[
{
"id": 0,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_pair_vector_and_distance(g: dgl.DGLGraph):\n dst_pos = g.ndata[\"pos\"][g.edges()[1]] + g.edata[\"pbc_offshift\"]\n src_pos = g.ndata[\"pos\"][g.edges()[0]]\n bond_vec = dst_pos - src_pos\n bond_dist = torch.norm(bond_vec, dim=1)\n\n return bond_vec, bond_dist\n```\n###test function signature:\n```python\n\n def test_compute_angle(self, graph_Mo, graph_CH4):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_angle",
"reference": "\n def test_compute_angle(self, graph_Mo, graph_CH4):\n s1, g1, state1 = graph_Mo\n lattice = torch.tensor(s1.lattice.matrix, dtype=matgl.float_th).unsqueeze(dim=0)\n g1.edata[\"pbc_offshift\"] = torch.matmul(g1.edata[\"pbc_offset\"], lattice[0])\n g1.ndata[\"pos\"] = g1.ndata[\"frac_coords\"] @ lattice[0]\n bv, bd = compute_pair_vector_and_distance(g1)\n g1.edata[\"bond_vec\"] = bv\n g1.edata[\"bond_dist\"] = bd\n cos_loop = _calculate_cos_loop(g1, 4.0)\n\n line_graph = create_line_graph(g1, 4.0)\n line_graph.apply_edges(compute_theta_and_phi)\n np.testing.assert_array_almost_equal(\n np.sort(np.array(cos_loop)), np.sort(np.array(line_graph.edata[\"cos_theta\"]))\n )\n\n # test only compute theta\n line_graph.apply_edges(partial(compute_theta, directed=False))\n theta = np.arccos(np.clip(cos_loop, -1.0 + 1e-7, 1.0 - 1e-7))\n np.testing.assert_array_almost_equal(np.sort(theta), np.sort(np.array(line_graph.edata[\"theta\"])), decimal=4)\n\n # test only compute theta with cosine\n _ = line_graph.edata.pop(\"cos_theta\")\n line_graph.apply_edges(partial(compute_theta, cosine=True, directed=False))\n np.testing.assert_array_almost_equal(\n np.sort(np.array(cos_loop)), np.sort(np.array(line_graph.edata[\"cos_theta\"]))\n )\n\n s2, g2, state2 = graph_CH4\n lattice = torch.tensor(np.identity(3), dtype=matgl.float_th).unsqueeze(dim=0)\n g2.edata[\"pbc_offshift\"] = torch.matmul(g2.edata[\"pbc_offset\"], lattice[0])\n g2.ndata[\"pos\"] = g2.ndata[\"frac_coords\"] @ lattice[0]\n bv, bd = compute_pair_vector_and_distance(g2)\n g2.edata[\"bond_vec\"] = bv\n g2.edata[\"bond_dist\"] = bd\n cos_loop = _calculate_cos_loop(g2, 2.0)\n\n line_graph = create_line_graph(g2, 2.0)\n line_graph.apply_edges(compute_theta_and_phi)\n np.testing.assert_array_almost_equal(\n np.sort(np.array(cos_loop)), np.sort(np.array(line_graph.edata[\"cos_theta\"]))\n )\n\n # test only compute theta\n line_graph.apply_edges(partial(compute_theta, directed=False))\n np.testing.assert_array_almost_equal(\n np.sort(np.arccos(np.array(cos_loop))), np.sort(np.array(line_graph.edata[\"theta\"]))\n )\n\n # test only compute theta with cosine\n _ = line_graph.edata.pop(\"cos_theta\")\n line_graph.apply_edges(partial(compute_theta, cosine=True, directed=False))\n np.testing.assert_array_almost_equal(\n np.sort(np.array(cos_loop)), np.sort(np.array(line_graph.edata[\"cos_theta\"]))\n )",
"completions": [
"```python\n def test_compute_angle(self, graph_Mo, graph_CH4):\n \"\"\"\n Test compute_angle function\n \"\"\"\n # compute angle between Mo-C and Mo-H\n angle_Mo_C = compute_angle(graph_Mo, graph_CH4, 0, 1)\n angle_Mo_H = compute_angle(graph_Mo, graph_CH4, 0, 2)\n\n # check angle between Mo-C and Mo-H\n self.assertAlmostEqual(angle_Mo_C, 104.5, places=1)\n self.assertAlmostEqual(angle_Mo_H, 109.5, places=1)\n```\n"
],
"line": 16,
"token": 148,
"line_diff": 0,
"token_diff": 0
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{
"id": 1,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def _make_schedule(self, targets: List[Task]) -> List[Task]:\n self.schedule = []\n self.dependencies = self._build_dependencies(targets)\n\n edge_tups = []\n for node in self.dependencies:\n for dependency in self.dependencies[node]:\n edge_tups.append((dependency, node))\n\n for task in targets:\n # add edges from a dummy node to each target to guarantee\n # they will be included in the final schedule\n edge_tups.append((Executor.DUMMY_TASK_VALUE, task))\n\n def _compare_key(task: Union[Task, str]):\n if task == Executor.DUMMY_TASK_VALUE:\n return (\"\", 0)\n return (\n task.group_label() or \"\",\n -task.priority(),\n )\n\n graph = networkx.DiGraph(edge_tups)\n res = [\n t\n for t in networkx.lexicographical_topological_sort(graph, key=_compare_key)\n if t != Executor.DUMMY_TASK_VALUE\n ]\n return res\n```\n###test function signature:\n```python\n\n def test_group_label_with_dependencies(self):\n # Creating tasks with dependencies and group labels\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_group_label_with_dependencies",
"reference": "\n def test_group_label_with_dependencies(self):\n # Creating tasks with dependencies and group labels\n task1 = create_mock_task(\"task1\", result=1, group_label=\"group1\")\n task2 = create_mock_task(\n \"task2\", result=2, dependencies={\"task1\": task1}, group_label=\"group2\"\n )\n task3 = create_mock_task(\n \"task3\", result=3, dependencies={\"task2\": task2}, group_label=\"group1\"\n )\n\n executor = Executor([task3])\n schedule = executor._make_schedule([task3])\n scheduled_labels = [\n task.group_label() for task in schedule if task.group_label()\n ]\n\n # Check if task3 is scheduled after task1 and task2 due to dependency, even though it has the same group label as task1\n group1_indices = [\n i for i, label in enumerate(scheduled_labels) if label == \"group1\"\n ]\n group2_index = scheduled_labels.index(\"group2\")\n\n assert (\n group1_indices[-1] > group2_index\n ), \"Task with the same group label but later dependency was not scheduled after different group label\"",
"completions": [
"```python\n\n def test_group_label_with_dependencies(self):\n # Creating tasks with dependencies and group labels\n task1 = Task(name=\"task1\", group_label=\"group1\")\n task2 = Task(name=\"task2\", group_label=\"group1\")\n task3 = Task(name=\"task3\", group_label=\"group2\")\n task4 = Task(name=\"task4\", group_label=\"group2\")\n task5 = Task(name=\"task5\", group_label=\"group3\")\n task6 = Task(name=\"task6\", group_label=\"group3\")\n task7 = Task(name=\"task7\", group_label=\"group4\")\n task8 = Task(name=\"task8\", group_label=\"group4\")\n task9 = Task(name=\"task9\", group_label=\"group5\")\n task10 = Task(name=\"task10\", group_label=\"group5\")\n\n # Creating dependencies between tasks\n task1.depends_on(task2)\n task2.depends_on(task3)\n task3.depends_on(task4)\n task4.depends_on(task5)\n task5.depends_on(task6)\n task6.depends_on(task7)\n task7.depends_on(task8)\n task8.depends_on(task9)\n task9.depends_on(task10)\n\n # Creating a list of tasks\n tasks = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies\n tasks_with_dependencies = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with group labels\n tasks_with_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_group_labels = [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n\n # Creating a list of tasks with dependencies and group labels\n tasks_with_dependencies_and_"
],
"line": 36,
"token": 242,
"line_diff": 0,
"token_diff": 0
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{
"id": 2,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef setup_cache(\n max_batch_size, max_seq_len, dim, heads, layers, block_size, rope_base\n):\n if max_seq_len >= max_seq_len and max_batch_size >= max_batch_size:\n return\n\n head_dim = dim // heads\n max_seq_len = find_multiple(max_seq_len, 8)\n\n for b in layers:\n b.attention.kv_cache = KVCache(\n max_batch_size, max_seq_len, heads, head_dim\n )\n\n freq_cis = precompute_freq_cis(block_size, dim // heads, rope_base)\n causal_mask = torch.tril(\n torch.ones(max_seq_len, max_seq_len, dtype=torch.bool)\n )\n\n return causal_mask, freq_cis\n```\n###test function signature:\n```python\n\ndef test_setup_cache_max_batch_size_greater_than_max():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_setup_cache_max_batch_size_greater_than_max",
"reference": "\ndef test_setup_cache_max_batch_size_greater_than_max():\n layers = [\n Mock(attention=Mock(kw_cache=None)),\n Mock(attention=Mock(kw_cache=None)),\n ]\n max_batch_size = 64\n max_seq_len = 32\n heads = 4\n head_dim = 32\n block_size = 32\n rope_base = 1000\n setup_cache(\n max_batch_size + 10,\n max_seq_len,\n head_dim * heads,\n heads,\n layers,\n block_size,\n rope_base,\n )\n for layer in layers:\n assert isinstance(layer.attention.kw_cache, KVCache)\n assert layer.attention.kw_cache.k_cache.shape == torch.Size(\n [max_batch_size + 10, heads, max_seq_len, head_dim]\n )\n assert layer.attention.kw_cache.v_cache.shape == torch.Size(\n [max_batch_size + 10, heads, max_seq_len, head_dim]\n )",
"completions": [
"```python\ndef test_setup_cache_max_batch_size_greater_than_max():\n max_batch_size = 10\n max_seq_len = 10\n dim = 10\n heads = 10\n layers = [1, 2, 3]\n block_size = 10\n rope_base = 10\n\n causal_mask, freq_cis = setup_cache(\n max_batch_size, max_seq_len, dim, heads, layers, block_size, rope_base\n )\n\n assert causal_mask.shape == (max_seq_len, max_seq_len)\n assert causal_mask.dtype == torch.bool\n assert causal_mask.all()\n\n assert freq_cis.shape == (block_size, dim // heads)\n assert freq_cis.dtype == torch.float32\n assert freq_cis.all()\n\n for b in layers:\n assert b.attention.kv_cache.max_batch_size == max_batch_size\n assert b.attention.kv_cache.max_seq_len == max_seq_len\n assert b.attention.kv_cache.heads == heads\n assert b.attention.kv_cache.head_dim == dim // heads\n\n assert causal_mask.shape == (max_seq_len, max_seq_len)\n assert causal_mask.dtype == torch.bool\n assert causal_mask.all()\n\n assert freq_cis.shape == (block_size, dim // heads)\n assert freq_cis.dtype == torch.float32\n assert freq_cis.all()\n\n for b in layers:\n assert b.attention.kv_cache.max_batch_size == max_batch_size\n assert b.attention.kv_cache.max_seq_len == max_seq_len\n assert b.attention.kv_cache.heads == heads\n assert b.attention.kv_cache.head_dim == dim // heads\n\n assert causal_mask.shape == (max_seq_len, max_seq_len)\n assert causal_mask.dtype == torch.bool\n assert causal_mask.all()\n\n assert freq_cis.shape == (block_size, dim // heads)\n assert freq_cis.dtype == torch.float32\n assert freq_cis.all()\n\n for b in layers:\n assert b.attention.kv_cache.max_batch_size == max_batch_size\n assert b.attention.kv_cache.max_seq_len == max_seq_len\n assert b.attention.kv_cache.heads == heads\n assert b.attention.kv_cache.head_dim == dim // heads\n\n assert causal_mask.shape == (max_seq_len, max_seq_len)\n assert causal_mask.dtype == torch.bool\n assert causal_mask.all()\n\n assert freq_cis.shape == (block_size, dim // heads)\n assert freq_cis.dtype == torch.float32\n assert freq_cis.all()\n\n for b in layers:\n assert b.attention.kv_cache.max_batch_size == max_batch_size\n assert b.attention.kv_cache.max_seq_len == max_seq_len\n assert b.attention.kv_cache.heads == heads\n assert b.attention.kv_cache.head_dim == dim // heads\n\n assert causal_mask.shape == (max_seq_len, max_seq_len)\n assert causal_mask.dtype == torch.bool\n assert causal_mask.all()\n\n assert freq_cis.shape == (block_size, dim // heads)\n assert freq_cis.dtype == torch.float32\n assert freq_cis.all()\n\n for b in layers:\n assert b.attention.kv_cache.max_batch_size"
],
"line": 26,
"token": 168,
"line_diff": 0,
"token_diff": 0
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{
"id": 3,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef load_image_from_string(\n reference: str,\n max_height: Optional[int] = None,\n max_width: Optional[int] = None,\n) -> Tuple[str, Optional[float]]:\n if uri_is_http_link(uri=reference):\n return load_image_from_url(\n url=reference, max_height=max_height, max_width=max_width\n )\n if os.path.exists(reference):\n if max_height is None or max_width is None:\n with open(reference, \"rb\") as f:\n img_bytes = f.read()\n img_base64_str = encode_base_64(payload=img_bytes)\n return img_base64_str, None\n local_image = cv2.imread(reference)\n if local_image is None:\n raise EncodingError(f\"Could not load image from {reference}\")\n local_image, scaling_factor = resize_opencv_image(\n image=local_image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=local_image), scaling_factor\n if max_height is not None and max_width is not None:\n image_bytes = base64.b64decode(reference)\n image = bytes_to_opencv_image(payload=image_bytes)\n image, scaling_factor = resize_opencv_image(\n image=image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=image), scaling_factor\n return reference, None\n```\n###test function signature:\n```python\n\ndef test_load_image_from_string_when_file_to_be_downloaded(\n requests_get_mock: MagicMock,\n) -> None:\n # given\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_load_image_from_string_when_file_to_be_downloaded",
"reference": "\ndef test_load_image_from_string_when_file_to_be_downloaded(\n requests_get_mock: MagicMock,\n) -> None:\n # given\n response = Response()\n response.status_code = 200\n image = np.zeros((128, 128, 3), dtype=np.uint8)\n _, encoded_image = cv2.imencode(\".jpg\", image)\n response._content = encoded_image\n requests_get_mock.return_value = response\n\n # when\n serialised_image, scaling_factor = load_image_from_string(\n reference=\"https://some/file.jpg\"\n )\n\n # then\n recovered_image = base64.b64decode(serialised_image)\n bytes_array = np.frombuffer(recovered_image, dtype=np.uint8)\n decoding_result = cv2.imdecode(bytes_array, cv2.IMREAD_UNCHANGED)\n\n # then\n assert scaling_factor is None\n assert decoding_result.shape == image.shape\n assert np.allclose(decoding_result, image)\n requests_get_mock.assert_called_once_with(\"https://some/file.jpg\")",
"completions": [
"```python\n\ndef test_load_image_from_string_when_file_to_be_downloaded(\n requests_get_mock: MagicMock,\n) -> None:\n # given\n reference = \"https://www.example.com/image.jpg\"\n max_height = 100\n max_width = 100\n requests_get_mock.return_value.content = b\"image_bytes\"\n # when\n result = load_image_from_string(\n reference=reference, max_height=max_height, max_width=max_width\n )\n # then\n assert result == (\n \"data:image/jpeg;base64,image_bytes\",\n None,\n )\n```\n"
],
"line": 47,
"token": 276,
"line_diff": 0,
"token_diff": 0
},
{
"id": 4,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef assert_connectivity(connectivity: nx.Graph, circuit: Circuit):\n if list(connectivity.nodes) != list(range(connectivity.number_of_nodes())):\n node_mapping = {node: i for i, node in enumerate(connectivity.nodes)}\n new_connectivity = nx.Graph()\n new_connectivity.add_edges_from(\n [(node_mapping[u], node_mapping[v]) for u, v in connectivity.edges]\n )\n connectivity = new_connectivity\n for gate in circuit.queue:\n if len(gate.qubits) > 2 and not isinstance(gate, gates.M):\n raise_error(ConnectivityError, f\"{gate.name} acts on more than two qubits.\")\n if len(gate.qubits) == 2:\n if (gate.qubits[0], gate.qubits[1]) not in connectivity.edges:\n raise_error(\n ConnectivityError,\n f\"Circuit does not respect connectivity. {gate.name} acts on {gate.qubits}.\",\n )\n```\n###test function signature:\n```python\n\ndef test_sabre_random_circuits(n_gates, look, decay, placer, connectivity):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_sabre_random_circuits",
"reference": "\ndef test_sabre_random_circuits(n_gates, look, decay, placer, connectivity):\n placer = placer(connectivity=connectivity)\n layout_circ = Circuit(5)\n initial_layout = placer(layout_circ)\n router = Sabre(connectivity=connectivity, lookahead=look, decay_lookahead=decay)\n circuit = generate_random_circuit(nqubits=5, ngates=n_gates)\n measurement = gates.M(*range(5))\n circuit.add(measurement)\n transpiled_circuit, final_qubit_map = router(circuit, initial_layout)\n assert router.added_swaps >= 0\n assert_connectivity(connectivity, transpiled_circuit)\n assert_placement(transpiled_circuit, final_qubit_map)\n assert n_gates + router.added_swaps + 1 == transpiled_circuit.ngates\n assert_circuit_equivalence(\n original_circuit=circuit,\n transpiled_circuit=transpiled_circuit,\n final_map=final_qubit_map,\n initial_map=initial_layout,\n )\n assert transpiled_circuit.queue[-1].register_name == measurement.register_name",
"completions": [
"```python\ndef test_sabre_random_circuits(n_gates, look, decay, placer, connectivity):\n import random\n import networkx as nx\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import Program\n from pyquil.gates import *\n from pyquil.paulis import PauliTerm, PauliSum\n from pyquil.api import QVMConnection\n from pyquil.quil import"
],
"line": 27,
"token": 250,
"line_diff": 0,
"token_diff": 0
},
{
"id": 5,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef xx_circuit_step(source, strength, target, embodiment):\n\n permute_source_for_overlap, permute_target_for_overlap = None, None\n\n # apply all possible reflections, shifts to the source\n for source_reflection_name in reflection_options:\n reflected_source_coord, source_reflection, reflection_phase_shift = apply_reflection(\n source_reflection_name, source\n )\n for source_shift_name in shift_options:\n shifted_source_coord, source_shift, shift_phase_shift = apply_shift(\n source_shift_name, reflected_source_coord\n )\n\n # check for overlap, back out permutation\n source_shared, target_shared = None, None\n for i, j in [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)]:\n\n if (\n abs(np.mod(abs(shifted_source_coord[i] - target[j]), np.pi)) < EPSILON\n or abs(np.mod(abs(shifted_source_coord[i] - target[j]), np.pi) - np.pi)\n < EPSILON\n ):\n source_shared, target_shared = i, j\n break\n if source_shared is None:\n continue\n\n # pick out the other coordinates\n source_first, source_second = (x for x in [0, 1, 2] if x != source_shared)\n target_first, target_second = (x for x in [0, 1, 2] if x != target_shared)\n\n # check for arccos validity\n r, s, u, v, x, y = decompose_xxyy_into_xxyy_xx(\n float(target[target_first]),\n float(target[target_second]),\n float(shifted_source_coord[source_first]),\n float(shifted_source_coord[source_second]),\n float(strength),\n )\n if any(math.isnan(val) for val in (r, s, u, v, x, y)):\n continue\n\n # OK: this combination of things works.\n # save the permutation which rotates the shared coordinate into ZZ.\n permute_source_for_overlap = canonical_rotation_circuit(source_first, source_second)\n permute_target_for_overlap = canonical_rotation_circuit(target_first, target_second)\n break\n\n if permute_source_for_overlap is not None:\n break\n\n if permute_source_for_overlap is None:\n raise QiskitError(\n \"Error during RZX decomposition: Could not find a suitable Weyl \"\n f\"reflection to match {source} to {target} along {strength}.\"\n )\n\n prefix_circuit, affix_circuit = QuantumCircuit(2), QuantumCircuit(2)\n\n # the basic formula we're trying to work with is:\n # target^p_t_f_o =\n # rs * (source^s_reflection * s_shift)^p_s_f_o * uv * operation * xy\n # but we're rearranging it into the form\n # target = affix source prefix\n # and computing just the prefix / affix circuits.\n\n # the outermost prefix layer comes from the (inverse) target permutation.\n prefix_circuit.compose(permute_target_for_overlap.inverse(), inplace=True)\n # the middle prefix layer comes from the local Z rolls.\n prefix_circuit.rz(2 * x, [0])\n prefix_circuit.rz(2 * y, [1])\n prefix_circuit.compose(embodiment, inplace=True)\n prefix_circuit.rz(2 * u, [0])\n prefix_circuit.rz(2 * v, [1])\n # the innermost prefix layer is source_reflection, shifted by source_shift,\n # finally conjugated by p_s_f_o.\n prefix_circuit.compose(permute_source_for_overlap, inplace=True)\n prefix_circuit.compose(source_reflection, inplace=True)\n prefix_circuit.global_phase += -np.log(reflection_phase_shift).imag\n prefix_circuit.global_phase += -np.log(shift_phase_shift).imag\n\n # the affix circuit is constructed in reverse.\n # first (i.e., innermost), we install the other half of the source transformations and p_s_f_o.\n affix_circuit.compose(source_reflection.inverse(), inplace=True)\n affix_circuit.compose(source_shift, inplace=True)\n affix_circuit.compose(permute_source_for_overlap.inverse(), inplace=True)\n # then, the other local rolls in the middle.\n affix_circuit.rz(2 * r, [0])\n affix_circuit.rz(2 * s, [1])\n # finally, the other half of the p_t_f_o conjugation.\n affix_circuit.compose(permute_target_for_overlap, inplace=True)\n\n return {\"prefix_circuit\": prefix_circuit, \"affix_circuit\": affix_circuit}\n```\n###test function signature:\n```python\n def test_xx_circuit_step(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_xx_circuit_step",
"reference": " def test_xx_circuit_step(self):\n\n for _ in range(100):\n source_coordinate, interaction, target_coordinate = self._generate_xxyy_test_case()\n\n source_embodiment = qiskit.QuantumCircuit(2)\n source_embodiment.append(UnitaryGate(canonical_matrix(*source_coordinate)), [0, 1])\n interaction_embodiment = qiskit.QuantumCircuit(2)\n interaction_embodiment.append(UnitaryGate(canonical_matrix(*interaction)), [0, 1])\n\n prefix_circuit, affix_circuit = itemgetter(\"prefix_circuit\", \"affix_circuit\")(\n xx_circuit_step(\n source_coordinate, interaction[0], target_coordinate, interaction_embodiment\n )\n )\n\n target_embodiment = QuantumCircuit(2)\n target_embodiment.compose(prefix_circuit, inplace=True)\n target_embodiment.compose(source_embodiment, inplace=True)\n target_embodiment.compose(affix_circuit, inplace=True)\n self.assertTrue(\n np.all(\n np.abs(\n qiskit.quantum_info.operators.Operator(target_embodiment).data\n - canonical_matrix(*target_coordinate)\n )\n < EPSILON\n )\n )",
"completions": [
"```python\n def test_xx_circuit_step(self):\n source = np.array([0.1, 0.2, 0.3])\n strength = 0.4\n target = np.array([0.5, 0.6, 0.7])\n embodiment = QuantumCircuit(2)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.cx(0, 1)\n embodiment.barrier()\n embodiment.rz(0.1, 0)\n embodiment.rz(0.2, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(0.3, 0)\n embodiment.rz(0.4, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(0.5, 0)\n embodiment.rz(0.6, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(0.7, 0)\n embodiment.rz(0.8, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(0.9, 0)\n embodiment.rz(1.0, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(1.1, 0)\n embodiment.rz(1.2, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(1.3, 0)\n embodiment.rz(1.4, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(1.5, 0)\n embodiment.rz(1.6, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(1.7, 0)\n embodiment.rz(1.8, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(1.9, 0)\n embodiment.rz(2.0, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment.h(1)\n embodiment.barrier()\n embodiment.rz(2.1, 0)\n embodiment.rz(2.2, 1)\n embodiment.barrier()\n embodiment.cx(0, 1)\n embodiment.h(0)\n embodiment"
],
"line": 90,
"token": 807,
"line_diff": 0,
"token_diff": 0
},
{
"id": 6,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef arange(start, stop=None, step=1, dtype=None):\n if dtype is None:\n dtypes_to_resolve = [\n getattr(start, \"dtype\", type(start)),\n getattr(step, \"dtype\", type(step)),\n ]\n if stop is not None:\n dtypes_to_resolve.append(getattr(stop, \"dtype\", type(stop)))\n dtype = dtypes.result_type(*dtypes_to_resolve)\n dtype = to_torch_dtype(dtype)\n if stop is None:\n return torch.arange(end=start, dtype=dtype, device=get_device())\n return torch.arange(\n start, stop, step=step, dtype=dtype, device=get_device()\n )\n```\n###test function signature:\n```python\n\n def test_einsum(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_einsum",
"reference": "\n def test_einsum(self):\n x = np.arange(24).reshape([2, 3, 4]).astype(\"float32\")\n y = np.arange(24).reshape([2, 4, 3]).astype(\"float32\")\n self.assertAllClose(\n knp.einsum(\"ijk,lkj->il\", x, y),\n np.einsum(\"ijk,lkj->il\", x, y),\n )\n self.assertAllClose(\n knp.einsum(\"ijk,ikj->i\", x, y),\n np.einsum(\"ijk,ikj->i\", x, y),\n )\n self.assertAllClose(\n knp.einsum(\"i...,j...k->...ijk\", x, y),\n np.einsum(\"i..., j...k->...ijk\", x, y),\n )\n self.assertAllClose(knp.einsum(\",ijk\", 5, y), np.einsum(\",ijk\", 5, y))\n\n self.assertAllClose(\n knp.Einsum(\"ijk,lkj->il\")(x, y),\n np.einsum(\"ijk,lkj->il\", x, y),\n )\n self.assertAllClose(\n knp.Einsum(\"ijk,ikj->i\")(x, y),\n np.einsum(\"ijk,ikj->i\", x, y),\n )\n self.assertAllClose(\n knp.Einsum(\"i...,j...k->...ijk\")(x, y),\n np.einsum(\"i...,j...k->...ijk\", x, y),\n )\n self.assertAllClose(knp.Einsum(\",ijk\")(5, y), np.einsum(\",ijk\", 5, y))",
"completions": [
"```python\n\n def test_arange(self):\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.float32),\n torch.arange(0, 10, 1, dtype=torch.float32),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.float64),\n torch.arange(0, 10, 1, dtype=torch.float64),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.int32),\n torch.arange(0, 10, 1, dtype=torch.int32),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.int64),\n torch.arange(0, 10, 1, dtype=torch.int64),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.bool),\n torch.arange(0, 10, 1, dtype=torch.bool),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.complex64),\n torch.arange(0, 10, 1, dtype=torch.complex64),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.complex128),\n torch.arange(0, 10, 1, dtype=torch.complex128),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.float32, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.float32, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.float64, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.float64, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.int32, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.int32, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.int64, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.int64, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.bool, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.bool, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.complex64, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.complex64, device=\"cuda\"),\n )\n self.assertEqual(\n arange(0, 10, 1, dtype=torch.complex128, device=\"cuda\"),\n torch.arange(0, 10, 1, dtype=torch.complex128, device=\"cuda\"),\n )\n```\n"
],
"line": 25,
"token": 195,
"line_diff": 0,
"token_diff": 0
},
{
"id": 7,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef spectrogram(\n waveform: np.ndarray,\n window: np.ndarray,\n frame_length: int,\n hop_length: int,\n fft_length: Optional[int] = None,\n power: Optional[float] = 1.0,\n center: bool = True,\n pad_mode: str = \"reflect\",\n onesided: bool = True,\n preemphasis: Optional[float] = None,\n mel_filters: Optional[np.ndarray] = None,\n mel_floor: float = 1e-10,\n log_mel: Optional[str] = None,\n reference: float = 1.0,\n min_value: float = 1e-10,\n db_range: Optional[float] = None,\n remove_dc_offset: Optional[bool] = None,\n dtype: np.dtype = np.float32,\n) -> np.ndarray:\n window_length = len(window)\n\n if fft_length is None:\n fft_length = frame_length\n\n if frame_length > fft_length:\n raise ValueError(f\"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})\")\n\n if window_length != frame_length:\n raise ValueError(f\"Length of the window ({window_length}) must equal frame_length ({frame_length})\")\n\n if hop_length <= 0:\n raise ValueError(\"hop_length must be greater than zero\")\n\n if waveform.ndim != 1:\n raise ValueError(f\"Input waveform must have only one dimension, shape is {waveform.shape}\")\n\n if np.iscomplexobj(waveform):\n raise ValueError(\"Complex-valued input waveforms are not currently supported\")\n\n if power is None and mel_filters is not None:\n raise ValueError(\n \"You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram.\"\n \"Specify `power` to fix this issue.\"\n )\n\n # center pad the waveform\n if center:\n padding = [(int(frame_length // 2), int(frame_length // 2))]\n waveform = np.pad(waveform, padding, mode=pad_mode)\n\n # promote to float64, since np.fft uses float64 internally\n waveform = waveform.astype(np.float64)\n window = window.astype(np.float64)\n\n # split waveform into frames of frame_length size\n num_frames = int(1 + np.floor((waveform.size - frame_length) / hop_length))\n\n num_frequency_bins = (fft_length // 2) + 1 if onesided else fft_length\n spectrogram = np.empty((num_frames, num_frequency_bins), dtype=np.complex64)\n\n # rfft is faster than fft\n fft_func = np.fft.rfft if onesided else np.fft.fft\n buffer = np.zeros(fft_length)\n\n timestep = 0\n for frame_idx in range(num_frames):\n buffer[:frame_length] = waveform[timestep : timestep + frame_length]\n\n if remove_dc_offset:\n buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean()\n\n if preemphasis is not None:\n buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1]\n buffer[0] *= 1 - preemphasis\n\n buffer[:frame_length] *= window\n\n spectrogram[frame_idx] = fft_func(buffer)\n timestep += hop_length\n\n # note: ** is much faster than np.power\n if power is not None:\n spectrogram = np.abs(spectrogram, dtype=np.float64) ** power\n\n spectrogram = spectrogram.T\n\n if mel_filters is not None:\n spectrogram = np.maximum(mel_floor, np.dot(mel_filters.T, spectrogram))\n\n if power is not None and log_mel is not None:\n if log_mel == \"log\":\n spectrogram = np.log(spectrogram)\n elif log_mel == \"log10\":\n spectrogram = np.log10(spectrogram)\n elif log_mel == \"dB\":\n if power == 1.0:\n spectrogram = amplitude_to_db(spectrogram, reference, min_value, db_range)\n elif power == 2.0:\n spectrogram = power_to_db(spectrogram, reference, min_value, db_range)\n else:\n raise ValueError(f\"Cannot use log_mel option '{log_mel}' with power {power}\")\n else:\n raise ValueError(f\"Unknown log_mel option: {log_mel}\")\n\n spectrogram = np.asarray(spectrogram, dtype)\n\n return spectrogram\n```\n###test function signature:\n```python\n\n def test_spectrogram_center_padding(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_spectrogram_center_padding",
"reference": "\n def test_spectrogram_center_padding(self):\n waveform = self._load_datasamples(1)[0]\n\n spec = spectrogram(\n waveform,\n window_function(512, \"hann\"),\n frame_length=512,\n hop_length=128,\n center=True,\n pad_mode=\"reflect\",\n )\n self.assertEqual(spec.shape, (257, 732))\n\n # fmt: off\n expected = np.array([\n 0.1287945 , 0.12792738, 0.08311573, 0.03155122, 0.02470202,\n 0.00727857, 0.00910694, 0.00686163, 0.01238981, 0.01473668,\n 0.00336144, 0.00370314, 0.00600871, 0.01120164, 0.01942998,\n 0.03132008, 0.0232842 , 0.01124642, 0.02754783, 0.02423725,\n 0.00147893, 0.00038027, 0.00112299, 0.00596233, 0.00571529,\n 0.02084235, 0.0231855 , 0.00810006, 0.01837943, 0.00651339,\n 0.00093931, 0.00067426, 0.01058399, 0.01270507, 0.00151734,\n 0.00331913, 0.00302416, 0.01081792, 0.00754549, 0.00148963,\n 0.00111943, 0.00152573, 0.00608017, 0.01749986, 0.01205949,\n 0.0143082 , 0.01910573, 0.00413786, 0.03916619, 0.09873404,\n 0.08302026, 0.02673891, 0.00401255, 0.01397392, 0.00751862,\n 0.01024884, 0.01544606, 0.00638907, 0.00623633, 0.0085103 ,\n 0.00217659, 0.00276204, 0.00260835, 0.00299299,\n ])\n # fmt: on\n self.assertTrue(np.allclose(spec[:64, 0], expected))\n\n spec = spectrogram(\n waveform,\n window_function(512, \"hann\"),\n frame_length=512,\n hop_length=128,\n center=True,\n pad_mode=\"constant\",\n )\n self.assertEqual(spec.shape, (257, 732))\n\n # fmt: off\n expected = np.array([\n 0.06558744, 0.06889656, 0.06263352, 0.04264418, 0.03404115,\n 0.03244197, 0.02279134, 0.01646339, 0.01452216, 0.00826055,\n 0.00062093, 0.0031821 , 0.00419456, 0.00689327, 0.01106367,\n 0.01712119, 0.01721762, 0.00977533, 0.01606626, 0.02275621,\n 0.01727687, 0.00992739, 0.01217688, 0.01049927, 0.01022947,\n 0.01302475, 0.01166873, 0.01081812, 0.01057327, 0.00767912,\n 0.00429567, 0.00089625, 0.00654583, 0.00912084, 0.00700984,\n 0.00225026, 0.00290545, 0.00667712, 0.00730663, 0.00410813,\n 0.00073102, 0.00219296, 0.00527618, 0.00996585, 0.01123781,\n 0.00872816, 0.01165121, 0.02047945, 0.03681747, 0.0514379 ,\n 0.05137928, 0.03960042, 0.02821562, 0.01813349, 0.01201322,\n 0.01260964, 0.00900654, 0.00207905, 0.00456714, 0.00850599,\n 0.00788239, 0.00664407, 0.00824227, 0.00628301,\n ])\n # fmt: on\n self.assertTrue(np.allclose(spec[:64, 0], expected))\n\n spec = spectrogram(\n waveform,\n window_function(512, \"hann\"),\n frame_length=512,\n hop_length=128,\n center=False,\n )\n self.assertEqual(spec.shape, (257, 728))\n\n # fmt: off\n expected = np.array([\n 0.00250445, 0.02161521, 0.06232229, 0.04339567, 0.00937727,\n 0.01080616, 0.00248685, 0.0095264 , 0.00727476, 0.0079152 ,\n 0.00839946, 0.00254932, 0.00716622, 0.005559 , 0.00272623,\n 0.00581774, 0.01896395, 0.01829788, 0.01020514, 0.01632692,\n 0.00870888, 0.02065827, 0.0136022 , 0.0132382 , 0.011827 ,\n 0.00194505, 0.0189979 , 0.026874 , 0.02194014, 0.01923883,\n 0.01621437, 0.00661967, 0.00289517, 0.00470257, 0.00957801,\n 0.00191455, 0.00431664, 0.00544359, 0.01126213, 0.00785778,\n 0.00423469, 0.01322504, 0.02226548, 0.02318576, 0.03428908,\n 0.03648811, 0.0202938 , 0.011902 , 0.03226198, 0.06347476,\n 0.01306318, 0.05308729, 0.05474771, 0.03127991, 0.00998512,\n 0.01449977, 0.01272741, 0.00868176, 0.00850386, 0.00313876,\n 0.00811857, 0.00538216, 0.00685749, 0.00535275,\n ])\n # fmt: on\n self.assertTrue(np.allclose(spec[:64, 0], expected))",
"completions": [
"```python\n def test_spectrogram_center_padding(self):\n waveform = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n window = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n frame_length = 4\n hop_length = 2\n fft_length = 16\n power = 1.0\n center = True\n pad_mode = \"reflect\"\n onesided = True\n preemphasis = None\n mel_filters = None\n mel_floor = 1e-10\n log_mel = None\n reference = 1.0\n min_value = 1e-10\n db_range = None\n remove_dc_offset = None\n dtype = np.float32\n\n spectrogram = spectrogram(\n waveform,\n window,\n frame_length,\n hop_length,\n fft_length,\n power,\n center,\n pad_mode,\n onesided,\n preemphasis,\n mel_filters,\n mel_floor,\n log_mel,\n reference,\n min_value,\n db_range,\n remove_dc_offset,\n dtype,\n )\n\n np.testing.assert_array_equal(\n spectrogram,\n np.array(\n [\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,"
],
"line": 95,
"token": 804,
"line_diff": 0,
"token_diff": 0
},
{
"id": 8,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _generate_nanoplot(\n y_vals: Union[List[Union[int, float]], List[int], List[float]],\n y_ref_line: Optional[str] = None,\n y_ref_area: Optional[str] = None,\n x_vals: \"List[Union[int, float]] | None\" = None,\n expand_x: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n expand_y: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n missing_vals: str = \"marker\",\n all_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n all_single_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n plot_type: str = \"line\",\n data_line_type: str = \"curved\",\n currency: Optional[str] = None,\n y_val_fmt_fn: Optional[Callable[..., str]] = None,\n y_axis_fmt_fn: Optional[Callable[..., str]] = None,\n y_ref_line_fmt_fn: Optional[Callable[..., str]] = None,\n data_point_radius: Union[int, List[int]] = 10,\n data_point_stroke_color: Union[str, List[str]] = \"#FFFFFF\",\n data_point_stroke_width: Union[int, List[int]] = 4,\n data_point_fill_color: Union[str, List[str]] = \"#FF0000\",\n data_line_stroke_color: str = \"#4682B4\",\n data_line_stroke_width: int = 8,\n data_area_fill_color: str = \"#FF0000\",\n data_bar_stroke_color: Union[str, List[str]] = \"#3290CC\",\n data_bar_stroke_width: Union[int, List[int]] = 4,\n data_bar_fill_color: Union[str, List[str]] = \"#3FB5FF\",\n data_bar_negative_stroke_color: str = \"#CC3243\",\n data_bar_negative_stroke_width: int = 4,\n data_bar_negative_fill_color: str = \"#D75A68\",\n reference_line_color: str = \"#75A8B0\",\n reference_area_fill_color: str = \"#A6E6F2\",\n vertical_guide_stroke_color: str = \"#911EB4\",\n vertical_guide_stroke_width: int = 12,\n show_data_points: bool = True,\n show_data_line: bool = True,\n show_data_area: bool = True,\n show_reference_line: bool = True,\n show_reference_area: bool = True,\n show_vertical_guides: bool = True,\n show_y_axis_guide: bool = True,\n interactive_data_values: bool = True,\n svg_height: str = \"2em\",\n) -> str:\n\n # Ensure that arguments are matched\n _match_arg(\n x=missing_vals,\n lst=[\"marker\", \"gap\", \"zero\", \"remove\"],\n )\n _match_arg(\n x=data_line_type,\n lst=[\"curved\", \"straight\"],\n )\n\n #\n # Determine where a zero line is considered and provide the stroke color and width\n #\n\n zero_line_considered = True if plot_type in [\"bar\", \"boxplot\"] else False\n\n zero_line_stroke_color = \"#BFBFBF\"\n zero_line_stroke_width = 4\n\n # Initialize several local `*_tags` variables with `None`\n ref_area_tags = None\n area_path_tags = None\n data_path_tags = None\n zero_line_tags = None\n bar_tags = None\n boxplot_tags = None\n ref_line_tags = None\n circle_tags = None\n g_y_axis_tags = None\n g_guide_tags = None\n\n # Initialize the `single_horizontal_plot` variable with `False`\n single_horizontal_plot = False\n\n # If the number of `y` values in a list is zero or if all consist of NA values,\n # return an empty string\n if type(y_vals) is list and len(y_vals) == 0:\n return \"\"\n\n # If all `y` values are NA, return an empty string\n # TODO: all([]) evaluates to True. In that case does this produce the intended behavior?\n if isinstance(y_vals, list) and all(_map_is_na(y_vals)):\n return \"\"\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # Handle case where `x_vals` exists (i.e., is not `NULL`)\n if x_vals is not None:\n\n # If the number of `x` values is zero or an empty string,\n # return an empty string\n if len(x_vals) == 0:\n return \"\"\n if all(_map_is_na(x_vals)):\n return \"\"\n\n # Get the number of data points for `x`\n num_x_vals = len(x_vals)\n\n # Ensure that, if there are `x` values, the number of `x`\n # and `y` values matches\n if num_x_vals != num_y_vals:\n raise ValueError(\n f\"\"\"The number of `x` and `y` values must match.\n The `x` value length is: {num_x_vals}\n The `y` value length is: {num_y_vals}\n \"\"\"\n )\n\n # Handle missing values in `x_vals` through removal (i.e., missing\n # values in `x_vals` means removal of positional values from both\n # `x_vals` and `y_vals`)\n if any(_map_is_na(x_vals)):\n # TODO: this code did not have test coverage and likely didn't\n # work. It should work now, but we need to test it.\n\n # Determine which values from `x_vals` are non-missing values\n x_vals_non_missing = [~_is_na(val) for val in x_vals]\n\n # Retain only `x_vals_non_missing` from `x_vals` and `y_vals`\n x_vals = [x for x, keep in zip(x_vals, x_vals_non_missing) if keep]\n y_vals = [y for y, keep in zip(y_vals, x_vals_non_missing) if keep]\n\n # If `x` values are present, we cannot use a curved line so\n # we'll force the use of the 'straight' line type\n # TODO: if someone specifies the options curved, and we can't do it\n # then we should raise an error.\n data_line_type = \"straight\"\n\n # If `missing_vals` is set to 'gap' raise an error\n # TODO: Implement the 'gap' option for missing values\n if missing_vals == \"gap\":\n raise NotImplementedError(\"The 'gap' option for missing values is not yet implemented.\")\n\n # For the `missing_vals` options of 'zero' or 'remove', either replace NAs\n # with `0` or remove NAs entirely\n if missing_vals == \"zero\":\n y_vals = y_vals.fillna(0)\n\n # If `missing_vals` is 'remove', remove NAs from `y_vals`\n if missing_vals == \"remove\":\n y_vals = y_vals.dropna()\n\n if x_vals is not None:\n # Remove the corresponding `x` values for the removed `y` values\n x_vals = x_vals[y_vals.index]\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # If `y_vals` is a scalar value we requested a 'line' or 'bar' plot, then\n # reset several parameters\n if type(y_vals) in [int, float] and plot_type in [\"line\", \"bar\"]:\n\n single_horizontal_plot = True\n show_data_points = True\n show_data_line = True\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n y_vals = [y_vals]\n\n # If this is a box plot, set several parameters\n if plot_type == \"boxplot\":\n\n show_data_points = False\n show_data_line = False\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n # Find out whether the collection of non-NA `y` values are all integer-like\n y_vals_integerlike = _is_integerlike(val_list=y_vals)\n\n # Get the max and min of the `y` scale from the `y` data values\n y_scale_max = _get_extreme_value(y_vals, stat=\"max\")\n y_scale_min = _get_extreme_value(y_vals, stat=\"min\")\n\n # Handle cases where collection of `y_vals` is invariant\n if y_scale_min == y_scale_max and expand_y is None:\n\n if y_scale_min == 0:\n expand_y_dist = 5\n else:\n expand_y_dist = (y_scale_min / 10) * 2\n\n # Expand the `y` scale, centering around the `y_scale_min` value\n expand_y = [y_scale_min - expand_y_dist, y_scale_min + expand_y_dist]\n\n # Ensure that a reference line or reference area isn't shown if None or\n # any of its directives is missing\n if _is_na(y_ref_line):\n show_reference_line = False\n\n if y_ref_area is None:\n show_reference_area = False\n elif _is_na(y_ref_area[0]) or _is_na(y_ref_area[1]):\n show_reference_area = False\n\n # Determine the width of the data plot area; for plots where `x_vals`\n # are available, we'll use a fixed width of `500` (px), and for plots\n # where `x_vals` aren't present, we'll adjust the final width based\n # on the fixed interval between data points (this is dependent on the\n # number of data points)\n if x_vals is not None or single_horizontal_plot or plot_type == \"boxplot\":\n data_x_width = 600\n # TODO: what should x_d be in this case?\n else:\n # Obtain a sensible, fixed interval between data points in px\n if num_y_vals <= 20:\n x_d = 50\n elif num_y_vals <= 30:\n x_d = 40\n elif num_y_vals <= 40:\n x_d = 30\n elif num_y_vals <= 50:\n x_d = 25\n else:\n x_d = 20\n\n data_x_width = num_y_vals * x_d\n\n # Define the top-left of the plot area\n left_x = 0\n top_y = 0\n\n # Define the safe zone distance from top/bottom and left/right edges\n safe_y_d = 15\n safe_x_d = 50\n\n # Define the height of the plot area that bounds the data points\n data_y_height = 100\n\n # Determine the bottom-right of the plot area based on the quantity of data\n bottom_y = safe_y_d + data_y_height + safe_y_d\n right_x = safe_x_d + data_x_width + safe_x_d\n\n viewbox = f\"{left_x} {top_y} {right_x} {bottom_y}\"\n\n #\n # If there is a reference line and/or reference area, the values for these\n # need to be generated and integrated in the `normalize_y_vals()` operation\n # so that there are normalized values in relation to the data points\n #\n\n if show_reference_line and show_reference_area:\n\n # Case where there is both a reference line and a reference area\n\n #\n # Resolve the reference line\n #\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n #\n # Resolve the reference area\n #\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n _all_y_data = [y_vals, y_ref_line, y_ref_area_l, y_ref_area_u, expand_y]\n\n # Recompute the `y` scale min and max values\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference line and reference area boundaries\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n elif show_reference_line:\n\n # Case where there is a reference line\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n # Recompute the `y` scale min and max values\n args = [y_vals, y_ref_line, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n\n # Scale reference line\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n\n elif show_reference_area:\n\n # Case where there is a reference area\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n # Recompute the `y` scale min and max values\n\n # Recompute the `y` scale min and max values\n _all_y_data = [y_vals, y_ref_area_l, y_ref_area_u, expand_y] + (\n [0] if zero_line_considered else []\n )\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference area boundaries\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n else:\n\n # Case where there is no reference line or reference area\n\n # Recompute the `y` scale min and max values\n args = [y_vals, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n\n # Calculate the `data_y0_point` value for zero-line-inclusive plots\n if zero_line_considered:\n y_proportions_zero = y_proportions_list[\"zero\"][0]\n data_y0_point = safe_y_d + ((1 - y_proportions_zero) * data_y_height)\n\n # If x values are present then normalize them between [0, 1]; if\n # there are no x values, generate equally-spaced x values according\n # to the number of y values\n if plot_type == \"line\" and x_vals is not None:\n\n if expand_x is not None and _val_is_str(expand_x):\n\n # TODO: the line below lacked tests, and called non-existent methods.\n # replace with something that doesn't use pandas and returns the correct thing.\n\n # Assume that string values are dates and convert them to timestamps\n # expand_x = pd.to_datetime(expand_x, utc=True).timestamp()\n raise NotImplementedError(\"Currently, passing expand_x as a string is unsupported.\")\n\n # Scale to proportional values\n x_proportions_list = _normalize_to_dict(vals=x_vals, expand_x=expand_x)\n\n x_proportions = x_proportions_list[\"vals\"]\n\n else:\n x_proportions = np.linspace(0, 1, num_y_vals)\n\n #\n # Create normalized (and inverted for SVG) data `x` and `y` values for the\n # plot area; these are named `data_x_points` and `data_y_points`\n #\n\n for i in range(len(y_proportions)):\n y_proportions[i] = safe_y_d + ((1 - y_proportions[i]) * data_y_height)\n\n for i in range(len(x_proportions)):\n x_proportions[i] = (data_x_width * x_proportions[i]) + safe_x_d\n\n data_y_points = y_proportions\n data_x_points = x_proportions\n\n #\n # Ensure that certain options have their lengths checked and\n # expanded to length `num_y_vals`\n #\n\n data_point_radius = _normalize_option_list(option_list=data_point_radius, num_y_vals=num_y_vals)\n data_point_stroke_color = _normalize_option_list(\n option_list=data_point_stroke_color, num_y_vals=num_y_vals\n )\n data_point_stroke_width = _normalize_option_list(\n option_list=data_point_stroke_width, num_y_vals=num_y_vals\n )\n data_point_fill_color = _normalize_option_list(\n option_list=data_point_fill_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_color = _normalize_option_list(\n option_list=data_bar_stroke_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_width = _normalize_option_list(\n option_list=data_bar_stroke_width, num_y_vals=num_y_vals\n )\n data_bar_fill_color = _normalize_option_list(\n option_list=data_bar_fill_color, num_y_vals=num_y_vals\n )\n\n #\n # Generate data segments by defining `start` and `end` vectors (these\n # are guaranteed to be of the same length); these the segments of data\n # they receive line segments and adjoining areas\n #\n\n # Use run-length encoding to determine the segments of data\n\n # rle_data_y_points = pd.Series(data_y_points).diff().ne(0).cumsum()\n\n # end_data_y_points = np.cumsum(rle_data_y_points.lengths)\n\n # start_data_y_points = end_data_y_points - rle_data_y_points.lengths + 1\n\n start_data_y_points = [0]\n end_data_y_points = [len(data_y_points)]\n n_segments = 1\n\n #\n # Generate a curved data line\n #\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"curved\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n curve_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n curve_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n curved_path_string = [f\"M {curve_x[0]},{curve_y[0]}\"]\n\n for j in range(1, len(curve_x)):\n\n point_b1 = f\"{curve_x[j - 1] + x_d / 2},{curve_y[j - 1]}\"\n point_b2 = f\"{curve_x[j] - x_d / 2},{curve_y[j]}\"\n point_i = f\"{curve_x[j]},{curve_y[j]}\"\n\n path_string_j = f\"C {point_b1} {point_b2} {point_i}\"\n\n curved_path_string.append(path_string_j)\n\n curved_path_string_i = \" \".join(curved_path_string)\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\\n\".join(data_path_tags)\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"straight\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n line_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n line_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n line_xy = \" \".join([f\"{x},{y}\" for x, y in zip(line_x, line_y)])\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\".join(data_path_tags)\n\n #\n # Generate data points\n #\n\n if plot_type == \"line\" and show_data_points:\n\n circle_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_point_stroke_color_i = data_point_stroke_color[i]\n data_point_stroke_width_i = data_point_stroke_width[i]\n data_point_fill_color_i = data_point_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n circle_strings_i = f''\n\n else:\n continue\n\n else:\n circle_strings_i = f''\n\n circle_strings.append(circle_strings_i)\n\n circle_tags = \"\".join(circle_strings)\n\n #\n # Generate data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n bar_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_bar_stroke_color_i = data_bar_stroke_color[i]\n data_bar_stroke_width_i = data_bar_stroke_width[i]\n data_bar_fill_color_i = data_bar_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n bar_strings_i = f''\n\n else:\n continue\n\n else:\n\n if y_vals[i] < 0:\n\n y_value_i = data_y0_point\n y_height = data_y_points[i] - data_y0_point\n data_bar_stroke_color_i = data_bar_negative_stroke_color\n data_bar_stroke_width_i = data_bar_negative_stroke_width\n data_bar_fill_color_i = data_bar_negative_fill_color\n\n elif y_vals[i] > 0:\n\n y_value_i = data_y_points[i]\n y_height = data_y0_point - data_y_points[i]\n\n elif y_vals[i] == 0:\n\n y_value_i = data_y0_point - 1\n y_height = 2\n data_bar_stroke_color_i = \"#808080\"\n data_bar_stroke_width_i = 4\n data_bar_fill_color_i = \"#808080\"\n\n bar_strings_i = f''\n\n bar_strings.append(bar_strings_i)\n\n bar_tags = \"\".join(bar_strings)\n\n #\n # Generate single horizontal data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n data_bar_stroke_color = data_bar_negative_stroke_color\n data_bar_stroke_width = data_bar_negative_stroke_width\n data_bar_fill_color = data_bar_negative_fill_color\n\n rect_x = y_width\n rect_width = y0_width - y_width\n\n elif y_vals[0] > 0:\n\n data_bar_stroke_color = data_bar_stroke_color[0]\n data_bar_stroke_width = data_bar_stroke_width[0]\n data_bar_fill_color = data_bar_fill_color[0]\n\n rect_x = y0_width\n rect_width = y_width - y0_width\n\n elif y_vals[0] == 0:\n\n data_bar_stroke_color = \"#808080\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#808080\"\n\n rect_x = y0_width - 2.5\n rect_width = 5\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n bar_tags = f'{g_guide_tags}'\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate single horizontal data lines\n #\n\n # TODO: Make this a line with a single point\n if plot_type == \"line\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n data_point_radius_i = data_point_radius[0]\n data_point_stroke_color_i = data_point_stroke_color[0]\n data_point_stroke_width_i = data_point_stroke_width[0]\n data_point_fill_color_i = data_point_fill_color[0]\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x1_val\n\n elif y_vals[0] > 0:\n\n x1_val = y0_width\n x2_val = y_width\n\n circle_x_val = x2_val\n\n elif y_vals[0] == 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x2_val\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 15\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n data_path_tags = f'{g_guide_tags}'\n\n circle_tags = f''\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate box plots\n #\n\n if plot_type == \"boxplot\":\n pass\n\n #\n # Generate zero line for vertical bar plots\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n zero_line_tags = f''\n\n #\n # Generate reference line\n #\n\n if show_reference_line:\n\n stroke = reference_line_color\n stroke_width = 1\n stroke_dasharray = \"4 3\"\n transform = \"\"\n stroke_linecap = \"round\"\n vector_effect = \"non-scaling-stroke\"\n\n # Format value in a compact manner\n y_ref_line = _format_number_compactly(\n val=y_ref_line, currency=currency, as_integer=y_vals_integerlike, fn=y_ref_line_fmt_fn\n )\n\n ref_line_tags = f'{y_ref_line}'\n\n #\n # Generate reference area\n #\n\n if show_reference_area:\n\n fill = reference_area_fill_color\n\n p_ul = f\"{data_x_points[0]},{data_y_ref_area_u}\"\n p_ur = f\"{data_x_points[-1]},{data_y_ref_area_u}\"\n p_lr = f\"{data_x_points[-1]},{data_y_ref_area_l}\"\n p_ll = f\"{data_x_points[0]},{data_y_ref_area_l}\"\n\n ref_area_path = f\"M{p_ul},{p_ur},{p_lr},{p_ll}Z\"\n\n ref_area_tags = f''\n\n #\n # Generate y-axis guide\n #\n\n if show_y_axis_guide:\n\n rect_tag = f''\n\n if _is_integerlike(val_list=[y_scale_max]) and _is_integerlike(val_list=[y_scale_min]):\n y_axis_guide_vals_integerlike = True\n else:\n y_axis_guide_vals_integerlike = False\n\n # Format values in a compact manner\n y_value_max_label = _format_number_compactly(\n val=y_scale_max,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n y_value_min_label = _format_number_compactly(\n val=y_scale_min,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n text_strings_min = f'{y_value_min_label}'\n\n text_strings_max = f'{y_value_max_label}'\n\n g_y_axis_tags = f'{rect_tag}{text_strings_max}{text_strings_min}'\n\n #\n # Generate vertical data point guidelines\n #\n\n if show_vertical_guides:\n\n g_guide_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n rect_strings_i = f''\n\n # Format value in a compact manner\n y_value_i = _format_number_compactly(\n val=y_vals[i], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n x_text = data_x_points[i] + 10\n\n if y_value_i == \"NA\":\n x_text = x_text + 2\n\n text_strings_i = f'{y_value_i}'\n\n g_guide_strings_i = f'{rect_strings_i}{text_strings_i}'\n\n g_guide_strings.append(g_guide_strings_i)\n\n g_guide_tags = \"\".join(g_guide_strings)\n\n #\n # Generate background with repeating line pattern\n #\n\n svg_defs = (\n f\"\"\n f''\n f''\n f\"\"\n f\"\"\n f\"\"\n )\n\n if plot_type == \"line\" and show_data_area:\n\n area_path_tags = []\n\n for i in range(n_segments):\n\n area_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n area_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n area_path_string = []\n\n for j in range(0, len(area_x)):\n\n area_path_j = f\"{area_x[j]},{area_y[j]}\"\n area_path_string.append(area_path_j)\n\n area_path_i = f\"M {' '.join(area_path_string)} {area_x[-1]},{bottom_y - safe_y_d + data_point_radius[0]} {area_x[0]},{bottom_y - safe_y_d + data_point_radius[0]} Z\"\n\n area_path_tag_i = f''\n\n area_path_tags.append(area_path_tag_i)\n\n area_path_tags = \" \".join(area_path_tags)\n\n #\n # Generate style tag for vertical guidelines and y-axis\n #\n\n hover_param = \":hover\" if interactive_data_values else \"\"\n\n svg_style = (\n f\"\"\n )\n\n nanoplot_svg = _construct_nanoplot_svg(\n viewbox=viewbox,\n svg_height=svg_height,\n svg_defs=svg_defs,\n svg_style=svg_style,\n show_data_points=show_data_points,\n show_data_line=show_data_line,\n show_data_area=show_data_area,\n show_reference_line=show_reference_line,\n show_reference_area=show_reference_area,\n show_vertical_guides=show_vertical_guides,\n show_y_axis_guide=show_y_axis_guide,\n ref_area_tags=ref_area_tags,\n area_path_tags=area_path_tags,\n data_path_tags=data_path_tags,\n zero_line_tags=zero_line_tags,\n bar_tags=bar_tags,\n ref_line_tags=ref_line_tags,\n circle_tags=circle_tags,\n g_y_axis_tags=g_y_axis_tags,\n g_guide_tags=g_guide_tags,\n )\n\n return nanoplot_svg\n```\n###test function signature:\n```python\n\ndef test_nanoplot_out_bars_with_kword_ref_line():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nanoplot_out_bars_with_kword_ref_line",
"reference": "\ndef test_nanoplot_out_bars_with_kword_ref_line():\n\n out_bars_with_kword_ref_line = _generate_nanoplot(**CASES[12])\n\n assert _is_nanoplot_output(out_bars_with_kword_ref_line)\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_kword_ref_line,\n tag=\"rect\",\n attrs=[\n (\"x\", \"30.0\"),\n (\"width\", \"40\"),\n (\"height\", \"27.179487179487168\"),\n (\"stroke\", \"#CC3243\"),\n (\"stroke-width\", \"4\"),\n (\"fill\", \"#D75A68\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_kword_ref_line,\n tag=\"rect\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"0\"),\n (\"width\", \"65\"),\n (\"height\", \"130\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"0\"),\n (\"fill\", \"transparent\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_kword_ref_line,\n tag=\"text\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"19.0\"),\n (\"fill\", \"transparent\"),\n (\"stroke\", \"transparent\"),\n (\"font-size\", \"25\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_kword_ref_line,\n tag=\"line\",\n attrs=[\n (\"class\", \"ref-line\"),\n (\"x1\", \"50.0\"),\n (\"x2\", \"550\"),\n (\"stroke\", \"#75A8B0\"),\n (\"stroke-width\", \"1\"),\n (\"stroke-dasharray\", \"4 3\"),\n (\"stroke-linecap\", \"round\"),\n (\"vector-effect\", \"non-scaling-stroke\"),\n ],\n )",
"completions": [
"```python\n\ndef test_nanoplot_out_bars_with_kword_ref_line():\n y_vals = [1, 2, 3, 4, 5]\n y_ref_line = \"max\"\n y_ref_area = [1, 2]\n x_vals = [1, 2, 3, 4, 5]\n expand_x = \"min\"\n expand_y = \"min\"\n missing_vals = \"marker\"\n all_y_vals = [1, 2, 3, 4, 5]\n all_single_y_vals = [1, 2, 3, 4, 5]\n plot_type = \"bar\"\n data_line_type = \"curved\"\n currency = None\n y_val_fmt_fn = None\n y_axis_fmt_fn = None\n y_ref_line_fmt_fn = None\n data_point_radius = 10\n data_point_stroke_color = \"#FFFFFF\"\n data_point_stroke_width = 4\n data_point_fill_color = \"#FF0000\"\n data_line_stroke_color = \"#4682B4\"\n data_line_stroke_width = 8\n data_area_fill_color = \"#FF0000\"\n data_bar_stroke_color = \"#3290CC\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#3FB5FF\"\n data_bar_negative_stroke_color = \"#CC3243\"\n data_bar_negative_stroke_width = 4\n data_bar_negative_fill_color = \"#D75A68\"\n reference_line_color = \"#75A8B4\"\n reference_area_fill_color = \"#A6E6F2\"\n vertical_guide_stroke_color = \"#911EB4\"\n vertical_guide_stroke_width = 12\n show_data_points = True\n show_data_line = True\n show_data_area = True\n show_reference_line = True\n show_reference_area = True\n show_vertical_guides = True\n show_y_axis_guide = True\n interactive_data_values = True\n svg_height = \"2em\"\n\n nanoplot_svg = _generate_nanoplot(\n y_vals=y_vals,\n y_ref_line=y_ref_line,\n y_ref_area=y_ref_area,\n x_vals=x_vals,\n expand_x=expand_x,\n expand_y=expand_y,\n missing_vals=missing_vals,\n all_y_vals=all_y_vals,\n all_single_y_vals=all_single_y_vals,\n plot_type=plot_type,\n data_line_type=data_line_type,\n currency=currency,\n y_val_fmt_fn=y_val_fmt_fn,\n y_axis_fmt_fn=y_axis_fmt_fn,\n y_ref_line_fmt_fn=y_ref_line_fmt_fn,\n data_point_radius=data_point_radius,\n data_point_stroke_color=data_point_stroke_color,\n data_point_stroke_width=data_point_stroke_width,\n data_point_fill_color=data_point_fill_color,\n data_line_stroke_color=data_line_stroke_color,\n data_line_stroke_width=data_line_stroke_width,\n data_area_fill_color=data_area_fill_color,\n data_bar_stroke_color=data_bar_stroke_color,\n data_bar_stroke_width=data_bar_stroke_width,\n data_bar_fill_color=data_bar_fill_color,\n data_bar_negative_stroke_color=data_bar_negative_stroke_"
],
"line": 749,
"token": 7038,
"line_diff": 2,
"token_diff": 2
},
{
"id": 9,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef arange(start, stop=None, step=1, dtype=None):\n if dtype is None:\n dtypes_to_resolve = [\n getattr(start, \"dtype\", type(start)),\n getattr(step, \"dtype\", type(step)),\n ]\n if stop is not None:\n dtypes_to_resolve.append(getattr(stop, \"dtype\", type(stop)))\n dtype = dtypes.result_type(*dtypes_to_resolve)\n dtype = to_torch_dtype(dtype)\n if stop is None:\n return torch.arange(end=start, dtype=dtype, device=get_device())\n return torch.arange(\n start, stop, step=step, dtype=dtype, device=get_device()\n )\n```\n###test function signature:\n```python\n\n def test_take_along_axis(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_take_along_axis",
"reference": "\n def test_take_along_axis(self):\n x = np.arange(24).reshape([1, 2, 3, 4])\n indices = np.ones([1, 4, 1, 1], dtype=np.int32)\n self.assertAllClose(\n knp.take_along_axis(x, indices, axis=1),\n np.take_along_axis(x, indices, axis=1),\n )\n self.assertAllClose(\n knp.TakeAlongAxis(axis=1)(x, indices),\n np.take_along_axis(x, indices, axis=1),\n )\n\n x = np.arange(12).reshape([1, 1, 3, 4])\n indices = np.ones([1, 4, 1, 1], dtype=np.int32)\n self.assertAllClose(\n knp.take_along_axis(x, indices, axis=2),\n np.take_along_axis(x, indices, axis=2),\n )\n self.assertAllClose(\n knp.TakeAlongAxis(axis=2)(x, indices),\n np.take_along_axis(x, indices, axis=2),\n )\n\n # Test with axis=None\n x = np.arange(12).reshape([1, 1, 3, 4])\n indices = np.array([1, 2, 3], dtype=np.int32)\n self.assertAllClose(\n knp.take_along_axis(x, indices, axis=None),\n np.take_along_axis(x, indices, axis=None),\n )\n self.assertAllClose(\n knp.TakeAlongAxis(axis=None)(x, indices),\n np.take_along_axis(x, indices, axis=None),\n )\n\n # Test with negative indices\n x = np.arange(12).reshape([1, 1, 3, 4])\n indices = np.full([1, 4, 1, 1], -1, dtype=np.int32)\n self.assertAllClose(\n knp.take_along_axis(x, indices, axis=2),\n np.take_along_axis(x, indices, axis=2),\n )\n self.assertAllClose(\n knp.TakeAlongAxis(axis=2)(x, indices),\n np.take_along_axis(x, indices, axis=2),\n )",
"completions": [
"```python\n\n def test_take_along_axis(self):\n # test arange\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=1, dtype=torch.float32),\n torch.tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=2, dtype=torch.float32),\n torch.tensor([0, 2, 4, 6, 8], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=3, dtype=torch.float32),\n torch.tensor([0, 3, 6, 9], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=4, dtype=torch.float32),\n torch.tensor([0, 4, 8], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=5, dtype=torch.float32),\n torch.tensor([0, 5], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=6, dtype=torch.float32),\n torch.tensor([0, 6], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=7, dtype=torch.float32),\n torch.tensor([0, 7], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=8, dtype=torch.float32),\n torch.tensor([0, 8], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=9, dtype=torch.float32),\n torch.tensor([0, 9], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=10, dtype=torch.float32),\n torch.tensor([0], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=11, dtype=torch.float32),\n torch.tensor([0], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=12, dtype=torch.float32),\n torch.tensor([0], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=13, dtype=torch.float32),\n torch.tensor([0], dtype=torch.float32),\n )\n )\n self.assertTrue(\n torch.equal(\n arange(start=0, stop=10, step=14, dtype=torch.float32),\n torch.tensor([0], dtype=torch.float32"
],
"line": 25,
"token": 195,
"line_diff": 0,
"token_diff": 0
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{
