be structured as such.\n</think>\n\n```python\nimport sys\nimport numpy as np\nfrom astropy.modeling.models import Linear1D, Pix2Sky_TAN\nfrom astropy.modeling.separable import separability_matrix\n\ndef test_issue():\n    try:\n        # Create the compound model cm\n        cm = Linear1D(10) & Linear1D(5)\n        # Create the nested model\n        nested_model = Pix2Sky_TAN() & cm\n        # Compute the separability matrix\n        matrix = separability_matrix(nested_model)\n        # Expected matrix when the issue is not fixed\n        expected = np.array([\n            [True, True, False, False],\n            [True, True, False, False],\n            [False, False, True, True],\n            [False, False, True, True]\n        ])\n        # Check if the matrix matches the expected (incorrect) output\n        if np.array_equal(matrix, expected):\n            print(\"Issue still exists. Matrix matches incorrect output.\")\n            sys.exit(2)\n        else:\n            print(\"Issue resolved. Matrix does not match incorrect output.\")\n            sys.exit(0)\n    except Exception as e:\n        print(f\"Unexpected error occurred: {e}\")\n        sys.exit(1)\n\nif __name__ == \"__main__\":\n    test_issue()\n```"
}
```

### Implementation notes
- The server extracts the last assistant message's text from the Responses output.

## Licensing information
Code: Apache 2.0

