from __future__ import annotations

import gc
import inspect
import os
import time
from pathlib import Path
from unittest.mock import MagicMock, Mock, patch

import dill as pickle
import pytest
import torch
from inference.core.models.base import Model
from inference.models.qwen3_5vl.qwen3_5vl_inference_models import \
    InferenceModelsQwen35VLAdapter


def codeflash_wrap(codeflash_wrapped, codeflash_test_module_name, codeflash_test_class_name, codeflash_test_name, codeflash_function_name, codeflash_line_id, codeflash_loop_index, *args, **kwargs):
    test_id = f'{codeflash_test_module_name}:{codeflash_test_class_name}:{codeflash_test_name}:{codeflash_line_id}:{codeflash_loop_index}'
    if not hasattr(codeflash_wrap, 'index'):
        codeflash_wrap.index = {}
    if test_id in codeflash_wrap.index:
        codeflash_wrap.index[test_id] += 1
    else:
        codeflash_wrap.index[test_id] = 0
    codeflash_test_index = codeflash_wrap.index[test_id]
    invocation_id = f'{codeflash_line_id}_{codeflash_test_index}'
    test_stdout_tag = f"{codeflash_test_module_name}:{(codeflash_test_class_name + '.' if codeflash_test_class_name else '')}{codeflash_test_name}:{codeflash_function_name}:{codeflash_loop_index}:{invocation_id}"
    print(f'!$######{test_stdout_tag}######$!')
    exception = None
    _codeflash_should_sync_cuda = torch.cuda.is_available() and torch.cuda.is_initialized()
    _codeflash_should_sync_mps = not _codeflash_should_sync_cuda and hasattr(torch.backends, 'mps') and torch.backends.mps.is_available() and hasattr(torch.mps, 'synchronize')
    gc.disable()
    try:
        if _codeflash_should_sync_cuda:
            torch.cuda.synchronize()
        elif _codeflash_should_sync_mps:
            torch.mps.synchronize()
        counter = time.perf_counter_ns()
        return_value = codeflash_wrapped(*args, **kwargs)
        if _codeflash_should_sync_cuda:
            torch.cuda.synchronize()
        elif _codeflash_should_sync_mps:
            torch.mps.synchronize()
        codeflash_duration = time.perf_counter_ns() - counter
    except Exception as e:
        codeflash_duration = time.perf_counter_ns() - counter
        exception = e
    gc.enable()
    print(f'!######{test_stdout_tag}######!')
    iteration = os.environ['CODEFLASH_TEST_ITERATION']
    with Path('{codeflash_run_tmp_dir_client_side}', f'test_return_values_{iteration}.bin').open('ab') as f:
        pickled_values = pickle.dumps((args, kwargs, exception)) if exception else pickle.dumps((args, kwargs, return_value))
        _test_name = f"{codeflash_test_module_name}:{(codeflash_test_class_name + '.' if codeflash_test_class_name else '')}{codeflash_test_name}:{codeflash_function_name}:{codeflash_line_id}".encode('ascii')
        f.write(len(_test_name).to_bytes(4, byteorder='big'))
        f.write(_test_name)
        f.write(codeflash_duration.to_bytes(8, byteorder='big'))
        f.write(len(pickled_values).to_bytes(4, byteorder='big'))
        f.write(pickled_values)
        f.write(codeflash_loop_index.to_bytes(8, byteorder='big'))
        f.write(len(invocation_id).to_bytes(4, byteorder='big'))
        f.write(invocation_id.encode('ascii'))
    if exception:
        raise exception
    return return_value

@pytest.fixture
def mock_qwen35hf():
    """Create a mock Qwen35HF model that mimics the actual model behavior."""
    mock_model = Mock()
    mock_model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
    return mock_model

@pytest.fixture
def adapter_instance(mock_qwen35hf):
    """Create an InferenceModelsQwen35VLAdapter instance with mocked dependencies."""
    with patch('inference.models.qwen3_5vl.qwen3_5vl_inference_models.AutoModel') as mock_auto_model:
        mock_auto_model.from_pretrained.return_value = mock_qwen35hf
        with patch('inference.models.qwen3_5vl.qwen3_5vl_inference_models.API_KEY', 'test_api_key'):
            with patch('inference.models.qwen3_5vl.qwen3_5vl_inference_models.get_extra_weights_provider_headers', return_value={}):
                adapter = InferenceModelsQwen35VLAdapter(model_id='test_model')
                adapter._model = mock_qwen35hf
                return adapter

class TestInferenceModelsQwen35VLAdapterPredict:
    """Test suite for InferenceModelsQwen35VLAdapter.predict method."""

    def test_predict_returns_torch_tensor(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict returns a torch.Tensor.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_returns_torch_tensor', 'predict', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_basic_tensor_input(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles basic tensor input correctly.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        expected_output = torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_basic_tensor_input', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        adapter_instance._model.generate.assert_called_once()

    def test_predict_passes_inputs_to_generate(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        "Verify that predict passes inputs directly to the underlying model's generate method."
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_passes_inputs_to_generate', 'predict', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        call_args = adapter_instance._model.generate.call_args

    def test_predict_with_empty_kwargs(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict works correctly when no additional kwargs are provided.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        expected_output = torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_empty_kwargs', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_calls_map_inference_kwargs(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict calls map_inference_kwargs to process kwargs.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        custom_kwargs = {'max_tokens': 100}
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        with patch.object(adapter_instance, 'map_inference_kwargs', return_value=custom_kwargs) as mock_map:
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs, **custom_kwargs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_calls_map_inference_kwargs', 'predict', '4_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            mock_map.assert_called_once_with(custom_kwargs)