"id": 10,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_correlation(\n response_level_scores: dict[str, list[dict[str, dict[str, Any]]]],\n name: str = '',\n) -> dict[str, dict[str, dict[str, float]]]:\n factscore_metrics = set(list_metrics(response_level_scores[FACTSCORE]))\n pred_metrics = set(list_metrics(response_level_scores[EVAL_METHOD]))\n result = {}\n\n for metric in factscore_metrics.intersection(pred_metrics):\n factscore_scores = [\n find_metric(response_level_scores[FACTSCORE][i], metric)\n for i in range(len(response_level_scores[FACTSCORE]))\n ]\n pred_scores = [\n find_metric(response_level_scores[EVAL_METHOD][i], metric)\n for i in range(len(response_level_scores[EVAL_METHOD]))\n ]\n\n # Remove failed runs\n for i in range(len(factscore_scores) - 1, -1, -1):\n if factscore_scores[i] == -1 or pred_scores[i] == -1:\n factscore_scores.pop(i)\n pred_scores.pop(i)\n\n # Cannot calculate correlation with only one data point\n if len(factscore_scores) <= 1 or len(pred_scores) <= 1:\n pearson_result, spearman_result = None, None\n else:\n pearson_result = stats.pearsonr(factscore_scores, pred_scores)\n spearman_result = stats.spearmanr(factscore_scores, pred_scores)\n\n scatter_plot(\n factscore_scores,\n pred_scores,\n f'{name}-{metric}',\n FACTSCORE,\n EVAL_METHOD,\n )\n result[metric] = {\n _PEARSON: CorrelationResult(pearson_result).result,\n _SPEARMAN: CorrelationResult(spearman_result).result,\n }\n\n return result\n```\n###test function signature:\n```python\n\n def test_compute_correlation_one_data_point(\n self,\n mock_spearmanr: mock.Mock,\n mock_pearsonr: mock.Mock,\n mock_scatter_plot: mock.Mock,\n ) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_correlation_one_data_point",
"reference": "\n def test_compute_correlation_one_data_point(\n self,\n mock_spearmanr: mock.Mock,\n mock_pearsonr: mock.Mock,\n mock_scatter_plot: mock.Mock,\n ) -> None:\n response_level_scores = {\n correlation_vs_factscore.FACTSCORE: [\n {correlation_vs_factscore.RAW_STR: {'S': 0, 'N': 1}},\n ],\n correlation_vs_factscore.EVAL_METHOD: [\n {correlation_vs_factscore.RAW_STR: {'S': 0, 'M': 1}},\n ],\n }\n actual_output = correlation_vs_factscore.compute_correlation(\n response_level_scores=response_level_scores\n )\n mock_pearsonr.assert_not_called()\n mock_spearmanr.assert_not_called()\n mock_scatter_plot.assert_called()\n self.assertIsInstance(actual_output, dict)\n self.assertLen(actual_output, 1)\n self.assertIn('S', actual_output)\n self.assertIsInstance(actual_output['S'], dict)\n self.assertLen(actual_output['S'], 2)\n self.assertIn('Pearson', actual_output['S'])\n self.assertIsInstance(actual_output['S']['Pearson'], dict)\n self.assertIn('correlation', actual_output['S']['Pearson'])\n self.assertTrue(math.isnan(actual_output['S']['Pearson']['correlation']))\n self.assertIn('p_value', actual_output['S']['Pearson'])\n self.assertTrue(math.isnan(actual_output['S']['Pearson']['p_value']))\n self.assertIn('Spearman', actual_output['S'])\n self.assertIsInstance(actual_output['S']['Spearman'], dict)\n self.assertIn('correlation', actual_output['S']['Spearman'])\n self.assertTrue(math.isnan(actual_output['S']['Spearman']['correlation']))\n self.assertIn('p_value', actual_output['S']['Spearman'])\n self.assertTrue(math.isnan(actual_output['S']['Spearman']['p_value']))",
"completions": [
"```python\ndef test_compute_correlation_one_data_point(\n self,\n mock_spearmanr: mock.Mock,\n mock_pearsonr: mock.Mock,\n mock_scatter_plot: mock.Mock,\n) -> None:\n response_level_scores = {\n FACTSCORE: [\n {\n 'metric': 'metric1',\n 'value': 0.5,\n },\n {\n 'metric': 'metric2',\n 'value': 0.6,\n },\n ],\n EVAL_METHOD: [\n {\n 'metric': 'metric1',\n 'value': 0.5,\n },\n {\n 'metric': 'metric2',\n 'value': 0.6,\n },\n ],\n }\n result = compute_correlation(response_level_scores)\n self.assertEqual(result, {})\n mock_spearmanr.assert_not_called()\n mock_pearsonr.assert_not_called()\n mock_scatter_plot.assert_not_called()\n```\n"
],
"line": 54,
"token": 384,
"line_diff": 0,
"token_diff": 0
},
{
"id": 11,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _generate_nanoplot(\n y_vals: Union[List[Union[int, float]], List[int], List[float]],\n y_ref_line: Optional[str] = None,\n y_ref_area: Optional[str] = None,\n x_vals: \"List[Union[int, float]] | None\" = None,\n expand_x: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n expand_y: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n missing_vals: str = \"marker\",\n all_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n all_single_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n plot_type: str = \"line\",\n data_line_type: str = \"curved\",\n currency: Optional[str] = None,\n y_val_fmt_fn: Optional[Callable[..., str]] = None,\n y_axis_fmt_fn: Optional[Callable[..., str]] = None,\n y_ref_line_fmt_fn: Optional[Callable[..., str]] = None,\n data_point_radius: Union[int, List[int]] = 10,\n data_point_stroke_color: Union[str, List[str]] = \"#FFFFFF\",\n data_point_stroke_width: Union[int, List[int]] = 4,\n data_point_fill_color: Union[str, List[str]] = \"#FF0000\",\n data_line_stroke_color: str = \"#4682B4\",\n data_line_stroke_width: int = 8,\n data_area_fill_color: str = \"#FF0000\",\n data_bar_stroke_color: Union[str, List[str]] = \"#3290CC\",\n data_bar_stroke_width: Union[int, List[int]] = 4,\n data_bar_fill_color: Union[str, List[str]] = \"#3FB5FF\",\n data_bar_negative_stroke_color: str = \"#CC3243\",\n data_bar_negative_stroke_width: int = 4,\n data_bar_negative_fill_color: str = \"#D75A68\",\n reference_line_color: str = \"#75A8B0\",\n reference_area_fill_color: str = \"#A6E6F2\",\n vertical_guide_stroke_color: str = \"#911EB4\",\n vertical_guide_stroke_width: int = 12,\n show_data_points: bool = True,\n show_data_line: bool = True,\n show_data_area: bool = True,\n show_reference_line: bool = True,\n show_reference_area: bool = True,\n show_vertical_guides: bool = True,\n show_y_axis_guide: bool = True,\n interactive_data_values: bool = True,\n svg_height: str = \"2em\",\n) -> str:\n\n # Ensure that arguments are matched\n _match_arg(\n x=missing_vals,\n lst=[\"marker\", \"gap\", \"zero\", \"remove\"],\n )\n _match_arg(\n x=data_line_type,\n lst=[\"curved\", \"straight\"],\n )\n\n #\n # Determine where a zero line is considered and provide the stroke color and width\n #\n\n zero_line_considered = True if plot_type in [\"bar\", \"boxplot\"] else False\n\n zero_line_stroke_color = \"#BFBFBF\"\n zero_line_stroke_width = 4\n\n # Initialize several local `*_tags` variables with `None`\n ref_area_tags = None\n area_path_tags = None\n data_path_tags = None\n zero_line_tags = None\n bar_tags = None\n boxplot_tags = None\n ref_line_tags = None\n circle_tags = None\n g_y_axis_tags = None\n g_guide_tags = None\n\n # Initialize the `single_horizontal_plot` variable with `False`\n single_horizontal_plot = False\n\n # If the number of `y` values in a list is zero or if all consist of NA values,\n # return an empty string\n if type(y_vals) is list and len(y_vals) == 0:\n return \"\"\n\n # If all `y` values are NA, return an empty string\n # TODO: all([]) evaluates to True. In that case does this produce the intended behavior?\n if isinstance(y_vals, list) and all(_map_is_na(y_vals)):\n return \"\"\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # Handle case where `x_vals` exists (i.e., is not `NULL`)\n if x_vals is not None:\n\n # If the number of `x` values is zero or an empty string,\n # return an empty string\n if len(x_vals) == 0:\n return \"\"\n if all(_map_is_na(x_vals)):\n return \"\"\n\n # Get the number of data points for `x`\n num_x_vals = len(x_vals)\n\n # Ensure that, if there are `x` values, the number of `x`\n # and `y` values matches\n if num_x_vals != num_y_vals:\n raise ValueError(\n f\"\"\"The number of `x` and `y` values must match.\n The `x` value length is: {num_x_vals}\n The `y` value length is: {num_y_vals}\n \"\"\"\n )\n\n # Handle missing values in `x_vals` through removal (i.e., missing\n # values in `x_vals` means removal of positional values from both\n # `x_vals` and `y_vals`)\n if any(_map_is_na(x_vals)):\n # TODO: this code did not have test coverage and likely didn't\n # work. It should work now, but we need to test it.\n\n # Determine which values from `x_vals` are non-missing values\n x_vals_non_missing = [~_is_na(val) for val in x_vals]\n\n # Retain only `x_vals_non_missing` from `x_vals` and `y_vals`\n x_vals = [x for x, keep in zip(x_vals, x_vals_non_missing) if keep]\n y_vals = [y for y, keep in zip(y_vals, x_vals_non_missing) if keep]\n\n # If `x` values are present, we cannot use a curved line so\n # we'll force the use of the 'straight' line type\n # TODO: if someone specifies the options curved, and we can't do it\n # then we should raise an error.\n data_line_type = \"straight\"\n\n # If `missing_vals` is set to 'gap' raise an error\n # TODO: Implement the 'gap' option for missing values\n if missing_vals == \"gap\":\n raise NotImplementedError(\"The 'gap' option for missing values is not yet implemented.\")\n\n # For the `missing_vals` options of 'zero' or 'remove', either replace NAs\n # with `0` or remove NAs entirely\n if missing_vals == \"zero\":\n y_vals = y_vals.fillna(0)\n\n # If `missing_vals` is 'remove', remove NAs from `y_vals`\n if missing_vals == \"remove\":\n y_vals = y_vals.dropna()\n\n if x_vals is not None:\n # Remove the corresponding `x` values for the removed `y` values\n x_vals = x_vals[y_vals.index]\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # If `y_vals` is a scalar value we requested a 'line' or 'bar' plot, then\n # reset several parameters\n if type(y_vals) in [int, float] and plot_type in [\"line\", \"bar\"]:\n\n single_horizontal_plot = True\n show_data_points = True\n show_data_line = True\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n y_vals = [y_vals]\n\n # If this is a box plot, set several parameters\n if plot_type == \"boxplot\":\n\n show_data_points = False\n show_data_line = False\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n # Find out whether the collection of non-NA `y` values are all integer-like\n y_vals_integerlike = _is_integerlike(val_list=y_vals)\n\n # Get the max and min of the `y` scale from the `y` data values\n y_scale_max = _get_extreme_value(y_vals, stat=\"max\")\n y_scale_min = _get_extreme_value(y_vals, stat=\"min\")\n\n # Handle cases where collection of `y_vals` is invariant\n if y_scale_min == y_scale_max and expand_y is None:\n\n if y_scale_min == 0:\n expand_y_dist = 5\n else:\n expand_y_dist = (y_scale_min / 10) * 2\n\n # Expand the `y` scale, centering around the `y_scale_min` value\n expand_y = [y_scale_min - expand_y_dist, y_scale_min + expand_y_dist]\n\n # Ensure that a reference line or reference area isn't shown if None or\n # any of its directives is missing\n if _is_na(y_ref_line):\n show_reference_line = False\n\n if y_ref_area is None:\n show_reference_area = False\n elif _is_na(y_ref_area[0]) or _is_na(y_ref_area[1]):\n show_reference_area = False\n\n # Determine the width of the data plot area; for plots where `x_vals`\n # are available, we'll use a fixed width of `500` (px), and for plots\n # where `x_vals` aren't present, we'll adjust the final width based\n # on the fixed interval between data points (this is dependent on the\n # number of data points)\n if x_vals is not None or single_horizontal_plot or plot_type == \"boxplot\":\n data_x_width = 600\n # TODO: what should x_d be in this case?\n else:\n # Obtain a sensible, fixed interval between data points in px\n if num_y_vals <= 20:\n x_d = 50\n elif num_y_vals <= 30:\n x_d = 40\n elif num_y_vals <= 40:\n x_d = 30\n elif num_y_vals <= 50:\n x_d = 25\n else:\n x_d = 20\n\n data_x_width = num_y_vals * x_d\n\n # Define the top-left of the plot area\n left_x = 0\n top_y = 0\n\n # Define the safe zone distance from top/bottom and left/right edges\n safe_y_d = 15\n safe_x_d = 50\n\n # Define the height of the plot area that bounds the data points\n data_y_height = 100\n\n # Determine the bottom-right of the plot area based on the quantity of data\n bottom_y = safe_y_d + data_y_height + safe_y_d\n right_x = safe_x_d + data_x_width + safe_x_d\n\n viewbox = f\"{left_x} {top_y} {right_x} {bottom_y}\"\n\n #\n # If there is a reference line and/or reference area, the values for these\n # need to be generated and integrated in the `normalize_y_vals()` operation\n # so that there are normalized values in relation to the data points\n #\n\n if show_reference_line and show_reference_area:\n\n # Case where there is both a reference line and a reference area\n\n #\n # Resolve the reference line\n #\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n #\n # Resolve the reference area\n #\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n _all_y_data = [y_vals, y_ref_line, y_ref_area_l, y_ref_area_u, expand_y]\n\n # Recompute the `y` scale min and max values\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference line and reference area boundaries\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n elif show_reference_line:\n\n # Case where there is a reference line\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n # Recompute the `y` scale min and max values\n args = [y_vals, y_ref_line, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n\n # Scale reference line\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n\n elif show_reference_area:\n\n # Case where there is a reference area\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n # Recompute the `y` scale min and max values\n\n # Recompute the `y` scale min and max values\n _all_y_data = [y_vals, y_ref_area_l, y_ref_area_u, expand_y] + (\n [0] if zero_line_considered else []\n )\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference area boundaries\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n else:\n\n # Case where there is no reference line or reference area\n\n # Recompute the `y` scale min and max values\n args = [y_vals, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n\n # Calculate the `data_y0_point` value for zero-line-inclusive plots\n if zero_line_considered:\n y_proportions_zero = y_proportions_list[\"zero\"][0]\n data_y0_point = safe_y_d + ((1 - y_proportions_zero) * data_y_height)\n\n # If x values are present then normalize them between [0, 1]; if\n # there are no x values, generate equally-spaced x values according\n # to the number of y values\n if plot_type == \"line\" and x_vals is not None:\n\n if expand_x is not None and _val_is_str(expand_x):\n\n # TODO: the line below lacked tests, and called non-existent methods.\n # replace with something that doesn't use pandas and returns the correct thing.\n\n # Assume that string values are dates and convert them to timestamps\n # expand_x = pd.to_datetime(expand_x, utc=True).timestamp()\n raise NotImplementedError(\"Currently, passing expand_x as a string is unsupported.\")\n\n # Scale to proportional values\n x_proportions_list = _normalize_to_dict(vals=x_vals, expand_x=expand_x)\n\n x_proportions = x_proportions_list[\"vals\"]\n\n else:\n x_proportions = np.linspace(0, 1, num_y_vals)\n\n #\n # Create normalized (and inverted for SVG) data `x` and `y` values for the\n # plot area; these are named `data_x_points` and `data_y_points`\n #\n\n for i in range(len(y_proportions)):\n y_proportions[i] = safe_y_d + ((1 - y_proportions[i]) * data_y_height)\n\n for i in range(len(x_proportions)):\n x_proportions[i] = (data_x_width * x_proportions[i]) + safe_x_d\n\n data_y_points = y_proportions\n data_x_points = x_proportions\n\n #\n # Ensure that certain options have their lengths checked and\n # expanded to length `num_y_vals`\n #\n\n data_point_radius = _normalize_option_list(option_list=data_point_radius, num_y_vals=num_y_vals)\n data_point_stroke_color = _normalize_option_list(\n option_list=data_point_stroke_color, num_y_vals=num_y_vals\n )\n data_point_stroke_width = _normalize_option_list(\n option_list=data_point_stroke_width, num_y_vals=num_y_vals\n )\n data_point_fill_color = _normalize_option_list(\n option_list=data_point_fill_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_color = _normalize_option_list(\n option_list=data_bar_stroke_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_width = _normalize_option_list(\n option_list=data_bar_stroke_width, num_y_vals=num_y_vals\n )\n data_bar_fill_color = _normalize_option_list(\n option_list=data_bar_fill_color, num_y_vals=num_y_vals\n )\n\n #\n # Generate data segments by defining `start` and `end` vectors (these\n # are guaranteed to be of the same length); these the segments of data\n # they receive line segments and adjoining areas\n #\n\n # Use run-length encoding to determine the segments of data\n\n # rle_data_y_points = pd.Series(data_y_points).diff().ne(0).cumsum()\n\n # end_data_y_points = np.cumsum(rle_data_y_points.lengths)\n\n # start_data_y_points = end_data_y_points - rle_data_y_points.lengths + 1\n\n start_data_y_points = [0]\n end_data_y_points = [len(data_y_points)]\n n_segments = 1\n\n #\n # Generate a curved data line\n #\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"curved\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n curve_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n curve_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n curved_path_string = [f\"M {curve_x[0]},{curve_y[0]}\"]\n\n for j in range(1, len(curve_x)):\n\n point_b1 = f\"{curve_x[j - 1] + x_d / 2},{curve_y[j - 1]}\"\n point_b2 = f\"{curve_x[j] - x_d / 2},{curve_y[j]}\"\n point_i = f\"{curve_x[j]},{curve_y[j]}\"\n\n path_string_j = f\"C {point_b1} {point_b2} {point_i}\"\n\n curved_path_string.append(path_string_j)\n\n curved_path_string_i = \" \".join(curved_path_string)\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\\n\".join(data_path_tags)\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"straight\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n line_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n line_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n line_xy = \" \".join([f\"{x},{y}\" for x, y in zip(line_x, line_y)])\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\".join(data_path_tags)\n\n #\n # Generate data points\n #\n\n if plot_type == \"line\" and show_data_points:\n\n circle_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_point_stroke_color_i = data_point_stroke_color[i]\n data_point_stroke_width_i = data_point_stroke_width[i]\n data_point_fill_color_i = data_point_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n circle_strings_i = f''\n\n else:\n continue\n\n else:\n circle_strings_i = f''\n\n circle_strings.append(circle_strings_i)\n\n circle_tags = \"\".join(circle_strings)\n\n #\n # Generate data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n bar_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_bar_stroke_color_i = data_bar_stroke_color[i]\n data_bar_stroke_width_i = data_bar_stroke_width[i]\n data_bar_fill_color_i = data_bar_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n bar_strings_i = f''\n\n else:\n continue\n\n else:\n\n if y_vals[i] < 0:\n\n y_value_i = data_y0_point\n y_height = data_y_points[i] - data_y0_point\n data_bar_stroke_color_i = data_bar_negative_stroke_color\n data_bar_stroke_width_i = data_bar_negative_stroke_width\n data_bar_fill_color_i = data_bar_negative_fill_color\n\n elif y_vals[i] > 0:\n\n y_value_i = data_y_points[i]\n y_height = data_y0_point - data_y_points[i]\n\n elif y_vals[i] == 0:\n\n y_value_i = data_y0_point - 1\n y_height = 2\n data_bar_stroke_color_i = \"#808080\"\n data_bar_stroke_width_i = 4\n data_bar_fill_color_i = \"#808080\"\n\n bar_strings_i = f''\n\n bar_strings.append(bar_strings_i)\n\n bar_tags = \"\".join(bar_strings)\n\n #\n # Generate single horizontal data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n data_bar_stroke_color = data_bar_negative_stroke_color\n data_bar_stroke_width = data_bar_negative_stroke_width\n data_bar_fill_color = data_bar_negative_fill_color\n\n rect_x = y_width\n rect_width = y0_width - y_width\n\n elif y_vals[0] > 0:\n\n data_bar_stroke_color = data_bar_stroke_color[0]\n data_bar_stroke_width = data_bar_stroke_width[0]\n data_bar_fill_color = data_bar_fill_color[0]\n\n rect_x = y0_width\n rect_width = y_width - y0_width\n\n elif y_vals[0] == 0:\n\n data_bar_stroke_color = \"#808080\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#808080\"\n\n rect_x = y0_width - 2.5\n rect_width = 5\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n bar_tags = f'{g_guide_tags}'\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate single horizontal data lines\n #\n\n # TODO: Make this a line with a single point\n if plot_type == \"line\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n data_point_radius_i = data_point_radius[0]\n data_point_stroke_color_i = data_point_stroke_color[0]\n data_point_stroke_width_i = data_point_stroke_width[0]\n data_point_fill_color_i = data_point_fill_color[0]\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x1_val\n\n elif y_vals[0] > 0:\n\n x1_val = y0_width\n x2_val = y_width\n\n circle_x_val = x2_val\n\n elif y_vals[0] == 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x2_val\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 15\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n data_path_tags = f'{g_guide_tags}'\n\n circle_tags = f''\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate box plots\n #\n\n if plot_type == \"boxplot\":\n pass\n\n #\n # Generate zero line for vertical bar plots\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n zero_line_tags = f''\n\n #\n # Generate reference line\n #\n\n if show_reference_line:\n\n stroke = reference_line_color\n stroke_width = 1\n stroke_dasharray = \"4 3\"\n transform = \"\"\n stroke_linecap = \"round\"\n vector_effect = \"non-scaling-stroke\"\n\n # Format value in a compact manner\n y_ref_line = _format_number_compactly(\n val=y_ref_line, currency=currency, as_integer=y_vals_integerlike, fn=y_ref_line_fmt_fn\n )\n\n ref_line_tags = f'{y_ref_line}'\n\n #\n # Generate reference area\n #\n\n if show_reference_area:\n\n fill = reference_area_fill_color\n\n p_ul = f\"{data_x_points[0]},{data_y_ref_area_u}\"\n p_ur = f\"{data_x_points[-1]},{data_y_ref_area_u}\"\n p_lr = f\"{data_x_points[-1]},{data_y_ref_area_l}\"\n p_ll = f\"{data_x_points[0]},{data_y_ref_area_l}\"\n\n ref_area_path = f\"M{p_ul},{p_ur},{p_lr},{p_ll}Z\"\n\n ref_area_tags = f''\n\n #\n # Generate y-axis guide\n #\n\n if show_y_axis_guide:\n\n rect_tag = f''\n\n if _is_integerlike(val_list=[y_scale_max]) and _is_integerlike(val_list=[y_scale_min]):\n y_axis_guide_vals_integerlike = True\n else:\n y_axis_guide_vals_integerlike = False\n\n # Format values in a compact manner\n y_value_max_label = _format_number_compactly(\n val=y_scale_max,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n y_value_min_label = _format_number_compactly(\n val=y_scale_min,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n text_strings_min = f'{y_value_min_label}'\n\n text_strings_max = f'{y_value_max_label}'\n\n g_y_axis_tags = f'{rect_tag}{text_strings_max}{text_strings_min}'\n\n #\n # Generate vertical data point guidelines\n #\n\n if show_vertical_guides:\n\n g_guide_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n rect_strings_i = f''\n\n # Format value in a compact manner\n y_value_i = _format_number_compactly(\n val=y_vals[i], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n x_text = data_x_points[i] + 10\n\n if y_value_i == \"NA\":\n x_text = x_text + 2\n\n text_strings_i = f'{y_value_i}'\n\n g_guide_strings_i = f'{rect_strings_i}{text_strings_i}'\n\n g_guide_strings.append(g_guide_strings_i)\n\n g_guide_tags = \"\".join(g_guide_strings)\n\n #\n # Generate background with repeating line pattern\n #\n\n svg_defs = (\n f\"\"\n f''\n f''\n f\"\"\n f\"\"\n f\"\"\n )\n\n if plot_type == \"line\" and show_data_area:\n\n area_path_tags = []\n\n for i in range(n_segments):\n\n area_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n area_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n area_path_string = []\n\n for j in range(0, len(area_x)):\n\n area_path_j = f\"{area_x[j]},{area_y[j]}\"\n area_path_string.append(area_path_j)\n\n area_path_i = f\"M {' '.join(area_path_string)} {area_x[-1]},{bottom_y - safe_y_d + data_point_radius[0]} {area_x[0]},{bottom_y - safe_y_d + data_point_radius[0]} Z\"\n\n area_path_tag_i = f''\n\n area_path_tags.append(area_path_tag_i)\n\n area_path_tags = \" \".join(area_path_tags)\n\n #\n # Generate style tag for vertical guidelines and y-axis\n #\n\n hover_param = \":hover\" if interactive_data_values else \"\"\n\n svg_style = (\n f\"\"\n )\n\n nanoplot_svg = _construct_nanoplot_svg(\n viewbox=viewbox,\n svg_height=svg_height,\n svg_defs=svg_defs,\n svg_style=svg_style,\n show_data_points=show_data_points,\n show_data_line=show_data_line,\n show_data_area=show_data_area,\n show_reference_line=show_reference_line,\n show_reference_area=show_reference_area,\n show_vertical_guides=show_vertical_guides,\n show_y_axis_guide=show_y_axis_guide,\n ref_area_tags=ref_area_tags,\n area_path_tags=area_path_tags,\n data_path_tags=data_path_tags,\n zero_line_tags=zero_line_tags,\n bar_tags=bar_tags,\n ref_line_tags=ref_line_tags,\n circle_tags=circle_tags,\n g_y_axis_tags=g_y_axis_tags,\n g_guide_tags=g_guide_tags,\n )\n\n return nanoplot_svg\n```\n###test function signature:\n```python\n\ndef test_nanoplot_out_data_lines():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nanoplot_out_data_lines",
"reference": "\ndef test_nanoplot_out_data_lines():\n\n out_data_lines = _generate_nanoplot(**CASES[0])\n\n assert _is_nanoplot_output(out_data_lines)\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"pattern\",\n attrs=[\n (\"width\", \"8\"),\n (\"height\", \"8\"),\n (\"patternUnits\", \"userSpaceOnUse\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"path\",\n attrs=[\n (\"class\", \"area-closed\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"2\"),\n (\"fill-opacity\", \"0.7\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"circle\",\n attrs=[\n (\"cx\", \"50.0\"),\n (\"cy\", \"115.0\"),\n (\"r\", \"10\"),\n (\"stroke\", \"#FFFFFF\"),\n (\"stroke-width\", \"4\"),\n (\"fill\", \"#FF0000\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"rect\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"0\"),\n (\"width\", \"65\"),\n (\"height\", \"130\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"0\"),\n (\"fill\", \"transparent\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"text\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"19.0\"),\n (\"fill\", \"transparent\"),\n (\"stroke\", \"transparent\"),\n (\"font-size\", \"25\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"g\",\n attrs=[\n (\"class\", \"vert-line\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_data_lines,\n tag=\"g\",\n attrs=[\n (\"class\", \"y-axis-line\"),\n ],\n )",
"completions": [
"```python\n\ndef test_nanoplot_out_data_lines():\n y_vals = [1, 2, 3, 4, 5]\n y_ref_line = 2\n y_ref_area = [1, 3]\n x_vals = [1, 2, 3, 4, 5]\n expand_x = [1, 2, 3, 4, 5]\n expand_y = [1, 2, 3, 4, 5]\n missing_vals = \"marker\"\n all_y_vals = [1, 2, 3, 4, 5]\n all_single_y_vals = [1, 2, 3, 4, 5]\n plot_type = \"line\"\n data_line_type = \"curved\"\n currency = None\n y_val_fmt_fn = None\n y_axis_fmt_fn = None\n y_ref_line_fmt_fn = None\n data_point_radius = 10\n data_point_stroke_color = \"#FFFFFF\"\n data_point_stroke_width = 4\n data_point_fill_color = \"#FF0000\"\n data_line_stroke_color = \"#4682B4\"\n data_line_stroke_width = 8\n data_area_fill_color = \"#FF0000\"\n data_bar_stroke_color = \"#3290CC\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#3FB5FF\"\n data_bar_negative_stroke_color = \"#CC3243\"\n data_bar_negative_stroke_width = 4\n data_bar_negative_fill_color = \"#D75A68\"\n reference_line_color = \"#75A8B4\"\n reference_area_fill_color = \"#A6E6F2\"\n vertical_guide_stroke_color = \"#911EB4\"\n vertical_guide_stroke_width = 12\n show_data_points = True\n show_data_line = True\n show_data_area = True\n show_reference_line = True\n show_reference_area = True\n show_vertical_guides = True\n show_y_axis_guide = True\n interactive_data_values = True\n svg_height = \"2em\"\n\n expected = _generate_nanoplot(\n y_vals=y_vals,\n y_ref_line=y_ref_line,\n y_ref_area=y_ref_area,\n x_vals=x_vals,\n expand_x=expand_x,\n expand_y=expand_y,\n missing_vals=missing_vals,\n all_y_vals=all_y_vals,\n all_single_y_vals=all_single_y_vals,\n plot_type=plot_type,\n data_line_type=data_line_type,\n currency=currency,\n y_val_fmt_fn=y_val_fmt_fn,\n y_axis_fmt_fn=y_axis_fmt_fn,\n y_ref_line_fmt_fn=y_ref_line_fmt_fn,\n data_point_radius=data_point_radius,\n data_point_stroke_color=data_point_stroke_color,\n data_point_stroke_width=data_point_stroke_width,\n data_point_fill_color=data_point_fill_color,\n data_line_stroke_color=data_line_stroke_color,\n data_line_stroke_width=data_line_stroke_width,\n data_area_fill_color=data_area_fill_color,\n data_bar_stroke_color=data_bar_stroke_color,\n data_bar_stroke_width=data_bar_stroke_width,\n data_bar_fill_color=data_bar_fill_color,\n data_bar_negative_"
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{
"id": 12,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_temperature_features(\n meter_data_index,\n temperature_data,\n heating_balance_points=None,\n cooling_balance_points=None,\n data_quality=False,\n temperature_mean=True,\n degree_day_method=\"daily\",\n percent_hourly_coverage_per_day=0.5,\n percent_hourly_coverage_per_billing_period=0.9,\n use_mean_daily_values=True,\n tolerance=None,\n keep_partial_nan_rows=False,\n):\n if temperature_data.index.freq != \"H\":\n raise ValueError(\n \"temperature_data.index must have hourly frequency (freq='H').\"\n \" Found: {}\".format(temperature_data.index.freq)\n )\n\n if not temperature_data.index.tz:\n raise ValueError(\n \"temperature_data.index must be timezone-aware. You can set it with\"\n \" temperature_data.tz_localize(...).\"\n )\n\n if meter_data_index.freq is None and meter_data_index.inferred_freq == \"H\":\n raise ValueError(\n \"If you have hourly data explicitly set the frequency\"\n \" of the dataframe by setting\"\n \"``meter_data_index.freq =\"\n \" pd.tseries.frequencies.to_offset('H').\"\n )\n\n if not meter_data_index.tz:\n raise ValueError(\n \"meter_data_index must be timezone-aware. You can set it with\"\n \" meter_data.tz_localize(...).\"\n )\n\n if meter_data_index.duplicated().any():\n raise ValueError(\"Duplicates found in input meter trace index.\")\n\n temp_agg_funcs = []\n temp_agg_column_renames = {}\n\n if heating_balance_points is None:\n heating_balance_points = []\n if cooling_balance_points is None:\n cooling_balance_points = []\n\n if meter_data_index.freq is not None:\n try:\n freq_timedelta = pd.Timedelta(meter_data_index.freq)\n except ValueError: # freq cannot be converted to timedelta\n freq_timedelta = None\n else:\n freq_timedelta = None\n\n if tolerance is None:\n tolerance = freq_timedelta\n\n if not (heating_balance_points == [] and cooling_balance_points == []):\n if degree_day_method == \"hourly\":\n pass\n elif degree_day_method == \"daily\":\n if meter_data_index.freq == \"H\":\n raise ValueError(\n \"degree_day_method='daily' must be used with\"\n \" daily meter data. Found: 'hourly'\".format(degree_day_method)\n )\n else:\n raise ValueError(\"method not supported: {}\".format(degree_day_method))\n\n if freq_timedelta == pd.Timedelta(\"1H\"):\n # special fast route for hourly data.\n df = temperature_data.to_frame(\"temperature_mean\").reindex(meter_data_index)\n\n if use_mean_daily_values:\n n_days = 1\n else:\n n_days = 1.0 / 24.0\n\n df = df.assign(\n **{\n \"cdd_{}\".format(bp): np.maximum(df.temperature_mean - bp, 0) * n_days\n for bp in cooling_balance_points\n }\n )\n df = df.assign(\n **{\n \"hdd_{}\".format(bp): np.maximum(bp - df.temperature_mean, 0) * n_days\n for bp in heating_balance_points\n }\n )\n df = df.assign(\n n_hours_dropped=df.temperature_mean.isnull().astype(int),\n n_hours_kept=df.temperature_mean.notnull().astype(int),\n )\n # TODO(philngo): bad interface or maybe this is just wrong for some reason?\n if data_quality:\n df = df.assign(\n temperature_null=df.n_hours_dropped,\n temperature_not_null=df.n_hours_kept,\n )\n if not temperature_mean:\n del df[\"temperature_mean\"]\n else:\n # daily/billing route\n # heating/cooling degree day aggregations. Needed for n_days fields as well.\n temp_agg_funcs.extend(\n _degree_day_columns(\n heating_balance_points=heating_balance_points,\n cooling_balance_points=cooling_balance_points,\n degree_day_method=degree_day_method,\n percent_hourly_coverage_per_day=percent_hourly_coverage_per_day,\n percent_hourly_coverage_per_billing_period=percent_hourly_coverage_per_billing_period,\n use_mean_daily_values=use_mean_daily_values,\n )\n )\n temp_agg_column_renames.update(\n {(\"temp\", \"degree_day_columns\"): \"degree_day_columns\"}\n )\n\n if data_quality:\n temp_agg_funcs.extend(\n [(\"not_null\", \"count\"), (\"null\", lambda x: x.isnull().sum())]\n )\n temp_agg_column_renames.update(\n {\n (\"temp\", \"not_null\"): \"temperature_not_null\",\n (\"temp\", \"null\"): \"temperature_null\",\n }\n )\n\n if temperature_mean:\n temp_agg_funcs.extend([(\"mean\", \"mean\")])\n temp_agg_column_renames.update({(\"temp\", \"mean\"): \"temperature_mean\"})\n\n # aggregate temperatures\n temp_df = temperature_data.to_frame(\"temp\")\n temp_groups = _matching_groups(meter_data_index, temp_df, tolerance)\n temp_aggregations = temp_groups.agg({\"temp\": temp_agg_funcs})\n\n # expand temp aggregations by faking and deleting the `meter_value` column.\n # I haven't yet figured out a way to avoid this and get the desired\n # structure and behavior. (philngo)\n meter_value = pd.DataFrame({\"meter_value\": 0}, index=meter_data_index)\n df = pd.concat([meter_value, temp_aggregations], axis=1).rename(\n columns=temp_agg_column_renames\n )\n del df[\"meter_value\"]\n\n if \"degree_day_columns\" in df:\n if df[\"degree_day_columns\"].dropna().empty:\n column_defaults = {\n column: np.full(df[\"degree_day_columns\"].shape, np.nan)\n for column in [\"n_days_dropped\", \"n_days_kept\"]\n }\n df = df.drop([\"degree_day_columns\"], axis=1).assign(**column_defaults)\n else:\n df = pd.concat(\n [\n df.drop([\"degree_day_columns\"], axis=1),\n df[\"degree_day_columns\"].dropna().apply(pd.Series),\n ],\n axis=1,\n )\n\n if not keep_partial_nan_rows:\n df = overwrite_partial_rows_with_nan(df)\n\n # nan last row\n df = df.iloc[:-1].reindex(df.index)\n return df\n```\n###test function signature:\n```python\n\ndef test_compute_temperature_features_daily_temp_mean(il_electricity_cdd_hdd_daily):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_temperature_features_daily_temp_mean",
"reference": "\ndef test_compute_temperature_features_daily_temp_mean(il_electricity_cdd_hdd_daily):\n meter_data = il_electricity_cdd_hdd_daily[\"meter_data\"]\n temperature_data = il_electricity_cdd_hdd_daily[\"temperature_data\"]\n df = compute_temperature_features(meter_data.index, temperature_data)\n assert df.shape == (810, 3)\n assert list(sorted(df.columns)) == [\n \"n_days_dropped\",\n \"n_days_kept\",\n \"temperature_mean\",\n ]\n\n assert round(df.temperature_mean.mean()) == 55.0",
"completions": [
"```python\n\ndef test_compute_temperature_features_daily_temp_mean(il_electricity_cdd_hdd_daily):\n meter_data_index = pd.date_range(\n start=\"2015-01-01\", end=\"2015-01-31\", freq=\"D\", tz=\"UTC\"\n )\n temperature_data = pd.DataFrame(\n {\"temperature_mean\": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10]},\n index=pd.date_range(\n start=\"2015-01-01\", end=\"2015-01-12\", freq=\"D\", tz=\"UTC\"\n ),\n )\n df = compute_temperature_features(\n meter_data_index,\n temperature_data,\n heating_balance_points=[],\n cooling_balance_points=[],\n data_quality=False,\n temperature_mean=True,\n degree_day_method=\"daily\",\n percent_hourly_coverage_per_day=0.5,\n percent_hourly_coverage_per_billing_period=0.9,\n use_mean_daily_values=True,\n tolerance=None,\n keep_partial_nan_rows=False,\n )\n expected = pd.DataFrame(\n {\n \"temperature_mean\": [10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10],\n \"n_hours_kept\": [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12],\n \"n_hours_dropped\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n },\n index=pd.date_range(\n start=\"2015-01-01\", end=\"2015-01-12\", freq=\"D\", tz=\"UTC\"\n ),\n )\n pd.testing.assert_frame_equal(df, expected)\n```\n"
],
"line": 166,
"token": 983,
"line_diff": 0,
"token_diff": 0
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{
"id": 13,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef power_color(\n frequency,\n power,\n power_err=None,\n freq_edges=[1 / 256, 1 / 32, 0.25, 2.0, 16.0],\n df=None,\n m=1,\n freqs_to_exclude=None,\n poisson_power=0,\n return_log=False,\n):\n freq_edges = np.asarray(freq_edges)\n if len(freq_edges) != 5:\n raise ValueError(\"freq_edges must have 5 elements\")\n\n frequency = np.asarray(frequency)\n power = np.asarray(power)\n\n if df is None:\n df = np.median(np.diff(frequency))\n input_frequency_low_edges = frequency - df / 2\n input_frequency_high_edges = frequency + df / 2\n\n if freq_edges.min() < input_frequency_low_edges[0]:\n raise ValueError(\"The minimum frequency is larger than the first frequency edge\")\n if freq_edges.max() > input_frequency_high_edges[-1]:\n raise ValueError(\"The maximum frequency is lower than the last frequency edge\")\n\n if power_err is None:\n power_err = power / np.sqrt(m)\n else:\n power_err = np.asarray(power_err)\n\n if freqs_to_exclude is not None:\n if len(np.shape(freqs_to_exclude)) == 1:\n freqs_to_exclude = [freqs_to_exclude]\n\n if (\n not isinstance(freqs_to_exclude, Iterable)\n or len(np.shape(freqs_to_exclude)) != 2\n or np.shape(freqs_to_exclude)[1] != 2\n ):\n raise ValueError(\"freqs_to_exclude must be of format [[f0, f1], [f2, f3], ...]\")\n for f0, f1 in freqs_to_exclude:\n frequency_mask = (input_frequency_low_edges > f0) & (input_frequency_high_edges < f1)\n idx0, idx1 = np.searchsorted(frequency, [f0, f1])\n power[frequency_mask] = np.mean([power[idx0], power[idx1]])\n\n var00, var00_err = integrate_power_in_frequency_range(\n frequency,\n power,\n freq_edges[:2],\n power_err=power_err,\n df=df,\n m=m,\n poisson_power=poisson_power,\n )\n var01, var01_err = integrate_power_in_frequency_range(\n frequency,\n power,\n freq_edges[2:4],\n power_err=power_err,\n df=df,\n m=m,\n poisson_power=poisson_power,\n )\n var10, var10_err = integrate_power_in_frequency_range(\n frequency,\n power,\n freq_edges[1:3],\n power_err=power_err,\n df=df,\n m=m,\n poisson_power=poisson_power,\n )\n var11, var11_err = integrate_power_in_frequency_range(\n frequency,\n power,\n freq_edges[3:5],\n power_err=power_err,\n df=df,\n m=m,\n poisson_power=poisson_power,\n )\n pc0 = var00 / var01\n pc1 = var10 / var11\n pc0_err = pc0 * (var00_err / var00 + var01_err / var01)\n pc1_err = pc1 * (var10_err / var10 + var11_err / var11)\n if return_log:\n pc0_err = 1 / pc0 * pc0_err\n pc1_err = 1 / pc1 * pc1_err\n pc0 = np.log10(pc0)\n pc1 = np.log10(pc1)\n return pc0, pc0_err, pc1, pc1_err\n```\n###test function signature:\n```python\n\n def test_with_power_err(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_with_power_err",
"reference": "\n def test_with_power_err(self):\n pc0, pc0_err, pc1, pc1_err = power_color(\n self.freq,\n self.power,\n power_err=self.power / 2,\n )\n pc0e, pc0e_err, pc1e, pc1e_err = power_color(\n self.freq,\n self.power,\n power_err=self.power,\n )\n assert np.isclose(pc0, 1, atol=0.001)\n assert np.isclose(pc1, 1, atol=0.001)\n assert np.isclose(pc0e, 1, atol=0.001)\n assert np.isclose(pc1e, 1, atol=0.001)\n assert np.isclose(pc0e_err / pc0_err, 2, atol=0.001)\n assert np.isclose(pc1e_err / pc1_err, 2, atol=0.001)",
"completions": [
"```python\n def test_with_power_err(self):\n frequency = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n power = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n power_err = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n pc0, pc0_err, pc1, pc1_err = power_color(\n frequency,\n power,\n power_err=power_err,\n freq_edges=[1 / 256, 1 / 32, 0.25, 2.0, 16.0],\n df=None,\n m=1,\n freqs_to_exclude=None,\n poisson_power=0,\n return_log=False,\n )\n self.assertEqual(pc0, 0.0)\n self.assertEqual(pc0_err, 0.0)\n self.assertEqual(pc1, 0.0)\n self.assertEqual(pc1_err, 0.0)\n```\n"
],
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"token": 560,
"line_diff": 0,
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{
"id": 14,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def apply_top_k(\n self, logits: numpy.ndarray, filter_value=_MIN_FLOAT\n ) -> numpy.ndarray:\n logits_shape = logits.shape\n logits = logits.reshape(logits.shape[-1])\n top_k_indices = numpy.argpartition(logits, -self.top_k)[-self.top_k :]\n logits[~numpy.isin(numpy.arange(len(logits)), top_k_indices)] = filter_value\n\n return logits.reshape(logits_shape)\n```\n###test function signature:\n```python\n\n def test_apply_topk(\n self,\n ):\n # logits for opt usually have shape (1,1,51200)\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_apply_topk",
"reference": "\n def test_apply_topk(\n self,\n ):\n # logits for opt usually have shape (1,1,51200)\n logits = numpy.linspace(0, 1, 11).reshape((1, 1, 11))\n\n token_generator = TokenGenerator(\n logits_shape=logits[-1].shape[-1],\n top_k=3,\n )\n\n filter_value = -float(\"Inf\")\n new_logits = token_generator.apply_top_k(\n logits.copy(), filter_value=filter_value\n )\n\n for _ in range(token_generator.top_k):\n curr_max, idx = numpy.max(new_logits), numpy.argmax(new_logits)\n assert curr_max > filter_value\n new_logits = numpy.delete(new_logits, idx)\n\n assert numpy.all(new_logits == filter_value)",
"completions": [
"```python\n def test_apply_topk(\n self,\n ):\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]])\n self.assertEqual(\n self.apply_top_k(logits, filter_value=_MIN_FLOAT).tolist(),\n [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]],\n )\n\n logits = numpy.array([[1, 2, 3, 4, 5, 6, 7, 8, 9,"
],
"line": 21,
"token": 152,
"line_diff": 0,
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{
"id": 15,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def run(\n self,\n sources: List[Union[str, Path, ByteStream]],\n meta: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,\n ):\n parser = MarkdownIt(renderer_cls=RendererPlain)\n if self.table_to_single_line:\n parser.enable(\"table\")\n\n documents = []\n meta_list = normalize_metadata(meta=meta, sources_count=len(sources))\n\n for source, metadata in tqdm(\n zip(sources, meta_list),\n total=len(sources),\n desc=\"Converting markdown files to Documents\",\n disable=not self.progress_bar,\n ):\n try:\n bytestream = get_bytestream_from_source(source)\n except Exception as e:\n logger.warning(\"Could not read {source}. Skipping it. Error: {error}\", source=source, error=e)\n continue\n try:\n file_content = bytestream.data.decode(\"utf-8\")\n text = parser.render(file_content)\n except Exception as conversion_e:\n logger.warning(\n \"Failed to extract text from {source}. Skipping it. Error: {error}\",\n source=source,\n error=conversion_e,\n )\n continue\n\n merged_metadata = {**bytestream.meta, **metadata}\n document = Document(content=text, meta=merged_metadata)\n documents.append(document)\n\n return {\"documents\": documents}\n```\n###test function signature:\n```python\n\n def test_run_calls_normalize_metadata(self, test_files_path):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_run_calls_normalize_metadata",
"reference": "\n def test_run_calls_normalize_metadata(self, test_files_path):\n bytestream = ByteStream(data=b\"test\", meta={\"author\": \"test_author\", \"language\": \"en\"})\n\n converter = MarkdownToDocument()\n\n with patch(\"haystack.components.converters.markdown.normalize_metadata\") as normalize_metadata, patch(\n \"haystack.components.converters.markdown.MarkdownIt\"\n ):\n converter.run(sources=[bytestream, test_files_path / \"markdown\" / \"sample.md\"], meta={\"language\": \"it\"})\n\n # check that the metadata normalizer is called properly\n normalize_metadata.assert_called_with(meta={\"language\": \"it\"}, sources_count=2)",
"completions": [
"```python\n def test_run_calls_normalize_metadata(self, test_files_path):\n # Arrange\n sources = [test_files_path / \"test_file.md\"]\n meta = {\"key\": \"value\"}\n\n # Act\n run(sources, meta)\n\n # Assert\n normalize_metadata.assert_called_once_with(meta=meta, sources_count=1)\n```\n"
],
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"line_diff": 0,
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{
"id": 16,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def add_config(config: object) -> None:\n if hasattr(config, \"kind\"):\n Config.running_config.classes[config.kind] = config # type: ignore\n Config.running_config.types[config.kind] = {}\n for field in fields(config): # type: ignore\n if hasattr(field, \"type\"):\n Config.running_config.types[config.kind][field.name] = optional_origin(field.type)\n else:\n raise RuntimeError(\"Config must have a 'kind' attribute\")\n```\n###test function signature:\n```python\n\ndef test_config():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_config",
"reference": "\ndef test_config():\n arg_parser = get_arg_parser()\n core_add_args(arg_parser)\n arg_parser.parse_known_args()\n cfg = Config(\"test\")\n cfg.add_config(ConfigTest)\n cfg.init_default_config()\n assert Config.dict() == {\n \"configtest\": {\n \"testvar1\": \"testing123\",\n \"testvar2\": 12345,\n \"testvar3\": {\n \"mydict\": {\"foo\": \"bar\", \"abc\": {\"def\": \"ghi\"}},\n \"myint\": 0,\n \"mystr\": \"Hello\",\n },\n }\n }\n cfg2 = Config(\"test2\")\n assert cfg.configtest.testvar1 == cfg2.configtest.testvar1\n assert cfg.configtest.testvar1 == Config.configtest.testvar1\n assert cfg.configtest.testvar2 == cfg2.configtest.testvar2\n assert cfg.configtest.testvar3 == cfg2.configtest.testvar3\n Config.configtest.testvar2 += 1\n assert cfg.configtest.testvar2 == 12346\n with pytest.raises(ConfigNotFoundError):\n Config.does_not_exist.foo = \"bar\"\n with pytest.raises(ConfigNotFoundError):\n cfg.does_not_exist.foo = \"bar\"",
"completions": [
"```python\ndef test_config():\n from dataclasses import dataclass, field\n from typing import Optional\n\n @dataclass\n class Config:\n kind: str\n a: Optional[int] = field(default=None)\n b: Optional[int] = field(default=None)\n\n add_config(Config)\n\n assert Config.running_config.classes[\"Config\"] == Config\n assert Config.running_config.types[\"Config\"] == {\"a\": int, \"b\": int}\n```\n"
],
"line": 19,
"token": 166,
"line_diff": 0,
"token_diff": 0
},
{
"id": 17,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _generate_nanoplot(\n y_vals: Union[List[Union[int, float]], List[int], List[float]],\n y_ref_line: Optional[str] = None,\n y_ref_area: Optional[str] = None,\n x_vals: \"List[Union[int, float]] | None\" = None,\n expand_x: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n expand_y: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n missing_vals: str = \"marker\",\n all_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n all_single_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n plot_type: str = \"line\",\n data_line_type: str = \"curved\",\n currency: Optional[str] = None,\n y_val_fmt_fn: Optional[Callable[..., str]] = None,\n y_axis_fmt_fn: Optional[Callable[..., str]] = None,\n y_ref_line_fmt_fn: Optional[Callable[..., str]] = None,\n data_point_radius: Union[int, List[int]] = 10,\n data_point_stroke_color: Union[str, List[str]] = \"#FFFFFF\",\n data_point_stroke_width: Union[int, List[int]] = 4,\n data_point_fill_color: Union[str, List[str]] = \"#FF0000\",\n data_line_stroke_color: str = \"#4682B4\",\n data_line_stroke_width: int = 8,\n data_area_fill_color: str = \"#FF0000\",\n data_bar_stroke_color: Union[str, List[str]] = \"#3290CC\",\n data_bar_stroke_width: Union[int, List[int]] = 4,\n data_bar_fill_color: Union[str, List[str]] = \"#3FB5FF\",\n data_bar_negative_stroke_color: str = \"#CC3243\",\n data_bar_negative_stroke_width: int = 4,\n data_bar_negative_fill_color: str = \"#D75A68\",\n reference_line_color: str = \"#75A8B0\",\n reference_area_fill_color: str = \"#A6E6F2\",\n vertical_guide_stroke_color: str = \"#911EB4\",\n vertical_guide_stroke_width: int = 12,\n show_data_points: bool = True,\n show_data_line: bool = True,\n show_data_area: bool = True,\n show_reference_line: bool = True,\n show_reference_area: bool = True,\n show_vertical_guides: bool = True,\n show_y_axis_guide: bool = True,\n interactive_data_values: bool = True,\n svg_height: str = \"2em\",\n) -> str:\n\n # Ensure that arguments are matched\n _match_arg(\n x=missing_vals,\n lst=[\"marker\", \"gap\", \"zero\", \"remove\"],\n )\n _match_arg(\n x=data_line_type,\n lst=[\"curved\", \"straight\"],\n )\n\n #\n # Determine where a zero line is considered and provide the stroke color and width\n #\n\n zero_line_considered = True if plot_type in [\"bar\", \"boxplot\"] else False\n\n zero_line_stroke_color = \"#BFBFBF\"\n zero_line_stroke_width = 4\n\n # Initialize several local `*_tags` variables with `None`\n ref_area_tags = None\n area_path_tags = None\n data_path_tags = None\n zero_line_tags = None\n bar_tags = None\n boxplot_tags = None\n ref_line_tags = None\n circle_tags = None\n g_y_axis_tags = None\n g_guide_tags = None\n\n # Initialize the `single_horizontal_plot` variable with `False`\n single_horizontal_plot = False\n\n # If the number of `y` values in a list is zero or if all consist of NA values,\n # return an empty string\n if type(y_vals) is list and len(y_vals) == 0:\n return \"\"\n\n # If all `y` values are NA, return an empty string\n # TODO: all([]) evaluates to True. In that case does this produce the intended behavior?\n if isinstance(y_vals, list) and all(_map_is_na(y_vals)):\n return \"\"\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # Handle case where `x_vals` exists (i.e., is not `NULL`)\n if x_vals is not None:\n\n # If the number of `x` values is zero or an empty string,\n # return an empty string\n if len(x_vals) == 0:\n return \"\"\n if all(_map_is_na(x_vals)):\n return \"\"\n\n # Get the number of data points for `x`\n num_x_vals = len(x_vals)\n\n # Ensure that, if there are `x` values, the number of `x`\n # and `y` values matches\n if num_x_vals != num_y_vals:\n raise ValueError(\n f\"\"\"The number of `x` and `y` values must match.\n The `x` value length is: {num_x_vals}\n The `y` value length is: {num_y_vals}\n \"\"\"\n )\n\n # Handle missing values in `x_vals` through removal (i.e., missing\n # values in `x_vals` means removal of positional values from both\n # `x_vals` and `y_vals`)\n if any(_map_is_na(x_vals)):\n # TODO: this code did not have test coverage and likely didn't\n # work. It should work now, but we need to test it.\n\n # Determine which values from `x_vals` are non-missing values\n x_vals_non_missing = [~_is_na(val) for val in x_vals]\n\n # Retain only `x_vals_non_missing` from `x_vals` and `y_vals`\n x_vals = [x for x, keep in zip(x_vals, x_vals_non_missing) if keep]\n y_vals = [y for y, keep in zip(y_vals, x_vals_non_missing) if keep]\n\n # If `x` values are present, we cannot use a curved line so\n # we'll force the use of the 'straight' line type\n # TODO: if someone specifies the options curved, and we can't do it\n # then we should raise an error.\n data_line_type = \"straight\"\n\n # If `missing_vals` is set to 'gap' raise an error\n # TODO: Implement the 'gap' option for missing values\n if missing_vals == \"gap\":\n raise NotImplementedError(\"The 'gap' option for missing values is not yet implemented.\")\n\n # For the `missing_vals` options of 'zero' or 'remove', either replace NAs\n # with `0` or remove NAs entirely\n if missing_vals == \"zero\":\n y_vals = y_vals.fillna(0)\n\n # If `missing_vals` is 'remove', remove NAs from `y_vals`\n if missing_vals == \"remove\":\n y_vals = y_vals.dropna()\n\n if x_vals is not None:\n # Remove the corresponding `x` values for the removed `y` values\n x_vals = x_vals[y_vals.index]\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # If `y_vals` is a scalar value we requested a 'line' or 'bar' plot, then\n # reset several parameters\n if type(y_vals) in [int, float] and plot_type in [\"line\", \"bar\"]:\n\n single_horizontal_plot = True\n show_data_points = True\n show_data_line = True\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n y_vals = [y_vals]\n\n # If this is a box plot, set several parameters\n if plot_type == \"boxplot\":\n\n show_data_points = False\n show_data_line = False\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n # Find out whether the collection of non-NA `y` values are all integer-like\n y_vals_integerlike = _is_integerlike(val_list=y_vals)\n\n # Get the max and min of the `y` scale from the `y` data values\n y_scale_max = _get_extreme_value(y_vals, stat=\"max\")\n y_scale_min = _get_extreme_value(y_vals, stat=\"min\")\n\n # Handle cases where collection of `y_vals` is invariant\n if y_scale_min == y_scale_max and expand_y is None:\n\n if y_scale_min == 0:\n expand_y_dist = 5\n else:\n expand_y_dist = (y_scale_min / 10) * 2\n\n # Expand the `y` scale, centering around the `y_scale_min` value\n expand_y = [y_scale_min - expand_y_dist, y_scale_min + expand_y_dist]\n\n # Ensure that a reference line or reference area isn't shown if None or\n # any of its directives is missing\n if _is_na(y_ref_line):\n show_reference_line = False\n\n if y_ref_area is None:\n show_reference_area = False\n elif _is_na(y_ref_area[0]) or _is_na(y_ref_area[1]):\n show_reference_area = False\n\n # Determine the width of the data plot area; for plots where `x_vals`\n # are available, we'll use a fixed width of `500` (px), and for plots\n # where `x_vals` aren't present, we'll adjust the final width based\n # on the fixed interval between data points (this is dependent on the\n # number of data points)\n if x_vals is not None or single_horizontal_plot or plot_type == \"boxplot\":\n data_x_width = 600\n # TODO: what should x_d be in this case?\n else:\n # Obtain a sensible, fixed interval between data points in px\n if num_y_vals <= 20:\n x_d = 50\n elif num_y_vals <= 30:\n x_d = 40\n elif num_y_vals <= 40:\n x_d = 30\n elif num_y_vals <= 50:\n x_d = 25\n else:\n x_d = 20\n\n data_x_width = num_y_vals * x_d\n\n # Define the top-left of the plot area\n left_x = 0\n top_y = 0\n\n # Define the safe zone distance from top/bottom and left/right edges\n safe_y_d = 15\n safe_x_d = 50\n\n # Define the height of the plot area that bounds the data points\n data_y_height = 100\n\n # Determine the bottom-right of the plot area based on the quantity of data\n bottom_y = safe_y_d + data_y_height + safe_y_d\n right_x = safe_x_d + data_x_width + safe_x_d\n\n viewbox = f\"{left_x} {top_y} {right_x} {bottom_y}\"\n\n #\n # If there is a reference line and/or reference area, the values for these\n # need to be generated and integrated in the `normalize_y_vals()` operation\n # so that there are normalized values in relation to the data points\n #\n\n if show_reference_line and show_reference_area:\n\n # Case where there is both a reference line and a reference area\n\n #\n # Resolve the reference line\n #\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n #\n # Resolve the reference area\n #\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n _all_y_data = [y_vals, y_ref_line, y_ref_area_l, y_ref_area_u, expand_y]\n\n # Recompute the `y` scale min and max values\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference line and reference area boundaries\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n elif show_reference_line:\n\n # Case where there is a reference line\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n # Recompute the `y` scale min and max values\n args = [y_vals, y_ref_line, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n\n # Scale reference line\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n\n elif show_reference_area:\n\n # Case where there is a reference area\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n # Recompute the `y` scale min and max values\n\n # Recompute the `y` scale min and max values\n _all_y_data = [y_vals, y_ref_area_l, y_ref_area_u, expand_y] + (\n [0] if zero_line_considered else []\n )\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference area boundaries\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n else:\n\n # Case where there is no reference line or reference area\n\n # Recompute the `y` scale min and max values\n args = [y_vals, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n\n # Calculate the `data_y0_point` value for zero-line-inclusive plots\n if zero_line_considered:\n y_proportions_zero = y_proportions_list[\"zero\"][0]\n data_y0_point = safe_y_d + ((1 - y_proportions_zero) * data_y_height)\n\n # If x values are present then normalize them between [0, 1]; if\n # there are no x values, generate equally-spaced x values according\n # to the number of y values\n if plot_type == \"line\" and x_vals is not None:\n\n if expand_x is not None and _val_is_str(expand_x):\n\n # TODO: the line below lacked tests, and called non-existent methods.\n # replace with something that doesn't use pandas and returns the correct thing.\n\n # Assume that string values are dates and convert them to timestamps\n # expand_x = pd.to_datetime(expand_x, utc=True).timestamp()\n raise NotImplementedError(\"Currently, passing expand_x as a string is unsupported.\")\n\n # Scale to proportional values\n x_proportions_list = _normalize_to_dict(vals=x_vals, expand_x=expand_x)\n\n x_proportions = x_proportions_list[\"vals\"]\n\n else:\n x_proportions = np.linspace(0, 1, num_y_vals)\n\n #\n # Create normalized (and inverted for SVG) data `x` and `y` values for the\n # plot area; these are named `data_x_points` and `data_y_points`\n #\n\n for i in range(len(y_proportions)):\n y_proportions[i] = safe_y_d + ((1 - y_proportions[i]) * data_y_height)\n\n for i in range(len(x_proportions)):\n x_proportions[i] = (data_x_width * x_proportions[i]) + safe_x_d\n\n data_y_points = y_proportions\n data_x_points = x_proportions\n\n #\n # Ensure that certain options have their lengths checked and\n # expanded to length `num_y_vals`\n #\n\n data_point_radius = _normalize_option_list(option_list=data_point_radius, num_y_vals=num_y_vals)\n data_point_stroke_color = _normalize_option_list(\n option_list=data_point_stroke_color, num_y_vals=num_y_vals\n )\n data_point_stroke_width = _normalize_option_list(\n option_list=data_point_stroke_width, num_y_vals=num_y_vals\n )\n data_point_fill_color = _normalize_option_list(\n option_list=data_point_fill_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_color = _normalize_option_list(\n option_list=data_bar_stroke_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_width = _normalize_option_list(\n option_list=data_bar_stroke_width, num_y_vals=num_y_vals\n )\n data_bar_fill_color = _normalize_option_list(\n option_list=data_bar_fill_color, num_y_vals=num_y_vals\n )\n\n #\n # Generate data segments by defining `start` and `end` vectors (these\n # are guaranteed to be of the same length); these the segments of data\n # they receive line segments and adjoining areas\n #\n\n # Use run-length encoding to determine the segments of data\n\n # rle_data_y_points = pd.Series(data_y_points).diff().ne(0).cumsum()\n\n # end_data_y_points = np.cumsum(rle_data_y_points.lengths)\n\n # start_data_y_points = end_data_y_points - rle_data_y_points.lengths + 1\n\n start_data_y_points = [0]\n end_data_y_points = [len(data_y_points)]\n n_segments = 1\n\n #\n # Generate a curved data line\n #\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"curved\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n curve_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n curve_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n curved_path_string = [f\"M {curve_x[0]},{curve_y[0]}\"]\n\n for j in range(1, len(curve_x)):\n\n point_b1 = f\"{curve_x[j - 1] + x_d / 2},{curve_y[j - 1]}\"\n point_b2 = f\"{curve_x[j] - x_d / 2},{curve_y[j]}\"\n point_i = f\"{curve_x[j]},{curve_y[j]}\"\n\n path_string_j = f\"C {point_b1} {point_b2} {point_i}\"\n\n curved_path_string.append(path_string_j)\n\n curved_path_string_i = \" \".join(curved_path_string)\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\\n\".join(data_path_tags)\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"straight\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n line_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n line_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n line_xy = \" \".join([f\"{x},{y}\" for x, y in zip(line_x, line_y)])\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\".join(data_path_tags)\n\n #\n # Generate data points\n #\n\n if plot_type == \"line\" and show_data_points:\n\n circle_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_point_stroke_color_i = data_point_stroke_color[i]\n data_point_stroke_width_i = data_point_stroke_width[i]\n data_point_fill_color_i = data_point_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n circle_strings_i = f''\n\n else:\n continue\n\n else:\n circle_strings_i = f''\n\n circle_strings.append(circle_strings_i)\n\n circle_tags = \"\".join(circle_strings)\n\n #\n # Generate data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n bar_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_bar_stroke_color_i = data_bar_stroke_color[i]\n data_bar_stroke_width_i = data_bar_stroke_width[i]\n data_bar_fill_color_i = data_bar_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n bar_strings_i = f''\n\n else:\n continue\n\n else:\n\n if y_vals[i] < 0:\n\n y_value_i = data_y0_point\n y_height = data_y_points[i] - data_y0_point\n data_bar_stroke_color_i = data_bar_negative_stroke_color\n data_bar_stroke_width_i = data_bar_negative_stroke_width\n data_bar_fill_color_i = data_bar_negative_fill_color\n\n elif y_vals[i] > 0:\n\n y_value_i = data_y_points[i]\n y_height = data_y0_point - data_y_points[i]\n\n elif y_vals[i] == 0:\n\n y_value_i = data_y0_point - 1\n y_height = 2\n data_bar_stroke_color_i = \"#808080\"\n data_bar_stroke_width_i = 4\n data_bar_fill_color_i = \"#808080\"\n\n bar_strings_i = f''\n\n bar_strings.append(bar_strings_i)\n\n bar_tags = \"\".join(bar_strings)\n\n #\n # Generate single horizontal data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n data_bar_stroke_color = data_bar_negative_stroke_color\n data_bar_stroke_width = data_bar_negative_stroke_width\n data_bar_fill_color = data_bar_negative_fill_color\n\n rect_x = y_width\n rect_width = y0_width - y_width\n\n elif y_vals[0] > 0:\n\n data_bar_stroke_color = data_bar_stroke_color[0]\n data_bar_stroke_width = data_bar_stroke_width[0]\n data_bar_fill_color = data_bar_fill_color[0]\n\n rect_x = y0_width\n rect_width = y_width - y0_width\n\n elif y_vals[0] == 0:\n\n data_bar_stroke_color = \"#808080\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#808080\"\n\n rect_x = y0_width - 2.5\n rect_width = 5\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n bar_tags = f'{g_guide_tags}'\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate single horizontal data lines\n #\n\n # TODO: Make this a line with a single point\n if plot_type == \"line\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n data_point_radius_i = data_point_radius[0]\n data_point_stroke_color_i = data_point_stroke_color[0]\n data_point_stroke_width_i = data_point_stroke_width[0]\n data_point_fill_color_i = data_point_fill_color[0]\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x1_val\n\n elif y_vals[0] > 0:\n\n x1_val = y0_width\n x2_val = y_width\n\n circle_x_val = x2_val\n\n elif y_vals[0] == 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x2_val\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 15\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n data_path_tags = f'{g_guide_tags}'\n\n circle_tags = f''\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate box plots\n #\n\n if plot_type == \"boxplot\":\n pass\n\n #\n # Generate zero line for vertical bar plots\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n zero_line_tags = f''\n\n #\n # Generate reference line\n #\n\n if show_reference_line:\n\n stroke = reference_line_color\n stroke_width = 1\n stroke_dasharray = \"4 3\"\n transform = \"\"\n stroke_linecap = \"round\"\n vector_effect = \"non-scaling-stroke\"\n\n # Format value in a compact manner\n y_ref_line = _format_number_compactly(\n val=y_ref_line, currency=currency, as_integer=y_vals_integerlike, fn=y_ref_line_fmt_fn\n )\n\n ref_line_tags = f'{y_ref_line}'\n\n #\n # Generate reference area\n #\n\n if show_reference_area:\n\n fill = reference_area_fill_color\n\n p_ul = f\"{data_x_points[0]},{data_y_ref_area_u}\"\n p_ur = f\"{data_x_points[-1]},{data_y_ref_area_u}\"\n p_lr = f\"{data_x_points[-1]},{data_y_ref_area_l}\"\n p_ll = f\"{data_x_points[0]},{data_y_ref_area_l}\"\n\n ref_area_path = f\"M{p_ul},{p_ur},{p_lr},{p_ll}Z\"\n\n ref_area_tags = f''\n\n #\n # Generate y-axis guide\n #\n\n if show_y_axis_guide:\n\n rect_tag = f''\n\n if _is_integerlike(val_list=[y_scale_max]) and _is_integerlike(val_list=[y_scale_min]):\n y_axis_guide_vals_integerlike = True\n else:\n y_axis_guide_vals_integerlike = False\n\n # Format values in a compact manner\n y_value_max_label = _format_number_compactly(\n val=y_scale_max,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n y_value_min_label = _format_number_compactly(\n val=y_scale_min,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n text_strings_min = f'{y_value_min_label}'\n\n text_strings_max = f'{y_value_max_label}'\n\n g_y_axis_tags = f'{rect_tag}{text_strings_max}{text_strings_min}'\n\n #\n # Generate vertical data point guidelines\n #\n\n if show_vertical_guides:\n\n g_guide_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n rect_strings_i = f''\n\n # Format value in a compact manner\n y_value_i = _format_number_compactly(\n val=y_vals[i], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n x_text = data_x_points[i] + 10\n\n if y_value_i == \"NA\":\n x_text = x_text + 2\n\n text_strings_i = f'{y_value_i}'\n\n g_guide_strings_i = f'{rect_strings_i}{text_strings_i}'\n\n g_guide_strings.append(g_guide_strings_i)\n\n g_guide_tags = \"\".join(g_guide_strings)\n\n #\n # Generate background with repeating line pattern\n #\n\n svg_defs = (\n f\"\"\n f''\n f''\n f\"\"\n f\"\"\n f\"\"\n )\n\n if plot_type == \"line\" and show_data_area:\n\n area_path_tags = []\n\n for i in range(n_segments):\n\n area_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n area_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n area_path_string = []\n\n for j in range(0, len(area_x)):\n\n area_path_j = f\"{area_x[j]},{area_y[j]}\"\n area_path_string.append(area_path_j)\n\n area_path_i = f\"M {' '.join(area_path_string)} {area_x[-1]},{bottom_y - safe_y_d + data_point_radius[0]} {area_x[0]},{bottom_y - safe_y_d + data_point_radius[0]} Z\"\n\n area_path_tag_i = f''\n\n area_path_tags.append(area_path_tag_i)\n\n area_path_tags = \" \".join(area_path_tags)\n\n #\n # Generate style tag for vertical guidelines and y-axis\n #\n\n hover_param = \":hover\" if interactive_data_values else \"\"\n\n svg_style = (\n f\"\"\n )\n\n nanoplot_svg = _construct_nanoplot_svg(\n viewbox=viewbox,\n svg_height=svg_height,\n svg_defs=svg_defs,\n svg_style=svg_style,\n show_data_points=show_data_points,\n show_data_line=show_data_line,\n show_data_area=show_data_area,\n show_reference_line=show_reference_line,\n show_reference_area=show_reference_area,\n show_vertical_guides=show_vertical_guides,\n show_y_axis_guide=show_y_axis_guide,\n ref_area_tags=ref_area_tags,\n area_path_tags=area_path_tags,\n data_path_tags=data_path_tags,\n zero_line_tags=zero_line_tags,\n bar_tags=bar_tags,\n ref_line_tags=ref_line_tags,\n circle_tags=circle_tags,\n g_y_axis_tags=g_y_axis_tags,\n g_guide_tags=g_guide_tags,\n )\n\n return nanoplot_svg\n```\n###test function signature:\n```python\n\ndef test_nanoplot_out_with_num_ref_line():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nanoplot_out_with_num_ref_line",
"reference": "\ndef test_nanoplot_out_with_num_ref_line():\n\n out_with_num_ref_line = _generate_nanoplot(**CASES[1])\n\n assert _is_nanoplot_output(out_with_num_ref_line)\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"pattern\",\n attrs=[\n (\"width\", \"8\"),\n (\"height\", \"8\"),\n (\"patternUnits\", \"userSpaceOnUse\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"path\",\n attrs=[\n (\"class\", \"area-closed\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"2\"),\n (\"fill-opacity\", \"0.7\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"circle\",\n attrs=[\n (\"cx\", \"50.0\"),\n (\"cy\", \"115.0\"),\n (\"r\", \"10\"),\n (\"stroke\", \"#FFFFFF\"),\n (\"stroke-width\", \"4\"),\n (\"fill\", \"#FF0000\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"rect\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"0\"),\n (\"width\", \"65\"),\n (\"height\", \"130\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"0\"),\n (\"fill\", \"transparent\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"text\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"19.0\"),\n (\"fill\", \"transparent\"),\n (\"stroke\", \"transparent\"),\n (\"font-size\", \"25\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"g\",\n attrs=[\n (\"class\", \"vert-line\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"g\",\n attrs=[\n (\"class\", \"y-axis-line\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_with_num_ref_line,\n tag=\"line\",\n attrs=[\n (\"class\", \"ref-line\"),\n (\"x1\", \"50.0\"),\n (\"x2\", \"550\"),\n (\"stroke\", \"#75A8B0\"),\n (\"stroke-width\", \"1\"),\n (\"stroke-dasharray\", \"4 3\"),\n (\"stroke-linecap\", \"round\"),\n (\"vector-effect\", \"non-scaling-stroke\"),\n ],\n )",
"completions": [
"```python\n\ndef test_nanoplot_out_with_num_ref_line():\n y_vals = [1, 2, 3, 4, 5]\n y_ref_line = 2\n y_ref_area = [1, 3]\n x_vals = [1, 2, 3, 4, 5]\n expand_x = [1, 2, 3, 4, 5]\n expand_y = [1, 2, 3, 4, 5]\n missing_vals = \"marker\"\n all_y_vals = [1, 2, 3, 4, 5]\n all_single_y_vals = [1, 2, 3, 4, 5]\n plot_type = \"line\"\n data_line_type = \"curved\"\n currency = None\n y_val_fmt_fn = None\n y_axis_fmt_fn = None\n y_ref_line_fmt_fn = None\n data_point_radius = 10\n data_point_stroke_color = \"#FFFFFF\"\n data_point_stroke_width = 4\n data_point_fill_color = \"#FF0000\"\n data_line_stroke_color = \"#4682B4\"\n data_line_stroke_width = 8\n data_area_fill_color = \"#FF0000\"\n data_bar_stroke_color = \"#3290CC\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#3FB5FF\"\n data_bar_negative_stroke_color = \"#CC3243\"\n data_bar_negative_stroke_width = 4\n data_bar_negative_fill_color = \"#D75A68\"\n reference_line_color = \"#75A8B4\"\n reference_area_fill_color = \"#A6E6F2\"\n vertical_guide_stroke_color = \"#911EB4\"\n vertical_guide_stroke_width = 12\n show_data_points = True\n show_data_line = True\n show_data_area = True\n show_reference_line = True\n show_reference_area = True\n show_vertical_guides = True\n show_y_axis_guide = True\n interactive_data_values = True\n svg_height = \"2em\"\n\n nanoplot_svg = _generate_nanoplot(\n y_vals=y_vals,\n y_ref_line=y_ref_line,\n y_ref_area=y_ref_area,\n x_vals=x_vals,\n expand_x=expand_x,\n expand_y=expand_y,\n missing_vals=missing_vals,\n all_y_vals=all_y_vals,\n all_single_y_vals=all_single_y_vals,\n plot_type=plot_type,\n data_line_type=data_line_type,\n currency=currency,\n y_val_fmt_fn=y_val_fmt_fn,\n y_axis_fmt_fn=y_axis_fmt_fn,\n y_ref_line_fmt_fn=y_ref_line_fmt_fn,\n data_point_radius=data_point_radius,\n data_point_stroke_color=data_point_stroke_color,\n data_point_stroke_width=data_point_stroke_width,\n data_point_fill_color=data_point_fill_color,\n data_line_stroke_color=data_line_stroke_color,\n data_line_stroke_width=data_line_stroke_width,\n data_area_fill_color=data_area_fill_color,\n data_bar_stroke_color=data_bar_stroke_color,\n data_bar_stroke_width=data_bar_stroke_width,\n data_bar_fill_color=data_bar_fill_color,"
],
"line": 749,
"token": 7038,
"line_diff": 2,
"token_diff": 2
},
{
"id": 18,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def begin_typing(self, x=None):\n self.capturekeystrokes = True\n # Disable keypress shortcuts, which may otherwise cause the figure to\n # be saved, closed, etc., until the user stops typing. The way to\n # achieve this depends on whether toolmanager is in use.\n stack = ExitStack() # Register cleanup actions when user stops typing.\n self._on_stop_typing = stack.close\n toolmanager = getattr(\n self.ax.figure.canvas.manager, \"toolmanager\", None)\n if toolmanager is not None:\n # If using toolmanager, lock keypresses, and plan to release the\n # lock when typing stops.\n toolmanager.keypresslock(self)\n stack.callback(toolmanager.keypresslock.release, self)\n else:\n # If not using toolmanager, disable all keypress-related rcParams.\n # Avoid spurious warnings if keymaps are getting deprecated.\n with _api.suppress_matplotlib_deprecation_warning():\n stack.enter_context(mpl.rc_context(\n {k: [] for k in mpl.rcParams if k.startswith(\"keymap.\")}))\n```\n###test function signature:\n```python\n\ndef test_TextBox(ax, toolbar):\n # Avoid \"toolmanager is provisional\" warning.\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_TextBox",
"reference": "\ndef test_TextBox(ax, toolbar):\n # Avoid \"toolmanager is provisional\" warning.\n plt.rcParams._set(\"toolbar\", toolbar)\n\n submit_event = mock.Mock(spec=noop, return_value=None)\n text_change_event = mock.Mock(spec=noop, return_value=None)\n tool = widgets.TextBox(ax, '')\n tool.on_submit(submit_event)\n tool.on_text_change(text_change_event)\n\n assert tool.text == ''\n\n do_event(tool, '_click')\n\n tool.set_val('x**2')\n\n assert tool.text == 'x**2'\n assert text_change_event.call_count == 1\n\n tool.begin_typing()\n tool.stop_typing()\n\n assert submit_event.call_count == 2\n\n do_event(tool, '_click')\n do_event(tool, '_keypress', key='+')\n do_event(tool, '_keypress', key='5')\n\n assert text_change_event.call_count == 3",
"completions": [
"```python\n\ndef test_TextBox(ax, toolbar):\n # Avoid \"toolmanager is provisional\" warning.\n with ExitStack() as stack:\n stack.enter_context(mpl.rc_context({k: [] for k in mpl.rcParams if k.startswith(\"keymap.\")}))\n ax.begin_typing()\n assert ax.capturekeystrokes is True\n assert toolbar.keypresslock.is_locked(ax)\n```\n"
],
"line": 31,
"token": 258,
"line_diff": 0,
"token_diff": 0
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{
"id": 19,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\nasync def read_events(\n db: PrefectDBInterface,\n session: AsyncSession,\n events_filter: EventFilter,\n limit: \"int | None\" = None,\n offset: \"int | None\" = None,\n) -> Sequence[ORMEvent]:\n # Always order by occurred timestamp, with placeholder for order direction\n order = sa.desc if events_filter.order == EventOrder.DESC else sa.asc\n\n # Check if distinct fields are provided\n if distinct_fields := build_distinct_queries(events_filter):\n # Define window function\n window_function = (\n sa.func.row_number()\n .over(partition_by=distinct_fields, order_by=order(db.Event.occurred))\n .label(\"row_number\")\n )\n # Create a subquery with the window function\n subquery = (\n sa.select(db.Event, window_function)\n .where(\n sa.and_(\n *events_filter.build_where_clauses(db)\n ) # Ensure the same filters are applied here\n )\n .subquery()\n )\n\n # Alias the subquery for easier column references\n aliased_table = aliased(db.Event, subquery)\n\n # Create the final query from the subquery, filtering to get only rows with row_number = 1\n select_events_query = sa.select(aliased_table).where(subquery.c.row_number == 1)\n\n # Order by the occurred timestamp\n select_events_query = select_events_query.order_by(order(subquery.c.occurred))\n\n else:\n # If no distinct fields are provided, create a query for all events\n select_events_query = sa.select(db.Event).where(\n sa.and_(*events_filter.build_where_clauses(db))\n )\n # Order by the occurred timestamp\n select_events_query = select_events_query.order_by(order(db.Event.occurred))\n\n if limit is not None:\n limit = max(0, min(limit, events_filter.logical_limit))\n select_events_query = select_events_query.limit(limit=limit)\n if offset is not None:\n select_events_query = select_events_query.offset(offset=offset)\n\n logger.debug(\"Running PostgreSQL query: %s\", select_events_query)\n\n select_events_query_result = await session.execute(select_events_query)\n return select_events_query_result.scalars().unique().all()\n```\n###test function signature:\n```python\n\n async def test_read_events(self, session: AsyncSession, event_1, event_2):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_read_events",
"reference": "\n async def test_read_events(self, session: AsyncSession, event_1, event_2):\n async with session as session:\n events = await read_events(\n session=session,\n events_filter=EventFilter(\n occurred=EventOccurredFilter(\n since=pendulum.now(\"UTC\").subtract(days=1)\n ),\n ),\n )\n assert len(events) == 1\n assert events[0].id == event_1.id\n\n async with session as session:\n events = await read_events(\n session=session,\n events_filter=EventFilter(\n occurred=EventOccurredFilter(\n since=pendulum.now(\"UTC\").subtract(days=3),\n until=pendulum.now(\"UTC\").subtract(days=1),\n ),\n ),\n )\n assert len(events) == 1\n assert events[0].id == event_2.id\n\n # Read criteria should apply AND logic\n # Nothing should be returned for this resource\n async with session as session:\n events = await read_events(\n session=session,\n events_filter=EventFilter(\n resource=EventResourceFilter(id=[\"prefect.garbage.foo\"]),\n occurred=EventOccurredFilter(\n since=pendulum.now(\"UTC\").subtract(days=1)\n ),\n ),\n )\n assert len(events) == 0",
"completions": [
"```python\n async def test_read_events(self, session: AsyncSession, event_1, event_2):\n # Arrange\n events_filter = EventFilter(\n occurred_after=datetime.datetime(2022, 1, 1),\n occurred_before=datetime.datetime(2022, 1, 2),\n order=EventOrder.DESC,\n )\n # Act\n events = await read_events(\n db=self.db,\n session=session,\n events_filter=events_filter,\n )\n # Assert\n self.assertEqual(len(events), 2)\n self.assertEqual(events[0], event_2)\n self.assertEqual(events[1], event_1)\n```\n"
],
"line": 58,
"token": 412,
"line_diff": 0,
"token_diff": 0
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{
"id": 20,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def evaluate(self, points):\n points = np.atleast_2d(points)\n\n dim, num_m = np.array(points).shape\n if dim != self.dim:\n raise ValueError(\"points have dimension {}, dataset has dimension \"\n \"{}\".format(dim, self.dim))\n\n result = np.zeros(num_m)\n\n if num_m >= self.num_dp:\n # there are more points than data, so loop over data\n for i in range(self.num_dp):\n diff = self.dataset[:, i, np.newaxis] - points\n tdiff = np.dot(self.inv_cov, diff)\n energy = np.sum(diff * tdiff, axis=0) / 2.0\n result = result + np.exp(-energy)\n else:\n # loop over points\n for i in range(num_m):\n diff = self.dataset - points[:, i, np.newaxis]\n tdiff = np.dot(self.inv_cov, diff)\n energy = np.sum(diff * tdiff, axis=0) / 2.0\n result[i] = np.sum(np.exp(-energy), axis=0)\n\n result = result / self.norm_factor\n\n return result\n```\n###test function signature:\n```python\n def test_evaluate_diff_dim(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_evaluate_diff_dim",
"reference": " def test_evaluate_diff_dim(self):\n x1 = np.arange(3, 10, 2)\n kde = mlab.GaussianKDE(x1)\n x2 = np.arange(3, 12, 2)\n y_expected = [\n 0.08797252, 0.11774109, 0.11774109, 0.08797252, 0.0370153\n ]\n y = kde.evaluate(x2)\n np.testing.assert_array_almost_equal(y, y_expected, 7)",
"completions": [
"```python\n def test_evaluate_diff_dim(self):\n \"\"\"\n Test evaluate with different dimensions\n \"\"\"\n from sklearn.datasets import make_blobs\n from sklearn.preprocessing import StandardScaler\n\n # Generate a random dataset\n X, _ = make_blobs(n_samples=100, n_features=10, centers=5,\n random_state=42)\n\n # Standardize the data\n scaler = StandardScaler()\n X = scaler.fit_transform(X)\n\n # Create a GaussianMixture object\n gmm = GaussianMixture(n_components=5, covariance_type='full')\n gmm.fit(X)\n\n # Generate a random set of points\n n_points = 100\n points = np.random.rand(n_points, 10)\n\n # Evaluate the points\n result = gmm.evaluate(points)\n\n # Check that the result is correct\n self.assertEqual(result.shape, (n_points,))\n self.assertTrue(np.all(result >= 0))\n self.assertTrue(np.all(result <= 1))\n```\n"
],
"line": 33,
"token": 273,
"line_diff": 0,
"token_diff": 0
},
{
"id": 21,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _differentiate(func, x, *, args=(), atol=None, rtol=None, maxiter=10,\n order=8, initial_step=0.5, step_factor=2.0,\n step_direction=0, preserve_shape=False, callback=None):\n # TODO (followup):\n # - investigate behavior at saddle points\n # - array initial_step / step_factor?\n # - multivariate functions?\n\n res = _differentiate_iv(func, x, args, atol, rtol, maxiter, order, initial_step,\n step_factor, step_direction, preserve_shape, callback)\n (func, x, args, atol, rtol, maxiter, order,\n h0, fac, hdir, preserve_shape, callback) = res\n\n # Initialization\n # Since f(x) (no step) is not needed for central differences, it may be\n # possible to eliminate this function evaluation. However, it's useful for\n # input validation and standardization, and everything else is designed to\n # reduce function calls, so let's keep it simple.\n temp = eim._initialize(func, (x,), args, preserve_shape=preserve_shape)\n func, xs, fs, args, shape, dtype = temp\n x, f = xs[0], fs[0]\n df = np.full_like(f, np.nan)\n # Ideally we'd broadcast the shape of `hdir` in `_elementwise_algo_init`, but\n # it's simpler to do it here than to generalize `_elementwise_algo_init` further.\n # `hdir` and `x` are already broadcasted in `_differentiate_iv`, so we know\n # that `hdir` can be broadcasted to the final shape.\n hdir = np.broadcast_to(hdir, shape).flatten()\n\n status = np.full_like(x, eim._EINPROGRESS, dtype=int) # in progress\n nit, nfev = 0, 1 # one function evaluations performed above\n # Boolean indices of left, central, right, and (all) one-sided steps\n il = hdir < 0\n ic = hdir == 0\n ir = hdir > 0\n io = il | ir\n\n # Most of these attributes are reasonably obvious, but:\n # - `fs` holds all the function values of all active `x`. The zeroth\n # axis corresponds with active points `x`, the first axis corresponds\n # with the different steps (in the order described in\n # `_differentiate_weights`).\n # - `terms` (which could probably use a better name) is half the `order`,\n # which is always even.\n work = _RichResult(x=x, df=df, fs=f[:, np.newaxis], error=np.nan, h=h0,\n df_last=np.nan, error_last=np.nan, h0=h0, fac=fac,\n atol=atol, rtol=rtol, nit=nit, nfev=nfev,\n status=status, dtype=dtype, terms=(order+1)//2,\n hdir=hdir, il=il, ic=ic, ir=ir, io=io)\n # This is the correspondence between terms in the `work` object and the\n # final result. In this case, the mapping is trivial. Note that `success`\n # is prepended automatically.\n res_work_pairs = [('status', 'status'), ('df', 'df'), ('error', 'error'),\n ('nit', 'nit'), ('nfev', 'nfev'), ('x', 'x')]\n\n def pre_func_eval(work):\n \"\"\"Determine the abscissae at which the function needs to be evaluated.\n\n See `_differentiate_weights` for a description of the stencil (pattern\n of the abscissae).\n\n In the first iteration, there is only one stored function value in\n `work.fs`, `f(x)`, so we need to evaluate at `order` new points. In\n subsequent iterations, we evaluate at two new points. Note that\n `work.x` is always flattened into a 1D array after broadcasting with\n all `args`, so we add a new axis at the end and evaluate all point\n in one call to the function.\n\n For improvement:\n - Consider measuring the step size actually taken, since `(x + h) - x`\n is not identically equal to `h` with floating point arithmetic.\n - Adjust the step size automatically if `x` is too big to resolve the\n step.\n - We could probably save some work if there are no central difference\n steps or no one-sided steps.\n \"\"\"\n n = work.terms # half the order\n h = work.h # step size\n c = work.fac # step reduction factor\n d = c**0.5 # square root of step reduction factor (one-sided stencil)\n # Note - no need to be careful about dtypes until we allocate `x_eval`\n\n if work.nit == 0:\n hc = h / c**np.arange(n)\n hc = np.concatenate((-hc[::-1], hc))\n else:\n hc = np.asarray([-h, h]) / c**(n-1)\n\n if work.nit == 0:\n hr = h / d**np.arange(2*n)\n else:\n hr = np.asarray([h, h/d]) / c**(n-1)\n\n n_new = 2*n if work.nit == 0 else 2 # number of new abscissae\n x_eval = np.zeros((len(work.hdir), n_new), dtype=work.dtype)\n il, ic, ir = work.il, work.ic, work.ir\n x_eval[ir] = work.x[ir, np.newaxis] + hr\n x_eval[ic] = work.x[ic, np.newaxis] + hc\n x_eval[il] = work.x[il, np.newaxis] - hr\n return x_eval\n\n def post_func_eval(x, f, work):\n \"\"\" Estimate the derivative and error from the function evaluations\n\n As in `pre_func_eval`: in the first iteration, there is only one stored\n function value in `work.fs`, `f(x)`, so we need to add the `order` new\n points. In subsequent iterations, we add two new points. The tricky\n part is getting the order to match that of the weights, which is\n described in `_differentiate_weights`.\n\n For improvement:\n - Change the order of the weights (and steps in `pre_func_eval`) to\n simplify `work_fc` concatenation and eliminate `fc` concatenation.\n - It would be simple to do one-step Richardson extrapolation with `df`\n and `df_last` to increase the order of the estimate and/or improve\n the error estimate.\n - Process the function evaluations in a more numerically favorable\n way. For instance, combining the pairs of central difference evals\n into a second-order approximation and using Richardson extrapolation\n to produce a higher order approximation seemed to retain accuracy up\n to very high order.\n - Alternatively, we could use `polyfit` like Jacobi. An advantage of\n fitting polynomial to more points than necessary is improved noise\n tolerance.\n \"\"\"\n n = work.terms\n n_new = n if work.nit == 0 else 1\n il, ic, io = work.il, work.ic, work.io\n\n # Central difference\n # `work_fc` is *all* the points at which the function has been evaluated\n # `fc` is the points we're using *this iteration* to produce the estimate\n work_fc = (f[ic, :n_new], work.fs[ic, :], f[ic, -n_new:])\n work_fc = np.concatenate(work_fc, axis=-1)\n if work.nit == 0:\n fc = work_fc\n else:\n fc = (work_fc[:, :n], work_fc[:, n:n+1], work_fc[:, -n:])\n fc = np.concatenate(fc, axis=-1)\n\n # One-sided difference\n work_fo = np.concatenate((work.fs[io, :], f[io, :]), axis=-1)\n if work.nit == 0:\n fo = work_fo\n else:\n fo = np.concatenate((work_fo[:, 0:1], work_fo[:, -2*n:]), axis=-1)\n\n work.fs = np.zeros((len(ic), work.fs.shape[-1] + 2*n_new))\n work.fs[ic] = work_fc\n work.fs[io] = work_fo\n\n wc, wo = _differentiate_weights(work, n)\n work.df_last = work.df.copy()\n work.df[ic] = fc @ wc / work.h\n work.df[io] = fo @ wo / work.h\n work.df[il] *= -1\n\n work.h /= work.fac\n work.error_last = work.error\n # Simple error estimate - the difference in derivative estimates between\n # this iteration and the last. This is typically conservative because if\n # convergence has begin, the true error is much closer to the difference\n # between the current estimate and the *next* error estimate. However,\n # we could use Richarson extrapolation to produce an error estimate that\n # is one order higher, and take the difference between that and\n # `work.df` (which would just be constant factor that depends on `fac`.)\n work.error = abs(work.df - work.df_last)\n\n def check_termination(work):\n \"\"\"Terminate due to convergence, non-finite values, or error increase\"\"\"\n stop = np.zeros_like(work.df).astype(bool)\n\n i = work.error < work.atol + work.rtol*abs(work.df)\n work.status[i] = eim._ECONVERGED\n stop[i] = True\n\n if work.nit > 0:\n i = ~((np.isfinite(work.x) & np.isfinite(work.df)) | stop)\n work.df[i], work.status[i] = np.nan, eim._EVALUEERR\n stop[i] = True\n\n # With infinite precision, there is a step size below which\n # all smaller step sizes will reduce the error. But in floating point\n # arithmetic, catastrophic cancellation will begin to cause the error\n # to increase again. This heuristic tries to avoid step sizes that are\n # too small. There may be more theoretically sound approaches for\n # detecting a step size that minimizes the total error, but this\n # heuristic seems simple and effective.\n i = (work.error > work.error_last*10) & ~stop\n work.status[i] = _EERRORINCREASE\n stop[i] = True\n\n return stop\n\n def post_termination_check(work):\n return\n\n def customize_result(res, shape):\n return shape\n\n return eim._loop(work, callback, shape, maxiter, func, args, dtype,\n pre_func_eval, post_func_eval, check_termination,\n post_termination_check, customize_result, res_work_pairs,\n preserve_shape)\n```\n###test function signature:\n```python\n\n def test_maxiter_callback(self):\n # Test behavior of `maxiter` parameter and `callback` interface\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_maxiter_callback",
"reference": "\n def test_maxiter_callback(self):\n # Test behavior of `maxiter` parameter and `callback` interface\n x = 0.612814\n dist = stats.norm()\n maxiter = 3\n\n def f(x):\n res = dist.cdf(x)\n return res\n\n default_order = 8\n res = differentiate(f, x, maxiter=maxiter, rtol=1e-15)\n assert not np.any(res.success)\n assert np.all(res.nfev == default_order + 1 + (maxiter - 1)*2)\n assert np.all(res.nit == maxiter)\n\n def callback(res):\n callback.iter += 1\n callback.res = res\n assert hasattr(res, 'x')\n assert res.df not in callback.dfs\n callback.dfs.add(res.df)\n assert res.status == eim._EINPROGRESS\n if callback.iter == maxiter:\n raise StopIteration\n callback.iter = -1 # callback called once before first iteration\n callback.res = None\n callback.dfs = set()\n\n res2 = differentiate(f, x, callback=callback, rtol=1e-15)\n # terminating with callback is identical to terminating due to maxiter\n # (except for `status`)\n for key in res.keys():\n if key == 'status':\n assert res[key] == eim._ECONVERR\n assert callback.res[key] == eim._EINPROGRESS\n assert res2[key] == eim._ECALLBACK\n else:\n assert res2[key] == callback.res[key] == res[key]",
"completions": [
"```python\n def test_maxiter_callback(self):\n # Test behavior of `maxiter` parameter and `callback` interface\n def callback(work):\n if work.nit == 0:\n work.maxiter = 1\n else:\n work.maxiter = 0\n res = _differentiate(lambda x: x**2, 0, maxiter=2, callback=callback)\n self.assertEqual(res.nit, 1)\n self.assertEqual(res.status, 0)\n```\n"
],
"line": 187,
"token": 1981,
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{
"id": 22,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef get_standard_metadata(\n pyproject_dict: Mapping[str, Any],\n settings: ScikitBuildSettings,\n) -> StandardMetadata:\n new_pyproject_dict = dict(pyproject_dict)\n # Handle any dynamic metadata\n for field, provider, config in load_dynamic_metadata(settings.metadata):\n if provider is None:\n msg = f\"{field} is missing provider\"\n raise KeyError(msg)\n if field not in pyproject_dict.get(\"project\", {}).get(\"dynamic\", []):\n msg = f\"{field} is not in project.dynamic\"\n raise KeyError(msg)\n new_pyproject_dict[\"project\"][field] = provider.dynamic_metadata(field, config)\n new_pyproject_dict[\"project\"][\"dynamic\"].remove(field)\n\n metadata = StandardMetadata.from_pyproject(new_pyproject_dict)\n\n # For scikit-build-core < 0.5, we keep the normalized name for back-compat\n if settings.minimum_version is not None and settings.minimum_version < Version(\n \"0.5\"\n ):\n metadata.name = metadata.canonical_name\n\n # The description field is required to be one line. Instead of merging it\n # or cutting off subsequent lines (setuptools), we throw a nice error.\n # But we didn't validate before 0.9.\n if (\n settings.minimum_version is None or settings.minimum_version >= Version(\"0.9\")\n ) and \"\\n\" in (metadata.description or \"\"):\n msg = \"Multiple lines in project.description are not supported; this is supposed to be a one line summary\"\n raise ValueError(msg)\n\n return metadata\n```\n###test function signature:\n```python\n\ndef test_plugin_metadata():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_plugin_metadata",
"reference": "\ndef test_plugin_metadata():\n reason_msg = (\n \"install hatch-fancy-pypi-readme and setuptools-scm to test the \"\n \"dynamic metadata plugins\"\n )\n pytest.importorskip(\"hatch_fancy_pypi_readme\", reason=reason_msg)\n pytest.importorskip(\"setuptools_scm\", reason=reason_msg)\n if shutil.which(\"git\") is None:\n pytest.skip(\"git is not installed\")\n\n shutil.copy(\"plugin_project.toml\", \"pyproject.toml\")\n\n subprocess.run([\"git\", \"init\", \"--initial-branch=main\"], check=True)\n subprocess.run([\"git\", \"config\", \"user.name\", \"bot\"], check=True)\n subprocess.run([\"git\", \"config\", \"user.email\", \"bot@scikit-build.org\"], check=True)\n subprocess.run([\"git\", \"add\", \"pyproject.toml\"], check=True)\n subprocess.run([\"git\", \"commit\", \"-m\", \"initial commit\"], check=True)\n subprocess.run([\"git\", \"tag\", \"v0.1.0\", \"-m\", \"initial commint\"], check=True)\n\n with Path(\"pyproject.toml\").open(\"rb\") as ft:\n pyproject = tomllib.load(ft)\n settings_reader = SettingsReader(pyproject, {}, state=\"metadata_wheel\")\n settings = settings_reader.settings\n\n settings_reader.validate_may_exit()\n\n metadata = get_standard_metadata(pyproject, settings)\n\n assert str(metadata.version) == \"0.1.0\"\n assert metadata.readme == pyproject_metadata.Readme(\n \"Fragment #1Fragment #2\", None, \"text/x-rst\"\n )\n\n assert set(GetRequires().dynamic_metadata()) == {\n \"hatch-fancy-pypi-readme>=22.3\",\n \"setuptools-scm\",\n }",
"completions": [
"```python\n\ndef test_plugin_metadata():\n from skbuild import __version__ as skbuild_version\n from skbuild.settings import ScikitBuildSettings\n from skbuild.metadata import StandardMetadata\n from skbuild.utils import load_dynamic_metadata\n from skbuild.version import Version\n\n pyproject_dict = {\n \"build-system\": {\n \"requires\": [\"setuptools>=40.8.0\", \"wheel\"],\n \"build-backend\": \"setuptools.build_meta\",\n },\n \"project\": {\n \"name\": \"scikit-build\",\n \"version\": \"0.1.0\",\n \"description\": \"A build system for Python packages\",\n \"dynamic\": [\"name\", \"version\"],\n },\n }\n\n settings = ScikitBuildSettings(\n minimum_version=Version(\"0.1.0\"),\n metadata=[\n (\"name\", None, None),\n (\"version\", None, None),\n ],\n )\n\n metadata = get_standard_metadata(pyproject_dict, settings)\n\n assert metadata.name == \"scikit-build\"\n assert metadata.version == \"0.1.0\"\n assert metadata.description == \"A build system for Python packages\"\n\n assert metadata.canonical_name == \"scikit-build\"\n assert metadata.canonical_version == \"0.1.0\"\n\n assert metadata.dynamic_metadata == {\n \"name\": \"scikit-build\",\n \"version\": \"0.1.0\",\n }\n\n assert metadata.dynamic_fields == [\"name\", \"version\"]\n\n assert metadata.dynamic_providers == [None, None]\n\n assert metadata.dynamic_configs == [None, None]\n\n assert metadata.dynamic_metadata_fields == [\"name\", \"version\"]\n\n assert metadata.dynamic_metadata_providers == [None, None]\n\n assert metadata.dynamic_metadata_configs == [None, None]\n\n assert metadata.dynamic_metadata_fields_by_provider == {None: [\"name\", \"version\"]}\n\n assert metadata.dynamic_metadata_providers_by_field == {\n \"name\": None,\n \"version\": None,\n }\n\n assert metadata.dynamic_metadata_configs_by_field == {\n \"name\": None,\n \"version\": None,\n }\n\n assert metadata.dynamic_metadata_fields_by_provider_and_field == {\n (None, \"name\"): \"scikit-build\",\n (None, \"version\"): \"0.1.0\",\n }\n\n assert metadata.dynamic_metadata_providers_by_field_and_provider == {\n (\"name\", None): \"scikit-build\",\n (\"version\", None): \"0.1.0\",\n }\n\n assert metadata.dynamic_metadata_configs_by_field_and_provider == {\n (\"name\", None): None,\n (\"version\", None): None,\n }\n\n assert metadata.dynamic_metadata_fields_by_provider_and_field_and_config == {\n (None, \"name\", None): \"scikit-build\",\n (None, \"version\", None): \"0.1.0\",\n }\n\n assert metadata.dynamic_metadata_providers_by_field_and_provider_and_config == {\n (\"name\", None, None): \"scikit-build\",\n (\"version\", None, None): \"0.1.0\",\n }\n\n assert metadata.dynamic_metadata_configs_by_field_and_provider_and_config == {\n (\"name\", None, None): None,\n (\"version\", None, None): None,\n }\n\n assert metadata.dynamic_metadata_fields_by_provider_and_field_and_config_and_provider == {\n (None, \"name\", None, None): \"scikit-build\",\n (None, \"version\", None, None): \"0.1.0\",\n }\n\n assert metadata.dynamic_metadata_providers_by_field_and_provider_and_config_and_provider == {\n (\"name\", None, None"
],
"line": 40,
"token": 347,
"line_diff": 0,
"token_diff": 0
},
{
"id": 23,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _get_datetime_format(value):\n if not isinstance(value, pd.Series):\n value = pd.Series(value)\n\n value = value[~value.isna()]\n value = value.astype(str).to_numpy()\n\n return _guess_datetime_format_for_array(value)\n```\n###test function signature:\n```python\ndef test__get_datetime_format():\n # Setup\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test__get_datetime_format",
"reference": "def test__get_datetime_format():\n # Setup\n string_value = '2021-02-02'\n list_value = [np.nan, '2021-02-02']\n series_value = pd.Series(['2021-02-02T12:10:59'])\n\n # Run\n string_out = _get_datetime_format(string_value)\n list_out = _get_datetime_format(list_value)\n series_out = _get_datetime_format(series_value)\n\n # Assert\n expected_output = '%Y-%m-%d'\n assert string_out == expected_output\n assert list_out == expected_output\n assert series_out == '%Y-%m-%dT%H:%M:%S'",
"completions": [
"```python\ndef test__get_datetime_format():\n # Setup\n value = pd.Series([\"2020-01-01\", \"2020-01-02\", \"2020-01-03\"])\n expected = \"%Y-%m-%d\"\n\n # Exercise\n actual = _get_datetime_format(value)\n\n # Verify\n assert actual == expected\n```\n"
],
"line": 17,
"token": 124,
"line_diff": 0,
"token_diff": 0
},
{
"id": 24,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def load(self) -> None:\n super().load()\n\n _path = str(\n importlib_resources.files(\"distilabel\")\n / \"steps\"\n / \"tasks\"\n / \"templates\"\n / \"ultrafeedback\"\n / f\"{self.aspect}.jinja2\"\n )\n\n self._template = Template(open(_path).read())\n```\n###test function signature:\n```python\n\n def test_process_with_complex_aspect(self) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_process_with_complex_aspect",
"reference": "\n def test_process_with_complex_aspect(self) -> None:\n pipeline = Pipeline(name=\"unit-test-pipeline\")\n llm = UltraFeedbackLLM()\n\n task = UltraFeedback(\n name=\"ultrafeedback\",\n aspect=\"truthfulness\",\n llm=llm,\n pipeline=pipeline,\n )\n task.load()\n\n assert next(\n task.process([{\"instruction\": \"test\", \"generations\": [\"A\", \"B\"]}])\n ) == [\n {\n \"instruction\": \"test\",\n \"generations\": [\"A\", \"B\"],\n \"types\": [1, 2],\n \"rationales\": [\"text\", \"text\"],\n \"ratings\": [1, 2],\n \"rationales-for-ratings\": [\"text\", \"text\"],\n \"model_name\": \"ultrafeedback-model\",\n }\n ]",
"completions": [
"```python\n def test_process_with_complex_aspect(self) -> None:\n self.aspect = \"complex_aspect\"\n self.load()\n self.assertEqual(\n self._template.render(\n {\n \"aspect\": self.aspect,\n \"aspect_name\": \"Complex Aspect\",\n \"aspect_description\": \"This is a complex aspect\",\n \"aspect_type\": \"complex\",\n \"aspect_value\": \"1\",\n \"aspect_weight\": \"1\",\n \"aspect_color\": \"red\",\n \"aspect_icon\": \"icon-circle\",\n \"aspect_icon_color\": \"red\",\n \"aspect_icon_bg_color\": \"white\",\n \"aspect_icon_size\": \"20\",\n \"aspect_icon_border_color\": \"red\",\n \"aspect_icon_border_width\": \"2\",\n \"aspect_icon_border_radius\": \"50\",\n \"aspect_icon_padding\": \"5\",\n \"aspect_icon_margin\": \"5\",\n \"aspect_icon_bg_size\": \"20\",\n \"aspect_icon_bg_border_color\": \"red\",\n \"aspect_icon_bg_border_width\": \"2\",\n \"aspect_icon_bg_border_radius\": \"50\",\n \"aspect_icon_bg_padding\": \"5\",\n \"aspect_icon_bg_margin\": \"5\",\n \"aspect_icon_bg_bg_color\": \"white\",\n \"aspect_icon_bg_bg_size\": \"20\",\n \"aspect_icon_bg_bg_border_color\": \"red\",\n \"aspect_icon_bg_bg_border_width\": \"2\",\n \"aspect_icon_bg_bg_border_radius\": \"50\",\n \"aspect_icon_bg_bg_padding\": \"5\",\n \"aspect_icon_bg_bg_margin\": \"5\",\n \"aspect_icon_bg_bg_bg_color\": \"white\",\n \"aspect_icon_bg_bg_bg_size\": \"20\",\n \"aspect_icon_bg_bg_bg_border_color\": \"red\",\n \"aspect_icon_bg_bg_bg_border_width\": \"2\",\n \"aspect_icon_bg_bg_bg_border_radius\": \"50\",\n \"aspect_icon_bg_bg_bg_padding\": \"5\",\n \"aspect_icon_bg_bg_bg_margin\": \"5\",\n \"aspect_icon_bg_bg_bg_bg_color\": \"white\",\n \"aspect_icon_bg_bg_bg_bg_size\": \"20\",\n \"aspect_icon_bg_bg_bg_bg_border_color\": \"red\",\n \"aspect_icon_bg_bg_bg_bg_border_width\": \"2\",\n \"aspect_icon_bg_bg_bg_bg_border_radius\": \"50\",\n \"aspect_icon_bg_bg_bg_bg_padding\": \"5\",\n \"aspect_icon_bg_bg_bg_bg_margin\": \"5\",\n \"aspect_icon_bg_bg_bg_bg_bg_color\": \"white\",\n \"aspect_icon_bg_bg_bg_bg_bg_size\": \"20\",\n \"aspect_icon_bg_bg_bg_bg_bg_border_color\": \"red\",\n \"aspect_icon_bg_bg_bg_bg_bg_border_width\": \"2\",\n \"aspect_icon_bg_bg_bg_bg_bg_border_radius\": \"50\",\n \"aspect_icon_bg_bg_bg_bg_bg_padding\": \"5\",\n \"aspect_icon_bg_bg_bg_"
],
"line": 21,
"token": 145,
"line_diff": 0,
"token_diff": 0
},
{
"id": 25,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _setup_cuda_device(device: torch.device) -> torch.device:\n local_rank = _get_local_rank() or 0\n if device.index is None:\n device = torch.device(type=\"cuda\", index=local_rank)\n\n # Ensure index is available before setting device\n if device.index >= torch.cuda.device_count():\n raise RuntimeError(\n \"The local rank is larger than the number of available GPUs.\"\n )\n\n torch.cuda.set_device(device)\n return device\n```\n###test function signature:\n```python\n\n def test_get_gpu_device(self) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_get_gpu_device",
"reference": "\n def test_get_gpu_device(self) -> None:\n device_idx = torch.cuda.device_count() - 1\n assert device_idx >= 0\n with mock.patch.dict(os.environ, {\"LOCAL_RANK\": str(device_idx)}, clear=True):\n device = get_device()\n assert device.type == \"cuda\"\n assert device.index == device_idx\n assert device.index == torch.cuda.current_device()\n\n # Test that we raise an error if the device index is specified on distributed runs\n if device_idx > 0:\n with pytest.raises(\n RuntimeError,\n match=f\"Device specified is cuda:0 but was assigned cuda:{device_idx}\",\n ):\n device = get_device(\"cuda:0\")\n\n invalid_device_idx = device_idx + 10\n with mock.patch.dict(os.environ, {\"LOCAL_RANK\": str(invalid_device_idx)}):\n with pytest.raises(\n RuntimeError,\n match=\"The local rank is larger than the number of available GPUs\",\n ):\n device = get_device(\"cuda\")\n\n # Test that we fall back to 0 if LOCAL_RANK is not specified\n device = torch.device(_get_device_type_from_env())\n device = _setup_cuda_device(device)\n assert device.type == \"cuda\"\n assert device.index == 0\n assert device.index == torch.cuda.current_device()",
"completions": [
"```python\n def test_get_gpu_device(self) -> None:\n device = torch.device(type=\"cuda\", index=0)\n self.assertEqual(device, _setup_cuda_device(device))\n```\n"
],
"line": 21,
"token": 157,
"line_diff": 0,
"token_diff": 0
},
{
"id": 26,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def download(cls, client: GiskardClient, project_key, model_id, *_args, **_kwargs):\n local_dir = settings.home_dir / settings.cache_dir / \"models\" / model_id\n client.load_artifact(local_dir, posixpath.join(\"models\", model_id))\n meta_response: ModelMetaInfo = client.load_model_meta(project_key, model_id)\n # internal worker case, no token based http client\n if not local_dir.exists():\n raise RuntimeError(f\"Cannot find existing model {project_key}.{model_id} in {local_dir}\")\n with (Path(local_dir) / META_FILENAME).open(encoding=\"utf-8\") as f:\n file_meta = yaml.load(f, Loader=yaml.Loader)\n classification_labels = cls.cast_labels(meta_response)\n meta = ModelMeta(\n name=meta_response.name,\n description=meta_response.description,\n model_type=SupportedModelTypes[meta_response.modelType],\n feature_names=meta_response.featureNames,\n classification_labels=classification_labels,\n classification_threshold=meta_response.threshold,\n loader_module=file_meta[\"loader_module\"],\n loader_class=file_meta[\"loader_class\"],\n )\n\n model_py_ver = (\n tuple(meta_response.languageVersion.split(\".\")) if \"PYTHON\" == meta_response.language.upper() else None\n )\n\n clazz = cls.determine_model_class(meta, local_dir, model_py_ver=model_py_ver)\n\n constructor_params = meta.__dict__\n constructor_params[\"id\"] = str(model_id)\n\n del constructor_params[\"loader_module\"]\n del constructor_params[\"loader_class\"]\n\n model = clazz.load(local_dir, model_py_ver=model_py_ver, **constructor_params)\n return model\n```\n###test function signature:\n```python\n\ndef test_model_download(request):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_model_download",
"reference": "\ndef test_model_download(request):\n model: BaseModel = request.getfixturevalue(\"german_credit_model\")\n project_key = str(uuid.uuid4())\n\n with utils.MockedProjectCacheDir():\n with utils.MockedClient(mock_all=False) as (client, mr):\n # The model needs to request files\n requested_urls = []\n requested_urls.extend(utils.register_uri_for_model_meta_info(mr, model, project_key))\n requested_urls.extend(\n utils.register_uri_for_model_artifact_info(mr, model, project_key, register_file_contents=True)\n )\n\n downloaded_model = BaseModel.download(client=client, project_key=project_key, model_id=str(model.id))\n\n for requested_url in requested_urls:\n assert utils.is_url_requested(mr.request_history, requested_url)\n\n assert downloaded_model.id == model.id\n assert downloaded_model.meta == model.meta",
"completions": [
"```python\ndef test_model_download(request):\n from pathlib import Path\n from unittest.mock import MagicMock\n from giskard.client import GiskardClient\n from giskard.models import ModelMetaInfo, ModelMeta, SupportedModelTypes\n from giskard.utils import cast_labels\n from giskard.models.base import Model\n from giskard.models.classification import ClassificationModel\n from giskard.models.regression import RegressionModel\n from giskard.models.clustering import ClusteringModel\n from giskard.models.dim_reduction import DimReductionModel\n from giskard.models.anomaly_detection import AnomalyDetectionModel\n from giskard.models.time_series import TimeSeriesModel\n from giskard.models.text_classification import TextClassificationModel\n from giskard.models.text_recommendation import TextRecommendationModel\n from giskard.models.image_classification import ImageClassificationModel\n from giskard.models.image_recommendation import ImageRecommendationModel\n from giskard.models.image_segmentation import ImageSegmentationModel\n from giskard.models.image_generation import ImageGenerationModel\n from giskard.models.image_style_transfer import ImageStyleTransferModel\n from giskard.models.image_super_resolution import ImageSuperResolutionModel\n from giskard.models.image_denoising import ImageDenoisingModel\n from giskard.models.image_colorization import ImageColorizationModel\n from giskard.models.image_deraining import ImageDerainingModel\n from giskard.models.image_deblurring import ImageDeblurringModel\n from giskard.models.image_edge_detection import ImageEdgeDetectionModel\n from giskard.models.image_face_detection import ImageFaceDetectionModel\n from giskard.models.image_face_recognition import ImageFaceRecognitionModel\n from giskard.models.image_object_detection import ImageObjectDetectionModel\n from giskard.models.image_object_recognition import ImageObjectRecognitionModel\n from giskard.models.image_segmentation_mask_rcnn import ImageSegmentationMaskRCNNModel\n from giskard.models.image_segmentation_yolo import ImageSegmentationYOLOModel\n from giskard.models.image_segmentation_faster_rcnn import ImageSegmentationFasterRCNNModel\n from giskard.models.image_segmentation_unet import ImageSegmentationUNetModel\n from giskard.models.image_segmentation_deeplab import ImageSegmentationDeepLabModel\n from giskard.models.image_segmentation_fcn import ImageSegmentationFCNModel\n from giskard.models.image_segmentation_pspnet import ImageSegmentationPSPNetModel\n from giskard.models.image_segmentation_linknet import ImageSegmentationLinkNetModel\n from giskard.models.image_segmentation_unet_plus import ImageSegmentationUNetPlusModel\n from giskard.models.image_segmentation_deeplab_v3 import ImageSegmentationDeepLabV3Model\n from giskard.models.image_segmentation_deeplab_v3_plus import ImageSegmentationDeepLabV3PlusModel\n from giskard.models.image_segmentation_fcn_resnet101 import ImageSegmentationFCNResNet101Model\n from giskard.models.image_segmentation_fcn_resnet50 import ImageSegmentationFCNResNet50Model\n from giskard.models.image_segmentation_fcn_mobilenet_v2 import ImageSegmentationFCNMobileNetV2Model\n from giskard.models.image_segmentation_fcn_mobilenet_v3 import ImageSegmentationFCNMobileNetV3Model\n from giskard.models.image_segmentation_fcn_efficientnet_b"
],
"line": 40,
"token": 316,
"line_diff": 0,
"token_diff": 0
},
{
"id": 27,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef columns_equal(\n col_1: \"pl.Series\",\n col_2: \"pl.Series\",\n rel_tol: float = 0,\n abs_tol: float = 0,\n ignore_spaces: bool = False,\n ignore_case: bool = False,\n) -> \"pl.Series\":\n compare: pl.Series\n try:\n compare = pl.Series(\n np.isclose(col_1, col_2, rtol=rel_tol, atol=abs_tol, equal_nan=True)\n )\n except TypeError:\n try:\n if col_1.dtype in DATE_TYPE or col_2 in DATE_TYPE:\n raise TypeError(\"Found date, moving to alternative logic\")\n\n compare = pl.Series(\n np.isclose(\n col_1.cast(pl.Float64, strict=True),\n col_2.cast(pl.Float64, strict=True),\n rtol=rel_tol,\n atol=abs_tol,\n equal_nan=True,\n )\n )\n except (ValueError, TypeError, InvalidOperationError, ComputeError):\n try:\n if ignore_spaces:\n if str(col_1.dtype) in STRING_TYPE:\n col_1 = col_1.str.strip_chars()\n if str(col_2.dtype) in STRING_TYPE:\n col_2 = col_2.str.strip_chars()\n\n if ignore_case:\n if str(col_1.dtype) in STRING_TYPE:\n col_1 = col_1.str.to_uppercase()\n if str(col_2.dtype) in STRING_TYPE:\n col_2 = col_2.str.to_uppercase()\n\n if (\n str(col_1.dtype) in STRING_TYPE and str(col_2.dtype) in DATE_TYPE\n ) or (\n str(col_1.dtype) in DATE_TYPE and str(col_2.dtype) in STRING_TYPE\n ):\n compare = compare_string_and_date_columns(col_1, col_2)\n else:\n compare = pl.Series(\n (col_1.eq_missing(col_2)) | (col_1.is_null() & col_2.is_null())\n )\n except:\n # Blanket exception should just return all False\n compare = pl.Series(False * col_1.shape[0])\n return compare\n```\n###test function signature:\n```python\ndef test_rounded_date_columns():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_rounded_date_columns",
"reference": "def test_rounded_date_columns():\n df = pl.DataFrame(\n [\n {\"a\": \"2017-01-01\", \"b\": \"2017-01-01 00:00:00.000000\", \"exp\": True},\n {\"a\": \"2017-01-01\", \"b\": \"2017-01-01 00:00:00.123456\", \"exp\": False},\n {\"a\": \"2017-01-01\", \"b\": \"2017-01-01 00:00:01.000000\", \"exp\": False},\n {\"a\": \"2017-01-01\", \"b\": \"2017-01-01 00:00:00\", \"exp\": True},\n ]\n )\n\n df = df.with_columns(df[\"a\"].str.to_date().alias(\"a_dt\"))\n actual = columns_equal(df[\"a_dt\"], df[\"b\"])\n expected = df[\"exp\"]\n assert_series_equal(actual, expected, check_names=False)",
"completions": [
"```python\ndef test_rounded_date_columns():\n # given\n col_1 = pl.Series([\"2021-01-01\", \"2021-01-02\", \"2021-01-03\"])\n col_2 = pl.Series([\"2021-01-01\", \"2021-01-02\", \"2021-01-03\"])\n\n # when\n compare = columns_equal(col_1, col_2, ignore_spaces=True, ignore_case=True)\n\n # then\n assert compare.sum() == 3\n```\n"
],
"line": 62,
"token": 360,
"line_diff": 0,
"token_diff": 0
},
{
"id": 28,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef annotations_to_instances(annos, image_size, mask_format=\"polygon\"):\n boxes = [BoxMode.convert(obj[\"bbox\"], obj[\"bbox_mode\"], BoxMode.XYXY_ABS) for obj in annos]\n target = Instances(image_size)\n boxes = target.gt_boxes = Boxes(boxes)\n boxes.clip(image_size)\n\n classes = [obj[\"category_id\"] for obj in annos]\n classes = torch.tensor(classes, dtype=torch.int64)\n target.gt_classes = classes\n\n if len(annos) and \"segmentation\" in annos[0]:\n segms = [obj[\"segmentation\"] for obj in annos]\n if mask_format == \"polygon\":\n masks = PolygonMasks(segms)\n else:\n assert mask_format == \"bitmask\", mask_format\n masks = []\n for segm in segms:\n if isinstance(segm, list):\n # polygon\n masks.append(polygons_to_bitmask(segm, *image_size))\n elif isinstance(segm, dict):\n # COCO RLE\n masks.append(mask_util.decode(segm))\n elif isinstance(segm, np.ndarray):\n assert segm.ndim == 2, \"Expect segmentation of 2 dimensions, got {}.\".format(\n segm.ndim\n )\n # mask array\n masks.append(segm)\n else:\n raise ValueError(\n \"Cannot convert segmentation of type '{}' to BitMasks!\"\n \"Supported types are: polygons as list[list[float] or ndarray],\"\n \" COCO-style RLE as a dict, or a full-image segmentation mask \"\n \"as a 2D ndarray.\".format(type(segm))\n )\n # torch.from_numpy does not support array with negative stride.\n masks = BitMasks(\n torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks])\n )\n target.gt_masks = masks\n\n if len(annos) and \"keypoints\" in annos[0]:\n kpts = [obj.get(\"keypoints\", []) for obj in annos]\n target.gt_keypoints = Keypoints(kpts)\n\n return target\n```\n###test function signature:\n```python\n\n def test_transform_simple_annotation(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_transform_simple_annotation",
"reference": "\n def test_transform_simple_annotation(self):\n transforms = T.TransformList([T.HFlipTransform(400)])\n anno = {\n \"bbox\": np.asarray([10, 10, 200, 300]),\n \"bbox_mode\": BoxMode.XYXY_ABS,\n \"category_id\": 3,\n \"segmentation\": [[10, 10, 100, 100, 100, 10], [150, 150, 200, 150, 200, 200]],\n }\n\n output = detection_utils.transform_instance_annotations(anno, transforms, (400, 400))\n self.assertTrue(np.allclose(output[\"bbox\"], [200, 10, 390, 300]))\n self.assertEqual(len(output[\"segmentation\"]), len(anno[\"segmentation\"]))\n self.assertTrue(np.allclose(output[\"segmentation\"][0], [390, 10, 300, 100, 300, 10]))\n\n detection_utils.annotations_to_instances([output, output], (400, 400))",
"completions": [
"```python\n def test_transform_simple_annotation(self):\n annos = [\n {\n \"bbox\": [10, 10, 100, 100],\n \"bbox_mode\": BoxMode.XYXY_ABS,\n \"category_id\": 0,\n \"segmentation\": [\n [10, 10, 50, 50, 90, 90, 100, 100],\n [20, 20, 50, 50, 80, 80, 100, 100],\n ],\n \"keypoints\": [10, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n }\n ]\n image_size = (100, 100)\n target = annotations_to_instances(annos, image_size)\n assert target.gt_boxes.tensor.tolist() == [[10, 10, 100, 100]]\n assert target.gt_classes.tolist() == [0]\n assert target.gt_masks.polygons().tolist() == [\n [[10, 10], [50, 50], [90, 90], [100, 100]],\n [[20, 20], [50, 50], [80, 80], [100, 100]],\n ]\n assert target.gt_keypoints.tensor.tolist() == [[10, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n```\n"
],
"line": 54,
"token": 437,
"line_diff": 0,
"token_diff": 0
},
{
"id": 29,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef assert_connectivity(connectivity: nx.Graph, circuit: Circuit):\n if list(connectivity.nodes) != list(range(connectivity.number_of_nodes())):\n node_mapping = {node: i for i, node in enumerate(connectivity.nodes)}\n new_connectivity = nx.Graph()\n new_connectivity.add_edges_from(\n [(node_mapping[u], node_mapping[v]) for u, v in connectivity.edges]\n )\n connectivity = new_connectivity\n for gate in circuit.queue:\n if len(gate.qubits) > 2 and not isinstance(gate, gates.M):\n raise_error(ConnectivityError, f\"{gate.name} acts on more than two qubits.\")\n if len(gate.qubits) == 2:\n if (gate.qubits[0], gate.qubits[1]) not in connectivity.edges:\n raise_error(\n ConnectivityError,\n f\"Circuit does not respect connectivity. {gate.name} acts on {gate.qubits}.\",\n )\n```\n###test function signature:\n```python\n\ndef test_sabre_simple(seed):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_sabre_simple",
"reference": "\ndef test_sabre_simple(seed):\n placer = Trivial()\n circ = Circuit(5)\n circ.add(gates.CZ(0, 1))\n initial_layout = placer(circ)\n router = Sabre(connectivity=star_connectivity(), seed=seed)\n routed_circuit, final_map = router(circuit=circ, initial_layout=initial_layout)\n assert router.added_swaps == 1\n assert final_map == {\"q0\": 2, \"q1\": 1, \"q2\": 0, \"q3\": 3, \"q4\": 4}\n assert routed_circuit.queue[0].qubits == (0, 2)\n assert isinstance(routed_circuit.queue[0], gates.SWAP)\n assert isinstance(routed_circuit.queue[1], gates.CZ)\n assert_connectivity(circuit=routed_circuit, connectivity=star_connectivity())\n assert_circuit_equivalence(\n original_circuit=circ,\n transpiled_circuit=routed_circuit,\n final_map=final_map,\n initial_map=initial_layout,\n )",
"completions": [
"```python\ndef test_sabre_simple(seed):\n import random\n random.seed(seed)\n import networkx as nx\n from pyquil.quil import Program\n from pyquil.api import QVMConnection\n from pyquil.gates import CNOT, H, MEASURE\n from pyquil.quilbase import Declare\n from pyquil.quilbase import DefGate\n from pyquil.quilbase import DefCircuit\n from pyquil.quilbase import Gate\n from pyquil.quilbase import Circuit\n from pyquil.quilbase import QubitPlaceholder\n from pyquil.quilbase import MemoryReference\n from pyquil.quilbase import Parameter\n from pyquil.quilbase import Pragma\n from pyquil.quilbase import PragmaDeclare\n from pyquil.quilbase import PragmaDefGate\n from pyquil.quilbase import PragmaDefCircuit\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefQubit\n from pyquil.quilbase import PragmaDefMemory\n from pyquil.quilbase import PragmaDefRegister\n from pyquil.quilbase import PragmaDefLabel\n from pyquil.quilbase import PragmaDefFunction\n from pyquil.quilbase import PragmaDefProcedure\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyquil.quilbase import PragmaDefProgram\n from pyqu"
],
"line": 27,
"token": 242,
"line_diff": 0,
"token_diff": 0
},
{
"id": 30,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef unique_patterns_2d(\n agrid: NDArray[np.int64], ksize: int, periodic_input: bool\n) -> tuple[NDArray[np.int64], NDArray[np.int64], NDArray[np.int64]]:\n assert ksize >= 1\n if periodic_input:\n agrid = np.pad(\n agrid,\n ((0, ksize - 1), (0, ksize - 1), *(((0, 0),) * (len(agrid.shape) - 2))),\n mode=\"wrap\",\n )\n else:\n # TODO: implement non-wrapped image handling\n # a = np.pad(a, ((0,k-1),(0,k-1),*(((0,0),)*(len(a.shape)-2))), mode='constant', constant_values=None)\n agrid = np.pad(\n agrid,\n ((0, ksize - 1), (0, ksize - 1), *(((0, 0),) * (len(agrid.shape) - 2))),\n mode=\"wrap\",\n )\n\n patches: NDArray[np.int64] = np.lib.stride_tricks.as_strided(\n agrid,\n (\n agrid.shape[0] - ksize + 1,\n agrid.shape[1] - ksize + 1,\n ksize,\n ksize,\n *agrid.shape[2:],\n ),\n agrid.strides[:2] + agrid.strides[:2] + agrid.strides[2:],\n writeable=False,\n )\n patch_codes = hash_downto(patches, 2)\n uc, ui = np.unique(patch_codes, return_index=True)\n locs = np.unravel_index(ui, patch_codes.shape)\n up: NDArray[np.int64] = patches[locs[0], locs[1]]\n ids: NDArray[np.int64] = np.vectorize(\n {code: ind for ind, code in enumerate(uc)}.get\n )(patch_codes)\n return ids, up, patch_codes\n```\n###test function signature:\n```python\n\ndef test_unique_patterns_2d(img_redmaze) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_unique_patterns_2d",
"reference": "\ndef test_unique_patterns_2d(img_redmaze) -> None:\n img = img_redmaze\n tile_size = 1\n pattern_width = 2\n _tile_catalog, tile_grid, _code_list, _unique_tiles = wfc_tiles.make_tile_catalog(\n img, tile_size\n )\n\n (\n _patterns_in_grid,\n pattern_contents_list,\n patch_codes,\n ) = wfc_patterns.unique_patterns_2d(tile_grid, pattern_width, True)\n assert patch_codes[1][2] == 4867810695119132864\n assert pattern_contents_list[7][1][1] == 8253868773529191888",
"completions": [
"```python\n\ndef test_unique_patterns_2d(img_redmaze) -> None:\n ids, up, patch_codes = unique_patterns_2d(img_redmaze, 3, True)\n assert ids.shape == (10,)\n assert up.shape == (10, 3, 3)\n assert patch_codes.shape == (10,)\n```\n"
],
"line": 48,
"token": 425,
"line_diff": 0,
"token_diff": 0
},
{
"id": 31,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef serialize(data: Any, encoding: str | None = None) -> str | bytes:\n serialized: bytes | str\n\n if encoding is not None:\n serialized = yaml.dump(data, encoding=encoding, Dumper=AiiDADumper)\n else:\n serialized = yaml.dump(data, Dumper=AiiDADumper)\n\n return serialized\n```\n###test function signature:\n```python\ndef test_mixed_attribute_normal_dict():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_mixed_attribute_normal_dict",
"reference": "def test_mixed_attribute_normal_dict():\n from aiida.common.extendeddicts import AttributeDict\n\n # Construct a nested `AttributeDict`, which should make all nested dictionaries `AttributeDicts` recursively\n dictionary = {'nested': AttributeDict({'dict': 'string', 'value': 1})}\n attribute_dict = AttributeDict(dictionary)\n\n # Now add a normal dictionary in the attribute dictionary\n attribute_dict['nested']['normal'] = {'a': 2}\n\n serialized = serialize.serialize(attribute_dict)\n deserialized = serialize.deserialize_unsafe(serialized)\n\n assert attribute_dict, deserialized",
"completions": [
"```python\ndef test_mixed_attribute_normal_dict():\n data = {\n \"a\": 1,\n \"b\": 2,\n \"c\": 3,\n \"d\": 4,\n \"e\": 5,\n \"f\": 6,\n \"g\": 7,\n \"h\": 8,\n \"i\": 9,\n \"j\": 10,\n \"k\": 11,\n \"l\": 12,\n \"m\": 13,\n \"n\": 14,\n \"o\": 15,\n \"p\": 16,\n \"q\": 17,\n \"r\": 18,\n \"s\": 19,\n \"t\": 20,\n \"u\": 21,\n \"v\": 22,\n \"w\": 23,\n \"x\": 24,\n \"y\": 25,\n \"z\": 26,\n }\n assert serialize(data) == \"\"\"a: 1\nb: 2\nc: 3\nd: 4\ne: 5\nf: 6\ng: 7\nh: 8\ni: 9\nj: 10\nk: 11\nl: 12\nm: 13\nn: 14\no: 15\np: 16\nq: 17\nr: 18\ns: 19\nt: 20\nu: 21\nv: 22\nw: 23\nx: 24\ny: 25\nz: 26\n\"\"\"\n```\n"
],
"line": 17,
"token": 133,
"line_diff": 0,
"token_diff": 0
},
{
"id": 32,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def unload_model(self, model_id: str) -> RegisteredModels:\n self.__ensure_v1_client_mode()\n de_aliased_model_id = resolve_roboflow_model_alias(model_id=model_id)\n response = requests.post(\n f\"{self.__api_url}/model/remove\",\n json={\n \"model_id\": de_aliased_model_id,\n },\n headers=DEFAULT_HEADERS,\n )\n response.raise_for_status()\n response_payload = response.json()\n if (\n de_aliased_model_id == self.__selected_model\n or model_id == self.__selected_model\n ):\n self.__selected_model = None\n return RegisteredModels.from_dict(response_payload)\n```\n###test function signature:\n```python\n\ndef test_client_unload_single_model_when_successful_response_expected_against_alias(\n requests_mock: Mocker,\n) -> None:\n # given\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_client_unload_single_model_when_successful_response_expected_against_alias",
"reference": "\ndef test_client_unload_single_model_when_successful_response_expected_against_alias(\n requests_mock: Mocker,\n) -> None:\n # given\n api_url = \"http://some.com\"\n requests_mock.post(\n f\"{api_url}/model/remove\",\n json={\"models\": [{\"model_id\": \"some/1\", \"task_type\": \"classification\"}]},\n )\n http_client = InferenceHTTPClient(api_key=\"my-api-key\", api_url=api_url)\n http_client.select_model(model_id=\"yolov8n-640\")\n\n # when\n result = http_client.unload_model(model_id=\"yolov8n-640\")\n\n # then\n assert result == RegisteredModels(\n models=[ModelDescription(model_id=\"some/1\", task_type=\"classification\")]\n )\n assert requests_mock.last_request.json() == {\n \"model_id\": \"coco/3\",\n }\n assert (\n http_client.selected_model is None\n ), \"Even when alias is in use - selected model should be emptied\"",
"completions": [
"```python\n\ndef test_client_unload_single_model_when_successful_response_expected_against_alias(\n requests_mock: Mocker,\n) -> None:\n # given\n requests_mock.post(\n f\"{API_URL}/model/remove\",\n json={\n \"model_id\": \"test_model_id\",\n },\n headers=DEFAULT_HEADERS,\n )\n client = RoboflowClient(api_key=\"test_api_key\", api_secret=\"test_api_secret\")\n # when\n response = client.unload_model(model_id=\"test_model_alias\")\n # then\n assert response.model_id == \"test_model_id\"\n assert response.model_name == \"test_model_name\"\n assert response.model_version == \"test_model_version\"\n assert response.model_type == \"test_model_type\"\n assert response.model_status == \"test_model_status\"\n assert response.model_created_at == \"test_model_created_at\"\n assert response.model_updated_at == \"test_model_updated_at\"\n assert response.model_tags == \"test_model_tags\"\n assert response.model_description == \"test_model_description\"\n assert response.model_url == \"test_model_url\"\n assert response.model_thumbnail_url == \"test_model_thumbnail_url\"\n assert response.model_training_data_url == \"test_model_training_data_url\"\n assert response.model_training_data_type == \"test_model_training_data_type\"\n assert response.model_training_data_size == \"test_model_training_data_size\"\n assert response.model_training_data_description == \"test_model_training_data_description\"\n assert response.model_training_data_tags == \"test_model_training_data_tags\"\n assert response.model_training_data_created_at == \"test_model_training_data_created_at\"\n assert response.model_training_data_updated_at == \"test_model_training_data_updated_at\"\n assert response.model_training_data_status == \"test_model_training_data_status\"\n assert response.model_training_data_status_message == \"test_model_training_data_status_message\"\n assert response.model_training_data_status_details == \"test_model_training_data_status_details\"\n assert response.model_training_data_status_created_at == \"test_model_training_data_status_created_at\"\n assert response.model_training_data_status_updated_at == \"test_model_training_data_status_updated_at\"\n assert response.model_training_data_status_completed_at == \"test_model_training_data_status_completed_at\"\n assert response.model_training_data_status_duration == \"test_model_training_data_status_duration\"\n assert response.model_training_data_status_budget_type == \"test_model_training_data_status_budget_type\"\n assert response.model_training_data_status_budget_amount == \"test_model_training_data_status_budget_amount\"\n assert response.model_training_data_status_budget_unit == \"test_model_training_data_status_budget_unit\"\n assert response.model_training_data_status_budget_remaining == \"test_model_training_data_status_budget_remaining\"\n assert response.model_training_data_status_budget_message == \"test_model_training_data_status_budget_message\"\n assert response.model_training_data_status_budget_details == \"test_model_training_data_status_budget_details\"\n assert response.model_training_data_status_budget_created_at == \"test_model_training_data_status_budget_created_at\"\n assert response.model_training_data_status_budget_updated_at == \"test_"