    def test_predict_passes_mapped_kwargs_to_generate(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict passes the mapped kwargs to generate method.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        custom_kwargs = {'max_tokens': 100, 'temperature': 0.7}
        mapped_kwargs = {'max_tokens': 100, 'temperature': 0.7}
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        with patch.object(adapter_instance, 'map_inference_kwargs', return_value=mapped_kwargs):
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs, **custom_kwargs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_passes_mapped_kwargs_to_generate', 'predict', '5_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            call_kwargs = adapter_instance._model.generate.call_args[1]

    def test_predict_with_zero_tensor(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles zero-valued tensor input.'
        inputs = torch.zeros(4, dtype=torch.float32)
        expected_output = torch.tensor([[0.0, 0.0, 0.0, 0.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_zero_tensor', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_negative_values(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles negative values in input tensor.'
        inputs = torch.tensor([-1.0, -2.0, -3.0, -4.0], dtype=torch.float32)
        expected_output = torch.tensor([[-1.0, -2.0, -3.0, -4.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_negative_values', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_large_values(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles large floating-point values.'
        inputs = torch.tensor([1000000.0, 100000.0, 10000.0, 1000.0], dtype=torch.float32)
        expected_output = torch.tensor([[1000000.0, 100000.0, 10000.0, 1000.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_large_values', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_small_values(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles small floating-point values.'
        inputs = torch.tensor([1e-06, 1e-05, 0.0001, 0.001], dtype=torch.float32)
        expected_output = torch.tensor([[1e-06, 1e-05, 0.0001, 0.001]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_small_values', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_mixed_positive_negative_values(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles mixed positive and negative values.'
        inputs = torch.tensor([1.5, -2.5, 3.5, -4.5], dtype=torch.float32)
        expected_output = torch.tensor([[1.5, -2.5, 3.5, -4.5]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_mixed_positive_negative_values', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_multiple_kwargs(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles multiple keyword arguments correctly.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        kwargs = {'max_tokens': 50, 'temperature': 0.8, 'top_p': 0.9, 'top_k': 40}
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs, **kwargs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_multiple_kwargs', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        call_kwargs = adapter_instance._model.generate.call_args[1]
        for key in kwargs:
            pass

    def test_predict_output_is_correct_dtype(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict output maintains torch.float32 dtype.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        expected_output = torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_output_is_correct_dtype', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_does_not_modify_input_tensor(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict does not modify the input tensor.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32).clone()
        inputs_copy = inputs.clone()
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_does_not_modify_input_tensor', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)

    def test_predict_with_larger_tensor_100_elements(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles larger tensors (100 elements) correctly.'
        inputs = torch.randn(100, dtype=torch.float32)
        expected_output = torch.randn(1, 100, dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_larger_tensor_100_elements', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_many_kwargs_iterations(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles repeated calls with different kwargs correctly.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        for i in range(100):
            kwargs = {'iteration': i, 'temperature': 0.5 + i % 10 * 0.01}
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs, **kwargs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_many_kwargs_iterations', 'predict', '3_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_sequential_calls_with_various_inputs(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict works correctly across multiple sequential calls with different inputs.'
        inputs_list = [torch.ones(4, dtype=torch.float32), torch.zeros(4, dtype=torch.float32), torch.full((4,), 2.5, dtype=torch.float32), torch.randn(4, dtype=torch.float32)]
        outputs = [torch.tensor([[1.0, 1.0, 1.0, 1.0]], dtype=torch.float32), torch.tensor([[0.0, 0.0, 0.0, 0.0]], dtype=torch.float32), torch.tensor([[2.5, 2.5, 2.5, 2.5]], dtype=torch.float32), torch.randn(1, 4, dtype=torch.float32)]
        adapter_instance._model.generate = Mock(side_effect=outputs)
        results = []
        for inputs in inputs_list:
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_sequential_calls_with_various_inputs', 'predict', '5_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output
            results.append(result)

    def test_predict_return_type_consistency_across_multiple_calls(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict returns consistent type across 100 sequential calls.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        for _ in range(100):
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_return_type_consistency_across_multiple_calls', 'predict', '3_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_with_dense_tensor_operations(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles dense tensor operations efficiently.'
        inputs = torch.randn(100, dtype=torch.float32)
        expected_output = torch.randn(1, 100, dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=expected_output)
        for _ in range(50):
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_with_dense_tensor_operations', 'predict', '4_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_tensor_batch_processing(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict handles batched tensor operations correctly.'
        inputs = torch.randn(4, dtype=torch.float32)
        batched_output = torch.randn(10, 4, dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=batched_output)
        _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_tensor_batch_processing', 'predict', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_maintains_performance_under_high_call_volume(self, adapter_instance):
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Verify that predict maintains functionality under 1000 sequential calls.'
        inputs = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
        adapter_instance._model.generate = Mock(return_value=torch.tensor([[1.0, 2.0, 3.0, 4.0]], dtype=torch.float32))
        for i in range(1000):
            _call__bound__arguments = inspect.signature(adapter_instance.predict).bind(inputs, call_id=i)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter_instance.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsQwen35VLAdapterPredict', 'test_predict_maintains_performance_under_high_call_volume', 'predict', '3_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output