],
"line": 31,
"token": 162,
"line_diff": 0,
"token_diff": 0
},
{
"id": 33,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef edge_index_to_vector(\n edge_index: Tensor,\n size: Tuple[int, int],\n bipartite: bool,\n force_undirected: bool = False,\n) -> Tuple[Tensor, int]:\n\n row, col = edge_index\n\n if bipartite: # No need to account for self-loops.\n idx = (row * size[1]).add_(col)\n population = size[0] * size[1]\n return idx, population\n\n elif force_undirected:\n assert size[0] == size[1]\n num_nodes = size[0]\n\n # We only operate on the upper triangular matrix:\n mask = row < col\n row, col = row[mask], col[mask]\n offset = torch.arange(1, num_nodes, device=row.device).cumsum(0)[row]\n idx = row.mul_(num_nodes).add_(col).sub_(offset)\n population = (num_nodes * (num_nodes + 1)) // 2 - num_nodes\n return idx, population\n\n else:\n assert size[0] == size[1]\n num_nodes = size[0]\n\n # We remove self-loops as we do not want to take them into account\n # when sampling negative values.\n mask = row != col\n row, col = row[mask], col[mask]\n col[row < col] -= 1\n idx = row.mul_(num_nodes - 1).add_(col)\n population = num_nodes * num_nodes - num_nodes\n return idx, population\n```\n###test function signature:\n```python\n\ndef test_edge_index_to_vector_and_vice_versa():\n # Create a fully-connected graph:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_edge_index_to_vector_and_vice_versa",
"reference": "\ndef test_edge_index_to_vector_and_vice_versa():\n # Create a fully-connected graph:\n N = 10\n row = torch.arange(N).view(-1, 1).repeat(1, N).view(-1)\n col = torch.arange(N).view(1, -1).repeat(N, 1).view(-1)\n edge_index = torch.stack([row, col], dim=0)\n\n idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=True)\n assert population == N * N\n assert idx.tolist() == list(range(population))\n edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=True)\n assert is_undirected(edge_index2)\n assert edge_index.tolist() == edge_index2.tolist()\n\n idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=False)\n assert population == N * N - N\n assert idx.tolist() == list(range(population))\n mask = edge_index[0] != edge_index[1] # Remove self-loops.\n edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=False)\n assert is_undirected(edge_index2)\n assert edge_index[:, mask].tolist() == edge_index2.tolist()\n\n idx, population = edge_index_to_vector(edge_index, (N, N), bipartite=False,\n force_undirected=True)\n assert population == (N * (N + 1)) / 2 - N\n assert idx.tolist() == list(range(population))\n mask = edge_index[0] != edge_index[1] # Remove self-loops.\n edge_index2 = vector_to_edge_index(idx, (N, N), bipartite=False,\n force_undirected=True)\n assert is_undirected(edge_index2)\n assert edge_index[:, mask].tolist() == to_undirected(edge_index2).tolist()",
"completions": [
"```python\n\ndef test_edge_index_to_vector_and_vice_versa():\n # Create a fully-connected graph:\n edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])\n size = (4, 4)\n bipartite = False\n force_undirected = False\n idx, population = edge_index_to_vector(edge_index, size, bipartite, force_undirected)\n assert idx.tolist() == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n assert population == 16\n\n # Convert back to edge index:\n edge_index = vector_to_edge_index(idx, size, bipartite, force_undirected)\n assert torch.all(edge_index == torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]]))\n\n # Test bipartite graph:\n edge_index = torch.tensor([[0, 1], [1, 0]])\n size = (2, 2)\n bipartite = True\n force_undirected = False\n idx, population = edge_index_to_vector(edge_index, size, bipartite, force_undirected)\n assert idx.tolist() == [0, 1, 2, 3]\n assert population == 4\n\n # Convert back to edge index:\n edge_index = vector_to_edge_index(idx, size, bipartite, force_undirected)\n assert torch.all(edge_index == torch.tensor([[0, 1], [1, 0]]))\n\n # Test force_undirected:\n edge_index = torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]])\n size = (4, 4)\n bipartite = False\n force_undirected = True\n idx, population = edge_index_to_vector(edge_index, size, bipartite, force_undirected)\n assert idx.tolist() == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n assert population == 16\n\n # Convert back to edge index:\n edge_index = vector_to_edge_index(idx, size, bipartite, force_undirected)\n assert torch.all(edge_index == torch.tensor([[0, 1, 2, 3], [1, 0, 3, 2]]))\n\n # Test force_undirected with bipartite graph:\n edge_index = torch.tensor([[0, 1], [1, 0]])\n size = (2, 2)\n bipartite = True\n force_undirected = True\n idx, population = edge_index_to_vector(edge_index, size, bipartite, force_undirected)\n assert idx.tolist() == [0, 1, 2, 3]\n assert population == 4\n\n # Convert back to edge index:\n edge_index = vector_to_edge_index(idx, size, bipartite, force_undirected)\n assert torch.all(edge_index == torch.tensor([[0, 1], [1, 0]]))\n```\n"
],
"line": 43,
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{
"id": 34,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef spectrogram(\n waveform: np.ndarray,\n window: np.ndarray,\n frame_length: int,\n hop_length: int,\n fft_length: Optional[int] = None,\n power: Optional[float] = 1.0,\n center: bool = True,\n pad_mode: str = \"reflect\",\n onesided: bool = True,\n preemphasis: Optional[float] = None,\n mel_filters: Optional[np.ndarray] = None,\n mel_floor: float = 1e-10,\n log_mel: Optional[str] = None,\n reference: float = 1.0,\n min_value: float = 1e-10,\n db_range: Optional[float] = None,\n remove_dc_offset: Optional[bool] = None,\n dtype: np.dtype = np.float32,\n) -> np.ndarray:\n window_length = len(window)\n\n if fft_length is None:\n fft_length = frame_length\n\n if frame_length > fft_length:\n raise ValueError(f\"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})\")\n\n if window_length != frame_length:\n raise ValueError(f\"Length of the window ({window_length}) must equal frame_length ({frame_length})\")\n\n if hop_length <= 0:\n raise ValueError(\"hop_length must be greater than zero\")\n\n if waveform.ndim != 1:\n raise ValueError(f\"Input waveform must have only one dimension, shape is {waveform.shape}\")\n\n if np.iscomplexobj(waveform):\n raise ValueError(\"Complex-valued input waveforms are not currently supported\")\n\n if power is None and mel_filters is not None:\n raise ValueError(\n \"You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram.\"\n \"Specify `power` to fix this issue.\"\n )\n\n # center pad the waveform\n if center:\n padding = [(int(frame_length // 2), int(frame_length // 2))]\n waveform = np.pad(waveform, padding, mode=pad_mode)\n\n # promote to float64, since np.fft uses float64 internally\n waveform = waveform.astype(np.float64)\n window = window.astype(np.float64)\n\n # split waveform into frames of frame_length size\n num_frames = int(1 + np.floor((waveform.size - frame_length) / hop_length))\n\n num_frequency_bins = (fft_length // 2) + 1 if onesided else fft_length\n spectrogram = np.empty((num_frames, num_frequency_bins), dtype=np.complex64)\n\n # rfft is faster than fft\n fft_func = np.fft.rfft if onesided else np.fft.fft\n buffer = np.zeros(fft_length)\n\n timestep = 0\n for frame_idx in range(num_frames):\n buffer[:frame_length] = waveform[timestep : timestep + frame_length]\n\n if remove_dc_offset:\n buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean()\n\n if preemphasis is not None:\n buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1]\n buffer[0] *= 1 - preemphasis\n\n buffer[:frame_length] *= window\n\n spectrogram[frame_idx] = fft_func(buffer)\n timestep += hop_length\n\n # note: ** is much faster than np.power\n if power is not None:\n spectrogram = np.abs(spectrogram, dtype=np.float64) ** power\n\n spectrogram = spectrogram.T\n\n if mel_filters is not None:\n spectrogram = np.maximum(mel_floor, np.dot(mel_filters.T, spectrogram))\n\n if power is not None and log_mel is not None:\n if log_mel == \"log\":\n spectrogram = np.log(spectrogram)\n elif log_mel == \"log10\":\n spectrogram = np.log10(spectrogram)\n elif log_mel == \"dB\":\n if power == 1.0:\n spectrogram = amplitude_to_db(spectrogram, reference, min_value, db_range)\n elif power == 2.0:\n spectrogram = power_to_db(spectrogram, reference, min_value, db_range)\n else:\n raise ValueError(f\"Cannot use log_mel option '{log_mel}' with power {power}\")\n else:\n raise ValueError(f\"Unknown log_mel option: {log_mel}\")\n\n spectrogram = np.asarray(spectrogram, dtype)\n\n return spectrogram\n```\n###test function signature:\n```python\n\n def test_spectrogram_integration_test(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_spectrogram_integration_test",
"reference": "\n def test_spectrogram_integration_test(self):\n waveform = self._load_datasamples(1)[0]\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\", frame_length=512),\n frame_length=512,\n hop_length=128,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=True,\n )\n self.assertEqual(spec.shape, (257, 732))\n\n # fmt: off\n expected = np.array([\n 0.02464888, 0.04648664, 0.05872392, 0.02311783, 0.0327175 ,\n 0.02433643, 0.01198814, 0.02055709, 0.01559287, 0.01394357,\n 0.01299037, 0.01728045, 0.0254554 , 0.02486533, 0.02011792,\n 0.01755333, 0.02100457, 0.02337024, 0.01436963, 0.01464558,\n 0.0211017 , 0.0193489 , 0.01272165, 0.01858462, 0.03722598,\n 0.0456542 , 0.03281558, 0.00620586, 0.02226466, 0.03618042,\n 0.03508182, 0.02271432, 0.01051649, 0.01225771, 0.02315293,\n 0.02331886, 0.01417785, 0.0106844 , 0.01791214, 0.017177 ,\n 0.02125114, 0.05028201, 0.06830665, 0.05216664, 0.01963666,\n 0.06941418, 0.11513043, 0.12257859, 0.10948435, 0.08568069,\n 0.05509328, 0.05047818, 0.047112 , 0.05060737, 0.02982424,\n 0.02803827, 0.02933729, 0.01760491, 0.00587815, 0.02117637,\n 0.0293578 , 0.03452379, 0.02194803, 0.01676056,\n ])\n # fmt: on\n self.assertTrue(np.allclose(spec[:64, 400], expected))\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\"),\n frame_length=400,\n hop_length=128,\n fft_length=512,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=True,\n )\n self.assertEqual(spec.shape, (257, 732))\n self.assertTrue(np.allclose(spec[:64, 400], expected))\n\n mel_filters = mel_filter_bank(\n num_frequency_bins=256,\n num_mel_filters=400,\n min_frequency=20,\n max_frequency=8000,\n sampling_rate=16000,\n norm=None,\n mel_scale=\"kaldi\",\n triangularize_in_mel_space=True,\n )\n\n mel_filters = np.pad(mel_filters, ((0, 1), (0, 0)))\n\n spec = spectrogram(\n waveform,\n window_function(400, \"povey\", periodic=False),\n frame_length=400,\n hop_length=160,\n fft_length=512,\n power=2.0,\n center=False,\n pad_mode=\"reflect\",\n onesided=True,\n preemphasis=0.97,\n mel_filters=mel_filters,\n log_mel=\"log\",\n mel_floor=1.1920928955078125e-07,\n remove_dc_offset=True,\n )\n self.assertEqual(spec.shape, (400, 584))\n\n # fmt: off\n expected = np.array([-15.94238515, -8.20712299, -8.22704352, -15.94238515,\n -15.94238515, -15.94238515, -15.94238515, -15.94238515,\n -6.52463769, -7.73677889, -15.94238515, -15.94238515,\n -15.94238515, -15.94238515, -4.18650018, -3.37195286,\n -15.94238515, -15.94238515, -15.94238515, -15.94238515,\n -4.70190154, -2.4217066 , -15.94238515, -15.94238515,\n -15.94238515, -15.94238515, -5.62755239, -3.53385194,\n -15.94238515, -15.94238515, -15.94238515, -15.94238515,\n -9.43303023, -8.77480925, -15.94238515, -15.94238515,\n -15.94238515, -15.94238515, -4.2951092 , -5.51585994,\n -15.94238515, -15.94238515, -15.94238515, -4.40151721,\n -3.95228878, -15.94238515, -15.94238515, -15.94238515,\n -6.10365415, -4.59494697, -15.94238515, -15.94238515,\n -15.94238515, -8.10727767, -6.2585298 , -15.94238515,\n -15.94238515, -15.94238515, -5.60161702, -4.47217004,\n -15.94238515, -15.94238515, -15.94238515, -5.91641988]\n )\n # fmt: on\n self.assertTrue(np.allclose(spec[:64, 400], expected, atol=1e-5))",
"completions": [
"```python\n def test_spectrogram_integration_test(self):\n waveform = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])\n window = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n frame_length = 10\n hop_length = 1\n fft_length = 10\n power = 1.0\n center = True\n pad_mode = \"reflect\"\n onesided = True\n preemphasis = None\n mel_filters = None\n mel_floor = 1e-10\n log_mel = None\n reference = 1.0\n min_value = 1e-10\n db_range = None\n remove_dc_offset = None\n dtype = np.float32\n\n spectrogram = spectrogram(\n waveform,\n window,\n frame_length,\n hop_length,\n fft_length,\n power,\n center,\n pad_mode,\n onesided,\n preemphasis,\n mel_filters,\n mel_floor,\n log_mel,\n reference,\n min_value,\n db_range,\n remove_dc_offset,\n dtype,\n )\n\n expected_spectrogram = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]])\n\n self.assertTrue(np.allclose(spectrogram, expected_spectrogram))\n```\n"
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{
"id": 35,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef run_query_expansion_node(modules: List[Callable],\n module_params: List[Dict],\n previous_result: pd.DataFrame,\n node_line_dir: str,\n strategies: Dict,\n ) -> pd.DataFrame:\n if not os.path.exists(node_line_dir):\n os.makedirs(node_line_dir)\n node_dir = os.path.join(node_line_dir, \"query_expansion\")\n if not os.path.exists(node_dir):\n os.makedirs(node_dir)\n project_dir = pathlib.PurePath(node_line_dir).parent.parent\n\n # run query expansion\n results, execution_times = zip(*map(lambda task: measure_speed(\n task[0], project_dir=project_dir, previous_result=previous_result, **task[1]), zip(modules, module_params)))\n average_times = list(map(lambda x: x / len(results[0]), execution_times))\n\n # save results to folder\n pseudo_module_params = deepcopy(module_params)\n for i, module_param in enumerate(pseudo_module_params):\n if 'prompt' in module_params:\n module_param['prompt'] = str(i)\n filepaths = list(map(lambda x: os.path.join(node_dir, f'{x}.parquet'), range(len(modules))))\n list(map(lambda x: x[0].to_parquet(x[1], index=False), zip(results, filepaths))) # execute save to parquet\n filenames = list(map(lambda x: os.path.basename(x), filepaths))\n\n # make summary file\n summary_df = pd.DataFrame({\n 'filename': filenames,\n 'module_name': list(map(lambda module: module.__name__, modules)),\n 'module_params': module_params,\n 'execution_time': average_times,\n })\n\n # Run evaluation when there are more than one module.\n if len(modules) > 1:\n # pop general keys from strategies (e.g. metrics, speed_threshold)\n general_key = ['metrics', 'speed_threshold']\n general_strategy = dict(filter(lambda x: x[0] in general_key, strategies.items()))\n extra_strategy = dict(filter(lambda x: x[0] not in general_key, strategies.items()))\n\n # first, filter by threshold if it is enabled.\n if general_strategy.get('speed_threshold') is not None:\n results, filenames = filter_by_threshold(results, average_times, general_strategy['speed_threshold'],\n filenames)\n\n # check metrics in strategy\n if general_strategy.get('metrics') is None:\n raise ValueError(\"You must at least one metrics for query expansion evaluation.\")\n\n if extra_strategy.get('top_k') is None:\n extra_strategy['top_k'] = 10 # default value\n\n # get retrieval modules from strategy\n retrieval_callables, retrieval_params = make_retrieval_callable_params(extra_strategy)\n\n # get retrieval_gt\n retrieval_gt = pd.read_parquet(os.path.join(project_dir, \"data\", \"qa.parquet\"))['retrieval_gt'].tolist()\n\n # run evaluation\n evaluation_results = list(map(lambda result: evaluate_one_query_expansion_node(\n retrieval_callables, retrieval_params, result['queries'].tolist(), retrieval_gt,\n general_strategy['metrics'], project_dir, previous_result), results))\n\n evaluation_df = pd.DataFrame({\n 'filename': filenames,\n **{f'query_expansion_{metric_name}': list(map(lambda x: x[metric_name].mean(), evaluation_results))\n for metric_name in general_strategy['metrics']}\n })\n summary_df = pd.merge(on='filename', left=summary_df, right=evaluation_df, how='left')\n\n best_result, best_filename = select_best_average(evaluation_results, general_strategy['metrics'], filenames)\n # change metric name columns to query_expansion_metric_name\n best_result = best_result.rename(columns={\n metric_name: f'query_expansion_{metric_name}' for metric_name in strategies['metrics']})\n best_result = best_result.drop(columns=['retrieved_contents', 'retrieved_ids', 'retrieve_scores'])\n else:\n best_result, best_filename = results[0], filenames[0]\n best_result = pd.concat([previous_result, best_result], axis=1)\n\n # add 'is_best' column at summary file\n summary_df['is_best'] = summary_df['filename'] == best_filename\n\n # save files\n summary_df.to_csv(os.path.join(node_dir, \"summary.csv\"), index=False)\n best_result.to_parquet(os.path.join(node_dir, f\"best_{os.path.splitext(best_filename)[0]}.parquet\"), index=False)\n\n return best_result\n```\n###test function signature:\n```python\n\ndef test_run_query_expansion_node_default(node_line_dir):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_run_query_expansion_node_default",
"reference": "\ndef test_run_query_expansion_node_default(node_line_dir):\n generator_models['mock'] = MockLLM\n project_dir = pathlib.PurePath(node_line_dir).parent.parent\n qa_path = os.path.join(project_dir, \"data\", \"qa.parquet\")\n previous_result = pd.read_parquet(qa_path)\n\n modules = [query_decompose, hyde]\n module_params = [{'llm': \"mock\", 'temperature': 0.2, 'batch': 7}, {'llm': \"mock\"}]\n strategies = {\n 'metrics': metrics\n }\n best_result = run_query_expansion_node(modules, module_params, previous_result, node_line_dir, strategies)\n base_query_expansion_test(best_result, node_line_dir)",
"completions": [
"```python\ndef test_run_query_expansion_node_default(node_line_dir):\n modules = [query_expansion_module]\n module_params = [{'prompt': 'default'}]\n previous_result = pd.DataFrame({'query': ['a', 'b', 'c']})\n strategies = {'metrics': ['recall', 'precision', 'f1'], 'speed_threshold': 1000}\n run_query_expansion_node(modules, module_params, previous_result, node_line_dir, strategies)\n```\n"
],
"line": 84,
"token": 850,
"line_diff": 0,
"token_diff": 0
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{
"id": 36,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def run(self) -> Tuple[pd.DataFrame, FreqDetectionResult]:\n try:\n if self._prophet_detector_params[\"suppress_stan\"]:\n pd.set_option(\"mode.chained_assignment\", None)\n\n # Skip measurements based on feedbacks given from SODA Cloud\n preprocessed_df = self.preprocess(time_series_df=self.raw_time_series_df)\n\n # Automatically detect frequency of the time series\n freq_detector = FrequencyDetector(\n logs=self.logs,\n params=self.params,\n time_series_df=preprocessed_df,\n manual_freq=self.training_dataset_params.frequency,\n )\n freq_detection_result = freq_detector.detect_frequency()\n\n # Return if frequency detection failed\n if freq_detection_result.error_code_int >= ERROR_CODE_LEVEL_CUTTOFF:\n return self.exit_with_warning(freq_detection_result)\n\n # Apply training dataset configurations\n training_df = self.apply_training_dataset_configs(\n time_series_df=preprocessed_df, freq_detection_result=freq_detection_result\n )\n\n # Remove big gaps from the time series to not confuse Prophet\n training_df = self.remove_big_gaps_from_time_series(\n time_series_df=training_df, freq_detection_result=freq_detection_result\n )\n\n # Only use the last n points for training based on the window length\n window_length = self.get_window_length(training_df=training_df)\n training_df = training_df.iloc[-window_length:]\n\n training_df_shape = training_df[\"y\"].dropna().shape[0]\n if training_df_shape <= self._min_n_points:\n freq_detection_result = get_not_enough_measurements_freq_result(n_data_points=training_df_shape)\n return self.exit_with_warning(freq_detection_result)\n\n model_hyperparameters = self.get_prophet_hyperparameters(time_series_df=training_df)\n\n predictions_df = self.setup_fit_predict(\n time_series_df=training_df, model_hyperparameters=model_hyperparameters\n )\n anomalies_df = self.detect_anomalies(time_series_df=training_df, predictions_df=predictions_df)\n anomalies_df = self.generate_severity_zones(anomalies_df=anomalies_df)\n anomalies_df = self.compute_alert_level(anomalies_df=anomalies_df)\n return anomalies_df, freq_detection_result\n except Exception as e:\n raise e\n```\n###test function signature:\n```python\n\ndef test_with_exit() -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_with_exit",
"reference": "\ndef test_with_exit() -> None:\n time_series_df = generate_random_dataframe(size=2, n_rows_to_convert_none=0, frequency=\"D\")\n detector = ProphetDetector(\n logs=LOGS,\n params=PARAMS,\n time_series_df=time_series_df,\n model_cfg=ModelConfigs(),\n training_dataset_params=TrainingDatasetParameters(),\n severity_level_params=SeverityLevelParameters(),\n )\n df_anomalies, frequency_result = detector.run()\n assert df_anomalies.empty\n assert frequency_result.error_code_int == 100\n assert frequency_result.freq_detection_strategy == \"not_enough_measurements_custom\"",
"completions": [
"```python\ndef test_with_exit() -> None:\n from soda.sodacl.checks.check_frequency import FrequencyDetector\n from soda.sodacl.checks.check_frequency import get_not_enough_measurements_freq_result\n from soda.sodacl.checks.check_frequency import ERROR_CODE_LEVEL_CUTTOFF\n from soda.sodacl.checks.check_frequency import FreqDetectionResult\n from soda.sodacl.checks.check_frequency import CheckFrequency\n from soda.sodacl.checks.check_frequency import CheckFrequencyParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyLogs\n from soda.sodacl.checks.check_frequency import CheckFrequencyTrainingDatasetParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyProphetDetectorParams\n from soda.sodacl.checks.check_frequency import CheckFrequencyPro"
],
"line": 52,
"token": 310,
"line_diff": 0,
"token_diff": 0
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{
"id": 37,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef average_transforms(\n R: torch.Tensor,\n t: torch.Tensor,\n w: torch.Tensor,\n mask: torch.Tensor,\n dim: int,\n t_edge: Optional[torch.Tensor] = None,\n dither: Optional[bool] = True,\n dither_eps: float = 1e-4,\n) -> Tuple[torch.Tensor, torch.Tensor]:\n assert dim >= 0, \"dimension must index from the left\"\n w = torch.where(\n mask[..., None].bool(), w, torch.full_like(w, torch.finfo(w.dtype).min)\n )\n\n # We use different averaging models based on the number of weights\n num_transform_weights = w.size(-1)\n if num_transform_weights == 1:\n # Share a single scalar weight between t and R.\n probs = w.softmax(dim)\n t_probs = probs\n R_probs = probs[..., None]\n\n # Average translation.\n t_avg = (t * t_probs).sum(dim)\n elif num_transform_weights == 2:\n # Use separate scalar weights for each of t and R.\n probs = w.softmax(dim)\n t_probs, R_probs = probs.unbind(-1)\n t_probs = t_probs[..., None]\n R_probs = R_probs[..., None, None]\n\n # Average translation.\n t_avg = (t * t_probs).sum(dim)\n elif num_transform_weights == 3:\n # For R use a signed scalar weight.\n R_probs = w[..., 2].softmax(dim)[..., None, None]\n\n # For t use a two-parameter precision matrix P = P_isometric + P_radial.\n # We need to hand compute softmax over the shared dim x 2 elements.\n w_t = w[..., :2]\n w_t_total = w_t.logsumexp([dim, -1], True)\n p_iso, p_rad = (w_t - w_t_total).exp().unbind(-1)\n\n # Use Gaussian fusion for translation.\n t_edge = t_edge * mask.to(t_edge.dtype)[..., None]\n t_avg, _ = fuse_gaussians_isometric_plus_radial(t, p_iso, p_rad, t_edge, dim)\n else:\n raise NotImplementedError\n\n # Average rotation via SVD\n R_avg_unc = (R * R_probs).sum(dim)\n R_avg_unc = R_avg_unc + dither_eps * torch.randn_like(R_avg_unc)\n U, S, Vh = torch.linalg.svd(R_avg_unc, full_matrices=True)\n R_avg = U @ Vh\n\n # Enforce that matrix is rotation matrix\n d = torch.linalg.det(R_avg)\n d_expand = F.pad(d[..., None, None], (2, 0), value=1.0)\n Vh = Vh * d_expand\n R_avg = U @ Vh\n return R_avg, t_avg\n```\n###test function signature:\n```python\n\ndef test_average_transforms(rotations, translations):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_average_transforms",
"reference": "\ndef test_average_transforms(rotations, translations):\n R = torch.stack([rotations[0], torch.eye(3)])\n t = torch.stack([translations[0], torch.zeros(3)])\n w = torch.ones(2, 2)\n mask = torch.ones(2)\n # average of a transform with the identity is \"half\" the transform\n R_avg, t_avg = average_transforms(R, t, w, mask, dim=0, dither=False)\n R_total_fromavg, _ = compose_transforms(\n R_avg, torch.zeros(3), R_avg, torch.zeros(3)\n )\n _, t_total_fromavg = compose_transforms(torch.eye(3), t_avg, torch.eye(3), t_avg)\n assert torch.allclose(R_total_fromavg, R[0], atol=1e-3, rtol=1e-2)\n assert torch.allclose(t_total_fromavg, t[0], atol=1e-3, rtol=1e-2)",
"completions": [
"```python\ndef test_average_transforms(rotations, translations):\n # test case 1\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 2\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 3\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 4\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 5\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 6\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 7\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 8\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w, mask, 0)\n assert torch.allclose(R, rotations)\n assert torch.allclose(t, translations)\n\n # test case 9\n mask = torch.ones(1, 1, 1, 1)\n w = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0])\n R, t = average_transforms(rotations, translations, w"
],
"line": 65,
"token": 537,
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"token_diff": 0
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{
"id": 38,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef btlm_config_to_gpt2_config(btlm_config: PretrainedConfig) -> GPT2Config:\n return GPT2Config(\n vocab_size=btlm_config.vocab_size,\n n_positions=0 if btlm_config.position_embedding_type == \"alibi\" else btlm_config.n_positions,\n n_embd=btlm_config.hidden_size,\n n_layer=btlm_config.num_hidden_layers,\n n_head=btlm_config.num_attention_heads,\n n_inner=btlm_config.n_inner,\n activation_function=btlm_config.activation_function,\n resid_pdrop=btlm_config.resid_pdrop,\n embd_pdrop=btlm_config.embd_pdrop,\n attn_pdrop=btlm_config.attn_pdrop,\n layer_norm_epsilon=btlm_config.layer_norm_epsilon,\n initializer_range=btlm_config.initializer_range,\n bos_token_id=btlm_config.bos_token_id,\n eos_token_id=btlm_config.eos_token_id,\n # These are new arguments not in the original GPT2Config\n use_alibi=btlm_config.position_embedding_type == \"alibi\",\n use_flash_attn=btlm_config.position_embedding_type == \"alibi\", # Alibi code path requires flash_attn\n mup_width_scale=btlm_config.mup_width_scale,\n mup_embeddings_multiplier=btlm_config.mup_embeddings_scale,\n mup_output_multiplier=btlm_config.mup_output_alpha,\n mup_scale_qk_dot_by_d=btlm_config.mup_scale_qk_dot_by_d,\n mlp_multiple_of=1,\n )\n```\n###test function signature:\n```python\ndef test_btlm_optimized(model_name):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_btlm_optimized",
"reference": "def test_btlm_optimized(model_name):\n dtype = torch.float16\n device = \"cuda\"\n config = btlm_config_to_gpt2_config(\n AutoConfig.from_pretrained(model_name, trust_remote_code=True)\n )\n config.fused_bias_fc = True\n config.fused_dropout_add_ln = True\n config.residual_in_fp32 = True\n\n pretrained_state_dict = remap_state_dict_hf_btlm(state_dict_from_pretrained(model_name), config)\n model = GPTLMHeadModel(config, device=device, dtype=dtype)\n model.load_state_dict(pretrained_state_dict)\n model.eval()\n\n torch.manual_seed(0)\n batch_size = 2\n max_seqlen = 256\n seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device=device)\n input_ids = torch.randint(\n 0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long, device=device\n )\n with torch.no_grad():\n out = model.transformer(input_ids)\n logits = model(input_ids).logits\n del model\n\n # Without device_map, the model is loaded on the CPU, which is very slow\n # Need auto here since the 13B fp32 model doesn't fit in memory on a A100 40GB\n model_ref = AutoModelForCausalLM.from_pretrained(\n model_name, device_map=\"auto\", trust_remote_code=True\n )\n model_ref.eval()\n with torch.no_grad():\n out_ref = model_ref.transformer(input_ids).last_hidden_state.to(device=device)\n logits_ref = model_ref(input_ids).logits.to(device=device)\n del model_ref\n\n model_hf = AutoModelForCausalLM.from_pretrained(\n model_name,\n torch_dtype=dtype,\n device_map={\"\": device},\n trust_remote_code=True,\n )\n model_hf.eval()\n with torch.no_grad():\n out_hf = model_hf.transformer(input_ids).last_hidden_state\n logits_hf = model_hf(input_ids).logits\n del model_hf\n\n print(f\"Output max diff: {(out - out_ref).abs().max().item()}\")\n print(f\"Output mean diff: {(out - out_ref).abs().mean().item()}\")\n print(f\"HF fp16 max diff: {(out_hf - out_ref).abs().max().item()}\")\n print(f\"HF fp16 mean diff: {(out_hf - out_ref).abs().mean().item()}\")\n assert (out - out_ref).abs().max().item() < 3 * (out_hf - out_ref).abs().max().item()\n\n print(f\"Logits max diff: {(logits - logits_ref).abs().max().item()}\")\n print(f\"Logits mean diff: {(logits - logits_ref).abs().mean().item()}\")\n print(f\"HF fp16 max diff: {(logits_hf - logits_ref).abs().max().item()}\")\n print(f\"HF fp16 mean diff: {(logits_hf - logits_ref).abs().mean().item()}\")\n assert (logits - logits_ref).abs().max().item() < 3 * (\n logits_hf - logits_ref\n ).abs().max().item()",
"completions": [
"```python\ndef test_btlm_optimized(model_name):\n from transformers import GPT2Config, PretrainedConfig\n from bert_extended.optimized import btlm_config_to_gpt2_config\n\n config = PretrainedConfig.from_pretrained(model_name)\n gpt2_config = btlm_config_to_gpt2_config(config)\n gpt2_config.to_json_file(f\"{model_name}_optimized.json\")\n```\n"
],
"line": 35,
"token": 169,
"line_diff": 0,
"token_diff": 0
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{
"id": 39,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef grad_solid_harmonic(r: jnp.ndarray, l_max: int) -> jnp.ndarray:\n def cartesian_sum(r, ell):\n # computes all cartesian sums of order ell\n # i.e. all terms (a*x + b*y + c*z) for a+b+c = ell\n if ell == 0:\n return np.array([0.])\n return np.sum(np.stack(\n np.meshgrid(*[r]*ell, indexing='ij'), axis=0).reshape(ell, -1), axis=0)\n\n dharmonics = []\n for ell in range(l_max + 1):\n dx = cartesian_sum(np.array([1., 0., 0.]), ell)\n dy = cartesian_sum(np.array([0., 1., 0.]), ell)\n dz = cartesian_sum(np.array([0., 0., 1.]), ell)\n d = np.stack([dx, dy, dz], axis=1)\n cp = jax.vmap(functools.partial(cartesian_product, ell=ell), out_axes=-1)\n dcp = jnp.where(r.T[None, :, :] == 0, 0,\n d[:, :, None]*cp(r)[:, None, :] / r.T[None, :, :])\n sh = jnp.einsum('cs,cxn->snx', full_cart2sph(ell, True), dcp)\n pad = l_max - ell\n sh = jnp.pad(sh, [[pad, pad], [0, 0], [0, 0]])\n dharmonics.append(sh)\n\n return jnp.stack(dharmonics, axis=1) # [L, M, grid, xyz]\n```\n###test function signature:\n```python\n\n def test_grad_solid_harmonic(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_grad_solid_harmonic",
"reference": "\n def test_grad_solid_harmonic(self):\n np.random.seed(0)\n r = np.random.randn(100, 3)\n l_max = 5\n\n jax_grad = jax.jacfwd(lambda x: gto.solid_harmonic_from_cart(x, l_max))\n expected = jnp.transpose(jnp.squeeze(jax.vmap(jax_grad)(r[:, None, :])),\n [1, 2, 0, 3])\n\n with self.subTest('by hand'):\n observed = gto.grad_solid_harmonic(r, l_max)\n np.testing.assert_allclose(observed, expected, atol=1.e-4)\n with self.subTest('by jax'):\n observed_jacfwd = gto.grad_solid_harmonic_by_jacfwd(r, l_max)\n np.testing.assert_allclose(observed_jacfwd, expected, atol=1.e-4)",
"completions": [
"```python\n def test_grad_solid_harmonic(self):\n r = jnp.array([[0.5, 0.5, 0.5]])\n l_max = 2\n dharmonics = grad_solid_harmonic(r, l_max)\n self.assertEqual(dharmonics.shape, (l_max + 1, l_max + 1, 1, 3))\n self.assertTrue(jnp.allclose(dharmonics[0, 0, 0, :], jnp.array([0., 0., 0.])))\n self.assertTrue(jnp.allclose(dharmonics[1, 0, 0, :], jnp.array([1., 0., 0.])))\n self.assertTrue(jnp.allclose(dharmonics[1, 1, 0, :], jnp.array([0., 1., 0.])))\n self.assertTrue(jnp.allclose(dharmonics[2, 0, 0, :], jnp.array([0., 0., 1.])))\n self.assertTrue(jnp.allclose(dharmonics[2, 1, 0, :], jnp.array([0., 0., 0.])))\n self.assertTrue(jnp.allclose(dharmonics[2, 2, 0, :], jnp.array([0., 0., 0.])))\n```\n"
],
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"token": 389,
"line_diff": 0,
"token_diff": 0
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{
"id": 40,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef generate_basis(\n base_shape, angular_tesselation, remove_symmetries=True, eps=1e-4\n):\n\n if base_shape == 'tetrahedron':\n verts = np.array([\n (np.sqrt(8 / 9), 0, -1 / 3),\n (-np.sqrt(2 / 9), np.sqrt(2 / 3), -1 / 3),\n (-np.sqrt(2 / 9), -np.sqrt(2 / 3), -1 / 3),\n (0, 0, 1),\n ])\n faces = np.array([(0, 1, 2), (0, 2, 3), (0, 1, 3), (1, 2, 3)])\n elif base_shape == 'icosahedron':\n a = (np.sqrt(5) + 1) / 2\n verts = np.array([\n (-1, 0, a),\n (1, 0, a),\n (-1, 0, -a),\n (1, 0, -a),\n (0, a, 1),\n (0, a, -1),\n (0, -a, 1),\n (0, -a, -1),\n (a, 1, 0),\n (-a, 1, 0),\n (a, -1, 0),\n (-a, -1, 0),\n ]) / np.sqrt(a + 2)\n faces = np.array([\n (0, 4, 1),\n (0, 9, 4),\n (9, 5, 4),\n (4, 5, 8),\n (4, 8, 1),\n (8, 10, 1),\n (8, 3, 10),\n (5, 3, 8),\n (5, 2, 3),\n (2, 7, 3),\n (7, 10, 3),\n (7, 6, 10),\n (7, 11, 6),\n (11, 0, 6),\n (0, 1, 6),\n (6, 1, 10),\n (9, 0, 11),\n (9, 11, 2),\n (9, 2, 5),\n (7, 2, 11),\n ])\n elif base_shape == 'octahedron':\n verts = np.array(\n [(0, 0, -1), (0, 0, 1), (0, -1, 0), (0, 1, 0), (-1, 0, 0), (1, 0, 0)]\n )\n corners = np.array(list(itertools.product([-1, 1], repeat=3)))\n pairs = np.argwhere(compute_sq_dist(corners.T, verts.T) == 2)\n faces = np.sort(np.reshape(pairs[:, 1], [3, -1]).T, 1)\n else:\n raise ValueError(f'base_shape {base_shape} not supported')\n verts = tesselate_geodesic(verts, faces, angular_tesselation)\n\n if remove_symmetries:\n # Remove elements of `verts` that are reflections of each other.\n match = compute_sq_dist(verts.T, -verts.T) < eps\n verts = verts[~np.any(np.triu(match), axis=0), :]\n\n basis = verts[:, ::-1]\n return basis\n```\n###test function signature:\n```python\n def test_generate_basis_golden(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_generate_basis_golden",
"reference": " def test_generate_basis_golden(self):\n\n basis = geopoly.generate_basis('tetrahedron', 1)\n basis_golden = np.array([\n [-0.33333333, -0.81649658, -0.47140452],\n [-0.33333333, 0.81649658, -0.47140452],\n [-0.33333333, 0.00000000, 0.94280904],\n [1.00000000, 0.00000000, 0.00000000],\n ])\n self.assertTrue(is_same_basis(basis.T, basis_golden.T))\n\n basis = geopoly.generate_basis('tetrahedron', 2)\n basis_golden = np.array([\n [-0.33333333, -0.81649658, -0.47140452],\n [-0.57735027, 0.00000000, -0.81649658],\n [-0.33333333, 0.81649658, -0.47140452],\n [-0.57735027, -0.70710678, 0.40824829],\n [-0.57735027, 0.70710678, 0.40824829],\n [-0.33333333, 0.00000000, 0.94280904],\n [1.00000000, 0.00000000, 0.00000000],\n ])\n self.assertTrue(is_same_basis(basis.T, basis_golden.T))\n\n basis = geopoly.generate_basis('icosahedron', 2)\n basis_golden = np.array([\n [0.85065081, 0.00000000, 0.52573111],\n [0.80901699, 0.50000000, 0.30901699],\n [0.52573111, 0.85065081, 0.00000000],\n [1.00000000, 0.00000000, 0.00000000],\n [0.80901699, 0.50000000, -0.30901699],\n [0.85065081, 0.00000000, -0.52573111],\n [0.30901699, 0.80901699, -0.50000000],\n [0.00000000, 0.52573111, -0.85065081],\n [0.50000000, 0.30901699, -0.80901699],\n [0.00000000, 1.00000000, 0.00000000],\n [-0.52573111, 0.85065081, 0.00000000],\n [-0.30901699, 0.80901699, -0.50000000],\n [0.00000000, 0.52573111, 0.85065081],\n [-0.30901699, 0.80901699, 0.50000000],\n [0.30901699, 0.80901699, 0.50000000],\n [0.50000000, 0.30901699, 0.80901699],\n [0.50000000, -0.30901699, 0.80901699],\n [0.00000000, 0.00000000, 1.00000000],\n [-0.50000000, 0.30901699, 0.80901699],\n [-0.80901699, 0.50000000, 0.30901699],\n [-0.80901699, 0.50000000, -0.30901699],\n ])\n self.assertTrue(is_same_basis(basis.T, basis_golden.T))\n\n basis = geopoly.generate_basis('octahedron', 4)\n basis_golden = np.array([\n [0.00000000, 0.00000000, -1.00000000],\n [0.00000000, -0.31622777, -0.94868330],\n [0.00000000, -0.70710678, -0.70710678],\n [0.00000000, -0.94868330, -0.31622777],\n [0.00000000, -1.00000000, 0.00000000],\n [-0.31622777, 0.00000000, -0.94868330],\n [-0.40824829, -0.40824829, -0.81649658],\n [-0.40824829, -0.81649658, -0.40824829],\n [-0.31622777, -0.94868330, 0.00000000],\n [-0.70710678, 0.00000000, -0.70710678],\n [-0.81649658, -0.40824829, -0.40824829],\n [-0.70710678, -0.70710678, 0.00000000],\n [-0.94868330, 0.00000000, -0.31622777],\n [-0.94868330, -0.31622777, 0.00000000],\n [-1.00000000, 0.00000000, 0.00000000],\n [0.00000000, -0.31622777, 0.94868330],\n [0.00000000, -0.70710678, 0.70710678],\n [0.00000000, -0.94868330, 0.31622777],\n [0.40824829, -0.40824829, 0.81649658],\n [0.40824829, -0.81649658, 0.40824829],\n [0.31622777, -0.94868330, 0.00000000],\n [0.81649658, -0.40824829, 0.40824829],\n [0.70710678, -0.70710678, 0.00000000],\n [0.94868330, -0.31622777, 0.00000000],\n [0.31622777, 0.00000000, -0.94868330],\n [0.40824829, 0.40824829, -0.81649658],\n [0.40824829, 0.81649658, -0.40824829],\n [0.70710678, 0.00000000, -0.70710678],\n [0.81649658, 0.40824829, -0.40824829],\n [0.94868330, 0.00000000, -0.31622777],\n [0.40824829, -0.40824829, -0.81649658],\n [0.40824829, -0.81649658, -0.40824829],\n [0.81649658, -0.40824829, -0.40824829],\n ])\n self.assertTrue(is_same_basis(basis.T, basis_golden.T))",
"completions": [
"```python\n def test_generate_basis_golden(self):\n basis = generate_basis(\n base_shape='tetrahedron', angular_tesselation=10, remove_symmetries=True, eps=1e-4\n )\n self.assertTrue(np.allclose(basis, np.array([[0, 0, 1], [0, 0, -1], [0, 1, 0], [0, -1, 0]])))\n```\n"
],
"line": 75,
"token": 692,
"line_diff": 0,
"token_diff": 0
},
{
"id": 41,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def t_map(self, t: Union[float, torch.Tensor]) -> torch.Tensor:\n if not isinstance(t, torch.Tensor):\n t = torch.Tensor([t]).float()\n\n t_max = self.t_max.to(t.device)\n t_min = self.t_min.to(t.device)\n t_tilde = t_min + (t_max - t_min) * t\n\n return t_tilde\n```\n###test function signature:\n```python\n\ndef test_noise_schedule_diffusion_coeff(kind):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_noise_schedule_diffusion_coeff",
"reference": "\ndef test_noise_schedule_diffusion_coeff(kind):\n noise_schedule = GaussianNoiseSchedule(kind=kind, log_snr_range=(-6, 6))\n ts = torch.linspace(1e-2, 1 - 1e-2, 10)\n t_map = noise_schedule.t_map(ts) # map time to the prescribed log_SNR range\n\n if kind == \"log_snr\":\n g = noise_schedule.g(ts)\n\n # compute true beta_t\n l_range = noise_schedule.log_snr_range\n snr = torch.exp((1 - t_map) * l_range[1] + t_map * l_range[0])\n g_true = (-(l_range[0] - l_range[1]) / (snr + 1)).sqrt()\n\n assert torch.allclose(g, g_true, atol=1e-4)\n\n if kind == \"ot_linear\":\n g = noise_schedule.g(ts)\n g_true = (2.0 * t_map / (1.0 - t_map)).sqrt()\n assert torch.allclose(g, g_true, atol=1e-4)",
"completions": [
"```python\ndef test_noise_schedule_diffusion_coeff(kind):\n from torch.testing import assert_allclose\n from torch.nn import functional as F\n from torch import tensor\n from torch.distributions import Uniform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import Transform\n from torch.distributions.transforms import SigmoidTransform\n from torch.distributions.transforms import TanhTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import ComposeTransform\n from torch.distributions.transforms import PowerTransform\n from torch.distributions.transforms import ExpTransform\n from torch.distributions.transforms import LogTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import AffineTransform\n from torch.distributions.transforms import"
],
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"token": 143,
"line_diff": 0,
"token_diff": 0
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{
"id": 42,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_temperature_features(\n meter_data_index,\n temperature_data,\n heating_balance_points=None,\n cooling_balance_points=None,\n data_quality=False,\n temperature_mean=True,\n degree_day_method=\"daily\",\n percent_hourly_coverage_per_day=0.5,\n percent_hourly_coverage_per_billing_period=0.9,\n use_mean_daily_values=True,\n tolerance=None,\n keep_partial_nan_rows=False,\n):\n if temperature_data.index.freq != \"H\":\n raise ValueError(\n \"temperature_data.index must have hourly frequency (freq='H').\"\n \" Found: {}\".format(temperature_data.index.freq)\n )\n\n if not temperature_data.index.tz:\n raise ValueError(\n \"temperature_data.index must be timezone-aware. You can set it with\"\n \" temperature_data.tz_localize(...).\"\n )\n\n if meter_data_index.freq is None and meter_data_index.inferred_freq == \"H\":\n raise ValueError(\n \"If you have hourly data explicitly set the frequency\"\n \" of the dataframe by setting\"\n \"``meter_data_index.freq =\"\n \" pd.tseries.frequencies.to_offset('H').\"\n )\n\n if not meter_data_index.tz:\n raise ValueError(\n \"meter_data_index must be timezone-aware. You can set it with\"\n \" meter_data.tz_localize(...).\"\n )\n\n if meter_data_index.duplicated().any():\n raise ValueError(\"Duplicates found in input meter trace index.\")\n\n temp_agg_funcs = []\n temp_agg_column_renames = {}\n\n if heating_balance_points is None:\n heating_balance_points = []\n if cooling_balance_points is None:\n cooling_balance_points = []\n\n if meter_data_index.freq is not None:\n try:\n freq_timedelta = pd.Timedelta(meter_data_index.freq)\n except ValueError: # freq cannot be converted to timedelta\n freq_timedelta = None\n else:\n freq_timedelta = None\n\n if tolerance is None:\n tolerance = freq_timedelta\n\n if not (heating_balance_points == [] and cooling_balance_points == []):\n if degree_day_method == \"hourly\":\n pass\n elif degree_day_method == \"daily\":\n if meter_data_index.freq == \"H\":\n raise ValueError(\n \"degree_day_method='daily' must be used with\"\n \" daily meter data. Found: 'hourly'\".format(degree_day_method)\n )\n else:\n raise ValueError(\"method not supported: {}\".format(degree_day_method))\n\n if freq_timedelta == pd.Timedelta(\"1H\"):\n # special fast route for hourly data.\n df = temperature_data.to_frame(\"temperature_mean\").reindex(meter_data_index)\n\n if use_mean_daily_values:\n n_days = 1\n else:\n n_days = 1.0 / 24.0\n\n df = df.assign(\n **{\n \"cdd_{}\".format(bp): np.maximum(df.temperature_mean - bp, 0) * n_days\n for bp in cooling_balance_points\n }\n )\n df = df.assign(\n **{\n \"hdd_{}\".format(bp): np.maximum(bp - df.temperature_mean, 0) * n_days\n for bp in heating_balance_points\n }\n )\n df = df.assign(\n n_hours_dropped=df.temperature_mean.isnull().astype(int),\n n_hours_kept=df.temperature_mean.notnull().astype(int),\n )\n # TODO(philngo): bad interface or maybe this is just wrong for some reason?\n if data_quality:\n df = df.assign(\n temperature_null=df.n_hours_dropped,\n temperature_not_null=df.n_hours_kept,\n )\n if not temperature_mean:\n del df[\"temperature_mean\"]\n else:\n # daily/billing route\n # heating/cooling degree day aggregations. Needed for n_days fields as well.\n temp_agg_funcs.extend(\n _degree_day_columns(\n heating_balance_points=heating_balance_points,\n cooling_balance_points=cooling_balance_points,\n degree_day_method=degree_day_method,\n percent_hourly_coverage_per_day=percent_hourly_coverage_per_day,\n percent_hourly_coverage_per_billing_period=percent_hourly_coverage_per_billing_period,\n use_mean_daily_values=use_mean_daily_values,\n )\n )\n temp_agg_column_renames.update(\n {(\"temp\", \"degree_day_columns\"): \"degree_day_columns\"}\n )\n\n if data_quality:\n temp_agg_funcs.extend(\n [(\"not_null\", \"count\"), (\"null\", lambda x: x.isnull().sum())]\n )\n temp_agg_column_renames.update(\n {\n (\"temp\", \"not_null\"): \"temperature_not_null\",\n (\"temp\", \"null\"): \"temperature_null\",\n }\n )\n\n if temperature_mean:\n temp_agg_funcs.extend([(\"mean\", \"mean\")])\n temp_agg_column_renames.update({(\"temp\", \"mean\"): \"temperature_mean\"})\n\n # aggregate temperatures\n temp_df = temperature_data.to_frame(\"temp\")\n temp_groups = _matching_groups(meter_data_index, temp_df, tolerance)\n temp_aggregations = temp_groups.agg({\"temp\": temp_agg_funcs})\n\n # expand temp aggregations by faking and deleting the `meter_value` column.\n # I haven't yet figured out a way to avoid this and get the desired\n # structure and behavior. (philngo)\n meter_value = pd.DataFrame({\"meter_value\": 0}, index=meter_data_index)\n df = pd.concat([meter_value, temp_aggregations], axis=1).rename(\n columns=temp_agg_column_renames\n )\n del df[\"meter_value\"]\n\n if \"degree_day_columns\" in df:\n if df[\"degree_day_columns\"].dropna().empty:\n column_defaults = {\n column: np.full(df[\"degree_day_columns\"].shape, np.nan)\n for column in [\"n_days_dropped\", \"n_days_kept\"]\n }\n df = df.drop([\"degree_day_columns\"], axis=1).assign(**column_defaults)\n else:\n df = pd.concat(\n [\n df.drop([\"degree_day_columns\"], axis=1),\n df[\"degree_day_columns\"].dropna().apply(pd.Series),\n ],\n axis=1,\n )\n\n if not keep_partial_nan_rows:\n df = overwrite_partial_rows_with_nan(df)\n\n # nan last row\n df = df.iloc[:-1].reindex(df.index)\n return df\n```\n###test function signature:\n```python\n\ndef test_compute_temperature_features_billing_monthly_data_quality(\n il_electricity_cdd_hdd_billing_monthly,\n):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_temperature_features_billing_monthly_data_quality",
"reference": "\ndef test_compute_temperature_features_billing_monthly_data_quality(\n il_electricity_cdd_hdd_billing_monthly,\n):\n meter_data = il_electricity_cdd_hdd_billing_monthly[\"meter_data\"]\n temperature_data = il_electricity_cdd_hdd_billing_monthly[\"temperature_data\"]\n df = compute_temperature_features(\n meter_data.index, temperature_data, temperature_mean=False, data_quality=True\n )\n assert df.shape == (27, 4)\n assert list(sorted(df.columns)) == [\n \"n_days_dropped\",\n \"n_days_kept\",\n \"temperature_not_null\",\n \"temperature_null\",\n ]\n assert round(df.temperature_not_null.mean(), 2) == 729.23\n assert round(df.temperature_null.mean(), 2) == 0.0",
"completions": [
"```python\ndef test_compute_temperature_features_billing_monthly_data_quality(\n il_electricity_cdd_hdd_billing_monthly,\n):\n meter_data_index = pd.date_range(\n start=\"2015-01-01\", end=\"2015-01-31\", freq=\"1H\", tz=\"America/Chicago\"\n )\n temperature_data = pd.DataFrame(\n {\n \"temperature_mean\": [\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0,\n 10.0"
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{
"id": 43,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def unload_model(self, model_id: str) -> RegisteredModels:\n self.__ensure_v1_client_mode()\n de_aliased_model_id = resolve_roboflow_model_alias(model_id=model_id)\n response = requests.post(\n f\"{self.__api_url}/model/remove\",\n json={\n \"model_id\": de_aliased_model_id,\n },\n headers=DEFAULT_HEADERS,\n )\n response.raise_for_status()\n response_payload = response.json()\n if (\n de_aliased_model_id == self.__selected_model\n or model_id == self.__selected_model\n ):\n self.__selected_model = None\n return RegisteredModels.from_dict(response_payload)\n```\n###test function signature:\n```python\n\ndef test_client_unload_single_model_when_successful_response_expected(\n requests_mock: Mocker,\n) -> None:\n # given\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_client_unload_single_model_when_successful_response_expected",
"reference": "\ndef test_client_unload_single_model_when_successful_response_expected(\n requests_mock: Mocker,\n) -> None:\n # given\n api_url = \"http://some.com\"\n requests_mock.post(\n f\"{api_url}/model/remove\",\n json={\"models\": [{\"model_id\": \"some/1\", \"task_type\": \"classification\"}]},\n )\n http_client = InferenceHTTPClient(api_key=\"my-api-key\", api_url=api_url)\n\n # when\n result = http_client.unload_model(model_id=\"some/1\")\n\n # then\n assert result == RegisteredModels(\n models=[ModelDescription(model_id=\"some/1\", task_type=\"classification\")]\n )\n assert requests_mock.last_request.json() == {\n \"model_id\": \"some/1\",\n }",
"completions": [
"```python\n\ndef test_client_unload_single_model_when_successful_response_expected(\n requests_mock: Mocker,\n) -> None:\n # given\n model_id = \"my_model_id\"\n de_aliased_model_id = \"my_de_aliased_model_id\"\n response_payload = {\n \"model_id\": de_aliased_model_id,\n \"model_name\": \"my_model_name\",\n \"model_version\": \"my_model_version\",\n \"model_type\": \"my_model_type\",\n \"model_status\": \"my_model_status\",\n \"model_description\": \"my_model_description\",\n \"model_tags\": [\"my_model_tag1\", \"my_model_tag2\"],\n \"model_created_at\": \"my_model_created_at\",\n \"model_updated_at\": \"my_model_updated_at\",\n }\n requests_mock.post(\n f\"{self.__api_url}/model/remove\",\n json=response_payload,\n headers=DEFAULT_HEADERS,\n )\n # when\n response = self.client.unload_model(model_id=model_id)\n # then\n assert response.model_id == de_aliased_model_id\n assert response.model_name == \"my_model_name\"\n assert response.model_version == \"my_model_version\"\n assert response.model_type == \"my_model_type\"\n assert response.model_status == \"my_model_status\"\n assert response.model_description == \"my_model_description\"\n assert response.model_tags == [\"my_model_tag1\", \"my_model_tag2\"]\n assert response.model_created_at == \"my_model_created_at\"\n assert response.model_updated_at == \"my_model_updated_at\"\n assert self.client.selected_model is None\n```\n"
],
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{
"id": 44,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef generate_unit_locations(\n num_units,\n channel_locations,\n margin_um=20.0,\n minimum_z=5.0,\n maximum_z=40.0,\n minimum_distance=20.0,\n max_iteration=100,\n distance_strict=False,\n seed=None,\n):\n rng = np.random.default_rng(seed=seed)\n units_locations = np.zeros((num_units, 3), dtype=\"float32\")\n\n minimum_x, maximum_x = np.min(channel_locations[:, 0]) - margin_um, np.max(channel_locations[:, 0]) + margin_um\n minimum_y, maximum_y = np.min(channel_locations[:, 1]) - margin_um, np.max(channel_locations[:, 1]) + margin_um\n\n units_locations[:, 0] = rng.uniform(minimum_x, maximum_x, size=num_units)\n units_locations[:, 1] = rng.uniform(minimum_y, maximum_y, size=num_units)\n units_locations[:, 2] = rng.uniform(minimum_z, maximum_z, size=num_units)\n\n if minimum_distance is not None:\n solution_found = False\n renew_inds = None\n for i in range(max_iteration):\n distances = np.linalg.norm(units_locations[:, np.newaxis] - units_locations[np.newaxis, :], axis=2)\n inds0, inds1 = np.nonzero(distances < minimum_distance)\n mask = inds0 != inds1\n inds0 = inds0[mask]\n inds1 = inds1[mask]\n\n if inds0.size > 0:\n if renew_inds is None:\n renew_inds = np.unique(inds0)\n else:\n # random only bad ones in the previous set\n renew_inds = renew_inds[np.isin(renew_inds, np.unique(inds0))]\n\n units_locations[:, 0][renew_inds] = rng.uniform(minimum_x, maximum_x, size=renew_inds.size)\n units_locations[:, 1][renew_inds] = rng.uniform(minimum_y, maximum_y, size=renew_inds.size)\n units_locations[:, 2][renew_inds] = rng.uniform(minimum_z, maximum_z, size=renew_inds.size)\n else:\n solution_found = True\n break\n\n if not solution_found:\n if distance_strict:\n raise ValueError(\n f\"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=} \"\n \"You can use distance_strict=False or reduce minimum distance\"\n )\n else:\n warnings.warn(f\"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=}\")\n\n return units_locations\n```\n###test function signature:\n```python\n\ndef test_generate_unit_locations():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_generate_unit_locations",
"reference": "\ndef test_generate_unit_locations():\n seed = 0\n\n probe = generate_multi_columns_probe(num_columns=2, num_contact_per_column=20, xpitch=20, ypitch=20)\n channel_locations = probe.contact_positions\n\n num_units = 100\n minimum_distance = 20.0\n unit_locations = generate_unit_locations(\n num_units,\n channel_locations,\n margin_um=20.0,\n minimum_z=5.0,\n maximum_z=40.0,\n minimum_distance=minimum_distance,\n max_iteration=500,\n distance_strict=False,\n seed=seed,\n )\n distances = np.linalg.norm(unit_locations[:, np.newaxis] - unit_locations[np.newaxis, :], axis=2)\n dist_flat = np.triu(distances, k=1).flatten()\n dist_flat = dist_flat[dist_flat > 0]\n assert np.all(dist_flat > minimum_distance)\n\n # import matplotlib.pyplot as plt\n # fig, ax = plt.subplots()\n # ax.hist(dist_flat, bins = np.arange(0, 400, 10))\n # fig, ax = plt.subplots()\n # from probeinterface.plotting import plot_probe\n # plot_probe(probe, ax=ax)\n # ax.scatter(unit_locations[:, 0], unit_locations[:,1], marker='*', s=20)\n # plt.show()",
"completions": [
"```python\n\ndef test_generate_unit_locations():\n num_units = 10\n channel_locations = np.array([[0, 0, 0], [10, 10, 10], [20, 20, 20]])\n margin_um = 20.0\n minimum_z = 5.0\n maximum_z = 40.0\n minimum_distance = 20.0\n max_iteration = 100\n distance_strict = False\n seed = None\n\n units_locations = generate_unit_locations(\n num_units,\n channel_locations,\n margin_um=margin_um,\n minimum_z=minimum_z,\n maximum_z=maximum_z,\n minimum_distance=minimum_distance,\n max_iteration=max_iteration,\n distance_strict=distance_strict,\n seed=seed,\n )\n\n assert units_locations.shape == (num_units, 3)\n assert np.all(units_locations[:, 0] >= margin_um)\n assert np.all(units_locations[:, 0] <= 100 - margin_um)\n assert np.all(units_locations[:, 1] >= margin_um)\n assert np.all(units_locations[:, 1] <= 100 - margin_um)\n assert np.all(units_locations[:, 2] >= minimum_z)\n assert np.all(units_locations[:, 2] <= maximum_z)\n\n # check distance\n distances = np.linalg.norm(units_locations[:, np.newaxis] - units_locations[np.newaxis, :], axis=2)\n assert np.all(distances >= minimum_distance)\n\n # check no duplicate\n assert np.all(np.unique(units_locations, axis=0) == units_locations)\n```\n"
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{
"id": 45,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def to_dataframe(\n self, index: Optional[Union[str, None]] = \"date\", sort_by: Optional[str] = None\n ) -> pd.DataFrame:\n\n def is_list_of_basemodel(items: Union[List[T], T]) -> bool:\n return isinstance(items, list) and all(\n isinstance(item, BaseModel) for item in items\n )\n\n if self.results is None or not self.results:\n raise OpenBBError(\"Results not found.\")\n\n if isinstance(self.results, pd.DataFrame):\n return self.results\n\n try:\n res = self.results\n df = None\n sort_columns = True\n\n # List[Dict]\n if isinstance(res, list) and len(res) == 1 and isinstance(res[0], dict):\n r = res[0]\n dict_of_df = {}\n\n for k, v in r.items():\n # Dict[str, List[BaseModel]]\n if is_list_of_basemodel(v):\n dict_of_df[k] = basemodel_to_df(v, index)\n sort_columns = False\n # Dict[str, Any]\n else:\n dict_of_df[k] = pd.DataFrame(v)\n\n df = pd.concat(dict_of_df, axis=1)\n\n # List[BaseModel]\n elif is_list_of_basemodel(res):\n dt: Union[List[Data], Data] = res # type: ignore\n df = basemodel_to_df(dt, index)\n sort_columns = False\n # List[List | str | int | float] | Dict[str, Dict | List | BaseModel]\n else:\n try:\n df = pd.DataFrame(res)\n # Set index, if any\n if index is not None and index in df.columns:\n df.set_index(index, inplace=True)\n\n except ValueError:\n if isinstance(res, dict):\n df = pd.DataFrame([res])\n\n if df is None:\n raise OpenBBError(\"Unsupported data format.\")\n\n # Drop columns that are all NaN, but don't rearrange columns\n if sort_columns:\n df.sort_index(axis=1, inplace=True)\n df = df.dropna(axis=1, how=\"all\")\n\n # Sort by specified column\n if sort_by:\n df.sort_values(by=sort_by, inplace=True)\n\n except OpenBBError as e:\n raise e\n except ValueError as ve:\n raise OpenBBError(\n f\"ValueError: {ve}. Ensure the data format matches the expected format.\"\n ) from ve\n except TypeError as te:\n raise OpenBBError(\n f\"TypeError: {te}. Check the data types in your results.\"\n ) from te\n except Exception as ex:\n raise OpenBBError(f\"An unexpected error occurred: {ex}\") from ex\n\n return df\n```\n###test function signature:\n```python\ndef test_to_dataframe_w_args(results, index, sort_by):\n # Arrange\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_to_dataframe_w_args",
"reference": "def test_to_dataframe_w_args(results, index, sort_by):\n # Arrange\n co = OBBject(results=results)\n\n # Act and Assert\n result = co.to_dataframe(index=index, sort_by=sort_by)\n assert isinstance(result, pd.DataFrame)\n assert result.index.name == index\n\n # check if dataframe is properly sorted\n assert result[sort_by].is_monotonic_increasing",
"completions": [
"```python\ndef test_to_dataframe_w_args(results, index, sort_by):\n # Arrange\n from openbb_terminal.decorators import log_start_end\n from openbb_terminal.stocks.fundamental_analysis import financedatabase_model\n\n @log_start_end(log=logger)\n def test_to_dataframe_w_args(results, index, sort_by):\n # Arrange\n df = financedatabase_model.to_dataframe(results, index, sort_by)\n # Act\n # Assert\n assert df.shape[0] > 0\n assert df.shape[1] > 0\n assert df.index.name == index\n assert df.columns.tolist() == sort_by\n\n # Act\n test_to_dataframe_w_args(results, index, sort_by)\n # Assert\n```\n"
],
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{
"id": 46,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef unique(ar, return_index=False, return_inverse=False,\n return_counts=False, axis=None, *, equal_nan=True):\n ar = np.asanyarray(ar)\n if axis is None:\n ret = _unique1d(ar, return_index, return_inverse, return_counts, \n equal_nan=equal_nan)\n return _unpack_tuple(ret)\n\n # axis was specified and not None\n try:\n ar = np.moveaxis(ar, axis, 0)\n except np.AxisError:\n # this removes the \"axis1\" or \"axis2\" prefix from the error message\n raise np.AxisError(axis, ar.ndim) from None\n\n # Must reshape to a contiguous 2D array for this to work...\n orig_shape, orig_dtype = ar.shape, ar.dtype\n ar = ar.reshape(orig_shape[0], np.prod(orig_shape[1:], dtype=np.intp))\n ar = np.ascontiguousarray(ar)\n dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])]\n\n # At this point, `ar` has shape `(n, m)`, and `dtype` is a structured\n # data type with `m` fields where each field has the data type of `ar`.\n # In the following, we create the array `consolidated`, which has\n # shape `(n,)` with data type `dtype`.\n try:\n if ar.shape[1] > 0:\n consolidated = ar.view(dtype)\n else:\n # If ar.shape[1] == 0, then dtype will be `np.dtype([])`, which is\n # a data type with itemsize 0, and the call `ar.view(dtype)` will\n # fail. Instead, we'll use `np.empty` to explicitly create the\n # array with shape `(len(ar),)`. Because `dtype` in this case has\n # itemsize 0, the total size of the result is still 0 bytes.\n consolidated = np.empty(len(ar), dtype=dtype)\n except TypeError as e:\n # There's no good way to do this for object arrays, etc...\n msg = 'The axis argument to unique is not supported for dtype {dt}'\n raise TypeError(msg.format(dt=ar.dtype)) from e\n\n def reshape_uniq(uniq):\n n = len(uniq)\n uniq = uniq.view(orig_dtype)\n uniq = uniq.reshape(n, *orig_shape[1:])\n uniq = np.moveaxis(uniq, 0, axis)\n return uniq\n\n output = _unique1d(consolidated, return_index,\n return_inverse, return_counts, equal_nan=equal_nan)\n output = (reshape_uniq(output[0]),) + output[1:]\n return _unpack_tuple(output)\n```\n###test function signature:\n```python\n\n def test_unique_1d(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_unique_1d",
"reference": "\n def test_unique_1d(self):\n\n def check_all(a, b, i1, i2, c, dt):\n base_msg = 'check {0} failed for type {1}'\n\n msg = base_msg.format('values', dt)\n v = unique(a)\n assert_array_equal(v, b, msg)\n\n msg = base_msg.format('return_index', dt)\n v, j = unique(a, True, False, False)\n assert_array_equal(v, b, msg)\n assert_array_equal(j, i1, msg)\n\n msg = base_msg.format('return_inverse', dt)\n v, j = unique(a, False, True, False)\n assert_array_equal(v, b, msg)\n assert_array_equal(j, i2, msg)\n\n msg = base_msg.format('return_counts', dt)\n v, j = unique(a, False, False, True)\n assert_array_equal(v, b, msg)\n assert_array_equal(j, c, msg)\n\n msg = base_msg.format('return_index and return_inverse', dt)\n v, j1, j2 = unique(a, True, True, False)\n assert_array_equal(v, b, msg)\n assert_array_equal(j1, i1, msg)\n assert_array_equal(j2, i2, msg)\n\n msg = base_msg.format('return_index and return_counts', dt)\n v, j1, j2 = unique(a, True, False, True)\n assert_array_equal(v, b, msg)\n assert_array_equal(j1, i1, msg)\n assert_array_equal(j2, c, msg)\n\n msg = base_msg.format('return_inverse and return_counts', dt)\n v, j1, j2 = unique(a, False, True, True)\n assert_array_equal(v, b, msg)\n assert_array_equal(j1, i2, msg)\n assert_array_equal(j2, c, msg)\n\n msg = base_msg.format(('return_index, return_inverse '\n 'and return_counts'), dt)\n v, j1, j2, j3 = unique(a, True, True, True)\n assert_array_equal(v, b, msg)\n assert_array_equal(j1, i1, msg)\n assert_array_equal(j2, i2, msg)\n assert_array_equal(j3, c, msg)\n\n a = [5, 7, 1, 2, 1, 5, 7]*10\n b = [1, 2, 5, 7]\n i1 = [2, 3, 0, 1]\n i2 = [2, 3, 0, 1, 0, 2, 3]*10\n c = np.multiply([2, 1, 2, 2], 10)\n\n # test for numeric arrays\n types = []\n types.extend(np.typecodes['AllInteger'])\n types.extend(np.typecodes['AllFloat'])\n types.append('datetime64[D]')\n types.append('timedelta64[D]')\n for dt in types:\n aa = np.array(a, dt)\n bb = np.array(b, dt)\n check_all(aa, bb, i1, i2, c, dt)\n\n # test for object arrays\n dt = 'O'\n aa = np.empty(len(a), dt)\n aa[:] = a\n bb = np.empty(len(b), dt)\n bb[:] = b\n check_all(aa, bb, i1, i2, c, dt)\n\n # test for structured arrays\n dt = [('', 'i'), ('', 'i')]\n aa = np.array(list(zip(a, a)), dt)\n bb = np.array(list(zip(b, b)), dt)\n check_all(aa, bb, i1, i2, c, dt)\n\n # test for ticket #2799\n aa = [1. + 0.j, 1 - 1.j, 1]\n assert_array_equal(np.unique(aa), [1. - 1.j, 1. + 0.j])\n\n # test for ticket #4785\n a = [(1, 2), (1, 2), (2, 3)]\n unq = [1, 2, 3]\n inv = [0, 1, 0, 1, 1, 2]\n a1 = unique(a)\n assert_array_equal(a1, unq)\n a2, a2_inv = unique(a, return_inverse=True)\n assert_array_equal(a2, unq)\n assert_array_equal(a2_inv, inv)\n\n # test for chararrays with return_inverse (gh-5099)\n a = np.chararray(5)\n a[...] = ''\n a2, a2_inv = np.unique(a, return_inverse=True)\n assert_array_equal(a2_inv, np.zeros(5))\n\n # test for ticket #9137\n a = []\n a1_idx = np.unique(a, return_index=True)[1]\n a2_inv = np.unique(a, return_inverse=True)[1]\n a3_idx, a3_inv = np.unique(a, return_index=True,\n return_inverse=True)[1:]\n assert_equal(a1_idx.dtype, np.intp)\n assert_equal(a2_inv.dtype, np.intp)\n assert_equal(a3_idx.dtype, np.intp)\n assert_equal(a3_inv.dtype, np.intp)\n\n # test for ticket 2111 - float\n a = [2.0, np.nan, 1.0, np.nan]\n ua = [1.0, 2.0, np.nan]\n ua_idx = [2, 0, 1]\n ua_inv = [1, 2, 0, 2]\n ua_cnt = [1, 1, 2]\n assert_equal(np.unique(a), ua)\n assert_equal(np.unique(a, return_index=True), (ua, ua_idx))\n assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))\n assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))\n\n # test for ticket 2111 - complex\n a = [2.0-1j, np.nan, 1.0+1j, complex(0.0, np.nan), complex(1.0, np.nan)]\n ua = [1.0+1j, 2.0-1j, complex(0.0, np.nan)]\n ua_idx = [2, 0, 3]\n ua_inv = [1, 2, 0, 2, 2]\n ua_cnt = [1, 1, 3]\n assert_equal(np.unique(a), ua)\n assert_equal(np.unique(a, return_index=True), (ua, ua_idx))\n assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))\n assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))\n\n # test for ticket 2111 - datetime64\n nat = np.datetime64('nat')\n a = [np.datetime64('2020-12-26'), nat, np.datetime64('2020-12-24'), nat]\n ua = [np.datetime64('2020-12-24'), np.datetime64('2020-12-26'), nat]\n ua_idx = [2, 0, 1]\n ua_inv = [1, 2, 0, 2]\n ua_cnt = [1, 1, 2]\n assert_equal(np.unique(a), ua)\n assert_equal(np.unique(a, return_index=True), (ua, ua_idx))\n assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))\n assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))\n\n # test for ticket 2111 - timedelta\n nat = np.timedelta64('nat')\n a = [np.timedelta64(1, 'D'), nat, np.timedelta64(1, 'h'), nat]\n ua = [np.timedelta64(1, 'h'), np.timedelta64(1, 'D'), nat]\n ua_idx = [2, 0, 1]\n ua_inv = [1, 2, 0, 2]\n ua_cnt = [1, 1, 2]\n assert_equal(np.unique(a), ua)\n assert_equal(np.unique(a, return_index=True), (ua, ua_idx))\n assert_equal(np.unique(a, return_inverse=True), (ua, ua_inv))\n assert_equal(np.unique(a, return_counts=True), (ua, ua_cnt))\n\n # test for gh-19300\n all_nans = [np.nan] * 4\n ua = [np.nan]\n ua_idx = [0]\n ua_inv = [0, 0, 0, 0]\n ua_cnt = [4]\n assert_equal(np.unique(all_nans), ua)\n assert_equal(np.unique(all_nans, return_index=True), (ua, ua_idx))\n assert_equal(np.unique(all_nans, return_inverse=True), (ua, ua_inv))\n assert_equal(np.unique(all_nans, return_counts=True), (ua, ua_cnt))",
"completions": [
"```python\n def test_unique_1d(self):\n ar = np.array([1, 2, 3, 4, 5, 5, 5, 5, 5, 5])\n self.assertEqual(unique(ar), np.array([1, 2, 3, 4, 5]))\n self.assertEqual(unique(ar, return_index=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_inverse=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_counts=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_index=True, return_inverse=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_index=True, return_counts=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_inverse=True, return_counts=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n self.assertEqual(unique(ar, return_index=True, return_inverse=True, return_counts=True), (np.array([1, 2, 3, 4, 5]), np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))\n```\n"
],
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"token": 571,
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{
"id": 47,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef scaled_dot_product_attention(\n q: torch.Tensor,\n k: torch.Tensor,\n v: torch.Tensor,\n att_mask: Optional[Union[AttentionMask, \"SparseCS\", torch.Tensor]],\n dropout: Optional[torch.nn.Module] = None,\n block_size: int = 128,\n) -> torch.Tensor:\n autocast_disabled = (\n _has_cpp_library\n and isinstance(att_mask, SparseCS)\n or (att_mask is not None and att_mask.is_sparse)\n )\n seq_len = q.shape[-2]\n\n # switch if:\n # causal is required but mask is not sparse\n # fp16 or under amp context\n # sequence length is divisible by block size\n # same seq len for K and Q\n switch_to_blocksparse = (\n _is_blocksparse_available\n and (att_mask is not None and not att_mask.is_sparse)\n and (isinstance(att_mask, AttentionMask) and att_mask.is_causal)\n and (q.dtype == torch.float16 or torch.is_autocast_enabled())\n and not seq_len % block_size\n and q.shape[-2] == k.shape[-2]\n )\n\n if switch_to_blocksparse:\n logger.info(\"Switching causal attention to Triton blocksparse...\")\n return blocksparse_attention(q, k, v, dropout, block_size)\n\n with torch.cuda.amp.autocast(enabled=False) if autocast_disabled else nullcontext(): # type: ignore\n if autocast_disabled:\n q, k, v = q.float(), k.float(), v.float()\n\n att = scaled_query_key_softmax(q, k, att_mask=att_mask)\n\n # Optional dropout, could be part of the masking in the future\n att = _apply_dropout(att, dropout)\n\n # Get to the predicted values, for all heads\n # y = att @ v # (N, S, S) x (N, S, hs) -> (N, S, hs)\n y = bmm(att, v)\n return y\n```\n###test function signature:\n```python\n\ndef test_amp_attention_dense(device):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_amp_attention_dense",
"reference": "\ndef test_amp_attention_dense(device):\n b, s, d = 8, 64, 32\n prob = 0.9\n\n a = torch.rand(b, s, d, device=device)\n m = torch.rand(s, s, device=device) > prob\n\n with torch.cuda.amp.autocast():\n r = scaled_dot_product_attention(a, a, a, m)\n\n expected_device = torch.float16 if device == \"cuda\" else torch.float32\n assert r.dtype == expected_device",
"completions": [
"```python\n\ndef test_amp_attention_dense(device):\n q = torch.randn(1, 128, 128, device=device)\n k = torch.randn(1, 128, 128, device=device)\n v = torch.randn(1, 128, 128, device=device)\n att_mask = torch.randn(1, 128, 128, device=device)\n dropout = torch.nn.Dropout(0.1)\n block_size = 128\n y = scaled_dot_product_attention(q, k, v, att_mask, dropout, block_size)\n assert y.shape == (1, 128, 128)\n```\n"
],
"line": 50,
"token": 390,
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{
"id": 48,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def quantize(\n cls,\n tensor: Tensor,\n nbits: int = 4,\n channel_wise: bool = True,\n group_size: int = 64,\n optimize: bool = False,\n round_zero: bool = False,\n axis: int = 0,\n bitpack: bool = True,\n compute_dtype: torch.dtype | None = None,\n view_as_float: bool = False,\n device: str = \"cuda\",\n ) -> tuple:\n assert nbits in Quantizer.SUPPORTED_BITS, (\n \"nbits=\" + str(nbits) + \" not supported.\"\n )\n assert axis in [0, 1], \"axis should be either 0 or 1\"\n if group_size is not None:\n assert is_divisible(tensor.numel(), group_size), (\n \"group_size should be divisble by the total tensor dimensions. shape: \"\n + str(tensor.shape)\n + \", group_size: \"\n + str(group_size)\n )\n\n W = tensor.float()\n shape = W.shape\n\n # Reshape for grouping\n if (group_size is not None) and channel_wise:\n W = (\n W.reshape([-1, group_size])\n if (axis == 1)\n else W.reshape([group_size, -1])\n )\n\n # Get min/max values\n if not channel_wise:\n _min, _max = W.min(), W.max()\n optimize = False\n else:\n _min = W.min(axis=axis, keepdim=True)[0]\n _max = W.max(axis=axis, keepdim=True)[0]\n\n max_v = 2**nbits - 1\n min_v = 0\n min_max = [min_v, max_v]\n\n # Note: here we work with the inverse of the scale to avoid division and quantize instead via W*scale + zero, the scale is inverted later on.\n scale = (max_v / (_max - _min)).clamp(\n max=2e4\n ) # clamp to avoid half-precision problems\n zero = -_min * scale\n\n # Round zero as in: https://github.com/casper-hansen/AutoAWQ/blob/main/awq/quantize/quantizer.py#L42C9-L42C14\n if round_zero:\n zero = torch.round(zero)\n\n # Fine-tune weights\n if optimize:\n W_q, scale, zero = Quantizer.optimize_weights(\n tensor=W,\n scale=scale,\n zero=zero,\n min_max=min_max,\n axis=axis,\n device=device,\n )\n else:\n W_q = torch.round(W * scale + zero).clamp(min_max[0], min_max[1])\n\n # Store meta-data (we invert the scale for dequantization)\n meta = {\n \"nbits\": nbits,\n \"group_size\": group_size,\n \"shape\": shape,\n \"scale\": 1.0 / scale,\n \"zero\": zero,\n \"axis\": axis,\n \"packing\": Quantizer.bit_to_packing[nbits],\n }\n meta[\"unpack_view_dtype\"] = Quantizer.unpack_view_dtype[meta[\"packing\"]]\n\n # Pack bits\n meta[\"view_as_float\"] = view_as_float\n if bitpack:\n W_q = Quantizer.pack[meta[\"packing\"]](W_q)\n if view_as_float:\n W_q = W_q.view(\n torch.float32 if compute_dtype is None else compute_dtype\n ) # store quantized weights as compute_dtype\n else:\n W_q = W_q.to(tensor.dtype)\n meta[\"packing\"] = None\n\n # cleanup\n del W, _min, _max\n torch.cuda.empty_cache()\n\n return W_q, meta\n```\n###test function signature:\n```python\n\n def test_quantizer(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_quantizer",
"reference": "\n def test_quantizer(self): \n for compute_dtype in [torch.float32, torch.float16, torch.bfloat16]:\n for nbits in [8,4,3,2,1]:\n W_q, meta = Quantizer.quantize(self.w, nbits=nbits, round_zero=True, optimize=True, view_as_float=False)\n if nbits == 3:\n assert W_q.dtype == torch.int32\n else:\n assert W_q.dtype == torch.uint8\n w_dq = Quantizer.dequantize(W_q, meta)\n norm1 = torch.norm(self.w - w_dq, p=0.7)\n \n W_q, meta = Quantizer.quantize(self.w, nbits=nbits, round_zero=True, optimize=True, compute_dtype=compute_dtype, view_as_float=True)\n assert W_q.dtype == compute_dtype\n w_dq = Quantizer.dequantize(W_q, meta)\n norm2 = torch.norm(self.w - w_dq, p=0.7)\n \n self.assertTrue(torch.equal(norm1, norm2))",
"completions": [
"```python\n\n def test_quantizer(self):\n # test case 1\n tensor = torch.randn(1, 3, 224, 224)\n W_q, meta = quantize(tensor, nbits=4, channel_wise=True, group_size=64, optimize=False, round_zero=False, axis=0, bitpack=True, compute_dtype=None, view_as_float=False, device=\"cuda\")\n self.assertEqual(W_q.shape, (1, 3, 224, 224))\n self.assertEqual(meta[\"nbits\"], 4)\n self.assertEqual(meta[\"group_size\"], 64)\n self.assertEqual(meta[\"shape\"], (1, 3, 224, 224))\n self.assertEqual(meta[\"scale\"], 1.0 / 15.999999999999998)\n self.assertEqual(meta[\"zero\"], -1.0)\n self.assertEqual(meta[\"axis\"], 0)\n self.assertEqual(meta[\"packing\"], \"uint4\")\n self.assertEqual(meta[\"unpack_view_dtype\"], torch.float32)\n self.assertEqual(meta[\"view_as_float\"], False)\n\n # test case 2\n tensor = torch.randn(1, 3, 224, 224)\n W_q, meta = quantize(tensor, nbits=4, channel_wise=True, group_size=64, optimize=False, round_zero=True, axis=0, bitpack=True, compute_dtype=None, view_as_float=False, device=\"cuda\")\n self.assertEqual(W_q.shape, (1, 3, 224, 224))\n self.assertEqual(meta[\"nbits\"], 4)\n self.assertEqual(meta[\"group_size\"], 64)\n self.assertEqual(meta[\"shape\"], (1, 3, 224, 224))\n self.assertEqual(meta[\"scale\"], 1.0 / 15.999999999999998)\n self.assertEqual(meta[\"zero\"], 0.0)\n self.assertEqual(meta[\"axis\"], 0)\n self.assertEqual(meta[\"packing\"], \"uint4\")\n self.assertEqual(meta[\"unpack_view_dtype\"], torch.float32)\n self.assertEqual(meta[\"view_as_float\"], False)\n\n # test case 3\n tensor = torch.randn(1, 3, 224, 224)\n W_q, meta = quantize(tensor, nbits=4, channel_wise=True, group_size=64, optimize=True, round_zero=False, axis=0, bitpack=True, compute_dtype=None, view_as_float=False, device=\"cuda\")\n self.assertEqual(W_q.shape, (1, 3, 224, 224))\n self.assertEqual(meta[\"nbits\"], 4)\n self.assertEqual(meta[\"group_size\"], 64)\n self.assertEqual(meta[\"shape\"], (1, 3, 224, 224))\n self.assertEqual(meta[\"scale\"], 1.0 / 15.999999999999998)\n self.assertEqual(meta[\"zero\"], -1.0)\n self.assertEqual(meta[\"axis\"], 0)\n self.assertEqual(meta[\"packing\"], \"uint4\")\n self.assertEqual(meta[\"unpack_view_dtype\"], torch.float32)\n self.assertEqual(meta[\"view_as_float\"], False)\n\n # test case 4\n tensor = torch.randn(1, 3, 224, 224)\n W_q, meta = quantize(tensor,"
],
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{
"id": 49,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _generate_nanoplot(\n y_vals: Union[List[Union[int, float]], List[int], List[float]],\n y_ref_line: Optional[str] = None,\n y_ref_area: Optional[str] = None,\n x_vals: \"List[Union[int, float]] | None\" = None,\n expand_x: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n expand_y: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n missing_vals: str = \"marker\",\n all_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n all_single_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n plot_type: str = \"line\",\n data_line_type: str = \"curved\",\n currency: Optional[str] = None,\n y_val_fmt_fn: Optional[Callable[..., str]] = None,\n y_axis_fmt_fn: Optional[Callable[..., str]] = None,\n y_ref_line_fmt_fn: Optional[Callable[..., str]] = None,\n data_point_radius: Union[int, List[int]] = 10,\n data_point_stroke_color: Union[str, List[str]] = \"#FFFFFF\",\n data_point_stroke_width: Union[int, List[int]] = 4,\n data_point_fill_color: Union[str, List[str]] = \"#FF0000\",\n data_line_stroke_color: str = \"#4682B4\",\n data_line_stroke_width: int = 8,\n data_area_fill_color: str = \"#FF0000\",\n data_bar_stroke_color: Union[str, List[str]] = \"#3290CC\",\n data_bar_stroke_width: Union[int, List[int]] = 4,\n data_bar_fill_color: Union[str, List[str]] = \"#3FB5FF\",\n data_bar_negative_stroke_color: str = \"#CC3243\",\n data_bar_negative_stroke_width: int = 4,\n data_bar_negative_fill_color: str = \"#D75A68\",\n reference_line_color: str = \"#75A8B0\",\n reference_area_fill_color: str = \"#A6E6F2\",\n vertical_guide_stroke_color: str = \"#911EB4\",\n vertical_guide_stroke_width: int = 12,\n show_data_points: bool = True,\n show_data_line: bool = True,\n show_data_area: bool = True,\n show_reference_line: bool = True,\n show_reference_area: bool = True,\n show_vertical_guides: bool = True,\n show_y_axis_guide: bool = True,\n interactive_data_values: bool = True,\n svg_height: str = \"2em\",\n) -> str:\n\n # Ensure that arguments are matched\n _match_arg(\n x=missing_vals,\n lst=[\"marker\", \"gap\", \"zero\", \"remove\"],\n )\n _match_arg(\n x=data_line_type,\n lst=[\"curved\", \"straight\"],\n )\n\n #\n # Determine where a zero line is considered and provide the stroke color and width\n #\n\n zero_line_considered = True if plot_type in [\"bar\", \"boxplot\"] else False\n\n zero_line_stroke_color = \"#BFBFBF\"\n zero_line_stroke_width = 4\n\n # Initialize several local `*_tags` variables with `None`\n ref_area_tags = None\n area_path_tags = None\n data_path_tags = None\n zero_line_tags = None\n bar_tags = None\n boxplot_tags = None\n ref_line_tags = None\n circle_tags = None\n g_y_axis_tags = None\n g_guide_tags = None\n\n # Initialize the `single_horizontal_plot` variable with `False`\n single_horizontal_plot = False\n\n # If the number of `y` values in a list is zero or if all consist of NA values,\n # return an empty string\n if type(y_vals) is list and len(y_vals) == 0:\n return \"\"\n\n # If all `y` values are NA, return an empty string\n # TODO: all([]) evaluates to True. In that case does this produce the intended behavior?\n if isinstance(y_vals, list) and all(_map_is_na(y_vals)):\n return \"\"\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # Handle case where `x_vals` exists (i.e., is not `NULL`)\n if x_vals is not None:\n\n # If the number of `x` values is zero or an empty string,\n # return an empty string\n if len(x_vals) == 0:\n return \"\"\n if all(_map_is_na(x_vals)):\n return \"\"\n\n # Get the number of data points for `x`\n num_x_vals = len(x_vals)\n\n # Ensure that, if there are `x` values, the number of `x`\n # and `y` values matches\n if num_x_vals != num_y_vals:\n raise ValueError(\n f\"\"\"The number of `x` and `y` values must match.\n The `x` value length is: {num_x_vals}\n The `y` value length is: {num_y_vals}\n \"\"\"\n )\n\n # Handle missing values in `x_vals` through removal (i.e., missing\n # values in `x_vals` means removal of positional values from both\n # `x_vals` and `y_vals`)\n if any(_map_is_na(x_vals)):\n # TODO: this code did not have test coverage and likely didn't\n # work. It should work now, but we need to test it.\n\n # Determine which values from `x_vals` are non-missing values\n x_vals_non_missing = [~_is_na(val) for val in x_vals]\n\n # Retain only `x_vals_non_missing` from `x_vals` and `y_vals`\n x_vals = [x for x, keep in zip(x_vals, x_vals_non_missing) if keep]\n y_vals = [y for y, keep in zip(y_vals, x_vals_non_missing) if keep]\n\n # If `x` values are present, we cannot use a curved line so\n # we'll force the use of the 'straight' line type\n # TODO: if someone specifies the options curved, and we can't do it\n # then we should raise an error.\n data_line_type = \"straight\"\n\n # If `missing_vals` is set to 'gap' raise an error\n # TODO: Implement the 'gap' option for missing values\n if missing_vals == \"gap\":\n raise NotImplementedError(\"The 'gap' option for missing values is not yet implemented.\")\n\n # For the `missing_vals` options of 'zero' or 'remove', either replace NAs\n # with `0` or remove NAs entirely\n if missing_vals == \"zero\":\n y_vals = y_vals.fillna(0)\n\n # If `missing_vals` is 'remove', remove NAs from `y_vals`\n if missing_vals == \"remove\":\n y_vals = y_vals.dropna()\n\n if x_vals is not None:\n # Remove the corresponding `x` values for the removed `y` values\n x_vals = x_vals[y_vals.index]\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # If `y_vals` is a scalar value we requested a 'line' or 'bar' plot, then\n # reset several parameters\n if type(y_vals) in [int, float] and plot_type in [\"line\", \"bar\"]:\n\n single_horizontal_plot = True\n show_data_points = True\n show_data_line = True\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n y_vals = [y_vals]\n\n # If this is a box plot, set several parameters\n if plot_type == \"boxplot\":\n\n show_data_points = False\n show_data_line = False\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n # Find out whether the collection of non-NA `y` values are all integer-like\n y_vals_integerlike = _is_integerlike(val_list=y_vals)\n\n # Get the max and min of the `y` scale from the `y` data values\n y_scale_max = _get_extreme_value(y_vals, stat=\"max\")\n y_scale_min = _get_extreme_value(y_vals, stat=\"min\")\n\n # Handle cases where collection of `y_vals` is invariant\n if y_scale_min == y_scale_max and expand_y is None:\n\n if y_scale_min == 0:\n expand_y_dist = 5\n else:\n expand_y_dist = (y_scale_min / 10) * 2\n\n # Expand the `y` scale, centering around the `y_scale_min` value\n expand_y = [y_scale_min - expand_y_dist, y_scale_min + expand_y_dist]\n\n # Ensure that a reference line or reference area isn't shown if None or\n # any of its directives is missing\n if _is_na(y_ref_line):\n show_reference_line = False\n\n if y_ref_area is None:\n show_reference_area = False\n elif _is_na(y_ref_area[0]) or _is_na(y_ref_area[1]):\n show_reference_area = False\n\n # Determine the width of the data plot area; for plots where `x_vals`\n # are available, we'll use a fixed width of `500` (px), and for plots\n # where `x_vals` aren't present, we'll adjust the final width based\n # on the fixed interval between data points (this is dependent on the\n # number of data points)\n if x_vals is not None or single_horizontal_plot or plot_type == \"boxplot\":\n data_x_width = 600\n # TODO: what should x_d be in this case?\n else:\n # Obtain a sensible, fixed interval between data points in px\n if num_y_vals <= 20:\n x_d = 50\n elif num_y_vals <= 30:\n x_d = 40\n elif num_y_vals <= 40:\n x_d = 30\n elif num_y_vals <= 50:\n x_d = 25\n else:\n x_d = 20\n\n data_x_width = num_y_vals * x_d\n\n # Define the top-left of the plot area\n left_x = 0\n top_y = 0\n\n # Define the safe zone distance from top/bottom and left/right edges\n safe_y_d = 15\n safe_x_d = 50\n\n # Define the height of the plot area that bounds the data points\n data_y_height = 100\n\n # Determine the bottom-right of the plot area based on the quantity of data\n bottom_y = safe_y_d + data_y_height + safe_y_d\n right_x = safe_x_d + data_x_width + safe_x_d\n\n viewbox = f\"{left_x} {top_y} {right_x} {bottom_y}\"\n\n #\n # If there is a reference line and/or reference area, the values for these\n # need to be generated and integrated in the `normalize_y_vals()` operation\n # so that there are normalized values in relation to the data points\n #\n\n if show_reference_line and show_reference_area:\n\n # Case where there is both a reference line and a reference area\n\n #\n # Resolve the reference line\n #\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n #\n # Resolve the reference area\n #\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n _all_y_data = [y_vals, y_ref_line, y_ref_area_l, y_ref_area_u, expand_y]\n\n # Recompute the `y` scale min and max values\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference line and reference area boundaries\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n elif show_reference_line:\n\n # Case where there is a reference line\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n # Recompute the `y` scale min and max values\n args = [y_vals, y_ref_line, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n\n # Scale reference line\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n\n elif show_reference_area:\n\n # Case where there is a reference area\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n # Recompute the `y` scale min and max values\n\n # Recompute the `y` scale min and max values\n _all_y_data = [y_vals, y_ref_area_l, y_ref_area_u, expand_y] + (\n [0] if zero_line_considered else []\n )\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference area boundaries\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n else:\n\n # Case where there is no reference line or reference area\n\n # Recompute the `y` scale min and max values\n args = [y_vals, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n\n # Calculate the `data_y0_point` value for zero-line-inclusive plots\n if zero_line_considered:\n y_proportions_zero = y_proportions_list[\"zero\"][0]\n data_y0_point = safe_y_d + ((1 - y_proportions_zero) * data_y_height)\n\n # If x values are present then normalize them between [0, 1]; if\n # there are no x values, generate equally-spaced x values according\n # to the number of y values\n if plot_type == \"line\" and x_vals is not None:\n\n if expand_x is not None and _val_is_str(expand_x):\n\n # TODO: the line below lacked tests, and called non-existent methods.\n # replace with something that doesn't use pandas and returns the correct thing.\n\n # Assume that string values are dates and convert them to timestamps\n # expand_x = pd.to_datetime(expand_x, utc=True).timestamp()\n raise NotImplementedError(\"Currently, passing expand_x as a string is unsupported.\")\n\n # Scale to proportional values\n x_proportions_list = _normalize_to_dict(vals=x_vals, expand_x=expand_x)\n\n x_proportions = x_proportions_list[\"vals\"]\n\n else:\n x_proportions = np.linspace(0, 1, num_y_vals)\n\n #\n # Create normalized (and inverted for SVG) data `x` and `y` values for the\n # plot area; these are named `data_x_points` and `data_y_points`\n #\n\n for i in range(len(y_proportions)):\n y_proportions[i] = safe_y_d + ((1 - y_proportions[i]) * data_y_height)\n\n for i in range(len(x_proportions)):\n x_proportions[i] = (data_x_width * x_proportions[i]) + safe_x_d\n\n data_y_points = y_proportions\n data_x_points = x_proportions\n\n #\n # Ensure that certain options have their lengths checked and\n # expanded to length `num_y_vals`\n #\n\n data_point_radius = _normalize_option_list(option_list=data_point_radius, num_y_vals=num_y_vals)\n data_point_stroke_color = _normalize_option_list(\n option_list=data_point_stroke_color, num_y_vals=num_y_vals\n )\n data_point_stroke_width = _normalize_option_list(\n option_list=data_point_stroke_width, num_y_vals=num_y_vals\n )\n data_point_fill_color = _normalize_option_list(\n option_list=data_point_fill_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_color = _normalize_option_list(\n option_list=data_bar_stroke_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_width = _normalize_option_list(\n option_list=data_bar_stroke_width, num_y_vals=num_y_vals\n )\n data_bar_fill_color = _normalize_option_list(\n option_list=data_bar_fill_color, num_y_vals=num_y_vals\n )\n\n #\n # Generate data segments by defining `start` and `end` vectors (these\n # are guaranteed to be of the same length); these the segments of data\n # they receive line segments and adjoining areas\n #\n\n # Use run-length encoding to determine the segments of data\n\n # rle_data_y_points = pd.Series(data_y_points).diff().ne(0).cumsum()\n\n # end_data_y_points = np.cumsum(rle_data_y_points.lengths)\n\n # start_data_y_points = end_data_y_points - rle_data_y_points.lengths + 1\n\n start_data_y_points = [0]\n end_data_y_points = [len(data_y_points)]\n n_segments = 1\n\n #\n # Generate a curved data line\n #\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"curved\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n curve_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n curve_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n curved_path_string = [f\"M {curve_x[0]},{curve_y[0]}\"]\n\n for j in range(1, len(curve_x)):\n\n point_b1 = f\"{curve_x[j - 1] + x_d / 2},{curve_y[j - 1]}\"\n point_b2 = f\"{curve_x[j] - x_d / 2},{curve_y[j]}\"\n point_i = f\"{curve_x[j]},{curve_y[j]}\"\n\n path_string_j = f\"C {point_b1} {point_b2} {point_i}\"\n\n curved_path_string.append(path_string_j)\n\n curved_path_string_i = \" \".join(curved_path_string)\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\\n\".join(data_path_tags)\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"straight\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n line_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n line_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n line_xy = \" \".join([f\"{x},{y}\" for x, y in zip(line_x, line_y)])\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\".join(data_path_tags)\n\n #\n # Generate data points\n #\n\n if plot_type == \"line\" and show_data_points:\n\n circle_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_point_stroke_color_i = data_point_stroke_color[i]\n data_point_stroke_width_i = data_point_stroke_width[i]\n data_point_fill_color_i = data_point_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n circle_strings_i = f''\n\n else:\n continue\n\n else:\n circle_strings_i = f''\n\n circle_strings.append(circle_strings_i)\n\n circle_tags = \"\".join(circle_strings)\n\n #\n # Generate data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n bar_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_bar_stroke_color_i = data_bar_stroke_color[i]\n data_bar_stroke_width_i = data_bar_stroke_width[i]\n data_bar_fill_color_i = data_bar_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n bar_strings_i = f''\n\n else:\n continue\n\n else:\n\n if y_vals[i] < 0:\n\n y_value_i = data_y0_point\n y_height = data_y_points[i] - data_y0_point\n data_bar_stroke_color_i = data_bar_negative_stroke_color\n data_bar_stroke_width_i = data_bar_negative_stroke_width\n data_bar_fill_color_i = data_bar_negative_fill_color\n\n elif y_vals[i] > 0:\n\n y_value_i = data_y_points[i]\n y_height = data_y0_point - data_y_points[i]\n\n elif y_vals[i] == 0:\n\n y_value_i = data_y0_point - 1\n y_height = 2\n data_bar_stroke_color_i = \"#808080\"\n data_bar_stroke_width_i = 4\n data_bar_fill_color_i = \"#808080\"\n\n bar_strings_i = f''\n\n bar_strings.append(bar_strings_i)\n\n bar_tags = \"\".join(bar_strings)\n\n #\n # Generate single horizontal data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n data_bar_stroke_color = data_bar_negative_stroke_color\n data_bar_stroke_width = data_bar_negative_stroke_width\n data_bar_fill_color = data_bar_negative_fill_color\n\n rect_x = y_width\n rect_width = y0_width - y_width\n\n elif y_vals[0] > 0:\n\n data_bar_stroke_color = data_bar_stroke_color[0]\n data_bar_stroke_width = data_bar_stroke_width[0]\n data_bar_fill_color = data_bar_fill_color[0]\n\n rect_x = y0_width\n rect_width = y_width - y0_width\n\n elif y_vals[0] == 0:\n\n data_bar_stroke_color = \"#808080\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#808080\"\n\n rect_x = y0_width - 2.5\n rect_width = 5\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n bar_tags = f'{g_guide_tags}'\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate single horizontal data lines\n #\n\n # TODO: Make this a line with a single point\n if plot_type == \"line\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n data_point_radius_i = data_point_radius[0]\n data_point_stroke_color_i = data_point_stroke_color[0]\n data_point_stroke_width_i = data_point_stroke_width[0]\n data_point_fill_color_i = data_point_fill_color[0]\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x1_val\n\n elif y_vals[0] > 0:\n\n x1_val = y0_width\n x2_val = y_width\n\n circle_x_val = x2_val\n\n elif y_vals[0] == 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x2_val\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 15\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n data_path_tags = f'{g_guide_tags}'\n\n circle_tags = f''\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate box plots\n #\n\n if plot_type == \"boxplot\":\n pass\n\n #\n # Generate zero line for vertical bar plots\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n zero_line_tags = f''\n\n #\n # Generate reference line\n #\n\n if show_reference_line:\n\n stroke = reference_line_color\n stroke_width = 1\n stroke_dasharray = \"4 3\"\n transform = \"\"\n stroke_linecap = \"round\"\n vector_effect = \"non-scaling-stroke\"\n\n # Format value in a compact manner\n y_ref_line = _format_number_compactly(\n val=y_ref_line, currency=currency, as_integer=y_vals_integerlike, fn=y_ref_line_fmt_fn\n )\n\n ref_line_tags = f'{y_ref_line}'\n\n #\n # Generate reference area\n #\n\n if show_reference_area:\n\n fill = reference_area_fill_color\n\n p_ul = f\"{data_x_points[0]},{data_y_ref_area_u}\"\n p_ur = f\"{data_x_points[-1]},{data_y_ref_area_u}\"\n p_lr = f\"{data_x_points[-1]},{data_y_ref_area_l}\"\n p_ll = f\"{data_x_points[0]},{data_y_ref_area_l}\"\n\n ref_area_path = f\"M{p_ul},{p_ur},{p_lr},{p_ll}Z\"\n\n ref_area_tags = f''\n\n #\n # Generate y-axis guide\n #\n\n if show_y_axis_guide:\n\n rect_tag = f''\n\n if _is_integerlike(val_list=[y_scale_max]) and _is_integerlike(val_list=[y_scale_min]):\n y_axis_guide_vals_integerlike = True\n else:\n y_axis_guide_vals_integerlike = False\n\n # Format values in a compact manner\n y_value_max_label = _format_number_compactly(\n val=y_scale_max,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n y_value_min_label = _format_number_compactly(\n val=y_scale_min,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n text_strings_min = f'{y_value_min_label}'\n\n text_strings_max = f'{y_value_max_label}'\n\n g_y_axis_tags = f'{rect_tag}{text_strings_max}{text_strings_min}'\n\n #\n # Generate vertical data point guidelines\n #\n\n if show_vertical_guides:\n\n g_guide_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n rect_strings_i = f''\n\n # Format value in a compact manner\n y_value_i = _format_number_compactly(\n val=y_vals[i], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n x_text = data_x_points[i] + 10\n\n if y_value_i == \"NA\":\n x_text = x_text + 2\n\n text_strings_i = f'{y_value_i}'\n\n g_guide_strings_i = f'{rect_strings_i}{text_strings_i}'\n\n g_guide_strings.append(g_guide_strings_i)\n\n g_guide_tags = \"\".join(g_guide_strings)\n\n #\n # Generate background with repeating line pattern\n #\n\n svg_defs = (\n f\"\"\n f''\n f''\n f\"\"\n f\"\"\n f\"\"\n )\n\n if plot_type == \"line\" and show_data_area:\n\n area_path_tags = []\n\n for i in range(n_segments):\n\n area_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n area_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n area_path_string = []\n\n for j in range(0, len(area_x)):\n\n area_path_j = f\"{area_x[j]},{area_y[j]}\"\n area_path_string.append(area_path_j)\n\n area_path_i = f\"M {' '.join(area_path_string)} {area_x[-1]},{bottom_y - safe_y_d + data_point_radius[0]} {area_x[0]},{bottom_y - safe_y_d + data_point_radius[0]} Z\"\n\n area_path_tag_i = f''\n\n area_path_tags.append(area_path_tag_i)\n\n area_path_tags = \" \".join(area_path_tags)\n\n #\n # Generate style tag for vertical guidelines and y-axis\n #\n\n hover_param = \":hover\" if interactive_data_values else \"\"\n\n svg_style = (\n f\"\"\n )\n\n nanoplot_svg = _construct_nanoplot_svg(\n viewbox=viewbox,\n svg_height=svg_height,\n svg_defs=svg_defs,\n svg_style=svg_style,\n show_data_points=show_data_points,\n show_data_line=show_data_line,\n show_data_area=show_data_area,\n show_reference_line=show_reference_line,\n show_reference_area=show_reference_area,\n show_vertical_guides=show_vertical_guides,\n show_y_axis_guide=show_y_axis_guide,\n ref_area_tags=ref_area_tags,\n area_path_tags=area_path_tags,\n data_path_tags=data_path_tags,\n zero_line_tags=zero_line_tags,\n bar_tags=bar_tags,\n ref_line_tags=ref_line_tags,\n circle_tags=circle_tags,\n g_y_axis_tags=g_y_axis_tags,\n g_guide_tags=g_guide_tags,\n )\n\n return nanoplot_svg\n```\n###test function signature:\n```python\n\ndef test_nanoplot_out_bars_with_num_ref_area():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nanoplot_out_bars_with_num_ref_area",
"reference": "\ndef test_nanoplot_out_bars_with_num_ref_area():\n\n out_bars_with_num_ref_area = _generate_nanoplot(**CASES[13])\n\n assert _is_nanoplot_output(out_bars_with_num_ref_area)\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_num_ref_area,\n tag=\"rect\",\n attrs=[\n (\"x\", \"30.0\"),\n (\"width\", \"40\"),\n (\"height\", \"27.179487179487168\"),\n (\"stroke\", \"#CC3243\"),\n (\"stroke-width\", \"4\"),\n (\"fill\", \"#D75A68\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_num_ref_area,\n tag=\"rect\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"0\"),\n (\"width\", \"65\"),\n (\"height\", \"130\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"0\"),\n (\"fill\", \"transparent\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_num_ref_area,\n tag=\"text\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"19.0\"),\n (\"fill\", \"transparent\"),\n (\"stroke\", \"transparent\"),\n (\"font-size\", \"25\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_num_ref_area,\n tag=\"path\",\n attrs=[\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"2\"),\n (\"fill\", \"#A6E6F2\"),\n (\"fill-opacity\", \"0.8\"),\n ],\n )",
"completions": [
"```python\n\ndef test_nanoplot_out_bars_with_num_ref_area():\n y_vals = [1, 2, 3, 4, 5]\n y_ref_area = [1, 3]\n y_ref_line = 2\n x_vals = [1, 2, 3, 4, 5]\n expand_x = [1, 2, 3, 4, 5]\n expand_y = [1, 2, 3, 4, 5]\n missing_vals = \"marker\"\n all_y_vals = [1, 2, 3, 4, 5]\n all_single_y_vals = [1, 2, 3, 4, 5]\n plot_type = \"bar\"\n data_line_type = \"curved\"\n currency = None\n y_val_fmt_fn = None\n y_axis_fmt_fn = None\n y_ref_line_fmt_fn = None\n data_point_radius = 10\n data_point_stroke_color = \"#FFFFFF\"\n data_point_stroke_width = 4\n data_point_fill_color = \"#FF0000\"\n data_line_stroke_color = \"#4682B4\"\n data_line_stroke_width = 8\n data_area_fill_color = \"#FF0000\"\n data_bar_stroke_color = \"#3290CC\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#3FB5FF\"\n data_bar_negative_stroke_color = \"#CC3243\"\n data_bar_negative_stroke_width = 4\n data_bar_negative_fill_color = \"#D75A68\"\n reference_line_color = \"#75A8B4\"\n reference_area_fill_color = \"#A6E6F2\"\n vertical_guide_stroke_color = \"#911EB4\"\n vertical_guide_stroke_width = 12\n show_data_points = True\n show_data_line = True\n show_data_area = True\n show_reference_line = True\n show_reference_area = True\n show_vertical_guides = True\n show_y_axis_guide = True\n interactive_data_values = True\n svg_height = \"2em\"\n\n nanoplot_svg = _generate_nanoplot(\n y_vals=y_vals,\n y_ref_line=y_ref_line,\n y_ref_area=y_ref_area,\n x_vals=x_vals,\n expand_x=expand_x,\n expand_y=expand_y,\n missing_vals=missing_vals,\n all_y_vals=all_y_vals,\n all_single_y_vals=all_single_y_vals,\n plot_type=plot_type,\n data_line_type=data_line_type,\n currency=currency,\n y_val_fmt_fn=y_val_fmt_fn,\n y_axis_fmt_fn=y_axis_fmt_fn,\n y_ref_line_fmt_fn=y_ref_line_fmt_fn,\n data_point_radius=data_point_radius,\n data_point_stroke_color=data_point_stroke_color,\n data_point_stroke_width=data_point_stroke_width,\n data_point_fill_color=data_point_fill_color,\n data_line_stroke_color=data_line_stroke_color,\n data_line_stroke_width=data_line_stroke_width,\n data_area_fill_color=data_area_fill_color,\n data_bar_stroke_color=data_bar_stroke_color,\n data_bar_stroke_width=data_bar_stroke_width,\n data_bar_fill_color=data_bar_fill_"
],
"line": 749,
"token": 7038,
"line_diff": 2,
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{
"id": 50,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef mean(x, axis=None, keepdims=False):\n if isinstance(x, (list, tuple)):\n x = stack(x)\n x = convert_to_tensor(x)\n if axis == () or axis == []:\n # Torch handles the empty axis case differently from numpy.\n return x\n axis = to_tuple_or_list(axis) # see [NB] below\n\n ori_dtype = standardize_dtype(x.dtype)\n # torch.mean only supports floating point inputs\n compute_dtype = dtypes.result_type(x.dtype, \"float32\")\n if \"int\" in ori_dtype or ori_dtype == \"bool\":\n result_dtype = compute_dtype\n else:\n result_dtype = ori_dtype\n\n # [NB] the python torch op torch.mean() is generated into\n # `torch._C._VariableFunctions.pyi`, and the method\n # signature is overloaded.\n # Dynamo won't actually find the correct signature of\n # `torch.mean()` if arguments are passed via kwargs\n # So we have to pass the arguments via positional args\n # EXCEPT for those that are forced as kwargs via the `*`\n # delimiter in the overloaded method signatures.\n # Additionally, we have to create a singleton-tuple\n # when `axis` is an int to match the existing fn signature\n result = torch.mean(\n x,\n axis,\n keepdims,\n dtype=to_torch_dtype(compute_dtype),\n )\n return cast(result, result_dtype)\n```\n###test function signature:\n```python\n\n def test_mean(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_mean",
"reference": "\n def test_mean(self):\n x = np.array([[1, 2, 3], [3, 2, 1]])\n self.assertAllClose(knp.mean(x), np.mean(x))\n self.assertAllClose(knp.mean(x, axis=()), np.mean(x, axis=()))\n self.assertAllClose(knp.mean(x, axis=1), np.mean(x, axis=1))\n self.assertAllClose(knp.mean(x, axis=(1,)), np.mean(x, axis=(1,)))\n self.assertAllClose(\n knp.mean(x, axis=1, keepdims=True),\n np.mean(x, axis=1, keepdims=True),\n )\n\n self.assertAllClose(knp.Mean()(x), np.mean(x))\n self.assertAllClose(knp.Mean(axis=1)(x), np.mean(x, axis=1))\n self.assertAllClose(\n knp.Mean(axis=1, keepdims=True)(x),\n np.mean(x, axis=1, keepdims=True),\n )\n\n # test overflow\n x = np.array([65504, 65504, 65504], dtype=\"float16\")\n self.assertAllClose(knp.mean(x), np.mean(x))",
"completions": [
"```python\n\n def test_mean(self):\n x = torch.randn(2, 3, 4)\n self.assertEqual(mean(x), torch.mean(x))\n self.assertEqual(mean(x, axis=0), torch.mean(x, dim=0))\n self.assertEqual(mean(x, axis=1), torch.mean(x, dim=1))\n self.assertEqual(mean(x, axis=2), torch.mean(x, dim=2))\n self.assertEqual(mean(x, axis=(0, 1)), torch.mean(x, dim=(0, 1)))\n self.assertEqual(mean(x, axis=(0, 2)), torch.mean(x, dim=(0, 2)))\n self.assertEqual(mean(x, axis=(1, 2)), torch.mean(x, dim=(1, 2)))\n self.assertEqual(mean(x, axis=(0, 1, 2)), torch.mean(x, dim=(0, 1, 2)))\n self.assertEqual(mean(x, axis=0, keepdims=True), torch.mean(x, dim=0, keepdim=True))\n self.assertEqual(mean(x, axis=1, keepdims=True), torch.mean(x, dim=1, keepdim=True))\n self.assertEqual(mean(x, axis=2, keepdims=True), torch.mean(x, dim=2, keepdim=True))\n self.assertEqual(mean(x, axis=(0, 1), keepdims=True), torch.mean(x, dim=(0, 1), keepdim=True))\n self.assertEqual(mean(x, axis=(0, 2), keepdims=True), torch.mean(x, dim=(0, 2), keepdim=True))\n self.assertEqual(mean(x, axis=(1, 2), keepdims=True), torch.mean(x, dim=(1, 2), keepdim=True))\n self.assertEqual(mean(x, axis=(0, 1, 2), keepdims=True), torch.mean(x, dim=(0, 1, 2), keepdim=True))\n```\n"
],
"line": 42,
"token": 322,
"line_diff": 0,
"token_diff": 0
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{
"id": 51,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef get_intrinsic_value(\n cash_flow: float,\n growth_rate: float,\n perpetual_growth_rate: float,\n weighted_average_cost_of_capital: float,\n cash_and_cash_equivalents: float,\n total_debt: float,\n shares_outstanding: float,\n periods: int = 5,\n) -> pd.DataFrame:\n components = {}\n\n cash_flow_projection = [cash_flow]\n\n # Cash Flow to Use\n for period in range(1, periods + 1):\n if period == 1:\n cash_flow_projection.append(cash_flow_projection[0] * (1 + growth_rate))\n else:\n cash_flow_projection.append(\n cash_flow_projection[period - 1] * (1 + growth_rate)\n )\n\n # Calculate the Terminal Value\n terminal_value = (\n cash_flow_projection[-1]\n * (1 + perpetual_growth_rate)\n / (weighted_average_cost_of_capital - perpetual_growth_rate)\n )\n\n # Add Terminal Value to the end of the cash flow projection\n cash_flow_projection[-1] = cash_flow_projection[-1] + terminal_value\n\n # Calculate the Present Value based on the Discounted Cash Flow Formula\n cash_flow_present_value = []\n for index, cash_flow_value in enumerate(cash_flow_projection):\n cash_flow_present_value.append(\n cash_flow_value / (1 + weighted_average_cost_of_capital) ** (index + 1)\n )\n\n # Calculate the Enterprise Value\n enterprise_value = sum(cash_flow_present_value)\n\n # Calculate the Equity Value\n equity_value = enterprise_value + cash_and_cash_equivalents - total_debt\n\n # Calculate the Intrinsic Value\n intrinsic_value = equity_value / shares_outstanding\n\n # Combine the components into a dictionary\n components = {\n \"Terminal Value\": terminal_value,\n \"Cash Flow Projection\": cash_flow_projection[-1],\n \"Enterprise Value\": enterprise_value,\n \"Equity Value\": equity_value,\n \"Intrinsic Value\": intrinsic_value,\n }\n\n return pd.DataFrame.from_dict(\n components, orient=\"index\", columns=[f\"Periods = {periods}\"]\n )\n```\n###test function signature:\n```python\n\ndef test_get_intrinsic_value(recorder):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_get_intrinsic_value",
"reference": "\ndef test_get_intrinsic_value(recorder):\n recorder.capture(\n intrinsic_model.get_intrinsic_value(\n cash_flow=500,\n growth_rate=0.05,\n perpetual_growth_rate=0.02,\n weighted_average_cost_of_capital=0.1,\n cash_and_cash_equivalents=100,\n total_debt=100,\n shares_outstanding=100,\n periods=5,\n )\n )",
"completions": [
"```python\n\ndef test_get_intrinsic_value(recorder):\n cash_flow = 1000000\n growth_rate = 0.05\n perpetual_growth_rate = 0.05\n weighted_average_cost_of_capital = 0.1\n cash_and_cash_equivalents = 1000000\n total_debt = 1000000\n shares_outstanding = 1000000\n periods = 5\n\n result = get_intrinsic_value(\n cash_flow,\n growth_rate,\n perpetual_growth_rate,\n weighted_average_cost_of_capital,\n cash_and_cash_equivalents,\n total_debt,\n shares_outstanding,\n periods,\n )\n\n recorder.capture(result)\n```\n"
],
"line": 61,
"token": 379,
"line_diff": 0,
"token_diff": 0
},
{
"id": 52,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef to_numpy(\n evset: EventSet,\n timestamp_to_datetime: bool = True,\n timestamps: bool = True,\n) -> Dict[str, ndarray]:\n timestamp_key = \"timestamp\"\n index_names = evset.schema.index_names()\n feature_names = evset.schema.feature_names()\n\n column_names = index_names + feature_names\n if timestamps:\n column_names += [timestamp_key]\n\n dst = {column_name: [] for column_name in column_names}\n for index, data in evset.data.items():\n assert isinstance(index, tuple)\n\n if timestamps:\n # Timestamps\n if evset.schema.is_unix_timestamp and timestamp_to_datetime:\n dst[timestamp_key].append(\n data.timestamps.astype(\"datetime64[s]\")\n )\n else:\n dst[timestamp_key].append(data.timestamps)\n\n # Features\n for feature_name, feature in zip(feature_names, data.features):\n dst[feature_name].append(feature)\n\n # Indexes\n num_timestamps = len(data.timestamps)\n for index_name, index_item in zip(index_names, index):\n dst[index_name].append(np.repeat(index_item, num_timestamps))\n\n dst = {k: np.concatenate(v) for k, v in dst.items()}\n return dst\n```\n###test function signature:\n```python\n\n def test_correct(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_correct",
"reference": "\n def test_correct(self):\n evset = event_set(\n timestamps=[\"2023-11-08T17:14:38\", \"2023-11-29T21:44:46\"],\n features={\n \"feature_1\": [0.5, 0.6],\n \"my_index\": [\"red\", \"blue\"],\n },\n indexes=[\"my_index\"],\n )\n\n result = to_numpy(evset)\n\n expected = {\n \"timestamp\": np.array(\n [\"2023-11-08T17:14:38\", \"2023-11-29T21:44:46\"],\n dtype=\"datetime64[s]\",\n ),\n \"feature_1\": np.array([0.5, 0.6]),\n \"my_index\": np.array([b\"red\", b\"blue\"]),\n }\n\n for k in expected:\n np.testing.assert_array_equal(\n np.sort(result[k]), np.sort(expected[k])\n )",
"completions": [
"```python\n def test_correct(self):\n evset = EventSet(\n data={\n \"timestamp\": [1610000000, 1610000000, 1610000000, 1610000000],\n \"index\": [0, 1, 2, 3],\n \"feature1\": [1, 2, 3, 4],\n \"feature2\": [1, 2, 3, 4],\n },\n schema=EventSetSchema(\n index_names=[\"index\"],\n feature_names=[\"feature1\", \"feature2\"],\n is_unix_timestamp=True,\n ),\n )\n\n result = to_numpy(evset)\n\n expected = {\n \"index\": [0, 1, 2, 3],\n \"feature1\": [1, 2, 3, 4],\n \"feature2\": [1, 2, 3, 4],\n \"timestamp\": [\n np.datetime64(\"2021-01-01T00:00:00\"),\n np.datetime64(\"2021-01-01T00:00:00\"),\n np.datetime64(\"2021-01-01T00:00:00\"),\n np.datetime64(\"2021-01-01T00:00:00\"),\n ],\n }\n\n self.assertDictEqual(result, expected)\n```\n"
],
"line": 41,
"token": 276,
"line_diff": 0,
"token_diff": 0
},
{
"id": 53,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def from_file(cls, path: str) -> \"LayerConfig\":\n logger.info(f\"Loading route config from {path}\")\n _, ext = os.path.splitext(path)\n with open(path, \"r\") as f:\n if ext == \".json\":\n layer = json.load(f)\n elif ext in [\".yaml\", \".yml\"]:\n layer = yaml.safe_load(f)\n else:\n raise ValueError(\n \"Unsupported file type. Only .json and .yaml are supported\"\n )\n\n if not is_valid(json.dumps(layer)):\n raise Exception(\"Invalid config JSON or YAML\")\n\n encoder_type = layer[\"encoder_type\"]\n encoder_name = layer[\"encoder_name\"]\n routes = []\n for route_data in layer[\"routes\"]:\n # Handle the 'llm' field specially if it exists\n if \"llm\" in route_data and route_data[\"llm\"] is not None:\n llm_data = route_data.pop(\n \"llm\"\n ) # Remove 'llm' from route_data and handle it separately\n # Use the module path directly from llm_data without modification\n llm_module_path = llm_data[\"module\"]\n # Dynamically import the module and then the class from that module\n llm_module = importlib.import_module(llm_module_path)\n llm_class = getattr(llm_module, llm_data[\"class\"])\n # Instantiate the LLM class with the provided model name\n llm = llm_class(name=llm_data[\"model\"])\n # Reassign the instantiated llm object back to route_data\n route_data[\"llm\"] = llm\n\n # Dynamically create the Route object using the remaining route_data\n route = Route(**route_data)\n routes.append(route)\n\n return cls(\n encoder_type=encoder_type, encoder_name=encoder_name, routes=routes\n )\n```\n###test function signature:\n```python\n\n def test_from_file_yaml(openai_encoder, tmp_path, index_cls):\n # Create a temporary YAML file with layer configuration\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_from_file_yaml",
"reference": "\n def test_from_file_yaml(openai_encoder, tmp_path, index_cls):\n # Create a temporary YAML file with layer configuration\n config_path = tmp_path / \"config.yaml\"\n config_path.write_text(\n layer_yaml()\n ) # Assuming layer_yaml() returns a valid YAML string\n\n # Load the LayerConfig from the temporary file\n layer_config = LayerConfig.from_file(str(config_path))\n\n # Assertions to verify the loaded configuration\n assert layer_config.encoder_type == \"cohere\"\n assert layer_config.encoder_name == \"embed-english-v3.0\"\n assert len(layer_config.routes) == 2\n assert layer_config.routes[0].name == \"politics\"",
"completions": [
"```python\n\n def test_from_file_yaml(openai_encoder, tmp_path, index_cls):\n # Create a temporary YAML file with layer configuration\n yaml_file = tmp_path / \"test_layer.yaml\"\n yaml_file.write_text(\n \"\"\"\n encoder_type: openai\n encoder_name: gpt2\n routes:\n - name: test_route\n model_name: test_model\n llm:\n module: tests.test_layer\n class: TestLLM\n model: test_model\n encoder_kwargs:\n max_position_embeddings: 512\n num_attention_heads: 8\n intermediate_size: 2048\n hidden_act: gelu\n hidden_dropout_prob: 0.1\n attention_probs_dropout_prob: 0.1\n type_vocab_size: 16\n initializer_range: 0.02\n layer_norm_eps: 1e-12\n use_cache: True\n is_decoder: False\n classifier_dropout: 0.1\n use_multiplicative_input_mask: False\n num_labels: 2\n output_attentions: False\n output_hidden_states: False\n return_dict: True\n task_specific_params:\n summarization:\n max_length: 20\n min_length: 5\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n top_p: 0.9\n repetition_penalty: 1.5\n length_penalty: 1.0\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n top_p: 0.9\n repetition_penalty: 1.5\n length_penalty: 1.0\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n top_p: 0.9\n repetition_penalty: 1.5\n length_penalty: 1.0\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n top_p: 0.9\n repetition_penalty: 1.5\n length_penalty: 1.0\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n top_p: 0.9\n repetition_penalty: 1.5\n length_penalty: 1.0\n do_sample: True\n num_beams: 5\n early_stopping: True\n no_repeat_ngram_size: 3\n num_return_sequences: 3\n temperature: 0.7\n top_k: 10\n"
],
"line": 49,
"token": 405,
"line_diff": 0,
"token_diff": 0
},
{
"id": 54,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def bind_all(self, circuit: QuantumCircuit) -> np.ndarray:\n arr = np.empty(self.shape, dtype=object)\n for idx in np.ndindex(self.shape):\n arr[idx] = self.bind(circuit, idx)\n return arr\n```\n###test function signature:\n```python\n def test_data(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_data",
"reference": " def test_data(self):\n with self.subTest(\"binding a single value\"):\n vals = np.linspace(0, 1, 50)\n data = {self.params: vals}\n ba = BindingsArray(data=data)\n self.assertEqual(ba.num_parameters, 50)\n self.assertEqual(ba.ndim, 0)\n self.assertEqual(ba.shape, ())\n self.assertEqual(ba.size, 1)\n self.assertEqual(ba.data, {tuple(param.name for param in self.params): vals})\n\n bound_circuit = ba.bind(self.circuit, ())\n self.assertEqual(bound_circuit, self.circuit.assign_parameters(vals))\n\n with self.subTest(\"binding an array\"):\n vals = np.linspace(0, 1, 300).reshape((2, 3, 50))\n data = {self.params: vals}\n ba = BindingsArray(data=data)\n self.assertEqual(ba.num_parameters, 50)\n self.assertEqual(ba.ndim, 2)\n self.assertEqual(ba.shape, (2, 3))\n self.assertEqual(ba.size, 6)\n self.assertEqual(ba.data, {tuple(param.name for param in self.params): vals})\n\n bound_circuits = ba.bind_all(self.circuit)\n self.assertEqual(bound_circuits.shape, (2, 3))\n self.assertEqual(bound_circuits[0, 0], self.circuit.assign_parameters(vals[0, 0]))\n self.assertEqual(bound_circuits[0, 1], self.circuit.assign_parameters(vals[0, 1]))\n self.assertEqual(bound_circuits[0, 2], self.circuit.assign_parameters(vals[0, 2]))\n self.assertEqual(bound_circuits[1, 0], self.circuit.assign_parameters(vals[1, 0]))\n self.assertEqual(bound_circuits[1, 1], self.circuit.assign_parameters(vals[1, 1]))\n self.assertEqual(bound_circuits[1, 2], self.circuit.assign_parameters(vals[1, 2]))\n\n with self.subTest(\"binding a single param\"):\n vals = np.linspace(0, 1, 50)\n data = {self.params[0]: vals}\n ba = BindingsArray(data=data)\n self.assertEqual(ba.num_parameters, 1)\n self.assertEqual(ba.ndim, 1)\n self.assertEqual(ba.shape, (50,))\n self.assertEqual(ba.size, 50)\n self.assertEqual(list(ba.data.keys()), [(self.params[0].name,)])\n np.testing.assert_allclose(list(ba.data.values()), [vals[..., np.newaxis]])",
"completions": [
"```python\n def test_data(self):\n from qiskit import QuantumCircuit\n from qiskit.quantum_info import Pauli\n from qiskit.quantum_info import PauliTable\n from qiskit.quantum_info import PauliSumOp\n from qiskit.quantum_info import PauliOp\n from qiskit.quantum_info import PauliExpectation\n from qiskit.quantum_info import PauliMoment\n from qiskit.quantum_info import PauliTensor\n from qiskit.quantum_info import PauliList\n from qiskit.quantum_info import PauliTransform\n from qiskit.quantum_info import PauliTransformOp\n from qiskit.quantum_info import PauliTransformList\n from qiskit.quantum_info import PauliTransformSum\n from qiskit.quantum_info import PauliTransformProduct\n from qiskit.quantum_info import PauliTransformExpectation\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum_info import PauliTransformVariance\n from qiskit.quantum"
],
"line": 15,
"token": 122,
"line_diff": 0,
"token_diff": 0
},
{
"id": 55,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef set_seed(\n seed: Optional[int] = None, debug_mode: Optional[Union[str, int]] = None\n) -> int:\n world_size, rank = get_world_size_and_rank()\n max_val = np.iinfo(np.uint32).max - world_size + 1\n min_val = np.iinfo(np.uint32).min\n if seed is None:\n rand_seed = torch.randint(min_val, max_val, (1,))\n seed = _broadcast_tensor(rand_seed, 0).item() # sync seed across ranks\n if seed < min_val or seed > max_val:\n raise ValueError(\n f\"Invalid seed value provided: {seed}. Value must be in the range [{min_val}, {max_val}]\"\n )\n local_seed = seed + rank\n _log.debug(\n f\"Setting manual seed to local seed {local_seed}. Local seed is seed + rank = {seed} + {rank}\"\n )\n\n torch.manual_seed(local_seed)\n np.random.seed(local_seed)\n random.seed(local_seed)\n\n if debug_mode is not None:\n _log.debug(f\"Setting deterministic debug mode to {debug_mode}\")\n torch.set_deterministic_debug_mode(debug_mode)\n deterministic_debug_mode = torch.get_deterministic_debug_mode()\n if deterministic_debug_mode == 0:\n _log.debug(\"Disabling cuDNN deterministic mode\")\n torch.backends.cudnn.deterministic = False\n torch.backends.cudnn.benchmark = True\n else:\n _log.debug(\"Enabling cuDNN deterministic mode\")\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n # reference: https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility\n os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n\n return seed\n```\n###test function signature:\n```python\n\n def test_deterministic_true(self) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_deterministic_true",
"reference": "\n def test_deterministic_true(self) -> None:\n for det_debug_mode, det_debug_mode_str in [(1, \"warn\"), (2, \"error\")]:\n warn_only = det_debug_mode == 1\n for debug_mode in (det_debug_mode, det_debug_mode_str):\n set_seed(42, debug_mode=debug_mode)\n assert torch.backends.cudnn.deterministic\n assert not torch.backends.cudnn.benchmark\n assert det_debug_mode == torch.get_deterministic_debug_mode()\n assert torch.are_deterministic_algorithms_enabled()\n assert (\n warn_only == torch.is_deterministic_algorithms_warn_only_enabled()\n )\n assert os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] == \":4096:8\"",
"completions": [
"```python\n def test_deterministic_true(self) -> None:\n seed = set_seed(debug_mode=True)\n self.assertEqual(seed, 123456789)\n```\n"
],
"line": 45,
"token": 345,
"line_diff": 0,
"token_diff": 0
},
{
"id": 56,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef concat_compat(to_concat, axis: AxisInt = 0, ea_compat_axis: bool = False):\n\n # filter empty arrays\n # 1-d dtypes always are included here\n def is_nonempty(x) -> bool:\n if x.ndim <= axis:\n return True\n return x.shape[axis] > 0\n\n # If all arrays are empty, there's nothing to convert, just short-cut to\n # the concatenation, #3121.\n #\n # Creating an empty array directly is tempting, but the winnings would be\n # marginal given that it would still require shape & dtype calculation and\n # np.concatenate which has them both implemented is compiled.\n non_empties = [x for x in to_concat if is_nonempty(x)]\n if non_empties and axis == 0 and not ea_compat_axis:\n # ea_compat_axis see GH#39574\n to_concat = non_empties\n\n dtypes = {obj.dtype for obj in to_concat}\n kinds = {obj.dtype.kind for obj in to_concat}\n contains_datetime = any(\n isinstance(dtype, (np.dtype, DatetimeTZDtype)) and dtype.kind in [\"m\", \"M\"]\n for dtype in dtypes\n ) or any(isinstance(obj, ABCExtensionArray) and obj.ndim > 1 for obj in to_concat)\n\n all_empty = not len(non_empties)\n single_dtype = len({x.dtype for x in to_concat}) == 1\n any_ea = any(isinstance(x.dtype, ExtensionDtype) for x in to_concat)\n\n if contains_datetime:\n return _concat_datetime(to_concat, axis=axis)\n\n if any_ea:\n # we ignore axis here, as internally concatting with EAs is always\n # for axis=0\n if not single_dtype:\n target_dtype = find_common_type([x.dtype for x in to_concat])\n target_dtype = common_dtype_categorical_compat(to_concat, target_dtype)\n to_concat = [\n astype_array(arr, target_dtype, copy=False) for arr in to_concat\n ]\n\n if isinstance(to_concat[0], ABCExtensionArray):\n # TODO: what about EA-backed Index?\n cls = type(to_concat[0])\n return cls._concat_same_type(to_concat)\n else:\n return np.concatenate(to_concat)\n\n elif all_empty:\n # we have all empties, but may need to coerce the result dtype to\n # object if we have non-numeric type operands (numpy would otherwise\n # cast this to float)\n if len(kinds) != 1:\n if not len(kinds - {\"i\", \"u\", \"f\"}) or not len(kinds - {\"b\", \"i\", \"u\"}):\n # let numpy coerce\n pass\n else:\n # coerce to object\n to_concat = [x.astype(\"object\") for x in to_concat]\n kinds = {\"o\"}\n\n result = np.concatenate(to_concat, axis=axis)\n if \"b\" in kinds and result.dtype.kind in [\"i\", \"u\", \"f\"]:\n # GH#39817 cast to object instead of casting bools to numeric\n result = result.astype(object, copy=False)\n return result\n```\n###test function signature:\n```python\n\ndef test_concat_periodarray_2d():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_concat_periodarray_2d",
"reference": "\ndef test_concat_periodarray_2d():\n pi = pd.period_range(\"2016-01-01\", periods=36, freq=\"D\")\n arr = pi._data.reshape(6, 6)\n\n result = _concat.concat_compat([arr[:2], arr[2:]], axis=0)\n tm.assert_period_array_equal(result, arr)\n\n result = _concat.concat_compat([arr[:, :2], arr[:, 2:]], axis=1)\n tm.assert_period_array_equal(result, arr)\n\n msg = (\n \"all the input array dimensions.* for the concatenation axis must match exactly\"\n )\n with pytest.raises(ValueError, match=msg):\n _concat.concat_compat([arr[:, :2], arr[:, 2:]], axis=0)\n\n with pytest.raises(ValueError, match=msg):\n _concat.concat_compat([arr[:2], arr[2:]], axis=1)",
"completions": [
"```python\n\ndef test_concat_periodarray_2d():\n import numpy as np\n import pandas as pd\n from pandas.core.dtypes.common import is_period_dtype\n\n def concat_compat(to_concat, axis: AxisInt = 0, ea_compat_axis: bool = False):\n\n # filter empty arrays\n # 1-d dtypes always are included here\n def is_nonempty(x) -> bool:\n if x.ndim <= axis:\n return True\n return x.shape[axis] > 0\n\n # If all arrays are empty, there's nothing to convert, just short-cut to\n # the concatenation, #3121.\n #\n # Creating an empty array directly is tempting, but the winnings would be\n # marginal given that it would still require shape & dtype calculation and\n # np.concatenate which has them both implemented is compiled.\n non_empties = [x for x in to_concat if is_nonempty(x)]\n if non_empties and axis == 0 and not ea_compat_axis:\n # ea_compat_axis see GH#39574\n to_concat = non_empties\n\n dtypes = {obj.dtype for obj in to_concat}\n kinds = {obj.dtype.kind for obj in to_concat}\n contains_datetime = any(\n isinstance(dtype, (np.dtype, DatetimeTZDtype)) and dtype.kind in [\"m\", \"M\"]\n for dtype in dtypes\n ) or any(isinstance(obj, ABCExtensionArray) and obj.ndim > 1 for obj in to_concat)\n\n all_empty = not len(non_empties)\n single_dtype = len({x.dtype for x in to_concat}) == 1\n any_ea = any(isinstance(x.dtype, ExtensionDtype) for x in to_concat)\n\n if contains_datetime:\n return _concat_datetime(to_concat, axis=axis)\n\n if any_ea:\n # we ignore axis here, as internally concatting with EAs is always\n # for axis=0\n if not single_dtype:\n target_dtype = find_common_type([x.dtype for x in to_concat])\n target_dtype = common_dtype_categorical_compat(to_concat, target_dtype)\n to_concat = [\n astype_array(arr, target_dtype, copy=False) for arr in to_concat\n ]\n\n if isinstance(to_concat[0], ABCExtensionArray):\n # TODO: what about EA-backed Index?\n cls = type(to_concat[0])\n return cls._concat_same_type(to_concat)\n else:\n return np.concatenate(to_concat)\n\n elif all_empty:\n # we have all empties, but may need to coerce the result dtype to\n # object if we have non-numeric type operands (numpy would otherwise\n # cast this to float)\n if len(kinds) != 1:\n if not len(kinds - {\"i\", \"u\", \"f\"}) or not len(kinds - {\"b\", \"i\", \"u\"}):\n # let numpy coerce\n pass\n else:\n # coerce to object\n to_concat = [x.astype(\"object\") for x in to_concat]\n kinds = {\"o\"}\n\n result = np.concatenate(to_concat, axis=axis)\n if \"b\" in kinds and result.dtype.kind in [\"i\", \"u\", \"f\"]:\n # GH#39817 cast to object instead of casting bools to numeric\n result = result.astype(object, copy=False)\n return result\n\n def _concat_datetime(to_concat, axis: AxisInt = 0):\n from pandas.core.arrays import PeriodArray\n\n if axis == 0:\n "
],
"line": 70,
"token": 610,
"line_diff": 0,
"token_diff": 0
},
{
"id": 57,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef ingest_ocp_payload(request):\n request_id = uuid4().hex\n response_data = {\"request-id\": request_id, \"payload-name\": []}\n if request.method == \"POST\":\n if not request.FILES:\n response_data[\"error\"] = \"no payload sent\"\n return Response(response_data, status=HTTPStatus.BAD_REQUEST)\n for _, file in request.FILES.items():\n payload_name = file.name\n response_data[\"payload-name\"].append(payload_name)\n s3_signature = get_s3_signature(settings.S3_ENDPOINT, payload_name, method=\"put_object\")\n res = upload_file_to_s3(s3_signature, data=file.file)\n if res.status_code == HTTPStatus.OK:\n response_data[\"upload\"] = \"success\"\n else:\n response_data[\"upload\"] = \"failed\"\n response_data[\"failed-reason\"] = res.reason\n return Response(response_data, status=res.status_code)\n send_payload(request_id, payload_name)\n else:\n params = request.query_params\n payload_names = params.get(\"payload_name\")\n if not payload_names:\n response_data[\"error\"] = \"no payload sent\"\n return Response(response_data, status=HTTPStatus.BAD_REQUEST)\n for payload_name in payload_names.split(\",\"):\n send_payload(request_id, payload_name)\n response_data[\"payload-name\"].append(payload_name)\n\n response_data[\"ingest-started\"] = True\n\n return Response(response_data, status=HTTPStatus.ACCEPTED)\n```\n###test function signature:\n```python\n\n def test_ingest_ocp_payload(self):\n # Arrange\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_ingest_ocp_payload",
"reference": "\n def test_ingest_ocp_payload(self):\n # Arrange\n file1 = SimpleUploadedFile(\"file1.gz\", b\"file_content\", content_type=\"multipart/form-data\")\n file2 = SimpleUploadedFile(\"file2.gz\", b\"file_content\", content_type=\"multipart/form-data\")\n test_table = [\n # Happy path tests\n (\n \"POST\",\n {\"file1\": file1, \"file2\": file2},\n \"\",\n HTTPStatus.ACCEPTED,\n {\"upload\": \"success\", \"ingest-started\": True},\n \"happy_path_post\",\n ),\n (\"GET\", {}, \"?payload_name=file1,file2\", HTTPStatus.ACCEPTED, {\"ingest-started\": True}, \"happy_path_get\"),\n # Edge cases\n (\"POST\", {}, \"\", HTTPStatus.BAD_REQUEST, {\"error\": \"no payload sent\"}, \"edge_case_no_files_post\"),\n (\"GET\", {}, \"\", HTTPStatus.BAD_REQUEST, {\"error\": \"no payload sent\"}, \"edge_case_no_payload_name_get\"),\n # Error cases\n (\"POST\", {\"file1\": file1}, \"\", HTTPStatus.INTERNAL_SERVER_ERROR, {\"upload\": \"failed\"}, \"error_case_post\"),\n (\"GET\", {}, \"?payload_name=non_existent_file\", HTTPStatus.ACCEPTED, {}, \"error_case_get\"),\n ]\n for test in test_table:\n method, files, query_params, expected_status, expected_response, test_id = test\n with self.subTest(test=test_id):\n\n mock_response = MagicMock()\n mock_response.status_code = (\n HTTPStatus.OK if expected_response.get(\"upload\") != \"failed\" else HTTPStatus.INTERNAL_SERVER_ERROR\n )\n mock_response.reason = \"Some reason\"\n\n with patch(\"masu.api.ingest_ocp_payload.get_s3_signature\") as mock_get_s3_signature, patch(\n \"masu.api.ingest_ocp_payload.upload_file_to_s3\", return_value=mock_response\n ) as mock_upload_file_to_s3, patch(\"masu.api.ingest_ocp_payload.send_payload\") as mock_send_payload:\n\n request = (\n APIRequestFactory().get(f\"/{query_params}\")\n if method == \"GET\"\n else APIRequestFactory().post(\"/\", data=files)\n )\n\n # Act\n response = ingest_ocp_payload(request)\n\n # Assert\n self.assertEqual(response.status_code, expected_status)\n if method == \"POST\" and files:\n mock_get_s3_signature.assert_called()\n mock_upload_file_to_s3.assert_called()\n if method == \"GET\" and \"payload_name\" in query_params:\n mock_send_payload.assert_called()\n for key, value in expected_response.items():\n self.assertEqual(response.data[key], value)",
"completions": [
"```python\n def test_ingest_ocp_payload(self):\n # Arrange\n request = self.factory.post(\n \"/api/v1/ingest/ocp/payload\",\n data={\"payload_name\": \"payload_name\"},\n content_type=\"multipart/form-data\",\n )\n request.FILES = {\"payload_name\": \"payload_name\"}\n # Act\n response = ingest_ocp_payload(request)\n # Assert\n self.assertEqual(response.status_code, HTTPStatus.ACCEPTED)\n self.assertEqual(response.data[\"request-id\"], \"request_id\")\n self.assertEqual(response.data[\"payload-name\"], [\"payload_name\"])\n self.assertEqual(response.data[\"ingest-started\"], True)\n```\n"
],
"line": 41,
"token": 308,
"line_diff": 0,
"token_diff": 0
},
{
"id": 58,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\nasync def compute_nb_items_with_metadata_field(\n project_id: str,\n metadata_field: str,\n collection_name: str,\n quantile_value: float = 0.1,\n) -> Tuple[int, float, int]:\n\n mongo_db = await get_mongo_db()\n\n try:\n pipeline = [\n # Match on the project id and the existence of the metadata field\n {\n \"$match\": {\n \"project_id\": project_id,\n f\"metadata.{metadata_field}\": {\"$exists\": True, \"$ne\": None},\n }\n },\n {\n \"$group\": {\n \"_id\": \"$user_id\", # Group by 'user_id'\n \"count\": {\"$sum\": 1}, # Count the documents in each group\n }\n },\n {\n \"$sort\": {\"count\": -1} # Sort by 'count' in descending order\n },\n ]\n\n # Execute the aggregation pipeline\n results = [] # List to hold the results\n async for group in mongo_db[collection_name].aggregate(pipeline):\n results.append(group[\"count\"])\n\n logger.debug(\n f\"Results for {metadata_field} in collection {collection_name} for project {project_id}: {results}\"\n )\n\n # If no results, return 0\n if len(results) == 0:\n return 0, 0.0, 0\n\n # Get the average and the quantiles\n average = sum(results) / len(results)\n bottom_quantile = results[int(len(results) * quantile_value)]\n top_quantile = results[int(len(results) * (1 - quantile_value))]\n\n return bottom_quantile, average, top_quantile\n\n except Exception as e:\n logger.warning(\n f\"Failed to fetch the number of {metadata_field} in collection {collection_name} for project {project_id}: {e}\",\n )\n return 0, 0.0, 0\n```\n###test function signature:\n```python\n\nasync def test_main_pipeline(db, populated_project):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_main_pipeline",
"reference": "\nasync def test_main_pipeline(db, populated_project):\n async for mongo_db in db:\n test_project_id = populated_project.id\n\n # Run the function\n topics = await fetch_topics(test_project_id)\n\n logger.debug(f\"Topics: {topics}\")\n\n count = await nb_items_with_a_metadata_field(\n test_project_id, \"tasks\", \"user_id\"\n )\n\n assert count > 0\n\n (\n bottom_quantile,\n average,\n top_quantile,\n ) = await compute_successrate_metadata_quantiles(test_project_id, \"user_id\")\n\n logger.debug(f\"Bottom quantile: {bottom_quantile}\")\n logger.debug(f\"Top quantile: {top_quantile}\")\n\n assert bottom_quantile >= 0\n # assert bottom_quantile <= top_quantile\n\n test_project_id = populated_project.id\n test_metadata_field = \"user_id\"\n test_collection_name = \"tasks\"\n test_quantile_value = 0.1\n\n # Run the function\n (\n bottom_quantile,\n average,\n top_quantile,\n ) = await compute_nb_items_with_metadata_field(\n test_project_id,\n test_metadata_field,\n test_collection_name,\n test_quantile_value,\n )\n\n logger.debug(\"runned compute_nb_items_with_metadata_field\")\n logger.debug(f\"Bottom quantile: {bottom_quantile}\")\n logger.debug(f\"Average: {average}\")\n logger.debug(f\"Top quantile: {top_quantile}\")\n\n assert isinstance(bottom_quantile, int)\n assert isinstance(average, float)\n assert isinstance(top_quantile, int)\n # assert bottom_quantile <= average <= top_quantile\n\n (\n bottom_quantile,\n average,\n top_quantile,\n ) = await compute_session_length_per_metadata(\n test_project_id,\n test_metadata_field,\n test_quantile_value,\n )\n logger.debug(\"runned compute_session_length_per_metadata\")\n logger.debug(f\"Bottom quantile: {bottom_quantile}\")\n logger.debug(f\"Average: {average}\")\n logger.debug(f\"Top quantile: {top_quantile}\")\n\n assert isinstance(bottom_quantile, float)\n assert isinstance(average, float)\n assert isinstance(top_quantile, float)\n # assert bottom_quantile <= average <= top_quantile",
"completions": [
"```python\nasync def test_main_pipeline(db, populated_project):\n # Arrange\n project_id = populated_project.id\n collection_name = \"collection_name\"\n metadata_field = \"metadata_field\"\n quantile_value = 0.1\n\n # Act\n result = await compute_nb_items_with_metadata_field(\n project_id, metadata_field, collection_name, quantile_value\n )\n\n # Assert\n assert result == (0, 0.0, 0)\n```\n"
],
"line": 56,
"token": 430,
"line_diff": 0,
"token_diff": 0
},
{
"id": 59,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef get_engine(engine: str) -> BaseImpl:\n if engine == \"auto\":\n engine = get_option(\"io.parquet.engine\")\n\n if engine == \"auto\":\n # try engines in this order\n engine_classes = [PyArrowImpl, FastParquetImpl]\n\n error_msgs = \"\"\n for engine_class in engine_classes:\n try:\n return engine_class()\n except ImportError as err:\n error_msgs += \"\\n - \" + str(err)\n\n raise ImportError(\n \"Unable to find a usable engine; \"\n \"tried using: 'pyarrow', 'fastparquet'.\\n\"\n \"A suitable version of \"\n \"pyarrow or fastparquet is required for parquet \"\n \"support.\\n\"\n \"Trying to import the above resulted in these errors:\"\n f\"{error_msgs}\"\n )\n\n if engine == \"pyarrow\":\n return PyArrowImpl()\n elif engine == \"fastparquet\":\n return FastParquetImpl()\n\n raise ValueError(\"engine must be one of 'pyarrow', 'fastparquet'\")\n```\n###test function signature:\n```python\n\ndef test_get_engine_auto_error_message():\n # Expect different error messages from get_engine(engine=\"auto\")\n # if engines aren't installed vs. are installed but bad version\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_get_engine_auto_error_message",
"reference": "\ndef test_get_engine_auto_error_message():\n # Expect different error messages from get_engine(engine=\"auto\")\n # if engines aren't installed vs. are installed but bad version\n from pandas.compat._optional import VERSIONS\n\n # Do we have engines installed, but a bad version of them?\n pa_min_ver = VERSIONS.get(\"pyarrow\")\n fp_min_ver = VERSIONS.get(\"fastparquet\")\n have_pa_bad_version = (\n False\n if not _HAVE_PYARROW\n else Version(pyarrow.__version__) < Version(pa_min_ver)\n )\n have_fp_bad_version = (\n False\n if not _HAVE_FASTPARQUET\n else Version(fastparquet.__version__) < Version(fp_min_ver)\n )\n # Do we have usable engines installed?\n have_usable_pa = _HAVE_PYARROW and not have_pa_bad_version\n have_usable_fp = _HAVE_FASTPARQUET and not have_fp_bad_version\n\n if not have_usable_pa and not have_usable_fp:\n # No usable engines found.\n if have_pa_bad_version:\n match = f\"Pandas requires version .{pa_min_ver}. or newer of .pyarrow.\"\n with pytest.raises(ImportError, match=match):\n get_engine(\"auto\")\n else:\n match = \"Missing optional dependency .pyarrow.\"\n with pytest.raises(ImportError, match=match):\n get_engine(\"auto\")\n\n if have_fp_bad_version:\n match = f\"Pandas requires version .{fp_min_ver}. or newer of .fastparquet.\"\n with pytest.raises(ImportError, match=match):\n get_engine(\"auto\")\n else:\n match = \"Missing optional dependency .fastparquet.\"\n with pytest.raises(ImportError, match=match):\n get_engine(\"auto\")",
"completions": [
"```python\ndef test_get_engine_auto_error_message():\n # Expect different error messages from get_engine(engine=\"auto\")\n # if engines aren't installed vs. are installed but bad version\n import pyarrow\n import fastparquet\n from pyarrow.parquet import ParquetEngine\n from fastparquet.parquet import ParquetEngine as FastParquetEngine\n\n def get_engine(engine: str) -> BaseImpl:\n if engine == \"auto\":\n engine = get_option(\"io.parquet.engine\")\n\n if engine == \"auto\":\n # try engines in this order\n engine_classes = [PyArrowImpl, FastParquetImpl]\n\n error_msgs = \"\"\n for engine_class in engine_classes:\n try:\n return engine_class()\n except ImportError as err:\n error_msgs += \"\\n - \" + str(err)\n\n raise ImportError(\n \"Unable to find a usable engine; \"\n \"tried using: 'pyarrow', 'fastparquet'.\\n\"\n \"A suitable version of \"\n \"pyarrow or fastparquet is required for parquet \"\n \"support.\\n\"\n \"Trying to import the above resulted in these errors:\"\n f\"{error_msgs}\"\n )\n\n if engine == \"pyarrow\":\n return PyArrowImpl()\n elif engine == \"fastparquet\":\n return FastParquetImpl()\n\n raise ValueError(\"engine must be one of 'pyarrow', 'fastparquet'\")\n\n # pyarrow is installed\n assert get_engine(\"auto\").__class__ == PyArrowImpl\n assert get_engine(\"pyarrow\").__class__ == PyArrowImpl\n assert get_engine(\"fastparquet\").__class__ == FastParquetImpl\n\n # pyarrow is not installed\n with pytest.raises(ImportError) as excinfo:\n get_engine(\"auto\")\n assert \"Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'\" in str(excinfo.value)\n assert \"A suitable version of pyarrow is required for parquet support.\" in str(excinfo.value)\n assert \"Trying to import the above resulted in these errors:\" in str(excinfo.value)\n assert \"No module named 'pyarrow'\" in str(excinfo.value)\n\n # fastparquet is not installed\n with pytest.raises(ImportError) as excinfo:\n get_engine(\"auto\")\n assert \"Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'\" in str(excinfo.value)\n assert \"A suitable version of fastparquet is required for parquet support.\" in str(excinfo.value)\n assert \"Trying to import the above resulted in these errors:\" in str(excinfo.value)\n assert \"No module named 'fastparquet'\" in str(excinfo.value)\n\n # pyarrow is installed but bad version\n with pytest.raises(ImportError) as excinfo:\n get_engine(\"auto\")\n assert \"Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'\" in str(excinfo.value)\n assert \"A suitable version of pyarrow is required for parquet support.\" in str(excinfo.value)\n assert \"Trying to import the above resulted in these errors:\" in str(excinfo.value)\n assert \"pyarrow.parquet.ParquetEngine\" in str(excinfo.value)\n assert \"pyarrow.parquet.ParquetEngine\" in str(excinfo.value)\n\n # fastparquet is installed but bad version\n with pytest.raises(ImportError) as excinfo:\n get_engine(\"auto\")\n assert \"Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'\" in str(excinfo.value)\n assert \"A suitable version of fastparquet is required for parquet support.\" in str(excinfo.value)\n assert \"Trying to import the above resulted in these errors:\" in str(excinfo.value)\n"
],
"line": 38,
"token": 277,
"line_diff": 0,
"token_diff": 0
},
{
"id": 60,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def attribute(\n self,\n inp: InterpretableInput,\n target: Union[str, torch.Tensor, None] = None,\n num_trials: int = 1,\n gen_args: Optional[Dict] = None,\n # internal callback hook can be used for logging\n _inspect_forward: Optional[Callable] = None,\n **kwargs,\n ) -> LLMAttributionResult:\n\n assert isinstance(\n inp, self.SUPPORTED_INPUTS\n ), f\"LLMAttribution does not support input type {type(inp)}\"\n\n if target is None:\n # generate when None\n assert hasattr(self.model, \"generate\") and callable(self.model.generate), (\n \"The model does not have recognizable generate function.\"\n \"Target must be given for attribution\"\n )\n\n if not gen_args:\n gen_args = DEFAULT_GEN_ARGS\n\n model_inp = self._format_model_input(inp.to_model_input())\n output_tokens = self.model.generate(model_inp, **gen_args)\n target_tokens = output_tokens[0][model_inp.size(1) :]\n else:\n assert gen_args is None, \"gen_args must be None when target is given\"\n\n if type(target) is str:\n # exclude sos\n target_tokens = self.tokenizer.encode(target)[1:]\n target_tokens = torch.tensor(target_tokens)\n elif type(target) is torch.Tensor:\n target_tokens = target\n\n attr = torch.zeros(\n [\n 1 + len(target_tokens) if self.include_per_token_attr else 1,\n inp.n_itp_features,\n ],\n dtype=torch.float,\n device=self.device,\n )\n\n for _ in range(num_trials):\n attr_input = inp.to_tensor().to(self.device)\n\n cur_attr = self.attr_method.attribute(\n attr_input,\n additional_forward_args=(inp, target_tokens, _inspect_forward),\n **kwargs,\n )\n\n # temp necessary due to FA & Shapley's different return shape of multi-task\n # FA will flatten output shape internally (n_output_token, n_itp_features)\n # Shapley will keep output shape (batch, n_output_token, n_input_features)\n cur_attr = cur_attr.reshape(attr.shape)\n\n attr += cur_attr\n\n attr = attr / num_trials\n\n attr = inp.format_attr(attr)\n\n return LLMAttributionResult(\n attr[0],\n attr[1:]\n if self.include_per_token_attr\n else None, # shape(n_output_token, n_input_features)\n inp.values,\n self.tokenizer.convert_ids_to_tokens(target_tokens),\n )\n```\n###test function signature:\n```python\n\n def test_llm_attr(self, AttrClass) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_llm_attr",
"reference": "\n def test_llm_attr(self, AttrClass) -> None:\n llm = DummyLLM()\n llm.to(self.device)\n tokenizer = DummyTokenizer()\n llm_attr = LLMAttribution(AttrClass(llm), tokenizer)\n\n inp = TextTemplateInput(\"{} b {} {} e {}\", [\"a\", \"c\", \"d\", \"f\"])\n res = llm_attr.attribute(inp, \"m n o p q\")\n\n self.assertEqual(res.seq_attr.shape, (4,))\n self.assertEqual(cast(Tensor, res.token_attr).shape, (5, 4))\n self.assertEqual(res.input_tokens, [\"a\", \"c\", \"d\", \"f\"])\n self.assertEqual(res.output_tokens, [\"m\", \"n\", \"o\", \"p\", \"q\"])\n self.assertEqual(res.seq_attr.device.type, self.device)\n self.assertEqual(cast(Tensor, res.token_attr).device.type, self.device)",
"completions": [
"```python\n def test_llm_attr(self, AttrClass) -> None:\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for LLM attribute\n # test case for L"
],
"line": 72,
"token": 464,
"line_diff": 0,
"token_diff": 0
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{
"id": 61,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def plan(self, task: str):\n agent_pool_description = self.agent_pool.get_agent_pool_description()\n feedback = self.get_last_feedback()\n last_plan = self.get_last_plan()\n\n # run the planning\n plan = self.planning.execute(task, agent_pool_description, last_plan, feedback)\n\n logger.info(f\"Plan: {plan}\")\n\n self.shared_memory.add(\n event_type=EventType.planning, agent=self.name, content=str(plan)\n )\n\n return plan\n```\n###test function signature:\n```python\n\ndef test_planner_succeeds(get_llm): # noqa: F811\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_planner_succeeds",
"reference": "\ndef test_planner_succeeds(get_llm): # noqa: F811\n llm = get_llm(__file__, test_planner_succeeds.__name__)\n\n physicist_description = (\n \"The physicist agent answers questions or research about physics-related topics\"\n )\n physicist = Physicist(\n name=\"Physicist\",\n description=physicist_description,\n llm=llm,\n )\n\n agent_pool = AgentPool()\n agent_pool.add_agents([physicist])\n\n shared_memory = SharedMemory(\n objective=\"Share the information across different agents.\",\n agent_pool=agent_pool,\n )\n\n planner = Planner(\n name=\"planner\",\n agent_pool=agent_pool,\n shared_memory=shared_memory,\n llm=llm,\n )\n\n task = \"\"\"We need to render a highly complex 3D image on the solar system. We can use any publicly avaliable\n resources to achieve this task.\"\"\" # noqa: E501\n\n p = planner.plan(task=task)\n assert len(p.steps) > 0",
"completions": [
"```python\ndef test_planner_succeeds(get_llm): # noqa: F811\n planner = get_llm\n planner.agent_pool.set_agent_pool_description(\n {\n \"agent_pool\": {\n \"agents\": [\n {\n \"name\": \"agent1\",\n \"capabilities\": [\"cap1\", \"cap2\"],\n \"max_task_queue_size\": 10,\n },\n {\n \"name\": \"agent2\",\n \"capabilities\": [\"cap1\", \"cap2\"],\n \"max_task_queue_size\": 10,\n },\n ]\n }\n }\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent1\",\n content='{\"task\": \"task1\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent2\",\n content='{\"task\": \"task2\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent1\",\n content='{\"task\": \"task3\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent2\",\n content='{\"task\": \"task4\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent1\",\n content='{\"task\": \"task5\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent2\",\n content='{\"task\": \"task6\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}]}}',\n )\n planner.shared_memory.add(\n event_type=EventType.planning,\n agent=\"agent1\",\n content='{\"task\": \"task7\", \"agent_pool\": {\"agents\": [{\"name\": \"agent1\", \"capabilities\": [\"cap1\", \"cap2\"], \"max_task_queue_size\": 10}, {\"name\": \"agent2\", \"capabilities\": [\"cap1\","
],
"line": 21,
"token": 149,
"line_diff": 0,
"token_diff": 0
},
{
"id": 62,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def add_edge(self, from_step: str, to_step: str) -> None:\n if from_step not in self.G:\n raise ValueError(f\"Step with name '{from_step}' does not exist\")\n\n if to_step not in self.G:\n raise ValueError(f\"Step with name '{to_step}' does not exist\")\n\n if to_step in self.G[from_step]:\n raise ValueError(\n f\"There is already a edge from '{to_step}' to '{from_step}'\"\n )\n\n if to_step in nx.ancestors(self.G, from_step):\n raise ValueError(\n f\"Cannot add edge from '{from_step}' to '{to_step}' as it would create a cycle.\"\n )\n\n self.G.add_edge(from_step, to_step)\n```\n###test function signature:\n```python\n\n def test_validate_missing_step_input(\n self, dummy_generator_step: \"GeneratorStep\", pipeline: \"Pipeline\"\n ) -> None:\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_validate_missing_step_input",
"reference": "\n def test_validate_missing_step_input(\n self, dummy_generator_step: \"GeneratorStep\", pipeline: \"Pipeline\"\n ) -> None:\n class DummyStep3(Step):\n @property\n def inputs(self) -> List[str]:\n return [\"instruction\"]\n\n @property\n def outputs(self) -> List[str]:\n return [\"response\"]\n\n def process(self) -> \"StepOutput\": # type: ignore\n yield [{\"response\": \"response1\"}]\n\n dag = DAG()\n dag.add_step(dummy_generator_step)\n dag.add_step(DummyStep3(name=\"dummy_step_3\", pipeline=pipeline))\n dag.add_edge(\"dummy_generator_step\", \"dummy_step_3\")\n\n with pytest.raises(\n ValueError,\n match=\"Step 'dummy_step_3' should have a parameter with type hint `StepInput`\",\n ):\n dag.validate()",
"completions": [
"```python\n def test_validate_missing_step_input(\n self, dummy_generator_step: \"GeneratorStep\", pipeline: \"Pipeline\"\n ) -> None:\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_1\", \"step_2\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_2\", \"step_1\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_1\", \"step_1\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_2\", \"step_2\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_3\", \"step_1\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_1\", \"step_3\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_3\", \"step_3\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_2\", \"step_3\")\n\n with pytest.raises(ValueError):\n dummy_generator_step.add_edge(\"step_3\", \"step_2\")\n```\n"
],
"line": 26,
"token": 238,
"line_diff": 0,
"token_diff": 0
},
{
"id": 63,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_temperature_features(\n meter_data_index,\n temperature_data,\n heating_balance_points=None,\n cooling_balance_points=None,\n data_quality=False,\n temperature_mean=True,\n degree_day_method=\"daily\",\n percent_hourly_coverage_per_day=0.5,\n percent_hourly_coverage_per_billing_period=0.9,\n use_mean_daily_values=True,\n tolerance=None,\n keep_partial_nan_rows=False,\n):\n if temperature_data.index.freq != \"H\":\n raise ValueError(\n \"temperature_data.index must have hourly frequency (freq='H').\"\n \" Found: {}\".format(temperature_data.index.freq)\n )\n\n if not temperature_data.index.tz:\n raise ValueError(\n \"temperature_data.index must be timezone-aware. You can set it with\"\n \" temperature_data.tz_localize(...).\"\n )\n\n if meter_data_index.freq is None and meter_data_index.inferred_freq == \"H\":\n raise ValueError(\n \"If you have hourly data explicitly set the frequency\"\n \" of the dataframe by setting\"\n \"``meter_data_index.freq =\"\n \" pd.tseries.frequencies.to_offset('H').\"\n )\n\n if not meter_data_index.tz:\n raise ValueError(\n \"meter_data_index must be timezone-aware. You can set it with\"\n \" meter_data.tz_localize(...).\"\n )\n\n if meter_data_index.duplicated().any():\n raise ValueError(\"Duplicates found in input meter trace index.\")\n\n temp_agg_funcs = []\n temp_agg_column_renames = {}\n\n if heating_balance_points is None:\n heating_balance_points = []\n if cooling_balance_points is None:\n cooling_balance_points = []\n\n if meter_data_index.freq is not None:\n try:\n freq_timedelta = pd.Timedelta(meter_data_index.freq)\n except ValueError: # freq cannot be converted to timedelta\n freq_timedelta = None\n else:\n freq_timedelta = None\n\n if tolerance is None:\n tolerance = freq_timedelta\n\n if not (heating_balance_points == [] and cooling_balance_points == []):\n if degree_day_method == \"hourly\":\n pass\n elif degree_day_method == \"daily\":\n if meter_data_index.freq == \"H\":\n raise ValueError(\n \"degree_day_method='daily' must be used with\"\n \" daily meter data. Found: 'hourly'\".format(degree_day_method)\n )\n else:\n raise ValueError(\"method not supported: {}\".format(degree_day_method))\n\n if freq_timedelta == pd.Timedelta(\"1H\"):\n # special fast route for hourly data.\n df = temperature_data.to_frame(\"temperature_mean\").reindex(meter_data_index)\n\n if use_mean_daily_values:\n n_days = 1\n else:\n n_days = 1.0 / 24.0\n\n df = df.assign(\n **{\n \"cdd_{}\".format(bp): np.maximum(df.temperature_mean - bp, 0) * n_days\n for bp in cooling_balance_points\n }\n )\n df = df.assign(\n **{\n \"hdd_{}\".format(bp): np.maximum(bp - df.temperature_mean, 0) * n_days\n for bp in heating_balance_points\n }\n )\n df = df.assign(\n n_hours_dropped=df.temperature_mean.isnull().astype(int),\n n_hours_kept=df.temperature_mean.notnull().astype(int),\n )\n # TODO(philngo): bad interface or maybe this is just wrong for some reason?\n if data_quality:\n df = df.assign(\n temperature_null=df.n_hours_dropped,\n temperature_not_null=df.n_hours_kept,\n )\n if not temperature_mean:\n del df[\"temperature_mean\"]\n else:\n # daily/billing route\n # heating/cooling degree day aggregations. Needed for n_days fields as well.\n temp_agg_funcs.extend(\n _degree_day_columns(\n heating_balance_points=heating_balance_points,\n cooling_balance_points=cooling_balance_points,\n degree_day_method=degree_day_method,\n percent_hourly_coverage_per_day=percent_hourly_coverage_per_day,\n percent_hourly_coverage_per_billing_period=percent_hourly_coverage_per_billing_period,\n use_mean_daily_values=use_mean_daily_values,\n )\n )\n temp_agg_column_renames.update(\n {(\"temp\", \"degree_day_columns\"): \"degree_day_columns\"}\n )\n\n if data_quality:\n temp_agg_funcs.extend(\n [(\"not_null\", \"count\"), (\"null\", lambda x: x.isnull().sum())]\n )\n temp_agg_column_renames.update(\n {\n (\"temp\", \"not_null\"): \"temperature_not_null\",\n (\"temp\", \"null\"): \"temperature_null\",\n }\n )\n\n if temperature_mean:\n temp_agg_funcs.extend([(\"mean\", \"mean\")])\n temp_agg_column_renames.update({(\"temp\", \"mean\"): \"temperature_mean\"})\n\n # aggregate temperatures\n temp_df = temperature_data.to_frame(\"temp\")\n temp_groups = _matching_groups(meter_data_index, temp_df, tolerance)\n temp_aggregations = temp_groups.agg({\"temp\": temp_agg_funcs})\n\n # expand temp aggregations by faking and deleting the `meter_value` column.\n # I haven't yet figured out a way to avoid this and get the desired\n # structure and behavior. (philngo)\n meter_value = pd.DataFrame({\"meter_value\": 0}, index=meter_data_index)\n df = pd.concat([meter_value, temp_aggregations], axis=1).rename(\n columns=temp_agg_column_renames\n )\n del df[\"meter_value\"]\n\n if \"degree_day_columns\" in df:\n if df[\"degree_day_columns\"].dropna().empty:\n column_defaults = {\n column: np.full(df[\"degree_day_columns\"].shape, np.nan)\n for column in [\"n_days_dropped\", \"n_days_kept\"]\n }\n df = df.drop([\"degree_day_columns\"], axis=1).assign(**column_defaults)\n else:\n df = pd.concat(\n [\n df.drop([\"degree_day_columns\"], axis=1),\n df[\"degree_day_columns\"].dropna().apply(pd.Series),\n ],\n axis=1,\n )\n\n if not keep_partial_nan_rows:\n df = overwrite_partial_rows_with_nan(df)\n\n # nan last row\n df = df.iloc[:-1].reindex(df.index)\n return df\n```\n###test function signature:\n```python\n\ndef test_compute_temperature_features_hourly_temp_mean(il_electricity_cdd_hdd_hourly):\n # pick a slice with both hdd and cdd\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_temperature_features_hourly_temp_mean",
"reference": "\ndef test_compute_temperature_features_hourly_temp_mean(il_electricity_cdd_hdd_hourly):\n # pick a slice with both hdd and cdd\n meter_data = il_electricity_cdd_hdd_hourly[\"meter_data\"][\"2016-03-01\":\"2016-07-01\"]\n temperature_data = il_electricity_cdd_hdd_hourly[\"temperature_data\"][\n \"2016-03-01\":\"2016-07-01\"\n ]\n df = compute_temperature_features(meter_data.index, temperature_data)\n assert list(sorted(df.columns)) == [\n \"n_hours_dropped\",\n \"n_hours_kept\",\n \"temperature_mean\",\n ]\n assert df.shape == (2952, 3)\n\n assert round(df.temperature_mean.mean()) == 62.0",
"completions": [
"```python\n\ndef test_compute_temperature_features_hourly_temp_mean(il_electricity_cdd_hdd_hourly):\n # pick a slice with both hdd and cdd\n df = il_electricity_cdd_hdd_hourly.copy()\n df = df.iloc[100:150]\n df = df.assign(\n **{\n \"cdd_10\": np.maximum(df.temperature_mean - 10, 0),\n \"cdd_20\": np.maximum(df.temperature_mean - 20, 0),\n \"hdd_10\": np.maximum(10 - df.temperature_mean, 0),\n \"hdd_20\": np.maximum(20 - df.temperature_mean, 0),\n }\n )\n df = df.assign(\n n_hours_dropped=df.temperature_mean.isnull().astype(int),\n n_hours_kept=df.temperature_mean.notnull().astype(int),\n )\n df = df.assign(\n temperature_null=df.n_hours_dropped,\n temperature_not_null=df.n_hours_kept,\n )\n df = df.drop([\"temperature_mean\"], axis=1)\n df = df.reindex(il_electricity_cdd_hdd_hourly.index)\n df = overwrite_partial_rows_with_nan(df)\n df = df.iloc[:-1].reindex(df.index)\n assert_frame_equal(df, il_electricity_cdd_hdd_hourly)\n```\n"
],
"line": 167,
"token": 992,
"line_diff": 0,
"token_diff": 0
},
{
"id": 64,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef pandasSQL_builder(\n con,\n schema: str | None = None,\n need_transaction: bool = False,\n) -> PandasSQL:\n import sqlite3\n\n if isinstance(con, sqlite3.Connection) or con is None:\n return SQLiteDatabase(con)\n\n sqlalchemy = import_optional_dependency(\"sqlalchemy\", errors=\"ignore\")\n\n if isinstance(con, str) and sqlalchemy is None:\n raise ImportError(\"Using URI string without sqlalchemy installed.\")\n\n if sqlalchemy is not None and isinstance(con, (str, sqlalchemy.engine.Connectable)):\n return SQLDatabase(con, schema, need_transaction)\n\n warnings.warn(\n \"pandas only supports SQLAlchemy connectable (engine/connection) or \"\n \"database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 \"\n \"objects are not tested. Please consider using SQLAlchemy.\",\n UserWarning,\n stacklevel=find_stack_level(),\n )\n return SQLiteDatabase(con)\n```\n###test function signature:\n```python\n\n def test_execute_closed_connection(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_execute_closed_connection",
"reference": "\n def test_execute_closed_connection(self):\n create_sql = \"\"\"\n CREATE TABLE test\n (\n a TEXT,\n b TEXT,\n c REAL,\n PRIMARY KEY (a, b)\n );\n \"\"\"\n with contextlib.closing(sqlite3.connect(\":memory:\")) as conn:\n cur = conn.cursor()\n cur.execute(create_sql)\n\n with sql.pandasSQL_builder(conn) as pandas_sql:\n pandas_sql.execute('INSERT INTO test VALUES(\"foo\", \"bar\", 1.234)')\n\n msg = \"Cannot operate on a closed database.\"\n with pytest.raises(sqlite3.ProgrammingError, match=msg):\n tquery(\"select * from test\", con=conn)",
"completions": [
"```python\n def test_execute_closed_connection(self):\n from pandas.io.sql import PandasSQL\n from pandas.io.sql.sqldatabase import SQLiteDatabase\n from pandas.io.sql.pandas_sql import PandasSQLQueryCompiler\n from pandas.io.sql.pandas_sql import PandasSQLEngine\n from pandas.io.sql.pandas_sql import PandasSQLTable\n from pandas.io.sql.pandas_sql import PandasSQLConverter\n from pandas.io.sql.pandas_sql import PandasSQLAlchemy\n from pandas.io.sql.pandas_sql import PandasSQLQuery\n from pandas.io.sql.pandas_sql import PandasSQLExecutionContext\n from pandas.io.sql.pandas_sql import PandasSQLCompiler\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas.io.sql.pandas_sql import PandasSQLExecutionResult\n from pandas."
],
"line": 31,
"token": 237,
"line_diff": 0,
"token_diff": 0
},
{
"id": 65,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef compute_temperature_features(\n meter_data_index,\n temperature_data,\n heating_balance_points=None,\n cooling_balance_points=None,\n data_quality=False,\n temperature_mean=True,\n degree_day_method=\"daily\",\n percent_hourly_coverage_per_day=0.5,\n percent_hourly_coverage_per_billing_period=0.9,\n use_mean_daily_values=True,\n tolerance=None,\n keep_partial_nan_rows=False,\n):\n if temperature_data.index.freq != \"H\":\n raise ValueError(\n \"temperature_data.index must have hourly frequency (freq='H').\"\n \" Found: {}\".format(temperature_data.index.freq)\n )\n\n if not temperature_data.index.tz:\n raise ValueError(\n \"temperature_data.index must be timezone-aware. You can set it with\"\n \" temperature_data.tz_localize(...).\"\n )\n\n if meter_data_index.freq is None and meter_data_index.inferred_freq == \"H\":\n raise ValueError(\n \"If you have hourly data explicitly set the frequency\"\n \" of the dataframe by setting\"\n \"``meter_data_index.freq =\"\n \" pd.tseries.frequencies.to_offset('H').\"\n )\n\n if not meter_data_index.tz:\n raise ValueError(\n \"meter_data_index must be timezone-aware. You can set it with\"\n \" meter_data.tz_localize(...).\"\n )\n\n if meter_data_index.duplicated().any():\n raise ValueError(\"Duplicates found in input meter trace index.\")\n\n temp_agg_funcs = []\n temp_agg_column_renames = {}\n\n if heating_balance_points is None:\n heating_balance_points = []\n if cooling_balance_points is None:\n cooling_balance_points = []\n\n if meter_data_index.freq is not None:\n try:\n freq_timedelta = pd.Timedelta(meter_data_index.freq)\n except ValueError: # freq cannot be converted to timedelta\n freq_timedelta = None\n else:\n freq_timedelta = None\n\n if tolerance is None:\n tolerance = freq_timedelta\n\n if not (heating_balance_points == [] and cooling_balance_points == []):\n if degree_day_method == \"hourly\":\n pass\n elif degree_day_method == \"daily\":\n if meter_data_index.freq == \"H\":\n raise ValueError(\n \"degree_day_method='daily' must be used with\"\n \" daily meter data. Found: 'hourly'\".format(degree_day_method)\n )\n else:\n raise ValueError(\"method not supported: {}\".format(degree_day_method))\n\n if freq_timedelta == pd.Timedelta(\"1H\"):\n # special fast route for hourly data.\n df = temperature_data.to_frame(\"temperature_mean\").reindex(meter_data_index)\n\n if use_mean_daily_values:\n n_days = 1\n else:\n n_days = 1.0 / 24.0\n\n df = df.assign(\n **{\n \"cdd_{}\".format(bp): np.maximum(df.temperature_mean - bp, 0) * n_days\n for bp in cooling_balance_points\n }\n )\n df = df.assign(\n **{\n \"hdd_{}\".format(bp): np.maximum(bp - df.temperature_mean, 0) * n_days\n for bp in heating_balance_points\n }\n )\n df = df.assign(\n n_hours_dropped=df.temperature_mean.isnull().astype(int),\n n_hours_kept=df.temperature_mean.notnull().astype(int),\n )\n # TODO(philngo): bad interface or maybe this is just wrong for some reason?\n if data_quality:\n df = df.assign(\n temperature_null=df.n_hours_dropped,\n temperature_not_null=df.n_hours_kept,\n )\n if not temperature_mean:\n del df[\"temperature_mean\"]\n else:\n # daily/billing route\n # heating/cooling degree day aggregations. Needed for n_days fields as well.\n temp_agg_funcs.extend(\n _degree_day_columns(\n heating_balance_points=heating_balance_points,\n cooling_balance_points=cooling_balance_points,\n degree_day_method=degree_day_method,\n percent_hourly_coverage_per_day=percent_hourly_coverage_per_day,\n percent_hourly_coverage_per_billing_period=percent_hourly_coverage_per_billing_period,\n use_mean_daily_values=use_mean_daily_values,\n )\n )\n temp_agg_column_renames.update(\n {(\"temp\", \"degree_day_columns\"): \"degree_day_columns\"}\n )\n\n if data_quality:\n temp_agg_funcs.extend(\n [(\"not_null\", \"count\"), (\"null\", lambda x: x.isnull().sum())]\n )\n temp_agg_column_renames.update(\n {\n (\"temp\", \"not_null\"): \"temperature_not_null\",\n (\"temp\", \"null\"): \"temperature_null\",\n }\n )\n\n if temperature_mean:\n temp_agg_funcs.extend([(\"mean\", \"mean\")])\n temp_agg_column_renames.update({(\"temp\", \"mean\"): \"temperature_mean\"})\n\n # aggregate temperatures\n temp_df = temperature_data.to_frame(\"temp\")\n temp_groups = _matching_groups(meter_data_index, temp_df, tolerance)\n temp_aggregations = temp_groups.agg({\"temp\": temp_agg_funcs})\n\n # expand temp aggregations by faking and deleting the `meter_value` column.\n # I haven't yet figured out a way to avoid this and get the desired\n # structure and behavior. (philngo)\n meter_value = pd.DataFrame({\"meter_value\": 0}, index=meter_data_index)\n df = pd.concat([meter_value, temp_aggregations], axis=1).rename(\n columns=temp_agg_column_renames\n )\n del df[\"meter_value\"]\n\n if \"degree_day_columns\" in df:\n if df[\"degree_day_columns\"].dropna().empty:\n column_defaults = {\n column: np.full(df[\"degree_day_columns\"].shape, np.nan)\n for column in [\"n_days_dropped\", \"n_days_kept\"]\n }\n df = df.drop([\"degree_day_columns\"], axis=1).assign(**column_defaults)\n else:\n df = pd.concat(\n [\n df.drop([\"degree_day_columns\"], axis=1),\n df[\"degree_day_columns\"].dropna().apply(pd.Series),\n ],\n axis=1,\n )\n\n if not keep_partial_nan_rows:\n df = overwrite_partial_rows_with_nan(df)\n\n # nan last row\n df = df.iloc[:-1].reindex(df.index)\n return df\n```\n###test function signature:\n```python\n\ndef test_compute_temperature_features_billing_monthly_hourly_degree_days(\n il_electricity_cdd_hdd_billing_monthly, snapshot\n):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_compute_temperature_features_billing_monthly_hourly_degree_days",
"reference": "\ndef test_compute_temperature_features_billing_monthly_hourly_degree_days(\n il_electricity_cdd_hdd_billing_monthly, snapshot\n):\n meter_data = il_electricity_cdd_hdd_billing_monthly[\"meter_data\"]\n temperature_data = il_electricity_cdd_hdd_billing_monthly[\"temperature_data\"]\n df = compute_temperature_features(\n meter_data.index,\n temperature_data,\n heating_balance_points=[60, 61],\n cooling_balance_points=[65, 66],\n temperature_mean=False,\n degree_day_method=\"hourly\",\n )\n assert df.shape == (27, 6)\n assert list(sorted(df.columns)) == [\n \"cdd_65\",\n \"cdd_66\",\n \"hdd_60\",\n \"hdd_61\",\n \"n_hours_dropped\",\n \"n_hours_kept\",\n ]\n snapshot.assert_match(\n [\n round(df.hdd_60.mean(), 2),\n round(df.hdd_61.mean(), 2),\n round(df.cdd_65.mean(), 2),\n round(df.cdd_66.mean(), 2),\n round(df.n_hours_kept.mean(), 2),\n round(df.n_hours_dropped.mean(), 2),\n ],\n \"values\",\n )",
"completions": [
"```python\ndef test_compute_temperature_features_billing_monthly_hourly_degree_days(\n il_electricity_cdd_hdd_billing_monthly, snapshot\n):\n meter_data_index = pd.date_range(\n start=\"2015-01-01\", end=\"2015-01-31\", freq=\"H\"\n )\n temperature_data = pd.DataFrame(\n {\n \"temperature_mean\": [\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 10,\n 1"
],
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{
"id": 66,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef initialize_model_parallel(\n tensor_model_parallel_size: int = 1,\n pipeline_model_parallel_size: int = 1,\n virtual_pipeline_model_parallel_size: Optional[int] = None,\n pipeline_model_parallel_split_rank: Optional[int] = None,\n use_fp8: bool = False,\n) -> None:\n # Get world size and rank. Ensure some consistencies.\n assert torch.distributed.is_initialized()\n world_size: int = torch.distributed.get_world_size()\n\n if world_size % (tensor_model_parallel_size * pipeline_model_parallel_size) != 0:\n raise RuntimeError(\n f\"world_size ({world_size}) is not divisible by tensor_model_parallel_size \"\n f\"({tensor_model_parallel_size}) x pipeline_model_parallel_size ({pipeline_model_parallel_size})\"\n )\n\n data_parallel_size: int = world_size // (tensor_model_parallel_size * pipeline_model_parallel_size)\n\n num_tensor_model_parallel_groups: int = world_size // tensor_model_parallel_size\n num_pipeline_model_parallel_groups: int = world_size // pipeline_model_parallel_size\n num_data_parallel_groups: int = world_size // data_parallel_size\n\n if virtual_pipeline_model_parallel_size is not None:\n if not pipeline_model_parallel_size > 2:\n raise RuntimeError(\"pipeline-model-parallel size should be greater than 2 with \" \"interleaved schedule\")\n global _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK\n global _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE\n _VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = 0\n _VIRTUAL_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = virtual_pipeline_model_parallel_size\n\n if pipeline_model_parallel_split_rank is not None:\n global _PIPELINE_MODEL_PARALLEL_SPLIT_RANK\n _PIPELINE_MODEL_PARALLEL_SPLIT_RANK = pipeline_model_parallel_split_rank\n\n rank = torch.distributed.get_rank()\n\n # Build the data-parallel groups.\n global _DATA_PARALLEL_GROUP\n global _DATA_PARALLEL_GROUP_GLOO\n global _DATA_PARALLEL_GLOBAL_RANKS\n assert _DATA_PARALLEL_GROUP is None, 'data parallel group is already initialized'\n all_data_parallel_group_ranks = []\n for i in range(pipeline_model_parallel_size):\n start_rank = i * num_pipeline_model_parallel_groups\n end_rank = (i + 1) * num_pipeline_model_parallel_groups\n for j in range(tensor_model_parallel_size):\n ranks = range(start_rank + j, end_rank, tensor_model_parallel_size)\n all_data_parallel_group_ranks.append(list(ranks))\n group = torch.distributed.new_group(ranks)\n group_gloo = torch.distributed.new_group(ranks, backend=\"gloo\")\n if rank in ranks:\n _DATA_PARALLEL_GROUP = group\n _DATA_PARALLEL_GROUP_GLOO = group_gloo\n _DATA_PARALLEL_GLOBAL_RANKS = ranks\n\n # Build the model-parallel groups.\n global _MODEL_PARALLEL_GROUP\n assert _MODEL_PARALLEL_GROUP is None, 'model parallel group is already initialized'\n for i in range(data_parallel_size):\n ranks = [data_parallel_group_ranks[i] for data_parallel_group_ranks in all_data_parallel_group_ranks]\n group = torch.distributed.new_group(ranks)\n if rank in ranks:\n _MODEL_PARALLEL_GROUP = group\n\n # Build the tensor model-parallel groups.\n global _TENSOR_MODEL_PARALLEL_GROUP\n assert _TENSOR_MODEL_PARALLEL_GROUP is None, 'tensor model parallel group is already initialized'\n for i in range(num_tensor_model_parallel_groups):\n ranks = range(i * tensor_model_parallel_size, (i + 1) * tensor_model_parallel_size)\n group = torch.distributed.new_group(ranks)\n if rank in ranks:\n _TENSOR_MODEL_PARALLEL_GROUP = group\n\n # Build the pipeline model-parallel groups and embedding groups\n # (first and last rank in each pipeline model-parallel group).\n global _PIPELINE_MODEL_PARALLEL_GROUP\n global _PIPELINE_GLOBAL_RANKS\n assert _PIPELINE_MODEL_PARALLEL_GROUP is None, 'pipeline model parallel group is already initialized'\n global _EMBEDDING_GROUP\n global _EMBEDDING_GLOBAL_RANKS\n assert _EMBEDDING_GROUP is None, 'embedding group is already initialized'\n global _POSITION_EMBEDDING_GROUP\n global _POSITION_EMBEDDING_GLOBAL_RANKS\n assert _POSITION_EMBEDDING_GROUP is None, 'position embedding group is already initialized'\n for i in range(num_pipeline_model_parallel_groups):\n ranks = range(i, world_size, num_pipeline_model_parallel_groups)\n group = torch.distributed.new_group(ranks)\n if rank in ranks:\n _PIPELINE_MODEL_PARALLEL_GROUP = group\n _PIPELINE_GLOBAL_RANKS = ranks\n # Setup embedding group (to exchange gradients between\n # first and last stages).\n if len(ranks) > 1:\n embedding_ranks = [ranks[0], ranks[-1]]\n position_embedding_ranks = [ranks[0]]\n if pipeline_model_parallel_split_rank is not None:\n if ranks[pipeline_model_parallel_split_rank] not in embedding_ranks:\n embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank], ranks[-1]]\n if ranks[pipeline_model_parallel_split_rank] not in position_embedding_ranks:\n position_embedding_ranks = [ranks[0], ranks[pipeline_model_parallel_split_rank]]\n else:\n embedding_ranks = ranks\n position_embedding_ranks = ranks\n\n group = torch.distributed.new_group(embedding_ranks)\n if rank in embedding_ranks:\n _EMBEDDING_GROUP = group\n if rank in ranks:\n _EMBEDDING_GLOBAL_RANKS = embedding_ranks\n\n group = torch.distributed.new_group(position_embedding_ranks)\n if rank in position_embedding_ranks:\n _POSITION_EMBEDDING_GROUP = group\n if rank in ranks:\n _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks\n\n # Build the FP8 groups.\n global _AMAX_REDUCTION_GROUP\n assert _AMAX_REDUCTION_GROUP is None, \\\n 'FP8 amax reduction group is already initialized'\n if use_fp8:\n amax_group_size: int = tensor_model_parallel_size * data_parallel_size\n num_amax_groups: int = world_size // amax_group_size\n for i in range(num_amax_groups):\n start_rank = i * amax_group_size\n end_rank = (i + 1) * amax_group_size\n ranks = range(start_rank, end_rank)\n group = torch.distributed.new_group(ranks)\n if rank in ranks:\n _AMAX_REDUCTION_GROUP = group\n\n # Initialize global memory buffer\n # This isn't really \"parallel state\" but there isn't another good place to\n # put this. If we end up with a more generic initialization of megatron-core\n # we could stick it there\n _set_global_memory_buffer()\n```\n###test function signature:\n```python\n\ndef test_initialize__and_destroy_model_parallel():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_initialize__and_destroy_model_parallel",
"reference": "\ndef test_initialize__and_destroy_model_parallel():\n with pytest.raises(AssertionError):\n assert(ps.initialize_model_parallel())\n Utils.initialize_distributed()\n with pytest.raises(RuntimeError):\n assert(ps.initialize_model_parallel(tensor_model_parallel_size=2*world_size))\n with pytest.raises(RuntimeError):\n assert(ps.initialize_model_parallel(pipeline_model_parallel_size=2*world_size))\n with pytest.raises(RuntimeError):\n assert(ps.initialize_model_parallel(pipeline_model_parallel_size=world_size, tensor_model_parallel_size=world_size))\n with pytest.raises(RuntimeError):\n assert(ps.initialize_model_parallel(virtual_pipeline_model_parallel_size=2))\n Utils.initialize_model_parallel(tensor_model_parallel_size=2, pipeline_model_parallel_size=4)\n\n assert(ps.model_parallel_is_initialized())\n assert(ps.get_model_parallel_group() is not None)\n assert(ps.get_tensor_model_parallel_group() is not None)\n assert(ps.get_pipeline_model_parallel_group() is not None)\n assert(ps.get_data_parallel_group() is not None) \n Utils.destroy_model_parallel()\n assert(ps._MODEL_PARALLEL_GROUP is None)",
"completions": [
"```python\ndef test_initialize_and_destroy_model_parallel():\n initialize_model_parallel(\n tensor_model_parallel_size=1,\n pipeline_model_parallel_size=1,\n virtual_pipeline_model_parallel_size=None,\n pipeline_model_parallel_split_rank=None,\n use_fp8=False,\n )\n destroy_model_parallel()\n```\n"
],
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"token_diff": 0
},
{
"id": 67,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef spectrogram(\n waveform: np.ndarray,\n window: np.ndarray,\n frame_length: int,\n hop_length: int,\n fft_length: Optional[int] = None,\n power: Optional[float] = 1.0,\n center: bool = True,\n pad_mode: str = \"reflect\",\n onesided: bool = True,\n preemphasis: Optional[float] = None,\n mel_filters: Optional[np.ndarray] = None,\n mel_floor: float = 1e-10,\n log_mel: Optional[str] = None,\n reference: float = 1.0,\n min_value: float = 1e-10,\n db_range: Optional[float] = None,\n remove_dc_offset: Optional[bool] = None,\n dtype: np.dtype = np.float32,\n) -> np.ndarray:\n window_length = len(window)\n\n if fft_length is None:\n fft_length = frame_length\n\n if frame_length > fft_length:\n raise ValueError(f\"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})\")\n\n if window_length != frame_length:\n raise ValueError(f\"Length of the window ({window_length}) must equal frame_length ({frame_length})\")\n\n if hop_length <= 0:\n raise ValueError(\"hop_length must be greater than zero\")\n\n if waveform.ndim != 1:\n raise ValueError(f\"Input waveform must have only one dimension, shape is {waveform.shape}\")\n\n if np.iscomplexobj(waveform):\n raise ValueError(\"Complex-valued input waveforms are not currently supported\")\n\n if power is None and mel_filters is not None:\n raise ValueError(\n \"You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram.\"\n \"Specify `power` to fix this issue.\"\n )\n\n # center pad the waveform\n if center:\n padding = [(int(frame_length // 2), int(frame_length // 2))]\n waveform = np.pad(waveform, padding, mode=pad_mode)\n\n # promote to float64, since np.fft uses float64 internally\n waveform = waveform.astype(np.float64)\n window = window.astype(np.float64)\n\n # split waveform into frames of frame_length size\n num_frames = int(1 + np.floor((waveform.size - frame_length) / hop_length))\n\n num_frequency_bins = (fft_length // 2) + 1 if onesided else fft_length\n spectrogram = np.empty((num_frames, num_frequency_bins), dtype=np.complex64)\n\n # rfft is faster than fft\n fft_func = np.fft.rfft if onesided else np.fft.fft\n buffer = np.zeros(fft_length)\n\n timestep = 0\n for frame_idx in range(num_frames):\n buffer[:frame_length] = waveform[timestep : timestep + frame_length]\n\n if remove_dc_offset:\n buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean()\n\n if preemphasis is not None:\n buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1]\n buffer[0] *= 1 - preemphasis\n\n buffer[:frame_length] *= window\n\n spectrogram[frame_idx] = fft_func(buffer)\n timestep += hop_length\n\n # note: ** is much faster than np.power\n if power is not None:\n spectrogram = np.abs(spectrogram, dtype=np.float64) ** power\n\n spectrogram = spectrogram.T\n\n if mel_filters is not None:\n spectrogram = np.maximum(mel_floor, np.dot(mel_filters.T, spectrogram))\n\n if power is not None and log_mel is not None:\n if log_mel == \"log\":\n spectrogram = np.log(spectrogram)\n elif log_mel == \"log10\":\n spectrogram = np.log10(spectrogram)\n elif log_mel == \"dB\":\n if power == 1.0:\n spectrogram = amplitude_to_db(spectrogram, reference, min_value, db_range)\n elif power == 2.0:\n spectrogram = power_to_db(spectrogram, reference, min_value, db_range)\n else:\n raise ValueError(f\"Cannot use log_mel option '{log_mel}' with power {power}\")\n else:\n raise ValueError(f\"Unknown log_mel option: {log_mel}\")\n\n spectrogram = np.asarray(spectrogram, dtype)\n\n return spectrogram\n```\n###test function signature:\n```python\n\n def test_spectrogram_shapes(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_spectrogram_shapes",
"reference": "\n def test_spectrogram_shapes(self):\n waveform = self._load_datasamples(1)[0]\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\"),\n frame_length=400,\n hop_length=128,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=True,\n )\n self.assertEqual(spec.shape, (201, 732))\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\"),\n frame_length=400,\n hop_length=128,\n power=1.0,\n center=False,\n pad_mode=\"reflect\",\n onesided=True,\n )\n self.assertEqual(spec.shape, (201, 729))\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\"),\n frame_length=400,\n hop_length=128,\n fft_length=512,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=True,\n )\n self.assertEqual(spec.shape, (257, 732))\n\n spec = spectrogram(\n waveform,\n window_function(400, \"hann\", frame_length=512),\n frame_length=512,\n hop_length=64,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=False,\n )\n self.assertEqual(spec.shape, (512, 1464))\n\n spec = spectrogram(\n waveform,\n window_function(512, \"hann\"),\n frame_length=512,\n hop_length=64,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=False,\n )\n self.assertEqual(spec.shape, (512, 1464))\n\n spec = spectrogram(\n waveform,\n window_function(512, \"hann\"),\n frame_length=512,\n hop_length=512,\n power=1.0,\n center=True,\n pad_mode=\"reflect\",\n onesided=False,\n )\n self.assertEqual(spec.shape, (512, 183))",
"completions": [
"```python\n def test_spectrogram_shapes(self):\n waveform = np.random.rand(1000)\n window = np.hanning(100)\n frame_length = 100\n hop_length = 10\n fft_length = 100\n power = 1.0\n center = True\n pad_mode = \"reflect\"\n onesided = True\n preemphasis = None\n mel_filters = None\n mel_floor = 1e-10\n log_mel = None\n reference = 1.0\n min_value = 1e-10\n db_range = None\n remove_dc_offset = None\n dtype = np.float32\n\n spectrogram = spectrogram(\n waveform,\n window,\n frame_length,\n hop_length,\n fft_length,\n power,\n center,\n pad_mode,\n onesided,\n preemphasis,\n mel_filters,\n mel_floor,\n log_mel,\n reference,\n min_value,\n db_range,\n remove_dc_offset,\n dtype,\n )\n\n self.assertEqual(spectrogram.shape, (10, 100))\n```\n"
],
"line": 95,
"token": 804,
"line_diff": 0,
"token_diff": 0
},
{
"id": 68,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef load_image_from_string(\n reference: str,\n max_height: Optional[int] = None,\n max_width: Optional[int] = None,\n) -> Tuple[str, Optional[float]]:\n if uri_is_http_link(uri=reference):\n return load_image_from_url(\n url=reference, max_height=max_height, max_width=max_width\n )\n if os.path.exists(reference):\n if max_height is None or max_width is None:\n with open(reference, \"rb\") as f:\n img_bytes = f.read()\n img_base64_str = encode_base_64(payload=img_bytes)\n return img_base64_str, None\n local_image = cv2.imread(reference)\n if local_image is None:\n raise EncodingError(f\"Could not load image from {reference}\")\n local_image, scaling_factor = resize_opencv_image(\n image=local_image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=local_image), scaling_factor\n if max_height is not None and max_width is not None:\n image_bytes = base64.b64decode(reference)\n image = bytes_to_opencv_image(payload=image_bytes)\n image, scaling_factor = resize_opencv_image(\n image=image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=image), scaling_factor\n return reference, None\n```\n###test function signature:\n```python\n\ndef test_load_image_from_string_when_base64_image_given_and_no_resize_needed(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_load_image_from_string_when_base64_image_given_and_no_resize_needed",
"reference": "\ndef test_load_image_from_string_when_base64_image_given_and_no_resize_needed(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n _, image = example_local_image\n _, buffer = cv2.imencode(\".jpg\", image)\n base64_image = base64.b64encode(buffer).decode(\"ascii\")\n\n # when\n serialised_image, scaling_factor = load_image_from_string(\n reference=base64_image,\n max_width=None,\n max_height=None,\n )\n\n # then\n assert (\n scaling_factor is None\n ), \"No resize parameters given, so scaling factor should not be established\"\n assert (\n serialised_image == base64_image\n ), \"Serialised image should be identical with input\"",
"completions": [
"```python\n\ndef test_load_image_from_string_when_base64_image_given_and_no_resize_needed(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n image_bytes = base64.b64decode(example_local_image[0])\n image = bytes_to_opencv_image(payload=image_bytes)\n # when\n result = load_image_from_string(\n reference=example_local_image[0],\n max_height=None,\n max_width=None,\n )\n # then\n assert result[0] == example_local_image[0]\n assert result[1] is None\n assert np.array_equal(image, result[1])\n```\n"
],
"line": 47,
"token": 280,
"line_diff": 0,
"token_diff": 0
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{
"id": 69,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def submit_payout_address(self, robot_index=1):\n path = reverse(\"order\")\n params = f\"?order_id={self.order_id}\"\n headers = self.get_robot_auth(robot_index)\n\n payout_address = create_address(\"robot\")\n signed_payout_address = sign_message(\n payout_address,\n passphrase_path=f\"tests/robots/{robot_index}/token\",\n private_key_path=f\"tests/robots/{robot_index}/enc_priv_key\",\n )\n body = {\n \"action\": \"update_address\",\n \"address\": signed_payout_address,\n \"mining_fee_rate\": 50,\n }\n self.response = self.client.post(path + params, body, **headers)\n```\n###test function signature:\n```python\n def test_successful_onchain(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_successful_onchain",
"reference": " def test_successful_onchain(self):\n trade = Trade(self.client)\n trade.publish_order()\n trade.take_order()\n trade.lock_taker_bond()\n trade.lock_escrow(trade.taker_index)\n trade.submit_payout_address(trade.maker_index)\n trade.confirm_fiat(trade.maker_index)\n trade.confirm_fiat(trade.taker_index)\n\n trade.process_payouts(mine_a_block=True)\n trade.get_order(trade.maker_index)\n\n data = trade.response.json()\n\n self.assertEqual(trade.response.status_code, 200)\n self.assertResponse(trade.response)\n\n self.assertEqual(data[\"status_message\"], Order.Status(Order.Status.SUC).label)\n self.assertTrue(data[\"is_fiat_sent\"])\n self.assertFalse(data[\"is_disputed\"])\n self.assertIsInstance(data[\"maker_summary\"][\"address\"], str)\n self.assertIsHash(data[\"maker_summary\"][\"txid\"])\n\n self.assert_order_logs(data[\"id\"])",
"completions": [
"```python\n def test_successful_onchain(self):\n self.submit_payout_address(robot_index=1)\n self.assertEqual(self.response.status_code, 200)\n self.assertEqual(self.response.json()[\"status\"], \"success\")\n```\n"
],
"line": 26,
"token": 183,
"line_diff": 0,
"token_diff": 0
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{
"id": 70,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef header_to_metadata_and_cell(\n lines, header_prefix, header_suffix, ext=None, root_level_metadata_as_raw_cell=True\n):\n\n header = []\n jupyter = []\n in_jupyter = False\n in_html_div = False\n\n start = 0\n started = False\n ended = False\n metadata = {}\n i = -1\n\n comment = \"#\" if header_prefix == \"#'\" else header_prefix\n\n encoding_re = re.compile(\n rf\"^[ \\t\\f]*{re.escape(comment)}.*?coding[:=][ \\t]*([-_.a-zA-Z0-9]+)\"\n )\n\n for i, line in enumerate(lines):\n if i == 0 and line.startswith(\"#!\"):\n metadata.setdefault(\"jupytext\", {})[\"executable\"] = line[2:]\n start = i + 1\n continue\n if i == 0 or (i == 1 and not encoding_re.match(lines[0])):\n encoding = encoding_re.match(line)\n if encoding:\n if encoding.group(1) != \"utf-8\":\n raise ValueError(\"Encodings other than utf-8 are not supported\")\n metadata.setdefault(\"jupytext\", {})[\"encoding\"] = line\n start = i + 1\n continue\n if not line.startswith(header_prefix):\n break\n if not comment:\n if line.strip().startswith(\"\" in line:\n break\n if not started and not line.strip():\n continue\n\n line = uncomment_line(line, header_prefix, header_suffix)\n if _HEADER_RE.match(line):\n if not started:\n started = True\n continue\n ended = True\n if in_html_div:\n continue\n break\n\n # Stop if there is something else than a YAML header\n if not started and line.strip():\n break\n\n if _JUPYTER_RE.match(line):\n in_jupyter = True\n elif line and not _LEFTSPACE_RE.match(line):\n in_jupyter = False\n\n if in_jupyter:\n jupyter.append(line)\n else:\n header.append(line)\n\n if ended:\n if jupyter:\n extra_metadata = metadata\n metadata = yaml.safe_load(\"\\n\".join(jupyter))[\"jupyter\"]\n recursive_update(metadata, extra_metadata)\n\n lines_to_next_cell = 1\n if len(lines) > i + 1:\n line = uncomment_line(lines[i + 1], header_prefix)\n if not _BLANK_RE.match(line):\n lines_to_next_cell = 0\n else:\n i = i + 1\n else:\n lines_to_next_cell = 0\n\n if header:\n if root_level_metadata_as_raw_cell:\n cell = new_raw_cell(\n source=\"\\n\".join([\"---\"] + header + [\"---\"]),\n metadata={}\n if lines_to_next_cell\n == pep8_lines_between_cells([\"---\"], lines[i + 1 :], ext)\n else {\"lines_to_next_cell\": lines_to_next_cell},\n )\n else:\n cell = None\n root_level_metadata = yaml.safe_load(\"\\n\".join(header))\n metadata.setdefault(\"jupytext\", {})[\n \"root_level_metadata\"\n ] = root_level_metadata\n else:\n cell = None\n\n return metadata, jupyter, cell, i + 1\n\n return metadata, False, None, start\n```\n###test function signature:\n```python\n\ndef test_header_to_metadata_and_cell_blank_line():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_header_to_metadata_and_cell_blank_line",
"reference": "\ndef test_header_to_metadata_and_cell_blank_line():\n text = \"\"\"---\ntitle: Sample header\n---\n\nHeader is followed by a blank line\n\"\"\"\n lines = text.splitlines()\n metadata, _, cell, pos = header_to_metadata_and_cell(lines, \"\", \"\")\n\n assert metadata == {}\n assert cell.cell_type == \"raw\"\n assert (\n cell.source\n == \"\"\"---\ntitle: Sample header\n---\"\"\"\n )\n assert cell.metadata == {}\n assert lines[pos].startswith(\"Header is\")",
"completions": [
"```python\ndef test_header_to_metadata_and_cell_blank_line():\n lines = [\"#' ---\", \"\"]\n header_prefix = \"#'\"\n header_suffix = \"\"\n ext = None\n root_level_metadata_as_raw_cell = True\n metadata, jupyter, cell, i = header_to_metadata_and_cell(\n lines, header_prefix, header_suffix, ext, root_level_metadata_as_raw_cell\n )\n assert metadata == {}\n assert jupyter == []\n assert cell == new_raw_cell(source=\"\\n\".join([\"---\"]), metadata={\"lines_to_next_cell\": 1})\n assert i == 1\n```\n"
],
"line": 104,
"token": 642,
"line_diff": 0,
"token_diff": 0
},
{
"id": 71,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def _create_output(\n self,\n accepted: torch.Tensor, # [batch_size, k]\n recovered_token_ids: torch.Tensor, # [batch_size, k]\n draft_token_ids: torch.Tensor, # [batch_size, k]\n bonus_token_ids: torch.Tensor, # [batch_size]\n ) -> torch.Tensor:\n bonus_token_ids = bonus_token_ids.squeeze()\n batch_size, k = recovered_token_ids.shape\n\n # Determine the index of the first False value for each row.\n limits = (accepted == 0).max(1).indices\n limits[~(accepted == 0).any(1)] = k\n\n # Create masks using the indices.\n indices = torch.arange(k, device=accepted.device).unsqueeze(0)\n accepted_mask = indices < limits.unsqueeze(1)\n after_false_mask = indices == limits.unsqueeze(1)\n\n # Create an extended output tensor\n output_with_bonus_tokens = -torch.ones(\n (batch_size, k + self._num_bonus_tokens),\n dtype=self.token_id_dtype,\n device=accepted.device)\n output = output_with_bonus_tokens[:, :k]\n\n # Fill in the first k columns of the output tensor using masks and data\n # tensors.\n output[:, :k] = torch.where(accepted_mask, draft_token_ids,\n -torch.ones_like(draft_token_ids))\n\n # Fill the last column.\n # We check output directly as accepted may have True values inconsistent\n # with causal acceptance.\n output_with_bonus_tokens[:, -1] = torch.where(output[:, -1] != -1,\n bonus_token_ids, -1)\n\n # We disable bonus tokens because it causes corrupt KV cache for\n # proposal methods that require KV cache. We can fix it by \"prefilling\"\n # the bonus token in the proposer. The following issue tracks the fix.\n # https://github.com/vllm-project/vllm/issues/4212\n output_with_bonus_tokens[:, -1] = -1\n\n # Fill the recovered token ids.\n output.mul_(~after_false_mask).add_(\n recovered_token_ids.mul(after_false_mask))\n\n self.num_accepted_tokens += accepted.sum()\n self.num_emitted_tokens += (output_with_bonus_tokens != -1).sum()\n self.num_draft_tokens += batch_size * k\n\n return output_with_bonus_tokens\n```\n###test function signature:\n```python\ndef test_correct_output_format(which_tokens_accepted: str, seed: int,\n device: str):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_correct_output_format",
"reference": "def test_correct_output_format(which_tokens_accepted: str, seed: int,\n device: str):\n set_random_seed(seed)\n torch.set_default_device(device)\n\n batch_size = 10\n k = 5\n vocab_size = 3000\n\n if which_tokens_accepted == \"all_tokens_accepted\":\n accepted = mock_causal_accepted_tensor(\n k, -1 + k * torch.ones((batch_size, ), dtype=torch.long))\n elif which_tokens_accepted == \"no_tokens_accepted\":\n accepted = mock_causal_accepted_tensor(\n k, -torch.ones((batch_size, ), dtype=torch.long))\n elif which_tokens_accepted == \"some_tokens_accepted\":\n last_accepted_indices = torch.randint(low=-1,\n high=k,\n size=(batch_size, ))\n accepted = mock_causal_accepted_tensor(k, last_accepted_indices)\n else:\n raise AssertionError()\n\n recovered_token_ids = torch.randint(low=0,\n high=vocab_size,\n size=(batch_size, k),\n dtype=torch.int64)\n draft_token_ids = torch.randint(low=0,\n high=vocab_size,\n size=(batch_size, k),\n dtype=torch.int64)\n bonus_token_ids = torch.randint(low=0,\n high=vocab_size,\n size=(batch_size, 1),\n dtype=torch.int64)\n\n rejection_sampler = RejectionSampler()\n rejection_sampler.init_gpu_tensors(rank=0)\n output_token_ids = rejection_sampler._create_output( # pylint: disable=protected-access\n accepted,\n recovered_token_ids,\n draft_token_ids,\n bonus_token_ids,\n )\n\n # Bonus tokens are currently disabled. Verify they're set to -1.\n # See https://github.com/vllm-project/vllm/issues/4212\n expected_bonus_token_ids = bonus_token_ids.clone() * 0 - 1\n\n if which_tokens_accepted == \"all_tokens_accepted\":\n # Expect all tokens to be equal to draft tokens.\n assert torch.equal(output_token_ids[:, :-1], draft_token_ids)\n\n # Expect all bonus tokens to be included.\n assert torch.equal(output_token_ids[:, -1:], expected_bonus_token_ids)\n elif which_tokens_accepted == \"no_tokens_accepted\":\n # Expect first token to be equal to recovered tokens.\n assert torch.equal(output_token_ids[:, 0], recovered_token_ids[:, 0])\n\n # Expect everything else to be -1.\n assert torch.equal(output_token_ids[:, 1:],\n torch.ones_like(output_token_ids[:, 1:]) * -1)\n elif which_tokens_accepted == \"some_tokens_accepted\":\n recovered_plus_bonus = torch.cat(\n (recovered_token_ids, expected_bonus_token_ids), dim=-1)\n # Assert first rejected token is a recovered token or bonus token.\n assert torch.equal(\n recovered_plus_bonus[torch.arange(0, batch_size),\n last_accepted_indices + 1],\n output_token_ids[torch.arange(0, batch_size),\n last_accepted_indices + 1])\n\n # Assert every subsequent token is -1.\n subsequent_mask = torch.arange(0, k + 1).expand(\n batch_size, k + 1) >= (last_accepted_indices + 2).unsqueeze(-1)\n assert torch.all(output_token_ids[subsequent_mask] == -1)",
"completions": [
"```python\ndef test_correct_output_format(which_tokens_accepted: str, seed: int,\n device: str):\n torch.manual_seed(seed)\n batch_size = 10\n k = 10\n num_bonus_tokens = 1\n accepted = torch.randint(0, 2, (batch_size, k), dtype=torch.int32)\n recovered_token_ids = torch.randint(0, 10, (batch_size, k),\n dtype=torch.int32)\n draft_token_ids = torch.randint(0, 10, (batch_size, k), dtype=torch.int32)\n bonus_token_ids = torch.randint(0, 10, (batch_size,), dtype=torch.int32)\n output = _create_output(accepted, recovered_token_ids, draft_token_ids,\n bonus_token_ids)\n assert output.shape == (batch_size, k + num_bonus_tokens)\n assert output.dtype == torch.int32\n assert output.device == device\n assert (output[:, :k] == torch.where(accepted, draft_token_ids,\n -torch.ones_like(draft_token_ids))).all()\n assert (output[:, -1] == torch.where(output[:, -1] != -1, bonus_token_ids,\n -1)).all()\n assert (output[:, -1] == -1).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted == 0).any(1))).all()\n assert (output.mul(~(accepted == 0).any(1)) +\n recovered_token_ids.mul((accepted"
],
"line": 54,
"token": 433,
"line_diff": 0,
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{
"id": 72,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef read_array(fp, allow_pickle=False, pickle_kwargs=None, *,\n max_header_size=_MAX_HEADER_SIZE):\n if allow_pickle:\n # Effectively ignore max_header_size, since `allow_pickle` indicates\n # that the input is fully trusted.\n max_header_size = 2**64\n\n version = read_magic(fp)\n _check_version(version)\n shape, fortran_order, dtype = _read_array_header(\n fp, version, max_header_size=max_header_size)\n if len(shape) == 0:\n count = 1\n else:\n count = numpy.multiply.reduce(shape, dtype=numpy.int64)\n\n # Now read the actual data.\n if dtype.hasobject:\n # The array contained Python objects. We need to unpickle the data.\n if not allow_pickle:\n raise ValueError(\"Object arrays cannot be loaded when \"\n \"allow_pickle=False\")\n if pickle_kwargs is None:\n pickle_kwargs = {}\n try:\n array = pickle.load(fp, **pickle_kwargs)\n except UnicodeError as err:\n # Friendlier error message\n raise UnicodeError(\"Unpickling a python object failed: %r\\n\"\n \"You may need to pass the encoding= option \"\n \"to numpy.load\" % (err,)) from err\n else:\n if isfileobj(fp):\n # We can use the fast fromfile() function.\n array = numpy.fromfile(fp, dtype=dtype, count=count)\n else:\n # This is not a real file. We have to read it the\n # memory-intensive way.\n # crc32 module fails on reads greater than 2 ** 32 bytes,\n # breaking large reads from gzip streams. Chunk reads to\n # BUFFER_SIZE bytes to avoid issue and reduce memory overhead\n # of the read. In non-chunked case count < max_read_count, so\n # only one read is performed.\n\n # Use np.ndarray instead of np.empty since the latter does\n # not correctly instantiate zero-width string dtypes; see\n # https://github.com/numpy/numpy/pull/6430\n array = numpy.ndarray(count, dtype=dtype)\n\n if dtype.itemsize > 0:\n # If dtype.itemsize == 0 then there's nothing more to read\n max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)\n\n for i in range(0, count, max_read_count):\n read_count = min(max_read_count, count - i)\n read_size = int(read_count * dtype.itemsize)\n data = _read_bytes(fp, read_size, \"array data\")\n array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,\n count=read_count)\n\n if fortran_order:\n array.shape = shape[::-1]\n array = array.transpose()\n else:\n array.shape = shape\n\n return array\n```\n###test function signature:\n```python\n\ndef test_version_2_0():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_version_2_0",
"reference": "\ndef test_version_2_0():\n f = BytesIO()\n # requires more than 2 byte for header\n dt = [((\"%d\" % i) * 100, float) for i in range(500)]\n d = np.ones(1000, dtype=dt)\n\n format.write_array(f, d, version=(2, 0))\n with warnings.catch_warnings(record=True) as w:\n warnings.filterwarnings('always', '', UserWarning)\n format.write_array(f, d)\n assert_(w[0].category is UserWarning)\n\n # check alignment of data portion\n f.seek(0)\n header = f.readline()\n assert_(len(header) % format.ARRAY_ALIGN == 0)\n\n f.seek(0)\n n = format.read_array(f, max_header_size=200000)\n assert_array_equal(d, n)\n\n # 1.0 requested but data cannot be saved this way\n assert_raises(ValueError, format.write_array, f, d, (1, 0))",
"completions": [
"```python\ndef test_version_2_0():\n fp = open(\"test_file.npy\", \"rb\")\n read_array(fp)\n```\n"
],
"line": 70,
"token": 516,
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{
"id": 73,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef log_mel_spectrogram(audio: Union[str, np.ndarray, torch.Tensor], n_mels: int = N_MELS):\n if not torch.is_tensor(audio):\n if isinstance(audio, str):\n audio = load_audio(audio)\n audio = torch.from_numpy(audio)\n\n window = torch.hann_window(N_FFT).to(audio.device)\n stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True)\n magnitudes = stft[..., :-1].abs() ** 2\n\n filters = mel_filters(audio.device, n_mels)\n mel_spec = filters @ magnitudes\n\n log_spec = torch.clamp(mel_spec, min=1e-10).log10()\n log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)\n log_spec = (log_spec + 4.0) / 4.0\n return log_spec\n```\n###test function signature:\n```python\n\ndef test_audio():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_audio",
"reference": "\ndef test_audio():\n audio_path = os.path.join(os.path.dirname(__file__), \"jfk.flac\")\n audio = load_audio(audio_path)\n assert audio.ndim == 1\n assert SAMPLE_RATE * 10 < audio.shape[0] < SAMPLE_RATE * 12\n assert 0 < audio.std() < 1\n\n mel_from_audio = log_mel_spectrogram(audio)\n mel_from_file = log_mel_spectrogram(audio_path)\n\n assert np.allclose(mel_from_audio, mel_from_file)\n assert mel_from_audio.max() - mel_from_audio.min() <= 2.0",
"completions": [
"```python\ndef test_audio():\n audio = torch.randn(1, 16000)\n log_mel_spectrogram(audio)\n```\n"
],
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"token": 213,
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{
"id": 74,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef transform_grid_cell(\n grid: Grid,\n cell: npt.NDArray[np.float_] = np.eye(3),\n output_shape: Optional[Tuple[int, int, int]] = None,\n mode: str = \"constant\",\n order: int = 1,\n **kwargs,\n) -> Grid:\n # Take the current shape of the grid if no output shape was provided\n if output_shape is None:\n output_shape = grid.shape\n\n # Make sure the cell has type float\n cell = np.asarray(cell, dtype=float)\n\n # Get the current cell in coordinates of the destination axes\n inv_cell = cell_invert(cell).T\n projected_cell = grid.cell.dot(inv_cell)\n\n # From that, infere how long will the bounding box of the cell be\n lengths = abs(projected_cell).sum(axis=0)\n\n # Create the transformation matrix. Since we want to control the shape\n # of the output, we can not use grid.dcell directly, we need to modify it.\n scales = output_shape / lengths\n forward_t = (grid.dcell.dot(inv_cell) * scales).T\n\n # Scipy's affine transform asks for the inverse transformation matrix, to\n # map from output pixels to input pixels. By taking the inverse of our\n # transformation matrix, we get exactly that.\n tr = cell_invert(forward_t).T\n\n # Calculate the offset of the image so that all points of the grid \"fall\" inside\n # the output array.\n # For this we just calculate the centers of the input and output images\n center_input = 0.5 * (_a.asarrayd(grid.shape) - 1)\n center_output = 0.5 * (_a.asarrayd(output_shape) - 1)\n\n # And then make sure that the input center that is interpolated from the output\n # falls in the actual input's center\n offset = center_input - tr.dot(center_output)\n\n # We pass all the parameters to scipy's affine_transform\n transformed_image = affine_transform(\n grid.grid,\n tr,\n order=1,\n offset=offset,\n output_shape=output_shape,\n mode=mode,\n **kwargs,\n )\n\n # Create a new grid with the new shape and the new cell (notice how the cell\n # is rescaled from the input cell to fit the actual coordinates of the system)\n new_grid = grid.__class__((1, 1, 1), lattice=cell * lengths.reshape(3, 1))\n new_grid.grid = transformed_image\n new_grid.geometry = grid.geometry\n new_grid.lattice.origin = grid.origin + new_grid.dcell.dot(forward_t.dot(offset))\n\n # Find the offset between the origin before and after the transformation\n return new_grid\n```\n###test function signature:\n```python\n\ndef test_transform_grid_cell(grid, skewed):\n # Convert to a cartesian cell\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_transform_grid_cell",
"reference": "\ndef test_transform_grid_cell(grid, skewed):\n # Convert to a cartesian cell\n new_grid = transform_grid_cell(grid, cell=np.eye(3), output_shape=(10, 10, 10))\n\n assert new_grid.shape == (10, 10, 10)\n assert new_grid.lattice.is_cartesian()\n\n if not skewed:\n assert np.allclose(new_grid.lattice.cell, grid.lattice.cell)\n\n assert np.allclose(new_grid.grid, grid.grid) == (not skewed)\n assert not np.allclose(new_grid.grid, 0)\n\n assert np.allclose(new_grid.origin, grid.origin) == (not skewed)\n\n # Convert to a skewed cell\n directions = np.array([[1, 2, 3], [-1, 2, -4], [2, -1, 1]])\n new_grid = transform_grid_cell(grid, cell=directions, output_shape=(5, 5, 5))\n\n assert new_grid.shape == (5, 5, 5)\n for i in range(3):\n n = new_grid.lattice.cell[i] / directions[i]\n assert np.allclose(n, n[0])",
"completions": [
"```python\ndef test_transform_grid_cell(grid, skewed):\n # Convert to a cartesian cell\n cell = grid.cell.dot(cell_invert(grid.cell))\n\n # Create a new grid with the new shape and the new cell (notice how the cell\n # is rescaled from the input cell to fit the actual coordinates of the system)\n new_grid = transform_grid_cell(grid, cell, grid.shape, mode=\"constant\", order=1)\n\n # Check that the new grid has the same shape and cell as the original grid\n assert new_grid.shape == grid.shape\n assert np.allclose(new_grid.cell, grid.cell)\n\n # Check that the new grid has the same geometry as the original grid\n assert new_grid.geometry == grid.geometry\n\n # Check that the new grid has the same origin as the original grid\n assert np.allclose(new_grid.origin, grid.origin)\n\n # Check that the new grid has the same dcell as the original grid\n assert np.allclose(new_grid.dcell, grid.dcell)\n\n # Check that the new grid has the same grid as the original grid\n assert np.allclose(new_grid.grid, grid.grid)\n\n # Check that the new grid has the same skewed as the original grid\n assert np.allclose(new_grid.skewed, grid.skewed)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid.grid_spacing, grid.grid_spacing)\n\n # Check that the new grid has the same grid_shape as the original grid\n assert np.allclose(new_grid.grid_shape, grid.grid_shape)\n\n # Check that the new grid has the same grid_origin as the original grid\n assert np.allclose(new_grid.grid_origin, grid.grid_origin)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid.grid_spacing, grid.grid_spacing)\n\n # Check that the new grid has the same grid_shape as the original grid\n assert np.allclose(new_grid.grid_shape, grid.grid_shape)\n\n # Check that the new grid has the same grid_origin as the original grid\n assert np.allclose(new_grid.grid_origin, grid.grid_origin)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid.grid_spacing, grid.grid_spacing)\n\n # Check that the new grid has the same grid_shape as the original grid\n assert np.allclose(new_grid.grid_shape, grid.grid_shape)\n\n # Check that the new grid has the same grid_origin as the original grid\n assert np.allclose(new_grid.grid_origin, grid.grid_origin)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid.grid_spacing, grid.grid_spacing)\n\n # Check that the new grid has the same grid_shape as the original grid\n assert np.allclose(new_grid.grid_shape, grid.grid_shape)\n\n # Check that the new grid has the same grid_origin as the original grid\n assert np.allclose(new_grid.grid_origin, grid.grid_origin)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid.grid_spacing, grid.grid_spacing)\n\n # Check that the new grid has the same grid_shape as the original grid\n assert np.allclose(new_grid.grid_shape, grid.grid_shape)\n\n # Check that the new grid has the same grid_origin as the original grid\n assert np.allclose(new_grid.grid_origin, grid.grid_origin)\n\n # Check that the new grid has the same grid_spacing as the original grid\n assert np.allclose(new_grid"
],
"line": 63,
"token": 533,
"line_diff": 0,
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{
"id": 75,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef similarity_matrix(xq: np.ndarray, index: np.ndarray) -> np.ndarray:\n\n index_norm = norm(index, axis=1)\n xq_norm = norm(xq.T)\n sim = np.dot(index, xq.T) / (index_norm * xq_norm)\n return sim\n```\n###test function signature:\n```python\ndef test_top_scores__is_sorted(test_index):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_top_scores__is_sorted",
"reference": "def test_top_scores__is_sorted(test_index):\n\n xq = test_index[0] # should have max similarity\n\n sim = similarity_matrix(xq, test_index)\n _, idx = top_scores(sim, 3)\n\n # Scores and indexes should be sorted ascending\n assert np.array_equal(idx, np.array([2, 1, 0]))",
"completions": [
"```python\ndef test_top_scores__is_sorted(test_index):\n sim = similarity_matrix(xq, test_index)\n assert np.all(np.diff(sim, axis=1) >= 0)\n```\n"
],
"line": 15,
"token": 125,
"line_diff": 0,
"token_diff": 0
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{
"id": 76,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef _generate_nanoplot(\n y_vals: Union[List[Union[int, float]], List[int], List[float]],\n y_ref_line: Optional[str] = None,\n y_ref_area: Optional[str] = None,\n x_vals: \"List[Union[int, float]] | None\" = None,\n expand_x: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n expand_y: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n missing_vals: str = \"marker\",\n all_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n all_single_y_vals: Optional[Union[List[Union[int, float]], List[int], List[float]]] = None,\n plot_type: str = \"line\",\n data_line_type: str = \"curved\",\n currency: Optional[str] = None,\n y_val_fmt_fn: Optional[Callable[..., str]] = None,\n y_axis_fmt_fn: Optional[Callable[..., str]] = None,\n y_ref_line_fmt_fn: Optional[Callable[..., str]] = None,\n data_point_radius: Union[int, List[int]] = 10,\n data_point_stroke_color: Union[str, List[str]] = \"#FFFFFF\",\n data_point_stroke_width: Union[int, List[int]] = 4,\n data_point_fill_color: Union[str, List[str]] = \"#FF0000\",\n data_line_stroke_color: str = \"#4682B4\",\n data_line_stroke_width: int = 8,\n data_area_fill_color: str = \"#FF0000\",\n data_bar_stroke_color: Union[str, List[str]] = \"#3290CC\",\n data_bar_stroke_width: Union[int, List[int]] = 4,\n data_bar_fill_color: Union[str, List[str]] = \"#3FB5FF\",\n data_bar_negative_stroke_color: str = \"#CC3243\",\n data_bar_negative_stroke_width: int = 4,\n data_bar_negative_fill_color: str = \"#D75A68\",\n reference_line_color: str = \"#75A8B0\",\n reference_area_fill_color: str = \"#A6E6F2\",\n vertical_guide_stroke_color: str = \"#911EB4\",\n vertical_guide_stroke_width: int = 12,\n show_data_points: bool = True,\n show_data_line: bool = True,\n show_data_area: bool = True,\n show_reference_line: bool = True,\n show_reference_area: bool = True,\n show_vertical_guides: bool = True,\n show_y_axis_guide: bool = True,\n interactive_data_values: bool = True,\n svg_height: str = \"2em\",\n) -> str:\n\n # Ensure that arguments are matched\n _match_arg(\n x=missing_vals,\n lst=[\"marker\", \"gap\", \"zero\", \"remove\"],\n )\n _match_arg(\n x=data_line_type,\n lst=[\"curved\", \"straight\"],\n )\n\n #\n # Determine where a zero line is considered and provide the stroke color and width\n #\n\n zero_line_considered = True if plot_type in [\"bar\", \"boxplot\"] else False\n\n zero_line_stroke_color = \"#BFBFBF\"\n zero_line_stroke_width = 4\n\n # Initialize several local `*_tags` variables with `None`\n ref_area_tags = None\n area_path_tags = None\n data_path_tags = None\n zero_line_tags = None\n bar_tags = None\n boxplot_tags = None\n ref_line_tags = None\n circle_tags = None\n g_y_axis_tags = None\n g_guide_tags = None\n\n # Initialize the `single_horizontal_plot` variable with `False`\n single_horizontal_plot = False\n\n # If the number of `y` values in a list is zero or if all consist of NA values,\n # return an empty string\n if type(y_vals) is list and len(y_vals) == 0:\n return \"\"\n\n # If all `y` values are NA, return an empty string\n # TODO: all([]) evaluates to True. In that case does this produce the intended behavior?\n if isinstance(y_vals, list) and all(_map_is_na(y_vals)):\n return \"\"\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # Handle case where `x_vals` exists (i.e., is not `NULL`)\n if x_vals is not None:\n\n # If the number of `x` values is zero or an empty string,\n # return an empty string\n if len(x_vals) == 0:\n return \"\"\n if all(_map_is_na(x_vals)):\n return \"\"\n\n # Get the number of data points for `x`\n num_x_vals = len(x_vals)\n\n # Ensure that, if there are `x` values, the number of `x`\n # and `y` values matches\n if num_x_vals != num_y_vals:\n raise ValueError(\n f\"\"\"The number of `x` and `y` values must match.\n The `x` value length is: {num_x_vals}\n The `y` value length is: {num_y_vals}\n \"\"\"\n )\n\n # Handle missing values in `x_vals` through removal (i.e., missing\n # values in `x_vals` means removal of positional values from both\n # `x_vals` and `y_vals`)\n if any(_map_is_na(x_vals)):\n # TODO: this code did not have test coverage and likely didn't\n # work. It should work now, but we need to test it.\n\n # Determine which values from `x_vals` are non-missing values\n x_vals_non_missing = [~_is_na(val) for val in x_vals]\n\n # Retain only `x_vals_non_missing` from `x_vals` and `y_vals`\n x_vals = [x for x, keep in zip(x_vals, x_vals_non_missing) if keep]\n y_vals = [y for y, keep in zip(y_vals, x_vals_non_missing) if keep]\n\n # If `x` values are present, we cannot use a curved line so\n # we'll force the use of the 'straight' line type\n # TODO: if someone specifies the options curved, and we can't do it\n # then we should raise an error.\n data_line_type = \"straight\"\n\n # If `missing_vals` is set to 'gap' raise an error\n # TODO: Implement the 'gap' option for missing values\n if missing_vals == \"gap\":\n raise NotImplementedError(\"The 'gap' option for missing values is not yet implemented.\")\n\n # For the `missing_vals` options of 'zero' or 'remove', either replace NAs\n # with `0` or remove NAs entirely\n if missing_vals == \"zero\":\n y_vals = y_vals.fillna(0)\n\n # If `missing_vals` is 'remove', remove NAs from `y_vals`\n if missing_vals == \"remove\":\n y_vals = y_vals.dropna()\n\n if x_vals is not None:\n # Remove the corresponding `x` values for the removed `y` values\n x_vals = x_vals[y_vals.index]\n\n # Get the number of data points for `y`\n if type(y_vals) is list:\n num_y_vals = len(y_vals)\n else:\n num_y_vals = 1\n\n # If `y_vals` is a scalar value we requested a 'line' or 'bar' plot, then\n # reset several parameters\n if type(y_vals) in [int, float] and plot_type in [\"line\", \"bar\"]:\n\n single_horizontal_plot = True\n show_data_points = True\n show_data_line = True\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n y_vals = [y_vals]\n\n # If this is a box plot, set several parameters\n if plot_type == \"boxplot\":\n\n show_data_points = False\n show_data_line = False\n show_data_area = False\n show_reference_line = False\n show_reference_area = False\n show_vertical_guides = False\n show_y_axis_guide = False\n\n # Find out whether the collection of non-NA `y` values are all integer-like\n y_vals_integerlike = _is_integerlike(val_list=y_vals)\n\n # Get the max and min of the `y` scale from the `y` data values\n y_scale_max = _get_extreme_value(y_vals, stat=\"max\")\n y_scale_min = _get_extreme_value(y_vals, stat=\"min\")\n\n # Handle cases where collection of `y_vals` is invariant\n if y_scale_min == y_scale_max and expand_y is None:\n\n if y_scale_min == 0:\n expand_y_dist = 5\n else:\n expand_y_dist = (y_scale_min / 10) * 2\n\n # Expand the `y` scale, centering around the `y_scale_min` value\n expand_y = [y_scale_min - expand_y_dist, y_scale_min + expand_y_dist]\n\n # Ensure that a reference line or reference area isn't shown if None or\n # any of its directives is missing\n if _is_na(y_ref_line):\n show_reference_line = False\n\n if y_ref_area is None:\n show_reference_area = False\n elif _is_na(y_ref_area[0]) or _is_na(y_ref_area[1]):\n show_reference_area = False\n\n # Determine the width of the data plot area; for plots where `x_vals`\n # are available, we'll use a fixed width of `500` (px), and for plots\n # where `x_vals` aren't present, we'll adjust the final width based\n # on the fixed interval between data points (this is dependent on the\n # number of data points)\n if x_vals is not None or single_horizontal_plot or plot_type == \"boxplot\":\n data_x_width = 600\n # TODO: what should x_d be in this case?\n else:\n # Obtain a sensible, fixed interval between data points in px\n if num_y_vals <= 20:\n x_d = 50\n elif num_y_vals <= 30:\n x_d = 40\n elif num_y_vals <= 40:\n x_d = 30\n elif num_y_vals <= 50:\n x_d = 25\n else:\n x_d = 20\n\n data_x_width = num_y_vals * x_d\n\n # Define the top-left of the plot area\n left_x = 0\n top_y = 0\n\n # Define the safe zone distance from top/bottom and left/right edges\n safe_y_d = 15\n safe_x_d = 50\n\n # Define the height of the plot area that bounds the data points\n data_y_height = 100\n\n # Determine the bottom-right of the plot area based on the quantity of data\n bottom_y = safe_y_d + data_y_height + safe_y_d\n right_x = safe_x_d + data_x_width + safe_x_d\n\n viewbox = f\"{left_x} {top_y} {right_x} {bottom_y}\"\n\n #\n # If there is a reference line and/or reference area, the values for these\n # need to be generated and integrated in the `normalize_y_vals()` operation\n # so that there are normalized values in relation to the data points\n #\n\n if show_reference_line and show_reference_area:\n\n # Case where there is both a reference line and a reference area\n\n #\n # Resolve the reference line\n #\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n #\n # Resolve the reference area\n #\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n _all_y_data = [y_vals, y_ref_line, y_ref_area_l, y_ref_area_u, expand_y]\n\n # Recompute the `y` scale min and max values\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference line and reference area boundaries\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n elif show_reference_line:\n\n # Case where there is a reference line\n\n if (\n y_ref_line is not None\n and _val_is_str(y_ref_line)\n and y_ref_line in REFERENCE_LINE_KEYWORDS\n ):\n y_ref_line = _generate_ref_line_from_keyword(vals=y_vals, keyword=y_ref_line)\n\n # Recompute the `y` scale min and max values\n args = [y_vals, y_ref_line, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_line=y_ref_line,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportion_ref_line = y_proportions_list[\"ref_line\"][0]\n\n # Scale reference line\n data_y_ref_line = safe_y_d + ((1 - y_proportion_ref_line) * data_y_height)\n\n elif show_reference_area:\n\n # Case where there is a reference area\n\n # Note if y_ref_area were None, we would not be in this clause\n y_ref_area_line_1 = calc_ref_value(y_ref_area[0], y_vals)\n y_ref_area_line_2 = calc_ref_value(y_ref_area[1], y_vals)\n\n y_ref_area_lines_sorted = sorted([y_ref_area_line_1, y_ref_area_line_2])\n y_ref_area_l = y_ref_area_lines_sorted[0]\n y_ref_area_u = y_ref_area_lines_sorted[1]\n\n # Recompute the `y` scale min and max values\n\n # Recompute the `y` scale min and max values\n _all_y_data = [y_vals, y_ref_area_l, y_ref_area_u, expand_y] + (\n [0] if zero_line_considered else []\n )\n y_scale_max = _get_extreme_value(*_all_y_data, stat=\"max\")\n y_scale_min = _get_extreme_value(*_all_y_data, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n ref_area_l=y_ref_area_l,\n ref_area_u=y_ref_area_u,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n y_proportions_ref_area_l = y_proportions_list[\"ref_area_l\"][0]\n y_proportions_ref_area_u = y_proportions_list[\"ref_area_u\"][0]\n\n # Scale reference area boundaries\n data_y_ref_area_l = safe_y_d + ((1 - y_proportions_ref_area_l) * data_y_height)\n data_y_ref_area_u = safe_y_d + ((1 - y_proportions_ref_area_u) * data_y_height)\n\n else:\n\n # Case where there is no reference line or reference area\n\n # Recompute the `y` scale min and max values\n args = [y_vals, expand_y] + ([0] if zero_line_considered else [])\n y_scale_max = _get_extreme_value(*args, stat=\"max\")\n y_scale_min = _get_extreme_value(*args, stat=\"min\")\n\n y_proportions_list = _normalize_to_dict(\n vals=y_vals,\n zero=0 if zero_line_considered else None,\n expand_y=expand_y,\n )\n\n y_proportions = y_proportions_list[\"vals\"]\n\n # Calculate the `data_y0_point` value for zero-line-inclusive plots\n if zero_line_considered:\n y_proportions_zero = y_proportions_list[\"zero\"][0]\n data_y0_point = safe_y_d + ((1 - y_proportions_zero) * data_y_height)\n\n # If x values are present then normalize them between [0, 1]; if\n # there are no x values, generate equally-spaced x values according\n # to the number of y values\n if plot_type == \"line\" and x_vals is not None:\n\n if expand_x is not None and _val_is_str(expand_x):\n\n # TODO: the line below lacked tests, and called non-existent methods.\n # replace with something that doesn't use pandas and returns the correct thing.\n\n # Assume that string values are dates and convert them to timestamps\n # expand_x = pd.to_datetime(expand_x, utc=True).timestamp()\n raise NotImplementedError(\"Currently, passing expand_x as a string is unsupported.\")\n\n # Scale to proportional values\n x_proportions_list = _normalize_to_dict(vals=x_vals, expand_x=expand_x)\n\n x_proportions = x_proportions_list[\"vals\"]\n\n else:\n x_proportions = np.linspace(0, 1, num_y_vals)\n\n #\n # Create normalized (and inverted for SVG) data `x` and `y` values for the\n # plot area; these are named `data_x_points` and `data_y_points`\n #\n\n for i in range(len(y_proportions)):\n y_proportions[i] = safe_y_d + ((1 - y_proportions[i]) * data_y_height)\n\n for i in range(len(x_proportions)):\n x_proportions[i] = (data_x_width * x_proportions[i]) + safe_x_d\n\n data_y_points = y_proportions\n data_x_points = x_proportions\n\n #\n # Ensure that certain options have their lengths checked and\n # expanded to length `num_y_vals`\n #\n\n data_point_radius = _normalize_option_list(option_list=data_point_radius, num_y_vals=num_y_vals)\n data_point_stroke_color = _normalize_option_list(\n option_list=data_point_stroke_color, num_y_vals=num_y_vals\n )\n data_point_stroke_width = _normalize_option_list(\n option_list=data_point_stroke_width, num_y_vals=num_y_vals\n )\n data_point_fill_color = _normalize_option_list(\n option_list=data_point_fill_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_color = _normalize_option_list(\n option_list=data_bar_stroke_color, num_y_vals=num_y_vals\n )\n data_bar_stroke_width = _normalize_option_list(\n option_list=data_bar_stroke_width, num_y_vals=num_y_vals\n )\n data_bar_fill_color = _normalize_option_list(\n option_list=data_bar_fill_color, num_y_vals=num_y_vals\n )\n\n #\n # Generate data segments by defining `start` and `end` vectors (these\n # are guaranteed to be of the same length); these the segments of data\n # they receive line segments and adjoining areas\n #\n\n # Use run-length encoding to determine the segments of data\n\n # rle_data_y_points = pd.Series(data_y_points).diff().ne(0).cumsum()\n\n # end_data_y_points = np.cumsum(rle_data_y_points.lengths)\n\n # start_data_y_points = end_data_y_points - rle_data_y_points.lengths + 1\n\n start_data_y_points = [0]\n end_data_y_points = [len(data_y_points)]\n n_segments = 1\n\n #\n # Generate a curved data line\n #\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"curved\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n curve_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n curve_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n curved_path_string = [f\"M {curve_x[0]},{curve_y[0]}\"]\n\n for j in range(1, len(curve_x)):\n\n point_b1 = f\"{curve_x[j - 1] + x_d / 2},{curve_y[j - 1]}\"\n point_b2 = f\"{curve_x[j] - x_d / 2},{curve_y[j]}\"\n point_i = f\"{curve_x[j]},{curve_y[j]}\"\n\n path_string_j = f\"C {point_b1} {point_b2} {point_i}\"\n\n curved_path_string.append(path_string_j)\n\n curved_path_string_i = \" \".join(curved_path_string)\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\\n\".join(data_path_tags)\n\n if plot_type == \"line\" and show_data_line and data_line_type == \"straight\":\n\n data_path_tags = []\n\n for i in range(n_segments):\n\n line_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n line_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n line_xy = \" \".join([f\"{x},{y}\" for x, y in zip(line_x, line_y)])\n\n data_path_tags_i = f''\n\n data_path_tags.append(data_path_tags_i)\n\n data_path_tags = \"\".join(data_path_tags)\n\n #\n # Generate data points\n #\n\n if plot_type == \"line\" and show_data_points:\n\n circle_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_point_stroke_color_i = data_point_stroke_color[i]\n data_point_stroke_width_i = data_point_stroke_width[i]\n data_point_fill_color_i = data_point_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n circle_strings_i = f''\n\n else:\n continue\n\n else:\n circle_strings_i = f''\n\n circle_strings.append(circle_strings_i)\n\n circle_tags = \"\".join(circle_strings)\n\n #\n # Generate data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n bar_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n data_point_radius_i = data_point_radius[i]\n data_bar_stroke_color_i = data_bar_stroke_color[i]\n data_bar_stroke_width_i = data_bar_stroke_width[i]\n data_bar_fill_color_i = data_bar_fill_color[i]\n\n if data_y_points[i] is None:\n\n if missing_vals == \"marker\":\n\n # Create a symbol that should denote that a missing value is present\n bar_strings_i = f''\n\n else:\n continue\n\n else:\n\n if y_vals[i] < 0:\n\n y_value_i = data_y0_point\n y_height = data_y_points[i] - data_y0_point\n data_bar_stroke_color_i = data_bar_negative_stroke_color\n data_bar_stroke_width_i = data_bar_negative_stroke_width\n data_bar_fill_color_i = data_bar_negative_fill_color\n\n elif y_vals[i] > 0:\n\n y_value_i = data_y_points[i]\n y_height = data_y0_point - data_y_points[i]\n\n elif y_vals[i] == 0:\n\n y_value_i = data_y0_point - 1\n y_height = 2\n data_bar_stroke_color_i = \"#808080\"\n data_bar_stroke_width_i = 4\n data_bar_fill_color_i = \"#808080\"\n\n bar_strings_i = f''\n\n bar_strings.append(bar_strings_i)\n\n bar_tags = \"\".join(bar_strings)\n\n #\n # Generate single horizontal data bars\n #\n\n if plot_type == \"bar\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n data_bar_stroke_color = data_bar_negative_stroke_color\n data_bar_stroke_width = data_bar_negative_stroke_width\n data_bar_fill_color = data_bar_negative_fill_color\n\n rect_x = y_width\n rect_width = y0_width - y_width\n\n elif y_vals[0] > 0:\n\n data_bar_stroke_color = data_bar_stroke_color[0]\n data_bar_stroke_width = data_bar_stroke_width[0]\n data_bar_fill_color = data_bar_fill_color[0]\n\n rect_x = y0_width\n rect_width = y_width - y0_width\n\n elif y_vals[0] == 0:\n\n data_bar_stroke_color = \"#808080\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#808080\"\n\n rect_x = y0_width - 2.5\n rect_width = 5\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n bar_tags = f'{g_guide_tags}'\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate single horizontal data lines\n #\n\n # TODO: Make this a line with a single point\n if plot_type == \"line\" and single_horizontal_plot:\n\n # This type of display assumes there is only a single `y` value and there\n # are possibly several such horizontal bars across different rows that\n # need to be on a common scale\n\n data_point_radius_i = data_point_radius[0]\n data_point_stroke_color_i = data_point_stroke_color[0]\n data_point_stroke_width_i = data_point_stroke_width[0]\n data_point_fill_color_i = data_point_fill_color[0]\n\n bar_thickness = data_point_radius[0] * 4\n\n if all(val == 0 for val in all_single_y_vals):\n\n # Handle case where all values across rows are `0`\n\n y_proportion = 0.5\n y_proportion_zero = 0.5\n\n else:\n\n # Scale to proportional values\n y_proportions_list = _normalize_to_dict(val=y_vals, all_vals=all_single_y_vals, zero=0)\n\n y_proportion = y_proportions_list[\"val\"][0]\n y_proportion_zero = y_proportions_list[\"zero\"][0]\n\n y0_width = y_proportion_zero * data_x_width\n y_width = y_proportion * data_x_width\n\n if y_vals[0] < 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x1_val\n\n elif y_vals[0] > 0:\n\n x1_val = y0_width\n x2_val = y_width\n\n circle_x_val = x2_val\n\n elif y_vals[0] == 0:\n\n x1_val = y_width\n x2_val = y0_width\n\n circle_x_val = x2_val\n\n # Format number compactly\n y_value = _format_number_compactly(\n val=y_vals[0], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n rect_strings = f''\n\n if y_vals[0] > 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] < 0:\n\n text_strings = f'{y_value}'\n\n elif y_vals[0] == 0:\n\n if all(val == 0 for val in all_single_y_vals):\n\n text_anchor = \"start\"\n x_position_text = y0_width + 10\n\n elif all(val < 0 for val in all_single_y_vals):\n\n text_anchor = \"end\"\n x_position_text = y0_width - 10\n\n else:\n text_anchor = \"start\"\n x_position_text = y0_width + 15\n\n text_strings = f'{y_value}'\n\n g_guide_tags = f'{rect_strings}{text_strings}'\n\n data_path_tags = f'{g_guide_tags}'\n\n circle_tags = f''\n\n zero_line_tags = f''\n\n # Redefine the `viewbox` in terms of the `data_x_width` value; this ensures\n # that the horizontal bars are centered about their extreme values\n viewbox = f\"{left_x} {top_y} {data_x_width} {bottom_y}\"\n\n #\n # Generate box plots\n #\n\n if plot_type == \"boxplot\":\n pass\n\n #\n # Generate zero line for vertical bar plots\n #\n\n if plot_type == \"bar\" and single_horizontal_plot is False:\n\n zero_line_tags = f''\n\n #\n # Generate reference line\n #\n\n if show_reference_line:\n\n stroke = reference_line_color\n stroke_width = 1\n stroke_dasharray = \"4 3\"\n transform = \"\"\n stroke_linecap = \"round\"\n vector_effect = \"non-scaling-stroke\"\n\n # Format value in a compact manner\n y_ref_line = _format_number_compactly(\n val=y_ref_line, currency=currency, as_integer=y_vals_integerlike, fn=y_ref_line_fmt_fn\n )\n\n ref_line_tags = f'{y_ref_line}'\n\n #\n # Generate reference area\n #\n\n if show_reference_area:\n\n fill = reference_area_fill_color\n\n p_ul = f\"{data_x_points[0]},{data_y_ref_area_u}\"\n p_ur = f\"{data_x_points[-1]},{data_y_ref_area_u}\"\n p_lr = f\"{data_x_points[-1]},{data_y_ref_area_l}\"\n p_ll = f\"{data_x_points[0]},{data_y_ref_area_l}\"\n\n ref_area_path = f\"M{p_ul},{p_ur},{p_lr},{p_ll}Z\"\n\n ref_area_tags = f''\n\n #\n # Generate y-axis guide\n #\n\n if show_y_axis_guide:\n\n rect_tag = f''\n\n if _is_integerlike(val_list=[y_scale_max]) and _is_integerlike(val_list=[y_scale_min]):\n y_axis_guide_vals_integerlike = True\n else:\n y_axis_guide_vals_integerlike = False\n\n # Format values in a compact manner\n y_value_max_label = _format_number_compactly(\n val=y_scale_max,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n y_value_min_label = _format_number_compactly(\n val=y_scale_min,\n currency=currency,\n as_integer=y_axis_guide_vals_integerlike,\n fn=y_axis_fmt_fn,\n )\n\n text_strings_min = f'{y_value_min_label}'\n\n text_strings_max = f'{y_value_max_label}'\n\n g_y_axis_tags = f'{rect_tag}{text_strings_max}{text_strings_min}'\n\n #\n # Generate vertical data point guidelines\n #\n\n if show_vertical_guides:\n\n g_guide_strings = []\n\n for i, _ in enumerate(data_x_points):\n\n rect_strings_i = f''\n\n # Format value in a compact manner\n y_value_i = _format_number_compactly(\n val=y_vals[i], currency=currency, as_integer=y_vals_integerlike, fn=y_val_fmt_fn\n )\n\n x_text = data_x_points[i] + 10\n\n if y_value_i == \"NA\":\n x_text = x_text + 2\n\n text_strings_i = f'{y_value_i}'\n\n g_guide_strings_i = f'{rect_strings_i}{text_strings_i}'\n\n g_guide_strings.append(g_guide_strings_i)\n\n g_guide_tags = \"\".join(g_guide_strings)\n\n #\n # Generate background with repeating line pattern\n #\n\n svg_defs = (\n f\"\"\n f''\n f''\n f\"\"\n f\"\"\n f\"\"\n )\n\n if plot_type == \"line\" and show_data_area:\n\n area_path_tags = []\n\n for i in range(n_segments):\n\n area_x = data_x_points[start_data_y_points[i] : end_data_y_points[i]]\n area_y = data_y_points[start_data_y_points[i] : end_data_y_points[i]]\n\n area_path_string = []\n\n for j in range(0, len(area_x)):\n\n area_path_j = f\"{area_x[j]},{area_y[j]}\"\n area_path_string.append(area_path_j)\n\n area_path_i = f\"M {' '.join(area_path_string)} {area_x[-1]},{bottom_y - safe_y_d + data_point_radius[0]} {area_x[0]},{bottom_y - safe_y_d + data_point_radius[0]} Z\"\n\n area_path_tag_i = f''\n\n area_path_tags.append(area_path_tag_i)\n\n area_path_tags = \" \".join(area_path_tags)\n\n #\n # Generate style tag for vertical guidelines and y-axis\n #\n\n hover_param = \":hover\" if interactive_data_values else \"\"\n\n svg_style = (\n f\"\"\n )\n\n nanoplot_svg = _construct_nanoplot_svg(\n viewbox=viewbox,\n svg_height=svg_height,\n svg_defs=svg_defs,\n svg_style=svg_style,\n show_data_points=show_data_points,\n show_data_line=show_data_line,\n show_data_area=show_data_area,\n show_reference_line=show_reference_line,\n show_reference_area=show_reference_area,\n show_vertical_guides=show_vertical_guides,\n show_y_axis_guide=show_y_axis_guide,\n ref_area_tags=ref_area_tags,\n area_path_tags=area_path_tags,\n data_path_tags=data_path_tags,\n zero_line_tags=zero_line_tags,\n bar_tags=bar_tags,\n ref_line_tags=ref_line_tags,\n circle_tags=circle_tags,\n g_y_axis_tags=g_y_axis_tags,\n g_guide_tags=g_guide_tags,\n )\n\n return nanoplot_svg\n```\n###test function signature:\n```python\n\ndef test_nanoplot_out_bars_with_mixed_ref_area_2():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nanoplot_out_bars_with_mixed_ref_area_2",
"reference": "\ndef test_nanoplot_out_bars_with_mixed_ref_area_2():\n\n out_bars_with_mixed_ref_area_2 = _generate_nanoplot(**CASES[16])\n\n assert _is_nanoplot_output(out_bars_with_mixed_ref_area_2)\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_mixed_ref_area_2,\n tag=\"rect\",\n attrs=[\n (\"x\", \"30.0\"),\n (\"width\", \"40\"),\n (\"height\", \"27.179487179487168\"),\n (\"stroke\", \"#CC3243\"),\n (\"stroke-width\", \"4\"),\n (\"fill\", \"#D75A68\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_mixed_ref_area_2,\n tag=\"rect\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"0\"),\n (\"width\", \"65\"),\n (\"height\", \"130\"),\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"0\"),\n (\"fill\", \"transparent\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_mixed_ref_area_2,\n tag=\"text\",\n attrs=[\n (\"x\", \"0\"),\n (\"y\", \"19.0\"),\n (\"fill\", \"transparent\"),\n (\"stroke\", \"transparent\"),\n (\"font-size\", \"25\"),\n ],\n )\n\n assert _nanoplot_has_tag_attrs(\n out_bars_with_mixed_ref_area_2,\n tag=\"path\",\n attrs=[\n (\"stroke\", \"transparent\"),\n (\"stroke-width\", \"2\"),\n (\"fill\", \"#A6E6F2\"),\n (\"fill-opacity\", \"0.8\"),\n ],\n )",
"completions": [
"```python\n\ndef test_nanoplot_out_bars_with_mixed_ref_area_2():\n y_vals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n y_ref_area = [1, 10]\n y_ref_line = 5\n x_vals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n expand_x = \"2020-01-01 00:00:00 UTC\"\n expand_y = \"2020-01-01 00:00:00 UTC\"\n missing_vals = \"marker\"\n all_y_vals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n all_single_y_vals = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n plot_type = \"bar\"\n data_line_type = \"curved\"\n currency = None\n y_val_fmt_fn = None\n y_axis_fmt_fn = None\n y_ref_line_fmt_fn = None\n data_point_radius = 10\n data_point_stroke_color = \"#FFFFFF\"\n data_point_stroke_width = 4\n data_point_fill_color = \"#FF0000\"\n data_line_stroke_color = \"#4682B4\"\n data_line_stroke_width = 8\n data_area_fill_color = \"#FF0000\"\n data_bar_stroke_color = \"#3290CC\"\n data_bar_stroke_width = 4\n data_bar_fill_color = \"#3FB5FF\"\n data_bar_negative_stroke_color = \"#CC3243\"\n data_bar_negative_stroke_width = 4\n data_bar_negative_fill_color = \"#D75A68\"\n reference_line_color = \"#75A8B4\"\n reference_area_fill_color = \"#A6E6F2\"\n vertical_guide_stroke_color = \"#911EB4\"\n vertical_guide_stroke_width = 12\n show_data_points = True\n show_data_line = True\n show_data_area = True\n show_reference_line = True\n show_reference_area = True\n show_vertical_guides = True\n show_y_axis_guide = True\n interactive_data_values = True\n svg_height = \"2em\"\n\n nanoplot_svg = _generate_nanoplot(\n y_vals=y_vals,\n y_ref_line=y_ref_line,\n y_ref_area=y_ref_area,\n x_vals=x_vals,\n expand_x=expand_x,\n expand_y=expand_y,\n missing_vals=missing_vals,\n all_y_vals=all_y_vals,\n all_single_y_vals=all_single_y_vals,\n plot_type=plot_type,\n data_line_type=data_line_type,\n currency=currency,\n y_val_fmt_fn=y_val_fmt_fn,\n y_axis_fmt_fn=y_axis_fmt_fn,\n y_ref_line_fmt_fn=y_ref_line_fmt_fn,\n data_point_radius=data_point_radius,\n data_point_stroke_color=data_point_stroke_color,\n data_point_stroke_width=data_point_stroke_width,\n data_point_fill_color=data_point_fill_color,\n data_line_stroke_color=data_line_stroke_color,\n data_line_"
],
"line": 749,
"token": 7038,
"line_diff": 2,
"token_diff": 2
},
{
"id": 77,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def mean(self, lower=0.0, upper=np.inf):\n loc = []\n for idx in self.index:\n qpd = self.qpd.loc[idx, :].values[0]\n l, _ = quad(exp_func, args=(qpd), a=lower, b=upper)\n loc.append(l)\n loc_arr = np.array(loc)\n return pd.DataFrame(loc_arr, index=self.index, columns=self.columns)\n```\n###test function signature:\n```python\ndef test_qpd_b_simple_use():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_qpd_b_simple_use",
"reference": "def test_qpd_b_simple_use():\n from skpro.distributions.qpd import QPD_B\n\n qpd = QPD_B(\n alpha=0.2,\n qv_low=[1, 2],\n qv_median=[3, 4],\n qv_high=[5, 6],\n lower=0,\n upper=10,\n )\n\n qpd.mean()",
"completions": [
"```python\ndef test_qpd_b_simple_use():\n import numpy as np\n import pandas as pd\n from scipy.integrate import quad\n from scipy.special import expit\n\n def exp_func(x, qpd):\n return expit(qpd * x)\n\n qpd = pd.DataFrame([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]],\n index=['a', 'b', 'c'], columns=['x', 'y', 'z'])\n qpd_b = qpd.mean(lower=0.0, upper=np.inf)\n assert qpd_b.equals(pd.DataFrame([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]],\n index=['a', 'b', 'c'], columns=['x', 'y', 'z']))\n```\n"
],
"line": 18,
"token": 146,
"line_diff": 0,
"token_diff": 0
},
{
"id": 78,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef create(\n supply_settings: Callable[[], config.ResilienceSettings] = config.ResilienceSettings, include_post: bool = False\n) -> adapters.HTTPAdapter:\n settings = supply_settings()\n allowed_methods = set(util.Retry.DEFAULT_ALLOWED_METHODS)\n if include_post:\n allowed_methods.add(\"POST\")\n\n retries = util.Retry(\n total=settings.total,\n backoff_factor=settings.backoff_factor,\n status_forcelist=settings.statuses,\n allowed_methods=allowed_methods,\n )\n return adapters.HTTPAdapter(max_retries=retries)\n```\n###test function signature:\n```python\n\ndef test_should_create_adapter(include_post):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_should_create_adapter",
"reference": "\ndef test_should_create_adapter(include_post):\n total_retries = 5\n backoff_factor = 0.4\n statuses = (random.randint(400, 600),)\n settings = config.ResilienceSettings(total=total_retries, backoff_factor=backoff_factor, statuses=statuses)\n adapter = http_adapter.create(supply_settings=lambda: settings, include_post=include_post)\n assert adapter.max_retries.total == total_retries\n assert adapter.max_retries.backoff_factor == backoff_factor\n assert adapter.max_retries.status_forcelist == statuses\n allowed_methods = adapter.max_retries.allowed_methods or []\n if include_post:\n assert \"POST\" in allowed_methods\n else:\n assert \"POST\" not in allowed_methods",
"completions": [
"```python\n\ndef test_should_create_adapter(include_post):\n assert create(include_post=include_post) == adapters.HTTPAdapter(max_retries=util.Retry(\n total=config.ResilienceSettings().total,\n backoff_factor=config.ResilienceSettings().backoff_factor,\n status_forcelist=config.ResilienceSettings().statuses,\n allowed_methods=set(util.Retry.DEFAULT_ALLOWED_METHODS) if not include_post else set(util.Retry.DEFAULT_ALLOWED_METHODS).union({\"POST\"})\n ))\n```\n"
],
"line": 24,
"token": 143,
"line_diff": 0,
"token_diff": 0
},
{
"id": 79,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef extract_best_config(trial_path: str, output_path: Optional[str] = None) -> Dict:\n summary_path = os.path.join(trial_path, 'summary.csv')\n if not os.path.exists(summary_path):\n raise ValueError(f\"summary.csv does not exist in {trial_path}.\")\n trial_summary_df = load_summary_file(summary_path, dict_columns=['best_module_params'])\n config_yaml_path = os.path.join(trial_path, 'config.yaml')\n with open(config_yaml_path, 'r') as f:\n config_dict = yaml.safe_load(f)\n yaml_dict = summary_df_to_yaml(trial_summary_df, config_dict)\n if output_path is not None:\n with open(output_path, 'w') as f:\n yaml.dump(yaml_dict, f)\n return yaml_dict\n```\n###test function signature:\n```python\n\ndef test_runner(evaluator):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_runner",
"reference": "\ndef test_runner(evaluator):\n os.environ['BM25'] = 'bm25'\n evaluator.start_trial(os.path.join(resource_dir, 'simple.yaml'))\n project_dir = evaluator.project_dir\n\n def runner_test(runner: Runner):\n answer = runner.run('What is the best movie in Korea? Have Korea movie ever won Oscar?',\n 'retrieved_contents')\n assert len(answer) == 1\n assert isinstance(answer, list)\n assert isinstance(answer[0], str)\n\n runner = Runner.from_trial_folder(os.path.join(project_dir, '0'))\n runner_test(runner)\n runner_test(runner)\n\n with tempfile.NamedTemporaryFile(suffix='yaml', mode='w+t') as yaml_path:\n extract_best_config(os.path.join(project_dir, '0'), yaml_path.name)\n runner = Runner.from_yaml(yaml_path.name, project_dir=project_dir)\n runner_test(runner)",
"completions": [
"```python\ndef test_runner(evaluator):\n def test_extract_best_config():\n trial_path = 'path/to/trial'\n output_path = 'path/to/output'\n yaml_dict = extract_best_config(trial_path, output_path)\n assert yaml_dict is not None\n assert isinstance(yaml_dict, dict)\n assert 'best_module_params' in yaml_dict\n assert isinstance(yaml_dict['best_module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']\n assert isinstance(yaml_dict['best_module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['module_params'], dict)\n assert 'module_name' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['module_params']\n assert isinstance(yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['module_name'], str)\n assert 'module_params' in yaml_dict['best_module_params']['module_params']['module_params']['module_params']['module_params']['module_params']['"
],
"line": 23,
"token": 205,
"line_diff": 0,
"token_diff": 0
},
{
"id": 80,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\n def init_weights(self):\n logger = MMLogger.get_current_instance()\n if self.init_cfg is None:\n logger.warn(f'No pre-trained weights for '\n f'{self.__class__.__name__}, '\n f'training start from scratch')\n if self.use_abs_pos_embed:\n trunc_normal_(self.absolute_pos_embed, std=0.02)\n for m in self.modules():\n if isinstance(m, nn.Linear):\n trunc_normal_init(m, std=.02, bias=0.)\n elif isinstance(m, nn.LayerNorm):\n constant_init(m, 1.0)\n else:\n assert 'checkpoint' in self.init_cfg, f'Only support ' \\\n f'specify `Pretrained` in ' \\\n f'`init_cfg` in ' \\\n f'{self.__class__.__name__} '\n ckpt = CheckpointLoader.load_checkpoint(\n self.init_cfg.checkpoint, logger=logger, map_location='cpu')\n if 'state_dict' in ckpt:\n _state_dict = ckpt['state_dict']\n elif 'model' in ckpt:\n _state_dict = ckpt['model']\n else:\n _state_dict = ckpt\n if self.convert_weights:\n # supported loading weight from original repo,\n _state_dict = swin_converter(_state_dict)\n\n state_dict = OrderedDict()\n for k, v in _state_dict.items():\n if k.startswith('backbone.'):\n state_dict[k[9:]] = v\n\n # strip prefix of state_dict\n if list(state_dict.keys())[0].startswith('module.'):\n state_dict = {k[7:]: v for k, v in state_dict.items()}\n\n # reshape absolute position embedding\n if state_dict.get('absolute_pos_embed') is not None:\n absolute_pos_embed = state_dict['absolute_pos_embed']\n N1, L, C1 = absolute_pos_embed.size()\n N2, C2, H, W = self.absolute_pos_embed.size()\n if N1 != N2 or C1 != C2 or L != H * W:\n logger.warning('Error in loading absolute_pos_embed, pass')\n else:\n state_dict['absolute_pos_embed'] = absolute_pos_embed.view(\n N2, H, W, C2).permute(0, 3, 1, 2).contiguous()\n\n # interpolate position bias table if needed\n relative_position_bias_table_keys = [\n k for k in state_dict.keys()\n if 'relative_position_bias_table' in k\n ]\n for table_key in relative_position_bias_table_keys:\n table_pretrained = state_dict[table_key]\n table_current = self.state_dict()[table_key]\n L1, nH1 = table_pretrained.size()\n L2, nH2 = table_current.size()\n if nH1 != nH2:\n logger.warning(f'Error in loading {table_key}, pass')\n elif L1 != L2:\n S1 = int(L1**0.5)\n S2 = int(L2**0.5)\n table_pretrained_resized = F.interpolate(\n table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1),\n size=(S2, S2),\n mode='bicubic')\n state_dict[table_key] = table_pretrained_resized.view(\n nH2, L2).permute(1, 0).contiguous()\n\n # load state_dict\n self.load_state_dict(state_dict, False)\n```\n###test function signature:\n```python\ndef test_swin_transformer():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_swin_transformer",
"reference": "def test_swin_transformer():\n\n with pytest.raises(TypeError):\n # Pretrained arg must be str or None.\n SwinTransformer(pretrained=123)\n\n with pytest.raises(AssertionError):\n # Because swin uses non-overlapping patch embed, so the stride of patch\n # embed must be equal to patch size.\n SwinTransformer(strides=(2, 2, 2, 2), patch_size=4)\n\n # test pretrained image size\n with pytest.raises(AssertionError):\n SwinTransformer(pretrain_img_size=(224, 224, 224))\n\n # Test absolute position embedding\n temp = torch.randn((1, 3, 224, 224))\n model = SwinTransformer(pretrain_img_size=224, use_abs_pos_embed=True)\n model.init_weights()\n model(temp)\n\n # Test patch norm\n model = SwinTransformer(patch_norm=False)\n model(temp)\n\n # Test normal inference\n temp = torch.randn((1, 3, 32, 32))\n model = SwinTransformer()\n outs = model(temp)\n assert outs[0].shape == (1, 96, 8, 8)\n assert outs[1].shape == (1, 192, 4, 4)\n assert outs[2].shape == (1, 384, 2, 2)\n assert outs[3].shape == (1, 768, 1, 1)\n\n # Test abnormal inference size\n temp = torch.randn((1, 3, 31, 31))\n model = SwinTransformer()\n outs = model(temp)\n assert outs[0].shape == (1, 96, 8, 8)\n assert outs[1].shape == (1, 192, 4, 4)\n assert outs[2].shape == (1, 384, 2, 2)\n assert outs[3].shape == (1, 768, 1, 1)\n\n # Test abnormal inference size\n temp = torch.randn((1, 3, 112, 137))\n model = SwinTransformer()\n outs = model(temp)\n assert outs[0].shape == (1, 96, 28, 35)\n assert outs[1].shape == (1, 192, 14, 18)\n assert outs[2].shape == (1, 384, 7, 9)\n assert outs[3].shape == (1, 768, 4, 5)\n\n model = SwinTransformer(frozen_stages=4)\n model.train()\n for p in model.parameters():\n assert not p.requires_grad",
"completions": [
"```python\ndef test_swin_transformer():\n from mmcv.cnn import (build_pos_embed, build_pos_embed_sine,\n constant_init, trunc_normal_init)\n from mmcv.runner import CheckpointLoader\n from mmdet.models.builder import build_backbone\n from mmdet.models.utils import swin_converter\n from mmdet.utils import get_root_logger\n from mmdet.utils.logger import MMLogger\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import PatchEmbed\n from mmdet.models.backbones.swin import PatchMerging\n from mmdet.models.backbones.swin import BasicLayer\n from mmdet.models.backbones.swin import BasicBlock\n from mmdet.models.backbones.swin import DepthwiseSeparableConv\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransformer\n from mmdet.models.backbones.swin import SwinTransform"
],
"line": 79,
"token": 586,
"line_diff": 0,
"token_diff": 0
},
{
"id": 81,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef columns_equal(\n col_1: \"pl.Series\",\n col_2: \"pl.Series\",\n rel_tol: float = 0,\n abs_tol: float = 0,\n ignore_spaces: bool = False,\n ignore_case: bool = False,\n) -> \"pl.Series\":\n compare: pl.Series\n try:\n compare = pl.Series(\n np.isclose(col_1, col_2, rtol=rel_tol, atol=abs_tol, equal_nan=True)\n )\n except TypeError:\n try:\n if col_1.dtype in DATE_TYPE or col_2 in DATE_TYPE:\n raise TypeError(\"Found date, moving to alternative logic\")\n\n compare = pl.Series(\n np.isclose(\n col_1.cast(pl.Float64, strict=True),\n col_2.cast(pl.Float64, strict=True),\n rtol=rel_tol,\n atol=abs_tol,\n equal_nan=True,\n )\n )\n except (ValueError, TypeError, InvalidOperationError, ComputeError):\n try:\n if ignore_spaces:\n if str(col_1.dtype) in STRING_TYPE:\n col_1 = col_1.str.strip_chars()\n if str(col_2.dtype) in STRING_TYPE:\n col_2 = col_2.str.strip_chars()\n\n if ignore_case:\n if str(col_1.dtype) in STRING_TYPE:\n col_1 = col_1.str.to_uppercase()\n if str(col_2.dtype) in STRING_TYPE:\n col_2 = col_2.str.to_uppercase()\n\n if (\n str(col_1.dtype) in STRING_TYPE and str(col_2.dtype) in DATE_TYPE\n ) or (\n str(col_1.dtype) in DATE_TYPE and str(col_2.dtype) in STRING_TYPE\n ):\n compare = compare_string_and_date_columns(col_1, col_2)\n else:\n compare = pl.Series(\n (col_1.eq_missing(col_2)) | (col_1.is_null() & col_2.is_null())\n )\n except:\n # Blanket exception should just return all False\n compare = pl.Series(False * col_1.shape[0])\n return compare\n```\n###test function signature:\n```python\n\ndef test_date_columns_equal():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_date_columns_equal",
"reference": "\ndef test_date_columns_equal():\n data = \"\"\"a|b|expected\n2017-01-01|2017-01-01|True\n2017-01-02|2017-01-02|True\n2017-10-01|2017-10-10|False\n2017-01-01||False\n|2017-01-01|False\n||True\"\"\"\n df = pl.read_csv(\n io.StringIO(data),\n separator=\"|\",\n null_values=[\"NULL\"],\n missing_utf8_is_empty_string=True,\n )\n # First compare just the strings\n actual_out = columns_equal(df[\"a\"], df[\"b\"], rel_tol=0.2)\n expect_out = df[\"expected\"]\n assert_series_equal(expect_out, actual_out, check_names=False)\n\n # Then compare converted to datetime objects\n col_a = df[\"a\"].str.to_datetime(strict=False)\n col_b = df[\"b\"].str.to_datetime(strict=False)\n actual_out = columns_equal(col_a, col_b, rel_tol=0.2)\n expect_out = df[\"expected\"]\n assert_series_equal(expect_out, actual_out, check_names=False)",
"completions": [
"```python\ndef test_date_columns_equal():\n col_1 = pl.Series([\"2022-01-01\", \"2022-01-02\", \"2022-01-03\"])\n col_2 = pl.Series([\"2022-01-01\", \"2022-01-02\", \"2022-01-03\"])\n compare = columns_equal(col_1, col_2, ignore_spaces=True, ignore_case=True)\n assert compare.sum() == 3\n```\n"
],
"line": 62,
"token": 360,
"line_diff": 0,
"token_diff": 0
},
{
"id": 82,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\nasync def upsert_child_firing(\n db: PrefectDBInterface,\n session: AsyncSession,\n firing: Firing,\n):\n automation_id = firing.trigger.automation.id\n parent_trigger_id = firing.trigger.parent.id\n child_trigger_id = firing.trigger.id\n\n upsert = (\n postgresql.insert(db.CompositeTriggerChildFiring)\n .values(\n automation_id=automation_id,\n parent_trigger_id=parent_trigger_id,\n child_trigger_id=child_trigger_id,\n child_firing_id=firing.id,\n child_fired_at=firing.triggered,\n child_firing=firing.dict(),\n )\n .on_conflict_do_update(\n index_elements=[\n db.CompositeTriggerChildFiring.automation_id,\n db.CompositeTriggerChildFiring.parent_trigger_id,\n db.CompositeTriggerChildFiring.child_trigger_id,\n ],\n set_=dict(\n child_firing_id=firing.id,\n child_fired_at=firing.triggered,\n child_firing=firing.dict(),\n updated=pendulum.now(\"UTC\"),\n ),\n )\n )\n\n await session.execute(upsert)\n\n result = await session.execute(\n sa.select(db.CompositeTriggerChildFiring).filter(\n db.CompositeTriggerChildFiring.automation_id == automation_id,\n db.CompositeTriggerChildFiring.parent_trigger_id == parent_trigger_id,\n db.CompositeTriggerChildFiring.child_trigger_id == child_trigger_id,\n )\n )\n\n return result.scalars().one()\n```\n###test function signature:\n```python\n\nasync def test_insert_compound_trigger_child_firing(\n automations_session: AsyncSession,\n raise_the_alarm: Automation,\n saw_a_red_and_green_dragon: CompoundTrigger,\n saw_a_red_dragon: EventTrigger,\n baby_red_dragon_passed_by: ReceivedEvent,\n frozen_time: DateTime,\n):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_insert_compound_trigger_child_firing",
"reference": "\nasync def test_insert_compound_trigger_child_firing(\n automations_session: AsyncSession,\n raise_the_alarm: Automation,\n saw_a_red_and_green_dragon: CompoundTrigger,\n saw_a_red_dragon: EventTrigger,\n baby_red_dragon_passed_by: ReceivedEvent,\n frozen_time: DateTime,\n):\n firing = Firing(\n trigger=saw_a_red_dragon,\n trigger_states={TriggerState.Triggered},\n triggered=frozen_time.add(seconds=1),\n triggering_labels={},\n triggering_event=baby_red_dragon_passed_by,\n )\n res = await upsert_child_firing(\n session=automations_session,\n firing=firing,\n )\n\n assert res and res.id\n assert res.automation_id == raise_the_alarm.id\n assert res.parent_trigger_id == saw_a_red_and_green_dragon.id\n assert res.child_trigger_id == saw_a_red_dragon.id\n assert res.child_firing_id == firing.id\n assert res.child_fired_at == firing.triggered\n\n child_firings = await get_child_firings(\n session=automations_session, trigger=saw_a_red_and_green_dragon\n )\n assert len(child_firings) == 1\n assert child_firings[0].id == res.id\n assert child_firings[0].child_firing_id == firing.id",
"completions": [
"```python\n\nasync def test_insert_compound_trigger_child_firing(\n automations_session: AsyncSession,\n raise_the_alarm: Automation,\n saw_a_red_and_green_dragon: CompoundTrigger,\n saw_a_red_dragon: EventTrigger,\n baby_red_dragon_passed_by: ReceivedEvent,\n frozen_time: DateTime,\n):\n \"\"\"\n Test that a compound trigger child firing is inserted correctly\n \"\"\"\n # Arrange\n firing = Firing(\n id=uuid4(),\n triggered=frozen_time,\n trigger=saw_a_red_and_green_dragon,\n event=baby_red_dragon_passed_by,\n )\n\n # Act\n result = await upsert_child_firing(\n db=automations_session,\n session=automations_session,\n firing=firing,\n )\n\n # Assert\n assert result.automation_id == raise_the_alarm.id\n assert result.parent_trigger_id == saw_a_red_and_green_dragon.id\n assert result.child_trigger_id == saw_a_red_dragon.id\n assert result.child_firing_id == firing.id\n assert result.child_fired_at == firing.triggered\n assert result.child_firing == firing.dict()\n assert result.updated == frozen_time\n\n```\n"
],
"line": 58,
"token": 225,
"line_diff": 0,
"token_diff": 0
},
{
"id": 83,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef write_output(output_file, output_type, results):\n\n # If json or txt, write contents directly to file.\n if output_type == \"json\" or output_type == \"txt\":\n with open(output_file, \"w\") as f:\n json.dump(results, f, indent=4)\n return\n # If csv or xlsx, convert results to pandas dataframes.\n urls_df = get_urls_df(results)\n codes_df = get_codes_df(results)\n\n # If csv, write dataframes to separate csv files for urls, codes.\n if output_type == \"csv\":\n urls_output_file = output_file.replace(\".csv\", \"_urls.csv\")\n codes_output_file = output_file.replace(\".csv\", \"_codes.csv\")\n urls_df.to_csv(urls_output_file, index=False)\n codes_df.to_csv(codes_output_file, index=False)\n\n # If xlsx, write dataframes to separate sheets for urls, codes.\n if output_type == \"xlsx\":\n writer = pd.ExcelWriter(output_file, engine=\"xlsxwriter\")\n urls_df.to_excel(writer, sheet_name=\"URLs\", index=False)\n codes_df.to_excel(writer, sheet_name=\"Codes\", index=False)\n writer.close()\n```\n###test function signature:\n```python\n def test_write_output_csv(self, mock_urls, mock_codes):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_write_output_csv",
"reference": " def test_write_output_csv(self, mock_urls, mock_codes):\n\n test_file = \"./test_output/test_file.csv\"\n test_file_urls = \"./test_output/test_file_urls.csv\"\n test_file_codes = \"./test_output/test_file_codes.csv\"\n test_results = {\"test\": \"test\"}\n mock_urls.return_value = pd.DataFrame([test_results])\n mock_codes.return_value = pd.DataFrame([test_results])\n\n with open(test_file_urls, \"w\") as f:\n pass\n\n with open(test_file_codes, \"w\") as f:\n pass\n\n write_output(test_file, \"csv\", test_results)\n\n test_data_urls = pd.read_csv(test_file_urls).to_dict(orient=\"records\")[0]\n test_data_codes = pd.read_csv(test_file_codes).to_dict(orient=\"records\")[0]\n\n os.remove(test_file_urls)\n os.remove(test_file_codes)\n\n self.assertEqual(test_data_urls, test_results)\n self.assertEqual(test_data_codes, test_results)",
"completions": [
"```python\ndef test_write_output_csv(self, mock_urls, mock_codes):\n \"\"\"\n Test write_output function with csv output type.\n \"\"\"\n output_file = \"output.csv\"\n output_type = \"csv\"\n results = {\"urls\": mock_urls, \"codes\": mock_codes}\n write_output(output_file, output_type, results)\n\n # Check that the urls and codes dataframes are written to separate csv files.\n urls_output_file = output_file.replace(\".csv\", \"_urls.csv\")\n codes_output_file = output_file.replace(\".csv\", \"_codes.csv\")\n self.assertTrue(os.path.isfile(urls_output_file))\n self.assertTrue(os.path.isfile(codes_output_file))\n\n # Check that the dataframes are correct.\n urls_df = pd.read_csv(urls_output_file)\n codes_df = pd.read_csv(codes_output_file)\n self.assertEqual(urls_df.shape, (2, 2))\n self.assertEqual(codes_df.shape, (2, 2))\n self.assertEqual(urls_df.iloc[0, 0], \"url1\")\n self.assertEqual(codes_df.iloc[0, 0], \"code1\")\n self.assertEqual(urls_df.iloc[1, 0], \"url2\")\n self.assertEqual(codes_df.iloc[1, 0], \"code2\")\n```\n"
],
"line": 31,
"token": 279,
"line_diff": 0,
"token_diff": 0
},
{
"id": 84,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\nasync def terminate_idle_instance() -> None:\n async with get_session_ctx() as session:\n async with PROCESSING_POOL_LOCK:\n res = await session.execute(\n select(InstanceModel)\n .where(\n InstanceModel.termination_policy == TerminationPolicy.DESTROY_AFTER_IDLE,\n InstanceModel.deleted == False,\n InstanceModel.job == None, # noqa: E711\n InstanceModel.status == InstanceStatus.IDLE,\n )\n .options(joinedload(InstanceModel.project))\n )\n instances = res.scalars().all()\n\n for instance in instances:\n last_time = instance.created_at.replace(tzinfo=datetime.timezone.utc)\n if instance.last_job_processed_at is not None:\n last_time = instance.last_job_processed_at.replace(\n tzinfo=datetime.timezone.utc\n )\n\n idle_seconds = instance.termination_idle_time\n delta = datetime.timedelta(seconds=idle_seconds)\n\n current_time = get_current_datetime().replace(tzinfo=datetime.timezone.utc)\n\n if last_time + delta < current_time:\n jpd = JobProvisioningData.__response__.parse_raw(\n instance.job_provisioning_data\n )\n await terminate_job_provisioning_data_instance(\n project=instance.project, job_provisioning_data=jpd\n )\n instance.deleted = True\n instance.deleted_at = get_current_datetime()\n instance.finished_at = get_current_datetime()\n instance.status = InstanceStatus.TERMINATED\n instance.termination_reason = \"Idle timeout\"\n\n idle_time = current_time - last_time\n logger.info(\n \"Instance %s terminated by termination policy: idle time %ss\",\n instance.name,\n str(idle_time.seconds),\n extra={\n \"instance_name\": instance.name,\n \"instance_status\": InstanceStatus.TERMINATED.value,\n },\n )\n\n await session.commit()\n```\n###test function signature:\n```python\n\n async def test_terminate_by_idle_timeout(self, test_db, session: AsyncSession):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_terminate_by_idle_timeout",
"reference": "\n async def test_terminate_by_idle_timeout(self, test_db, session: AsyncSession):\n project = await create_project(session=session)\n pool = await create_pool(session, project)\n\n instance = await create_instance(session, project, pool, status=InstanceStatus.IDLE)\n instance.termination_idle_time = 300\n instance.termination_policy = TerminationPolicy.DESTROY_AFTER_IDLE\n instance.last_job_processed_at = get_current_datetime() + dt.timedelta(minutes=-19)\n await session.commit()\n\n with patch(\n \"dstack._internal.server.background.tasks.process_instances.terminate_job_provisioning_data_instance\"\n ):\n await terminate_idle_instance()\n\n await session.refresh(instance)\n\n assert instance is not None\n assert instance.status == InstanceStatus.TERMINATED\n assert instance.termination_reason == \"Idle timeout\"",
"completions": [
"```python\n\n async def test_terminate_by_idle_timeout(self, test_db, session: AsyncSession):\n # given\n project = ProjectModel(name=\"test_project\")\n await session.add(project)\n await session.commit()\n\n instance = InstanceModel(\n name=\"test_instance\",\n project=project,\n termination_policy=TerminationPolicy.DESTROY_AFTER_IDLE,\n termination_idle_time=10,\n status=InstanceStatus.IDLE,\n created_at=datetime.datetime.now(datetime.timezone.utc),\n )\n await session.add(instance)\n await session.commit()\n\n # when\n await terminate_idle_instance()\n\n # then\n instance = await session.get(InstanceModel, instance.id)\n assert instance.status == InstanceStatus.TERMINATED\n assert instance.termination_reason == \"Idle timeout\"\n```\n"
],
"line": 56,
"token": 289,
"line_diff": 0,
"token_diff": 0
},
{
"id": 85,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef nargsort(\n items,\n kind: str = \"quicksort\",\n ascending: bool = True,\n na_position: str = \"last\",\n key: Callable | None = None,\n mask: npt.NDArray[np.bool_] | None = None,\n) -> npt.NDArray[np.intp]:\n\n if key is not None:\n items = ensure_key_mapped(items, key)\n return nargsort(\n items,\n kind=kind,\n ascending=ascending,\n na_position=na_position,\n key=None,\n mask=mask,\n )\n\n if isinstance(items, ABCRangeIndex):\n return items.argsort(ascending=ascending) # TODO: test coverage with key?\n elif not isinstance(items, ABCMultiIndex):\n items = extract_array(items)\n if mask is None:\n mask = np.asarray(isna(items)) # TODO: does this exclude MultiIndex too?\n\n if is_extension_array_dtype(items):\n return items.argsort(ascending=ascending, kind=kind, na_position=na_position)\n else:\n items = np.asanyarray(items)\n\n idx = np.arange(len(items))\n non_nans = items[~mask]\n non_nan_idx = idx[~mask]\n\n nan_idx = np.nonzero(mask)[0]\n if not ascending:\n non_nans = non_nans[::-1]\n non_nan_idx = non_nan_idx[::-1]\n indexer = non_nan_idx[non_nans.argsort(kind=kind)]\n if not ascending:\n indexer = indexer[::-1]\n # Finally, place the NaNs at the end or the beginning according to\n # na_position\n if na_position == \"last\":\n indexer = np.concatenate([indexer, nan_idx])\n elif na_position == \"first\":\n indexer = np.concatenate([nan_idx, indexer])\n else:\n raise ValueError(f\"invalid na_position: {na_position}\")\n return ensure_platform_int(indexer)\n```\n###test function signature:\n```python\n\n def test_nargsort(self, ascending, na_position, exp, box):\n # list places NaNs last, np.array(..., dtype=\"O\") may not place NaNs first\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_nargsort",
"reference": "\n def test_nargsort(self, ascending, na_position, exp, box):\n # list places NaNs last, np.array(..., dtype=\"O\") may not place NaNs first\n items = box([np.nan] * 5 + list(range(100)) + [np.nan] * 5)\n\n # mergesort is the most difficult to get right because we want it to be\n # stable.\n\n # According to numpy/core/tests/test_multiarray, \"\"\"The number of\n # sorted items must be greater than ~50 to check the actual algorithm\n # because quick and merge sort fall over to insertion sort for small\n # arrays.\"\"\"\n\n result = nargsort(\n items, kind=\"mergesort\", ascending=ascending, na_position=na_position\n )\n tm.assert_numpy_array_equal(result, np.array(exp), check_dtype=False)",
"completions": [
"```python\n def test_nargsort(self, ascending, na_position, exp, box):\n items = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,"
],
"line": 58,
"token": 412,
"line_diff": 0,
"token_diff": 0
},
{
"id": 86,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef generate_rotated_rb_circuits(\n n_qubits: int,\n num_cliffords: int,\n theta: Optional[float] = None,\n trials: int = 1,\n return_type: Optional[str] = None,\n seed: Optional[int] = None,\n) -> List[QPROGRAM]:\n\n circuits = cast(\n List[cirq.Circuit],\n generate_rb_circuits(n_qubits, num_cliffords, trials, seed=seed),\n )\n\n if theta is None:\n theta = 2 * np.pi * np.random.rand()\n\n for circ in circuits:\n qubits = list(circ.all_qubits())\n circ.insert(len(circ) // 2, cirq.Rz(rads=theta).on(qubits[0]))\n\n return_type = \"cirq\" if not return_type else return_type\n return [convert_from_mitiq(circuit, return_type) for circuit in circuits]\n```\n###test function signature:\n```python\n\ndef test_rotated_rb_circuits(n_qubits, theta):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_rotated_rb_circuits",
"reference": "\ndef test_rotated_rb_circuits(n_qubits, theta):\n depth = 10\n for trials in [2, 3]:\n circuits = generate_rotated_rb_circuits(\n n_qubits=n_qubits, num_cliffords=depth, theta=theta, trials=trials\n )\n for circ in circuits:\n zero_prob = (\n cirq.DensityMatrixSimulator()\n .simulate(circ)\n .final_density_matrix[0, 0]\n .real\n )\n assert -1.0001 <= zero_prob <= 1.0001",
"completions": [
"```python\ndef test_rotated_rb_circuits(n_qubits, theta):\n from cirq import Circuit, LineQubit\n from cirq.testing import assert_has_diagram\n from mitiq.generate import generate_rotated_rb_circuits\n\n circuits = generate_rotated_rb_circuits(n_qubits, 10, theta)\n for circuit in circuits:\n assert_has_diagram(\n circuit,\n f\"\"\"\n0: ───Rz({theta})───\n1: ───Rz({theta})───\n2: ───Rz({theta})───\n3: ───Rz({theta})───\n4: ───Rz({theta})───\n5: ───Rz({theta})───\n6: ───Rz({theta})───\n7: ───Rz({theta})───\n8: ───Rz({theta})───\n9: ───Rz({theta})───\n10: ───Rz({theta})───\n11: ───Rz({theta})───\n12: ───Rz({theta})───\n13: ───Rz({theta})───\n14: ───Rz({theta})───\n15: ───Rz({theta})───\n16: ───Rz({theta})───\n17: ───Rz({theta})───\n18: ───Rz({theta})───\n19: ───Rz({theta})───\n20: ───Rz({theta})───\n21: ───Rz({theta})───\n22: ───Rz({theta})───\n23: ───Rz({theta})───\n24: ───Rz({theta})───\n25: ───Rz({theta})───\n26: ───Rz({theta})───\n27: ───Rz({theta})───\n28: ───Rz({theta})───\n29: ───Rz({theta})───\n30: ───Rz({theta})───\n31: ───Rz({theta})───\n32: ───Rz({theta})───\n33: ───Rz({theta})───\n34: ───Rz({theta})───\n35: ───Rz({theta})───\n36: ───Rz({theta})───\n37: ───Rz({theta})───\n38: ───Rz({theta})───\n39: ───Rz({theta})───\n40: ───Rz({theta})───\n41: ───Rz({theta})───\n42: ───Rz({theta})───\n43: ───Rz({theta})───\n44: ───Rz({theta})───\n45: ───Rz({theta})───\n46: ───Rz({theta})───\n47: ───Rz({theta})───\n48: ───Rz({theta})───\n49: ───Rz({theta})───\n50: ───Rz({theta})───\n51: ───Rz({theta})───\n52: ───Rz({theta})───\n53: ───Rz({theta})───\n54: ───Rz({theta})───\n55: ───Rz({theta})───\n56: ───Rz({theta})───\n57: ───Rz({theta})───\n58: ───Rz({theta})───\n59: ───Rz({theta})───\n60: ───Rz({theta})───\n61: ───Rz({theta})───\n62: ───Rz({theta})───\n63: ───"
],
"line": 29,
"token": 226,
"line_diff": 0,
"token_diff": 0
},
{
"id": 87,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef eye(N, M=None, k=None, dtype=None):\n dtype = to_torch_dtype(dtype or config.floatx())\n M = N if M is None else M\n k = 0 if k is None else k\n if k == 0:\n # TODO: torch.eye doesn't support bfloat16 with cpu\n if get_device() == \"cpu\" and dtype == torch.bfloat16:\n return cast(\n torch.eye(\n N, M, dtype=to_torch_dtype(\"float32\"), device=get_device()\n ),\n dtype,\n )\n return torch.eye(N, M, dtype=dtype, device=get_device())\n diag_length = builtins.max(N, M)\n diag = torch.ones(diag_length, dtype=dtype, device=get_device())\n return torch.diag(diag, diagonal=k)[:N, :M]\n```\n###test function signature:\n```python\n\n def test_eye(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_eye",
"reference": "\n def test_eye(self):\n self.assertAllClose(knp.eye(3), np.eye(3))\n self.assertAllClose(knp.eye(3, 4), np.eye(3, 4))\n self.assertAllClose(knp.eye(3, 4, 1), np.eye(3, 4, 1))\n\n self.assertAllClose(knp.Eye()(3), np.eye(3))\n self.assertAllClose(knp.Eye()(3, 4), np.eye(3, 4))\n self.assertAllClose(knp.Eye(k=1)(3, 4), np.eye(3, 4, k=1))\n\n # Test k >= N\n self.assertAllClose(knp.Eye(k=3)(3), np.eye(3, k=3))\n\n # Test k > 0 and N >= M\n self.assertAllClose(knp.Eye(k=1)(3), np.eye(3, k=1))\n\n # Test k > 0 and N < M and N + k > M\n self.assertAllClose(knp.Eye(k=2)(3, 4), np.eye(3, 4, k=2))\n\n # Test k < 0 and M >= N\n self.assertAllClose(knp.Eye(k=-1)(3), np.eye(3, k=-1))\n\n # Test k < 0 and M < N and M - k > N\n self.assertAllClose(knp.Eye(k=-2)(4, 3), np.eye(4, 3, k=-2))",
"completions": [
"```python\n\n def test_eye(self):\n self.assertEqual(eye(3), torch.eye(3))\n self.assertEqual(eye(3, 4), torch.eye(3, 4))\n self.assertEqual(eye(3, 4, 1), torch.eye(3, 4, 1))\n self.assertEqual(eye(3, 4, -1), torch.eye(3, 4, -1))\n self.assertEqual(eye(3, 4, 2), torch.eye(3, 4, 2))\n self.assertEqual(eye(3, 4, -2), torch.eye(3, 4, -2))\n self.assertEqual(eye(3, 4, 3), torch.eye(3, 4, 3))\n self.assertEqual(eye(3, 4, -3), torch.eye(3, 4, -3))\n self.assertEqual(eye(3, 4, 4), torch.eye(3, 4, 4))\n self.assertEqual(eye(3, 4, -4), torch.eye(3, 4, -4))\n self.assertEqual(eye(3, 4, 5), torch.eye(3, 4, 5))\n self.assertEqual(eye(3, 4, -5), torch.eye(3, 4, -5))\n self.assertEqual(eye(3, 4, 6), torch.eye(3, 4, 6))\n self.assertEqual(eye(3, 4, -6), torch.eye(3, 4, -6))\n self.assertEqual(eye(3, 4, 7), torch.eye(3, 4, 7))\n self.assertEqual(eye(3, 4, -7), torch.eye(3, 4, -7))\n self.assertEqual(eye(3, 4, 8), torch.eye(3, 4, 8))\n self.assertEqual(eye(3, 4, -8), torch.eye(3, 4, -8))\n self.assertEqual(eye(3, 4, 9), torch.eye(3, 4, 9))\n self.assertEqual(eye(3, 4, -9), torch.eye(3, 4, -9))\n self.assertEqual(eye(3, 4, 10), torch.eye(3, 4, 10))\n self.assertEqual(eye(3, 4, -10), torch.eye(3, 4, -10))\n self.assertEqual(eye(3, 4, 11), torch.eye(3, 4, 11))\n self.assertEqual(eye(3, 4, -11), torch.eye(3, 4, -11))\n self.assertEqual(eye(3, 4, 12), torch.eye(3, 4, 12))\n self.assertEqual(eye(3, 4, -12), torch.eye(3, 4, -12))\n self.assertEqual(eye(3, 4, 13), torch.eye(3, 4, 13))\n self.assertEqual(eye(3, 4, -13), torch.eye(3, 4, -13))\n self.assertEqual(eye(3, 4, 14), torch.eye(3, 4, 14))\n self.assertEqual(eye(3, 4, -14), torch.eye(3, 4, -14))\n self.assertEqual(eye(3, 4, 15), torch.eye("
],
"line": 27,
"token": 219,
"line_diff": 0,
"token_diff": 0
},
{
"id": 88,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef ragged_qk_dotprod(\n query: RaggedActivations, key: RaggedActivations, lut: RaggedQkPidLookupTable\n) -> torch.Tensor:\n device = query.device\n\n assert query.raw_tensor.is_contiguous()\n assert key.raw_tensor.is_contiguous()\n\n # check constraints\n total_ctx_q_across_all_seqs, d_head = query.raw_tensor.shape\n total_ctx_k_across_all_seqs, d_head_k = key.raw_tensor.shape\n assert d_head == d_head_k, f\"{query.raw_tensor.shape=} {key.raw_tensor.shape=}\"\n\n # allocates output\n # max_n_ctx_q_across_seqs = query.max_n_ctx_per_seq\n\n assert query.n_seqs == key.n_seqs\n # TODO: flag use zeros for garbage\n scores_out = torch.ones(\n (query.n_seqs, query.max_n_ctx_per_seq, key.max_n_ctx_per_seq),\n device=device,\n dtype=query.dtype,\n )\n\n # Stride along the d_head dimension must be 1\n assert query.raw_tensor.stride(1) == 1, f\"{query.raw_tensor.stride(1)}\"\n assert key.raw_tensor.stride(1) == 1, f\"{key.raw_tensor.stride(1)}\"\n\n # pid_to_seq_idx = [0, 0, 1, 2, 2]\n grid = (lut.n_pids_total,)\n _qk_dotprod_kernel[grid](\n q_ptr=query.raw_tensor,\n k_ptr=key.raw_tensor,\n scores_ptr=scores_out,\n # Lookup tables (sometimes referred to as a \"lut\")\n pid_to_in_q_token_offset_ptr=lut.pid_to_in_q_token_offset,\n pid_to_in_k_token_offset_ptr=lut.pid_to_in_k_token_offset,\n pid_to_out_q_block_ptr=lut.pid_to_out_q_block,\n pid_to_out_k_block_ptr=lut.pid_to_out_k_block,\n pid_to_out_seq_idx_ptr=lut.pid_to_out_seq_idx,\n # Integers\n max_n_ctx_q_across_seqs=query.max_n_ctx_per_seq,\n max_n_ctx_k_across_seqs=key.max_n_ctx_per_seq,\n d_head=d_head,\n stride_ctx_q=query.raw_tensor.stride(0),\n stride_ctx_k=key.raw_tensor.stride(0),\n stride_out_seq=scores_out.stride(0),\n stride_out_q=scores_out.stride(1),\n stride_out_k=scores_out.stride(2),\n total_ctx_q_across_all_seqs=total_ctx_q_across_all_seqs,\n total_ctx_k_across_all_seqs=total_ctx_k_across_all_seqs,\n )\n return scores_out\n```\n###test function signature:\n```python\n\ndef test_ragged_qk_dotprod_single_seq(dtype):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_ragged_qk_dotprod_single_seq",
"reference": "\ndef test_ragged_qk_dotprod_single_seq(dtype):\n d_head = 2\n\n key = RaggedActivations.from_list(\n [\n make_seq(n_ctx=3, value=42, d_model=d_head, dtype=dtype),\n ]\n )\n query = RaggedActivations.from_list(\n [\n make_seq(n_ctx=4, value=55, d_model=d_head, dtype=dtype),\n ]\n )\n torch_scores = scores_via_qk_dotprod(query, key)\n print(f\"{torch_scores=}\")\n\n lut = RaggedQkPidLookupTable.from_query_and_key_tokens_per_seq(\n n_ctx_q_per_seq=query.n_ctx_per_seq, n_ctx_k_per_seq=key.n_ctx_per_seq\n )\n\n scores = ragged_qk_dotprod(query, key, lut)\n assert_eq(torch_scores, scores)",
"completions": [
"```python\n\ndef test_ragged_qk_dotprod_single_seq(dtype):\n # query\n query_raw_tensor = torch.randn(\n 1, 1, 1, dtype=dtype, device=torch.device(\"cuda\")\n )\n query = RaggedActivations(\n raw_tensor=query_raw_tensor,\n n_seqs=1,\n max_n_ctx_per_seq=1,\n dtype=dtype,\n device=torch.device(\"cuda\"),\n )\n\n # key\n key_raw_tensor = torch.randn(\n 1, 1, 1, dtype=dtype, device=torch.device(\"cuda\")\n )\n key = RaggedActivations(\n raw_tensor=key_raw_tensor,\n n_seqs=1,\n max_n_ctx_per_seq=1,\n dtype=dtype,\n device=torch.device(\"cuda\"),\n )\n\n # lut\n pid_to_in_q_token_offset = torch.tensor(\n [0], dtype=torch.int32, device=torch.device(\"cuda\")\n )\n pid_to_in_k_token_offset = torch.tensor(\n [0], dtype=torch.int32, device=torch.device(\"cuda\")\n )\n pid_to_out_q_block = torch.tensor(\n [0], dtype=torch.int32, device=torch.device(\"cuda\")\n )\n pid_to_out_k_block = torch.tensor(\n [0], dtype=torch.int32, device=torch.device(\"cuda\")\n )\n pid_to_out_seq_idx = torch.tensor(\n [0], dtype=torch.int32, device=torch.device(\"cuda\")\n )\n lut = RaggedQkPidLookupTable(\n pid_to_in_q_token_offset=pid_to_in_q_token_offset,\n pid_to_in_k_token_offset=pid_to_in_k_token_offset,\n pid_to_out_q_block=pid_to_out_q_block,\n pid_to_out_k_block=pid_to_out_k_block,\n pid_to_out_seq_idx=pid_to_out_seq_idx,\n n_pids_total=1,\n )\n\n # scores_out\n scores_out = torch.ones(\n (1, 1, 1), dtype=dtype, device=torch.device(\"cuda\")\n )\n\n # Stride along the d_head dimension must be 1\n assert query.raw_tensor.stride(1) == 1, f\"{query.raw_tensor.stride(1)}\"\n assert key.raw_tensor.stride(1) == 1, f\"{key.raw_tensor.stride(1)}\"\n\n # pid_to_seq_idx = [0, 0, 1, 2, 2]\n grid = (lut.n_pids_total,)\n _qk_dotprod_kernel[grid](\n q_ptr=query.raw_tensor,\n k_ptr=key.raw_tensor,\n scores_ptr=scores_out,\n # Lookup tables (sometimes referred to as a \"lut\")\n pid_to_in_q_token_offset_ptr=lut.pid_to_in_q_token_offset,\n pid_to_in_k_token_offset_ptr=lut.pid_to_in_k_token_offset,\n pid_to_out_q_block_ptr=lut.pid_to_out_q_block,\n pid_to_out_k_block_ptr=lut.pid_to_out_k_block,\n pid_to_out_seq_idx_ptr=lut.pid_to_"
],
"line": 57,
"token": 310,
"line_diff": 0,
"token_diff": 0
},
{
"id": 89,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None):\n # Ravel both arrays, behavior for the first array could be different\n ar1 = np.asarray(ar1).ravel()\n ar2 = np.asarray(ar2).ravel()\n\n # Ensure that iteration through object arrays yields size-1 arrays\n if ar2.dtype == object:\n ar2 = ar2.reshape(-1, 1)\n\n if kind not in {None, 'sort', 'table'}:\n raise ValueError(\n f\"Invalid kind: '{kind}'. Please use None, 'sort' or 'table'.\")\n\n # Can use the table method if all arrays are integers or boolean:\n is_int_arrays = all(ar.dtype.kind in (\"u\", \"i\", \"b\") for ar in (ar1, ar2))\n use_table_method = is_int_arrays and kind in {None, 'table'}\n\n if use_table_method:\n if ar2.size == 0:\n if invert:\n return np.ones_like(ar1, dtype=bool)\n else:\n return np.zeros_like(ar1, dtype=bool)\n\n # Convert booleans to uint8 so we can use the fast integer algorithm\n if ar1.dtype == bool:\n ar1 = ar1.astype(np.uint8)\n if ar2.dtype == bool:\n ar2 = ar2.astype(np.uint8)\n\n ar2_min = np.min(ar2)\n ar2_max = np.max(ar2)\n\n ar2_range = int(ar2_max) - int(ar2_min)\n\n # Constraints on whether we can actually use the table method:\n # 1. Assert memory usage is not too large\n below_memory_constraint = ar2_range <= 6 * (ar1.size + ar2.size)\n # 2. Check overflows for (ar2 - ar2_min); dtype=ar2.dtype\n range_safe_from_overflow = ar2_range <= np.iinfo(ar2.dtype).max\n # 3. Check overflows for (ar1 - ar2_min); dtype=ar1.dtype\n if ar1.size > 0:\n ar1_min = np.min(ar1)\n ar1_max = np.max(ar1)\n\n # After masking, the range of ar1 is guaranteed to be\n # within the range of ar2:\n ar1_upper = min(int(ar1_max), int(ar2_max))\n ar1_lower = max(int(ar1_min), int(ar2_min))\n\n range_safe_from_overflow &= all((\n ar1_upper - int(ar2_min) <= np.iinfo(ar1.dtype).max,\n ar1_lower - int(ar2_min) >= np.iinfo(ar1.dtype).min\n ))\n\n # Optimal performance is for approximately\n # log10(size) > (log10(range) - 2.27) / 0.927.\n # However, here we set the requirement that by default\n # the intermediate array can only be 6x\n # the combined memory allocation of the original\n # arrays. See discussion on \n # https://github.com/numpy/numpy/pull/12065.\n\n if (\n range_safe_from_overflow and \n (below_memory_constraint or kind == 'table')\n ):\n\n if invert:\n outgoing_array = np.ones_like(ar1, dtype=bool)\n else:\n outgoing_array = np.zeros_like(ar1, dtype=bool)\n\n # Make elements 1 where the integer exists in ar2\n if invert:\n isin_helper_ar = np.ones(ar2_range + 1, dtype=bool)\n isin_helper_ar[ar2 - ar2_min] = 0\n else:\n isin_helper_ar = np.zeros(ar2_range + 1, dtype=bool)\n isin_helper_ar[ar2 - ar2_min] = 1\n\n # Mask out elements we know won't work\n basic_mask = (ar1 <= ar2_max) & (ar1 >= ar2_min)\n outgoing_array[basic_mask] = isin_helper_ar[ar1[basic_mask] -\n ar2_min]\n\n return outgoing_array\n elif kind == 'table': # not range_safe_from_overflow\n raise RuntimeError(\n \"You have specified kind='table', \"\n \"but the range of values in `ar2` or `ar1` exceed the \"\n \"maximum integer of the datatype. \"\n \"Please set `kind` to None or 'sort'.\"\n )\n elif kind == 'table':\n raise ValueError(\n \"The 'table' method is only \"\n \"supported for boolean or integer arrays. \"\n \"Please select 'sort' or None for kind.\"\n )\n\n\n # Check if one of the arrays may contain arbitrary objects\n contains_object = ar1.dtype.hasobject or ar2.dtype.hasobject\n\n # This code is run when\n # a) the first condition is true, making the code significantly faster\n # b) the second condition is true (i.e. `ar1` or `ar2` may contain\n # arbitrary objects), since then sorting is not guaranteed to work\n if len(ar2) < 10 * len(ar1) ** 0.145 or contains_object:\n if invert:\n mask = np.ones(len(ar1), dtype=bool)\n for a in ar2:\n mask &= (ar1 != a)\n else:\n mask = np.zeros(len(ar1), dtype=bool)\n for a in ar2:\n mask |= (ar1 == a)\n return mask\n\n # Otherwise use sorting\n if not assume_unique:\n ar1, rev_idx = np.unique(ar1, return_inverse=True)\n ar2 = np.unique(ar2)\n\n ar = np.concatenate((ar1, ar2))\n # We need this to be a stable sort, so always use 'mergesort'\n # here. The values from the first array should always come before\n # the values from the second array.\n order = ar.argsort(kind='mergesort')\n sar = ar[order]\n if invert:\n bool_ar = (sar[1:] != sar[:-1])\n else:\n bool_ar = (sar[1:] == sar[:-1])\n flag = np.concatenate((bool_ar, [invert]))\n ret = np.empty(ar.shape, dtype=bool)\n ret[order] = flag\n\n if assume_unique:\n return ret[:len(ar1)]\n else:\n return ret[rev_idx]\n```\n###test function signature:\n```python\n\n def test_in1d_ravel(self, kind):\n # Test that in1d ravels its input arrays. This is not documented\n # behavior however. The test is to ensure consistentency.\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_in1d_ravel",
"reference": "\n def test_in1d_ravel(self, kind):\n # Test that in1d ravels its input arrays. This is not documented\n # behavior however. The test is to ensure consistentency.\n a = np.arange(6).reshape(2, 3)\n b = np.arange(3, 9).reshape(3, 2)\n long_b = np.arange(3, 63).reshape(30, 2)\n ec = np.array([False, False, False, True, True, True])\n\n assert_array_equal(in1d(a, b, assume_unique=True, kind=kind),\n ec)\n assert_array_equal(in1d(a, b, assume_unique=False,\n kind=kind),\n ec)\n assert_array_equal(in1d(a, long_b, assume_unique=True,\n kind=kind),\n ec)\n assert_array_equal(in1d(a, long_b, assume_unique=False,\n kind=kind),\n ec)",
"completions": [
"```python\n def test_in1d_ravel(self, kind):\n # Test that in1d ravels its input arrays. This is not documented\n # behavior however. The test is to ensure consistentency.\n ar1 = np.array([1, 2, 3, 4, 5])\n ar2 = np.array([1, 2, 3, 4, 5])\n ar1_ravel = ar1.ravel()\n ar2_ravel = ar2.ravel()\n self.assertTrue(np.array_equal(ar1_ravel, ar1))\n self.assertTrue(np.array_equal(ar2_ravel, ar2))\n self.assertTrue(np.array_equal(in1d(ar1, ar2, kind=kind),\n in1d(ar1_ravel, ar2_ravel, kind=kind)))\n```\n"
],
"line": 133,
"token": 1112,
"line_diff": 0,
"token_diff": 0
},
{
"id": 90,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef header_to_metadata_and_cell(\n lines, header_prefix, header_suffix, ext=None, root_level_metadata_as_raw_cell=True\n):\n\n header = []\n jupyter = []\n in_jupyter = False\n in_html_div = False\n\n start = 0\n started = False\n ended = False\n metadata = {}\n i = -1\n\n comment = \"#\" if header_prefix == \"#'\" else header_prefix\n\n encoding_re = re.compile(\n rf\"^[ \\t\\f]*{re.escape(comment)}.*?coding[:=][ \\t]*([-_.a-zA-Z0-9]+)\"\n )\n\n for i, line in enumerate(lines):\n if i == 0 and line.startswith(\"#!\"):\n metadata.setdefault(\"jupytext\", {})[\"executable\"] = line[2:]\n start = i + 1\n continue\n if i == 0 or (i == 1 and not encoding_re.match(lines[0])):\n encoding = encoding_re.match(line)\n if encoding:\n if encoding.group(1) != \"utf-8\":\n raise ValueError(\"Encodings other than utf-8 are not supported\")\n metadata.setdefault(\"jupytext\", {})[\"encoding\"] = line\n start = i + 1\n continue\n if not line.startswith(header_prefix):\n break\n if not comment:\n if line.strip().startswith(\"\" in line:\n break\n if not started and not line.strip():\n continue\n\n line = uncomment_line(line, header_prefix, header_suffix)\n if _HEADER_RE.match(line):\n if not started:\n started = True\n continue\n ended = True\n if in_html_div:\n continue\n break\n\n # Stop if there is something else than a YAML header\n if not started and line.strip():\n break\n\n if _JUPYTER_RE.match(line):\n in_jupyter = True\n elif line and not _LEFTSPACE_RE.match(line):\n in_jupyter = False\n\n if in_jupyter:\n jupyter.append(line)\n else:\n header.append(line)\n\n if ended:\n if jupyter:\n extra_metadata = metadata\n metadata = yaml.safe_load(\"\\n\".join(jupyter))[\"jupyter\"]\n recursive_update(metadata, extra_metadata)\n\n lines_to_next_cell = 1\n if len(lines) > i + 1:\n line = uncomment_line(lines[i + 1], header_prefix)\n if not _BLANK_RE.match(line):\n lines_to_next_cell = 0\n else:\n i = i + 1\n else:\n lines_to_next_cell = 0\n\n if header:\n if root_level_metadata_as_raw_cell:\n cell = new_raw_cell(\n source=\"\\n\".join([\"---\"] + header + [\"---\"]),\n metadata={}\n if lines_to_next_cell\n == pep8_lines_between_cells([\"---\"], lines[i + 1 :], ext)\n else {\"lines_to_next_cell\": lines_to_next_cell},\n )\n else:\n cell = None\n root_level_metadata = yaml.safe_load(\"\\n\".join(header))\n metadata.setdefault(\"jupytext\", {})[\n \"root_level_metadata\"\n ] = root_level_metadata\n else:\n cell = None\n\n return metadata, jupyter, cell, i + 1\n\n return metadata, False, None, start\n```\n###test function signature:\n```python\n\ndef test_header_to_metadata_and_cell_metadata():\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_header_to_metadata_and_cell_metadata",
"reference": "\ndef test_header_to_metadata_and_cell_metadata():\n text = \"\"\"---\ntitle: Sample header\njupyter:\n mainlanguage: python\n---\n\"\"\"\n lines = text.splitlines()\n metadata, _, cell, pos = header_to_metadata_and_cell(lines, \"\", \"\")\n\n assert metadata == {\"mainlanguage\": \"python\"}\n assert cell.cell_type == \"raw\"\n assert (\n cell.source\n == \"\"\"---\ntitle: Sample header\n---\"\"\"\n )\n assert cell.metadata == {\"lines_to_next_cell\": 0}\n assert pos == len(lines)",
"completions": [
"```python\ndef test_header_to_metadata_and_cell_metadata():\n lines = [\n \"#' ---\",\n \"#' jupyter:\",\n \"#' jupytext:\",\n \"#' text_representation:\",\n \"#' extension: .py\",\n \"#' format_name: light\",\n \"#' format_version: '1.5'\",\n \"#' jupytext_version: 1.11.4\",\n \"#' kernelspec:\",\n \"#' display_name: Python 3\",\n \"#' language: python\",\n \"#' name: python3\",\n \"#' ---\",\n \"#'\",\n \"#' This is a comment\",\n \"#'\",\n \"#' And another one\",\n \"#'\",\n \"#' ```python\",\n \"#' import numpy as np\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' import matplotlib.pyplot as plt\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %matplotlib inline\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2, 3])\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.show()\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' %config InlineBackend.figure_format = 'retina'\",\n \"#' ```\",\n \"#'\",\n \"#' ```python\",\n \"#' plt.plot([1, 2"
],
"line": 104,
"token": 642,
"line_diff": 0,
"token_diff": 0
},
{
"id": 91,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef import_optional_dependency(\n name: str,\n extra: str = \"\",\n errors: str = \"raise\",\n min_version: str | None = None,\n):\n\n assert errors in {\"warn\", \"raise\", \"ignore\"}\n\n package_name = INSTALL_MAPPING.get(name)\n install_name = package_name if package_name is not None else name\n\n msg = (\n f\"Missing optional dependency '{install_name}'. {extra} \"\n f\"Use pip or conda to install {install_name}.\"\n )\n try:\n module = importlib.import_module(name)\n except ImportError:\n if errors == \"raise\":\n raise ImportError(msg)\n return None\n\n # Handle submodules: if we have submodule, grab parent module from sys.modules\n parent = name.split(\".\")[0]\n if parent != name:\n install_name = parent\n module_to_get = sys.modules[install_name]\n else:\n module_to_get = module\n minimum_version = min_version if min_version is not None else VERSIONS.get(parent)\n if minimum_version:\n version = get_version(module_to_get)\n if version and Version(version) < Version(minimum_version):\n msg = (\n f\"Pandas requires version '{minimum_version}' or newer of '{parent}' \"\n f\"(version '{version}' currently installed).\"\n )\n if errors == \"warn\":\n warnings.warn(\n msg,\n UserWarning,\n stacklevel=find_stack_level(),\n )\n return None\n elif errors == \"raise\":\n raise ImportError(msg)\n\n return module\n```\n###test function signature:\n```python\n\ndef test_bad_version(monkeypatch):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_bad_version",
"reference": "\ndef test_bad_version(monkeypatch):\n name = \"fakemodule\"\n module = types.ModuleType(name)\n module.__version__ = \"0.9.0\"\n sys.modules[name] = module\n monkeypatch.setitem(VERSIONS, name, \"1.0.0\")\n\n match = \"Pandas requires .*1.0.0.* of .fakemodule.*'0.9.0'\"\n with pytest.raises(ImportError, match=match):\n import_optional_dependency(\"fakemodule\")\n\n # Test min_version parameter\n result = import_optional_dependency(\"fakemodule\", min_version=\"0.8\")\n assert result is module\n\n with tm.assert_produces_warning(UserWarning):\n result = import_optional_dependency(\"fakemodule\", errors=\"warn\")\n assert result is None\n\n module.__version__ = \"1.0.0\" # exact match is OK\n result = import_optional_dependency(\"fakemodule\")\n assert result is module",
"completions": [
"```python\ndef test_bad_version(monkeypatch):\n monkeypatch.setattr(import_optional_dependency, \"get_version\", lambda x: \"0.1.0\")\n monkeypatch.setattr(import_optional_dependency, \"VERSIONS\", {\"pandas\": \"0.2.0\"})\n with pytest.raises(ImportError):\n import_optional_dependency(\"pandas\")\n```\n"
],
"line": 54,
"token": 352,
"line_diff": 0,
"token_diff": 0
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{
"id": 92,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef spectrogram(\n waveform: np.ndarray,\n window: np.ndarray,\n frame_length: int,\n hop_length: int,\n fft_length: Optional[int] = None,\n power: Optional[float] = 1.0,\n center: bool = True,\n pad_mode: str = \"reflect\",\n onesided: bool = True,\n preemphasis: Optional[float] = None,\n mel_filters: Optional[np.ndarray] = None,\n mel_floor: float = 1e-10,\n log_mel: Optional[str] = None,\n reference: float = 1.0,\n min_value: float = 1e-10,\n db_range: Optional[float] = None,\n remove_dc_offset: Optional[bool] = None,\n dtype: np.dtype = np.float32,\n) -> np.ndarray:\n window_length = len(window)\n\n if fft_length is None:\n fft_length = frame_length\n\n if frame_length > fft_length:\n raise ValueError(f\"frame_length ({frame_length}) may not be larger than fft_length ({fft_length})\")\n\n if window_length != frame_length:\n raise ValueError(f\"Length of the window ({window_length}) must equal frame_length ({frame_length})\")\n\n if hop_length <= 0:\n raise ValueError(\"hop_length must be greater than zero\")\n\n if waveform.ndim != 1:\n raise ValueError(f\"Input waveform must have only one dimension, shape is {waveform.shape}\")\n\n if np.iscomplexobj(waveform):\n raise ValueError(\"Complex-valued input waveforms are not currently supported\")\n\n if power is None and mel_filters is not None:\n raise ValueError(\n \"You have provided `mel_filters` but `power` is `None`. Mel spectrogram computation is not yet supported for complex-valued spectrogram.\"\n \"Specify `power` to fix this issue.\"\n )\n\n # center pad the waveform\n if center:\n padding = [(int(frame_length // 2), int(frame_length // 2))]\n waveform = np.pad(waveform, padding, mode=pad_mode)\n\n # promote to float64, since np.fft uses float64 internally\n waveform = waveform.astype(np.float64)\n window = window.astype(np.float64)\n\n # split waveform into frames of frame_length size\n num_frames = int(1 + np.floor((waveform.size - frame_length) / hop_length))\n\n num_frequency_bins = (fft_length // 2) + 1 if onesided else fft_length\n spectrogram = np.empty((num_frames, num_frequency_bins), dtype=np.complex64)\n\n # rfft is faster than fft\n fft_func = np.fft.rfft if onesided else np.fft.fft\n buffer = np.zeros(fft_length)\n\n timestep = 0\n for frame_idx in range(num_frames):\n buffer[:frame_length] = waveform[timestep : timestep + frame_length]\n\n if remove_dc_offset:\n buffer[:frame_length] = buffer[:frame_length] - buffer[:frame_length].mean()\n\n if preemphasis is not None:\n buffer[1:frame_length] -= preemphasis * buffer[: frame_length - 1]\n buffer[0] *= 1 - preemphasis\n\n buffer[:frame_length] *= window\n\n spectrogram[frame_idx] = fft_func(buffer)\n timestep += hop_length\n\n # note: ** is much faster than np.power\n if power is not None:\n spectrogram = np.abs(spectrogram, dtype=np.float64) ** power\n\n spectrogram = spectrogram.T\n\n if mel_filters is not None:\n spectrogram = np.maximum(mel_floor, np.dot(mel_filters.T, spectrogram))\n\n if power is not None and log_mel is not None:\n if log_mel == \"log\":\n spectrogram = np.log(spectrogram)\n elif log_mel == \"log10\":\n spectrogram = np.log10(spectrogram)\n elif log_mel == \"dB\":\n if power == 1.0:\n spectrogram = amplitude_to_db(spectrogram, reference, min_value, db_range)\n elif power == 2.0:\n spectrogram = power_to_db(spectrogram, reference, min_value, db_range)\n else:\n raise ValueError(f\"Cannot use log_mel option '{log_mel}' with power {power}\")\n else:\n raise ValueError(f\"Unknown log_mel option: {log_mel}\")\n\n spectrogram = np.asarray(spectrogram, dtype)\n\n return spectrogram\n```\n###test function signature:\n```python\n\n def test_mel_spectrogram(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_mel_spectrogram",
"reference": "\n def test_mel_spectrogram(self):\n waveform = self._load_datasamples(1)[0]\n\n mel_filters = mel_filter_bank(\n num_frequency_bins=513,\n num_mel_filters=13,\n min_frequency=100,\n max_frequency=4000,\n sampling_rate=16000,\n norm=None,\n mel_scale=\"htk\",\n )\n self.assertEqual(mel_filters.shape, (513, 13))\n\n spec = spectrogram(\n waveform,\n window_function(800, \"hann\", frame_length=1024),\n frame_length=1024,\n hop_length=128,\n power=2.0,\n )\n self.assertEqual(spec.shape, (513, 732))\n\n spec = spectrogram(\n waveform,\n window_function(800, \"hann\", frame_length=1024),\n frame_length=1024,\n hop_length=128,\n power=2.0,\n mel_filters=mel_filters,\n )\n self.assertEqual(spec.shape, (13, 732))\n\n # fmt: off\n expected = np.array([\n 1.08027889e+02, 1.48080673e+01, 7.70758213e+00, 9.57676639e-01,\n 8.81639061e-02, 5.26073833e-02, 1.52736155e-02, 9.95350117e-03,\n 7.95364356e-03, 1.01148004e-02, 4.29241020e-03, 9.90708797e-03,\n 9.44153646e-04\n ])\n # fmt: on\n self.assertTrue(np.allclose(spec[:, 300], expected))",
"completions": [
"```python\n def test_mel_spectrogram(self):\n waveform = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n window = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])\n frame_length = 10\n hop_length = 1\n fft_length = 10\n power = 1.0\n center = True\n pad_mode = \"reflect\"\n onesided = True\n preemphasis = None\n mel_filters = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])\n mel_floor = 1e-10\n log_mel = \"log\"\n reference = 1.0\n min_value = 1e-10\n db_range = None\n remove_dc_offset = None\n dtype = np.float32\n\n spectrogram = spectrogram(\n waveform,\n window,\n frame_length,\n hop_length,\n fft_length,\n power,\n center,\n pad_mode,\n onesided,\n preemphasis,\n mel_filters,\n mel_floor,\n log_mel,\n reference,\n min_value,\n db_range,\n remove_dc_offset,\n dtype,\n )\n\n self.assertEqual(spectrogram.shape, (1, 10))\n self.assertEqual(spectrogram[0][0], 1)\n self.assertEqual(spectrogram[0][1], 2)\n self.assertEqual(spectrogram[0][2], 3)\n self.assertEqual(spectrogram[0][3], 4)\n self.assertEqual(spectrogram[0][4], 5)\n self.assertEqual(spectrogram[0][5], 6)\n self.assertEqual(spectrogram[0][6], 7)\n self.assertEqual(spectrogram[0][7], 8)\n self.assertEqual(spectrogram[0][8], 9)\n self.assertEqual(spectrogram[0][9], 10)\n```\n"
],
"line": 95,
"token": 804,
"line_diff": 0,
"token_diff": 0
},
{
"id": 93,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef create(\n meta_schema,\n validators=(),\n version=None,\n type_checker=_types.draft202012_type_checker,\n format_checker=_format.draft202012_format_checker,\n id_of=_id_of,\n applicable_validators=methodcaller(\"items\"),\n):\n # preemptively don't shadow the `Validator.format_checker` local\n format_checker_arg = format_checker\n\n @attr.s\n class Validator:\n\n VALIDATORS = dict(validators)\n META_SCHEMA = dict(meta_schema)\n TYPE_CHECKER = type_checker\n FORMAT_CHECKER = format_checker_arg\n ID_OF = staticmethod(id_of)\n\n schema = attr.ib(repr=reprlib.repr)\n resolver = attr.ib(default=None, repr=False)\n format_checker = attr.ib(default=None)\n\n def __init_subclass__(cls):\n warnings.warn(\n (\n \"Subclassing validator classes is not intended to \"\n \"be part of their public API. A future version \"\n \"will make doing so an error, as the behavior of \"\n \"subclasses isn't guaranteed to stay the same \"\n \"between releases of jsonschema. Instead, prefer \"\n \"composition of validators, wrapping them in an object \"\n \"owned entirely by the downstream library.\"\n ),\n DeprecationWarning,\n stacklevel=2,\n )\n\n def __attrs_post_init__(self):\n if self.resolver is None:\n self.resolver = RefResolver.from_schema(\n self.schema,\n id_of=id_of,\n )\n\n @classmethod\n def check_schema(cls, schema, format_checker=_UNSET):\n Validator = validator_for(cls.META_SCHEMA, default=cls)\n if format_checker is _UNSET:\n format_checker = Validator.FORMAT_CHECKER\n validator = Validator(\n schema=cls.META_SCHEMA,\n format_checker=format_checker,\n )\n for error in validator.iter_errors(schema):\n raise exceptions.SchemaError.create_from(error)\n\n def evolve(self, **changes):\n # Essentially reproduces attr.evolve, but may involve instantiating\n # a different class than this one.\n cls = self.__class__\n\n schema = changes.setdefault(\"schema\", self.schema)\n NewValidator = validator_for(schema, default=cls)\n\n for field in attr.fields(cls):\n if not field.init:\n continue\n attr_name = field.name # To deal with private attributes.\n init_name = attr_name if attr_name[0] != \"_\" else attr_name[1:]\n if init_name not in changes:\n changes[init_name] = getattr(self, attr_name)\n\n return NewValidator(**changes)\n\n def iter_errors(self, instance, _schema=None):\n if _schema is not None:\n warnings.warn(\n (\n \"Passing a schema to Validator.iter_errors \"\n \"is deprecated and will be removed in a future \"\n \"release. Call validator.evolve(schema=new_schema).\"\n \"iter_errors(...) instead.\"\n ),\n DeprecationWarning,\n stacklevel=2,\n )\n else:\n _schema = self.schema\n\n if _schema is True:\n return\n elif _schema is False:\n yield exceptions.ValidationError(\n f\"False schema does not allow {instance!r}\",\n validator=None,\n validator_value=None,\n instance=instance,\n schema=_schema,\n )\n return\n\n scope = id_of(_schema)\n if scope:\n self.resolver.push_scope(scope)\n try:\n for k, v in applicable_validators(_schema):\n validator = self.VALIDATORS.get(k)\n if validator is None:\n continue\n\n errors = validator(self, v, instance, _schema) or ()\n for error in errors:\n # set details if not already set by the called fn\n error._set(\n validator=k,\n validator_value=v,\n instance=instance,\n schema=_schema,\n type_checker=self.TYPE_CHECKER,\n )\n if k not in {\"if\", \"$ref\"}:\n error.schema_path.appendleft(k)\n yield error\n finally:\n if scope:\n self.resolver.pop_scope()\n\n def descend(self, instance, schema, path=None, schema_path=None):\n for error in self.evolve(schema=schema).iter_errors(instance):\n if path is not None:\n error.path.appendleft(path)\n if schema_path is not None:\n error.schema_path.appendleft(schema_path)\n yield error\n\n def validate(self, *args, **kwargs):\n for error in self.iter_errors(*args, **kwargs):\n raise error\n\n def is_type(self, instance, type):\n try:\n return self.TYPE_CHECKER.is_type(instance, type)\n except exceptions.UndefinedTypeCheck:\n raise exceptions.UnknownType(type, instance, self.schema)\n\n def is_valid(self, instance, _schema=None):\n if _schema is not None:\n warnings.warn(\n (\n \"Passing a schema to Validator.is_valid is deprecated \"\n \"and will be removed in a future release. Call \"\n \"validator.evolve(schema=new_schema).is_valid(...) \"\n \"instead.\"\n ),\n DeprecationWarning,\n stacklevel=2,\n )\n self = self.evolve(schema=_schema)\n\n error = next(self.iter_errors(instance), None)\n return error is None\n\n if version is not None:\n safe = version.title().replace(\" \", \"\").replace(\"-\", \"\")\n Validator.__name__ = Validator.__qualname__ = f\"{safe}Validator\"\n Validator = validates(version)(Validator)\n\n return Validator\n```\n###test function signature:\n```python\n\n def test_validates_registers_meta_schema_id(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_validates_registers_meta_schema_id",
"reference": "\n def test_validates_registers_meta_schema_id(self):\n meta_schema_key = \"meta schema id\"\n my_meta_schema = {\"id\": meta_schema_key}\n\n validators.create(\n meta_schema=my_meta_schema,\n version=\"my version\",\n id_of=lambda s: s.get(\"id\", \"\"),\n )\n self.addCleanup(validators._META_SCHEMAS.pop, meta_schema_key)\n\n self.assertIn(meta_schema_key, validators._META_SCHEMAS)",
"completions": [
"```python\n def test_validates_registers_meta_schema_id(self):\n validator = create(\n meta_schema={\n \"$id\": \"https://example.com/schema.json\",\n \"type\": \"object\",\n \"properties\": {\"foo\": {\"type\": \"string\"}},\n }\n )\n self.assertEqual(\n validator.resolver.store[\"https://example.com/schema.json\"],\n validator.schema,\n )\n```\n"
],
"line": 160,
"token": 896,
"line_diff": 0,
"token_diff": 0
},
{
"id": 94,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef read_metrics(directory: str) -> pd.Series:\n\n dfs = []\n # Loop through all csv files in the directory\n csv_files = [\n filename for filename in os.listdir(directory) if filename.endswith(\".csv\")\n ]\n\n if len(csv_files) == 0:\n return pd.Series()\n\n for filename in csv_files:\n filepath = os.path.join(directory, filename)\n # Read the csv file into a pandas DataFrame\n df = pd.read_csv(\n filepath,\n header=None,\n names=[\n \"initial_time_iso\",\n \"lead_time_hours\",\n \"channel\",\n \"metric\",\n \"value\",\n ],\n )\n out = pd.DataFrame()\n out[\"initial_time\"] = pd.to_datetime(df[\"initial_time_iso\"])\n out[\"lead_time\"] = pd.to_timedelta(df[\"lead_time_hours\"], unit=\"h\")\n out[\"channel\"] = df[\"channel\"]\n out[\"metric\"] = df[\"metric\"]\n out[\"value\"] = df[\"value\"]\n dfs.append(out)\n\n df = pd.concat(dfs, axis=0)\n df.set_index([\"initial_time\", \"lead_time\", \"channel\", \"metric\"], inplace=True)\n return df[\"value\"]\n```\n###test function signature:\n```python\n\ndef test_write_and_read_metric(tmpdir):\n # Write a metric to the file object\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_write_and_read_metric",
"reference": "\ndef test_write_and_read_metric(tmpdir):\n # Write a metric to the file object\n initial_time = datetime.datetime(2022, 1, 1, 0, 0, 0)\n lead_time = datetime.timedelta(hours=24)\n channel = \"t2m\"\n metric = \"rmse\"\n value = 25.6\n\n # Read the metric from the directory\n directory = tmpdir.mkdir(\"metrics\")\n filename = \"test_metric.csv\"\n filepath = os.path.join(directory, filename)\n with open(filepath, \"w\") as f:\n forecast_metrics_io.write_metric(\n f, initial_time, lead_time, channel, metric, value\n )\n metrics = forecast_metrics_io.read_metrics(directory)\n\n # Check that the metric was written correctly\n expected_index = pd.MultiIndex.from_tuples(\n [(initial_time, lead_time, channel, metric)],\n names=[\"initial_time\", \"lead_time\", \"channel\", \"metric\"],\n )\n expected_value = pd.Series([value], index=expected_index, name=\"value\")\n pd.testing.assert_series_equal(metrics, expected_value)",
"completions": [
"```python\ndef test_write_and_read_metric(tmpdir):\n # Write a metric to the file object\n metric = pd.Series(\n data=[1, 2, 3],\n index=[\n pd.Timestamp(\"2020-01-01 00:00:00\"),\n pd.Timestamp(\"2020-01-01 01:00:00\"),\n pd.Timestamp(\"2020-01-01 02:00:00\"),\n ],\n name=\"metric\",\n )\n metric.to_csv(tmpdir.join(\"metric.csv\"), header=False)\n\n # Read the metric from the file object\n read_metric = read_metrics(tmpdir.strpath)\n\n # Check that the metric is the same\n assert_series_equal(metric, read_metric)\n```\n"
],
"line": 43,
"token": 320,
"line_diff": 0,
"token_diff": 0
},
{
"id": 95,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n def evaluate(self, points):\n points = np.atleast_2d(points)\n\n dim, num_m = np.array(points).shape\n if dim != self.dim:\n raise ValueError(\"points have dimension {}, dataset has dimension \"\n \"{}\".format(dim, self.dim))\n\n result = np.zeros(num_m)\n\n if num_m >= self.num_dp:\n # there are more points than data, so loop over data\n for i in range(self.num_dp):\n diff = self.dataset[:, i, np.newaxis] - points\n tdiff = np.dot(self.inv_cov, diff)\n energy = np.sum(diff * tdiff, axis=0) / 2.0\n result = result + np.exp(-energy)\n else:\n # loop over points\n for i in range(num_m):\n diff = self.dataset - points[:, i, np.newaxis]\n tdiff = np.dot(self.inv_cov, diff)\n energy = np.sum(diff * tdiff, axis=0) / 2.0\n result[i] = np.sum(np.exp(-energy), axis=0)\n\n result = result / self.norm_factor\n\n return result\n```\n###test function signature:\n```python\n\n def test_kde_bandwidth_method(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_kde_bandwidth_method",
"reference": "\n def test_kde_bandwidth_method(self):\n\n np.random.seed(8765678)\n n_basesample = 50\n xn = np.random.randn(n_basesample)\n\n # Default\n gkde = mlab.GaussianKDE(xn)\n # Supply a callable\n gkde2 = mlab.GaussianKDE(xn, 'scott')\n # Supply a scalar\n gkde3 = mlab.GaussianKDE(xn, bw_method=gkde.factor)\n\n xs = np.linspace(-7, 7, 51)\n kdepdf = gkde.evaluate(xs)\n kdepdf2 = gkde2.evaluate(xs)\n assert kdepdf.all() == kdepdf2.all()\n kdepdf3 = gkde3.evaluate(xs)\n assert kdepdf.all() == kdepdf3.all()",
"completions": [
"```python\n def test_kde_bandwidth_method(self):\n from sklearn.neighbors import KernelDensity\n from sklearn.metrics import euclidean_distances\n from sklearn.datasets import make_blobs\n from sklearn.preprocessing import StandardScaler\n from sklearn.model_selection import train_test_split\n from sklearn.metrics import accuracy_score\n from sklearn.metrics import f1_score\n from sklearn.metrics import precision_score\n from sklearn.metrics import recall_score\n from sklearn.metrics import roc_auc_score\n from sklearn.metrics import confusion_matrix\n from sklearn.metrics import classification_report\n from sklearn.metrics import cohen_kappa_score\n from sklearn.metrics import mean_squared_error\n from sklearn.metrics import mean_absolute_error\n from sklearn.metrics import mean_squared_log_error\n from sklearn.metrics import median_absolute_error\n from sklearn.metrics import r2_score\n from sklearn.metrics import explained_variance_score\n from sklearn.metrics import silhouette_score\n from sklearn.metrics import calinski_harabasz_score\n from sklearn.metrics import davies_bouldin_score\n from sklearn.metrics import mutual_info_score\n from sklearn.metrics import normalized_mutual_info_score\n from sklearn.metrics import adjusted_mutual_info_score\n from sklearn.metrics import adjusted_rand_score\n from sklearn.metrics import completeness_score\n from sklearn.metrics import contingency_matrix\n from sklearn.metrics import homogeneity_completeness_v_measure\n from sklearn.metrics import homogeneity_score\n from sklearn.metrics import v_measure_score\n from sklearn.metrics import normalized_v_measure_score\n from sklearn.metrics import poisson\n from sklearn.metrics import kld\n from sklearn.metrics import kld_normalized\n from sklearn.metrics import kld_unnormalized\n from sklearn.metrics import kld_unnormalized_normalized\n from sklearn.metrics import kld_unnormalized_normalized_sym\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from sklearn.metrics import kld_unnormalized_normalized_sym_\n from"
],
"line": 33,
"token": 273,
"line_diff": 0,
"token_diff": 0
},
{
"id": 96,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef get_top_k_embeddings(\n query_embedding: List[float],\n embeddings: List[List[float]],\n similarity_fn: Optional[Callable[..., float]] = None,\n similarity_top_k: Optional[int] = None,\n embedding_ids: Optional[List] = None,\n similarity_cutoff: Optional[float] = None,\n) -> Tuple[List[float], List]:\n if embedding_ids is None:\n embedding_ids = list(range(len(embeddings)))\n\n similarity_fn = similarity_fn or default_similarity_fn\n\n embeddings_np = np.array(embeddings)\n query_embedding_np = np.array(query_embedding)\n\n similarity_heap: List[Tuple[float, Any]] = []\n for i, emb in enumerate(embeddings_np):\n similarity = similarity_fn(query_embedding_np, emb)\n if similarity_cutoff is None or similarity > similarity_cutoff:\n heapq.heappush(similarity_heap, (similarity, embedding_ids[i]))\n if similarity_top_k and len(similarity_heap) > similarity_top_k:\n heapq.heappop(similarity_heap)\n result_tups = sorted(similarity_heap, key=lambda x: x[0], reverse=True)\n\n result_similarities = [s for s, _ in result_tups]\n result_ids = [n for _, n in result_tups]\n\n return result_similarities, result_ids\n```\n###test function signature:\n```python\ndef test_get_top_k_mmr_embeddings() -> None:\n # Results score should follow from the mmr algorithm\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_get_top_k_mmr_embeddings",
"reference": "def test_get_top_k_mmr_embeddings() -> None:\n # Results score should follow from the mmr algorithm\n query_embedding = [5.0, 0.0, 0.0]\n embeddings = [[4.0, 3.0, 0.0], [3.0, 4.0, 0.0], [-4.0, 3.0, 0.0]]\n result_similarities, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, mmr_threshold=0.8\n )\n\n assert np.isclose(0.8 * 4 / 5, result_similarities[0], atol=0.00001)\n assert np.isclose(\n 0.8 * 3 / 5 - (1 - 0.8) * (3 * 4 / 25 + 3 * 4 / 25),\n result_similarities[1],\n atol=0.00001,\n )\n assert np.isclose(\n 0.8 * -4 / 5 - (1 - 0.8) * (3 * -4 / 25 + 4 * 3 / 25),\n result_similarities[2],\n atol=0.00001,\n )\n assert result_ids == [0, 1, 2]\n\n # Tests that if the first embedding vector is close to the second,\n # it will return the third\n query_embedding = [1.0, 0.0, 1.0]\n embeddings = [[1.0, 0.0, 0.9], [1.0, 0.0, 0.8], [0.7, 0.0, 1.0]]\n\n _, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, mmr_threshold=0.5\n )\n assert result_ids == [0, 2, 1]\n\n # Tests that embedding ids map properly to results\n _, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, embedding_ids=[\"A\", \"B\", \"C\"], mmr_threshold=0.5\n )\n assert result_ids == [\"A\", \"C\", \"B\"]\n # Test that it will go back to the original order under a high threshold\n _, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, mmr_threshold=1\n )\n assert result_ids == [0, 1, 2]\n\n # Test similarity_top_k works\n _, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, mmr_threshold=1, similarity_top_k=2\n )\n assert result_ids == [0, 1]\n\n # Test the results for get_top_k_embeddings and get_top_k_mmr_embeddings are the\n # same for threshold = 1\n query_embedding = [10, 23, 90, 78]\n embeddings = [[1, 23, 89, 68], [1, 74, 144, 23], [0.23, 0.0, 1.0, 9]]\n result_similarities_no_mmr, result_ids_no_mmr = get_top_k_embeddings(\n query_embedding, embeddings\n )\n result_similarities, result_ids = get_top_k_mmr_embeddings(\n query_embedding, embeddings, mmr_threshold=1\n )\n\n for result_no_mmr, result_with_mmr in zip(\n result_similarities_no_mmr, result_similarities\n ):\n assert np.isclose(result_no_mmr, result_with_mmr, atol=0.00001)",
"completions": [
"```python\ndef test_get_top_k_mmr_embeddings() -> None:\n query_embedding = [0.1, 0.2, 0.3, 0.4, 0.5]\n embeddings = [\n [0.1, 0.2, 0.3, 0.4, 0.5],\n [0.1, 0.2, 0.3, 0.4, 0.5],\n [0.1, 0.2, 0.3, 0.4, 0.5],\n [0.1, 0.2, 0.3, 0.4, 0.5],\n [0.1, 0.2, 0.3, 0.4, 0.5],\n ]\n result_similarities, result_ids = get_top_k_embeddings(\n query_embedding, embeddings, similarity_top_k=3\n )\n assert result_similarities == [0.5, 0.4, 0.3]\n assert result_ids == [0, 1, 2]\n```\n"
],
"line": 35,
"token": 309,
"line_diff": 0,
"token_diff": 0
},
{
"id": 97,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef frame_accuracy(\n logits: np.ndarray,\n label: np.ndarray,\n background_logit_threshold: float = 0.0,\n) -> float:\n top1_idx = np.argmax(logits, axis=-1)\n background_label = np.sum(label, axis=-1) == 0\n pred_background = (\n np.max(logits, axis=-1) <= background_logit_threshold\n ).astype(np.int32)\n\n # Extracts the label at the highest logit index for each input.\n top1_correct = np.take_along_axis(label, top1_idx[..., None], axis=-1)\n top1_correct = np.squeeze(top1_correct)\n foreground_correct = ~pred_background.astype(bool) * top1_correct\n\n # Count correctly classified background samples.\n background_correct = pred_background * background_label\n correct = foreground_correct + background_correct\n return np.sum(correct) / len(logits)\n```\n###test function signature:\n```python\n\n def test_frame_accuracy_all_background(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_frame_accuracy_all_background",
"reference": "\n def test_frame_accuracy_all_background(self):\n logits = np.array(\n [\n [1.5, 0.0, -1.0],\n [0.1, 0.7, 0.2],\n [0.1, 0.7, 0.2],\n [0.1, -0.7, 0.2],\n [-0.1, -0.7, -0.2],\n ],\n np.float32,\n )\n label = np.array(\n [\n [0, 0, 0],\n [0, 0, 0],\n [0, 0, 0],\n [0, 0, 0],\n [0, 0, 0],\n ],\n np.int32,\n )\n actual = metrics.frame_accuracy(logits, label)\n self.assertAlmostEqual(actual, 0.2)",
"completions": [
"```python\ndef test_frame_accuracy_all_background(self):\n logits = np.array([[0.1, 0.2, 0.3, 0.4]])\n label = np.array([[0, 0, 0, 0]])\n self.assertAlmostEqual(frame_accuracy(logits, label), 1.0)\n```\n"
],
"line": 28,
"token": 206,
"line_diff": 0,
"token_diff": 0
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{
"id": 98,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\n\ndef load_image_from_string(\n reference: str,\n max_height: Optional[int] = None,\n max_width: Optional[int] = None,\n) -> Tuple[str, Optional[float]]:\n if uri_is_http_link(uri=reference):\n return load_image_from_url(\n url=reference, max_height=max_height, max_width=max_width\n )\n if os.path.exists(reference):\n if max_height is None or max_width is None:\n with open(reference, \"rb\") as f:\n img_bytes = f.read()\n img_base64_str = encode_base_64(payload=img_bytes)\n return img_base64_str, None\n local_image = cv2.imread(reference)\n if local_image is None:\n raise EncodingError(f\"Could not load image from {reference}\")\n local_image, scaling_factor = resize_opencv_image(\n image=local_image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=local_image), scaling_factor\n if max_height is not None and max_width is not None:\n image_bytes = base64.b64decode(reference)\n image = bytes_to_opencv_image(payload=image_bytes)\n image, scaling_factor = resize_opencv_image(\n image=image,\n max_height=max_height,\n max_width=max_width,\n )\n return numpy_array_to_base64_jpeg(image=image), scaling_factor\n return reference, None\n```\n###test function signature:\n```python\n\ndef test_load_image_from_string_when_local_image_to_be_loaded(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_load_image_from_string_when_local_image_to_be_loaded",
"reference": "\ndef test_load_image_from_string_when_local_image_to_be_loaded(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n file_path, _ = example_local_image\n\n # when\n serialised_image, scaling_factor = load_image_from_string(\n reference=file_path,\n max_width=64,\n max_height=128,\n )\n recovered_image = base64.b64decode(serialised_image)\n bytes_array = np.frombuffer(recovered_image, dtype=np.uint8)\n decoding_result = cv2.imdecode(bytes_array, cv2.IMREAD_UNCHANGED)\n\n # then\n assert abs(scaling_factor - 0.5) < 1e-5\n assert decoding_result.shape == (64, 64, 3)\n assert (decoding_result == 0).all()",
"completions": [
"```python\n\ndef test_load_image_from_string_when_local_image_to_be_loaded(\n example_local_image: Tuple[str, np.ndarray]\n) -> None:\n # given\n reference, image = example_local_image\n # when\n result = load_image_from_string(\n reference=reference,\n max_height=image.shape[0],\n max_width=image.shape[1],\n )\n # then\n assert result[0] == reference\n assert result[1] is None\n```\n"
],
"line": 47,
"token": 280,
"line_diff": 0,
"token_diff": 0
},
{
"id": 99,
"prompt": "\nYou are an expert Python programmer. You will be given some information and will generate a correct test case for the given function.\nYour response will only be python test function and without anything else.\n\n### focal function:\n```python\ndef hertz_to_mel(freq: Union[float, np.ndarray], mel_scale: str = \"htk\") -> Union[float, np.ndarray]:\n\n if mel_scale not in [\"slaney\", \"htk\", \"kaldi\"]:\n raise ValueError('mel_scale should be one of \"htk\", \"slaney\" or \"kaldi\".')\n\n if mel_scale == \"htk\":\n return 2595.0 * np.log10(1.0 + (freq / 700.0))\n elif mel_scale == \"kaldi\":\n return 1127.0 * np.log(1.0 + (freq / 700.0))\n\n min_log_hertz = 1000.0\n min_log_mel = 15.0\n logstep = 27.0 / np.log(6.4)\n mels = 3.0 * freq / 200.0\n\n if isinstance(freq, np.ndarray):\n log_region = freq >= min_log_hertz\n mels[log_region] = min_log_mel + np.log(freq[log_region] / min_log_hertz) * logstep\n elif freq >= min_log_hertz:\n mels = min_log_mel + np.log(freq / min_log_hertz) * logstep\n\n return mels\n```\n###test function signature:\n```python\n\n def test_hertz_to_mel(self):\n```\n### Answer: (use the provided format with backticks)\n",
"name": "test_hertz_to_mel",
"reference": "\n def test_hertz_to_mel(self):\n self.assertEqual(hertz_to_mel(0.0), 0.0)\n self.assertAlmostEqual(hertz_to_mel(100), 150.48910241)\n\n inputs = np.array([100, 200])\n expected = np.array([150.48910241, 283.22989816])\n self.assertTrue(np.allclose(hertz_to_mel(inputs), expected))\n\n self.assertEqual(hertz_to_mel(0.0, \"slaney\"), 0.0)\n self.assertEqual(hertz_to_mel(100, \"slaney\"), 1.5)\n\n inputs = np.array([60, 100, 200, 1000, 1001, 2000])\n expected = np.array([0.9, 1.5, 3.0, 15.0, 15.01453781, 25.08188016])\n self.assertTrue(np.allclose(hertz_to_mel(inputs, \"slaney\"), expected))\n\n inputs = np.array([60, 100, 200, 1000, 1001, 2000])\n expected = np.array([92.6824, 150.4899, 283.2313, 999.9907, 1000.6534, 1521.3674])\n self.assertTrue(np.allclose(hertz_to_mel(inputs, \"kaldi\"), expected))\n\n with pytest.raises(ValueError):\n hertz_to_mel(100, mel_scale=None)",
"completions": [
"```python\n def test_hertz_to_mel(self):\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"slaney\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"htk\"), 0)\n self.assertEqual(hertz_to_mel(1000, \"kaldi\"), 0)\n self.assertEqual(hertz_to_mel("
],
"line": 27,
"token": 262,
"line_diff": 0,
"token_diff": 0
}
]