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 numpy as np
import pytest
from inference.models.moondream2.moondream2_inference_models import \
    InferenceModelsMoondream2Adapter
from PIL import Image


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
    gc.disable()
    try:
        counter = time.perf_counter_ns()
        return_value = codeflash_wrapped(*args, **kwargs)
        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

def test_caption_with_pil_image():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption function with PIL Image input - fundamental functionality'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['A beautiful sunset over mountains']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (3, 3), color='red')
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_pil_image', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_numpy_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption function with numpy array input - BGR format'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Test caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.zeros((10, 10, 3), dtype=np.uint8)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_numpy_array', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        mock_model_instance.caption.assert_called_once()

def test_caption_returns_first_element():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that caption returns the first element from model.caption() result'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['First caption', 'Second caption', 'Third caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (5, 5), color='blue')
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_returns_first_element', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_api_key_parameter():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption initialization with custom API key'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Caption']
        mock_from_pretrained.return_value = mock_model_instance
        custom_api_key = 'custom_test_key_12345'
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model', api_key=custom_api_key)

def test_caption_small_image():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with very small image (1x1 pixel)'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Tiny image']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (1, 1), color='green')
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_small_image', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_rgba_image():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with RGBA image (4 channels) - should convert to RGB internally'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['RGBA image caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGBA', (10, 10), color=(255, 0, 0, 255))
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_rgba_image', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_grayscale_image():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with grayscale image - should be converted to RGB internally'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Grayscale image']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('L', (10, 10), color=128)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_grayscale_image', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_uint8_numpy_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with uint8 numpy array (standard format)'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Uint8 array caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.random.randint(0, 256, size=(20, 20, 3), dtype=np.uint8)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_uint8_numpy_array', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_float32_numpy_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with float32 numpy array (different dtype)'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Float array caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.random.rand(15, 15, 3).astype(np.float32)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_float32_numpy_array', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_empty_caption():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption when model returns empty string'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (10, 10))
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_empty_caption', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_very_long_caption():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with very long output text'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        long_caption = 'This is a very long caption. ' * 100
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = [long_caption]
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (10, 10))
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_very_long_caption', 'caption', '1_6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_special_characters():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with special characters and unicode'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        special_caption = 'Image with emoji 🎨, symbols @#$%, and unicode: éèê'
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = [special_caption]
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (10, 10))
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_special_characters', 'caption', '1_6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_numpy_array_shape_3d():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption ensures numpy array is passed without shape modification'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Shape test']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.ones((32, 64, 3), dtype=np.uint8)
        original_shape = test_array.shape
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_numpy_array_shape_3d', 'caption', '1_6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_large_image_1080p():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with large 1080p resolution image'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Large image caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (1920, 1080), color='white')
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_large_image_1080p', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_with_large_numpy_array_4k():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with large 4K resolution numpy array'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['4K image caption']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.random.randint(0, 256, size=(2160, 3840, 3), dtype=np.uint8)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_large_numpy_array_4k', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_multiple_calls_sequential():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption function with multiple sequential calls'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.side_effect = [['Caption 1'], ['Caption 2'], ['Caption 3'], ['Caption 4'], ['Caption 5']]
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        results = []
        for i in range(5):
            test_image = Image.new('RGB', (50, 50), color=(i * 50, i * 50, i * 50))
            _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_multiple_calls_sequential', 'caption', '1_5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output
            results.append(result)

def test_caption_with_various_image_dimensions():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with various image dimensions to ensure scalability'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Dimension test']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        dimensions = [(100, 100), (256, 512), (512, 256), (800, 600), (1024, 768)]
        for width, height in dimensions:
            test_image = Image.new('RGB', (width, height))
            _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_various_image_dimensions', 'caption', '1_5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_caption_high_bit_depth_consistency():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption maintains consistency with high bit depth data'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['High bit depth']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.random.randint(0, 65536, size=(100, 100, 3), dtype=np.uint16)
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_high_bit_depth_consistency', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_stress_test_rapid_calls():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Stress test with rapid consecutive caption calls'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Stress test']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        num_calls = 50
        for _ in range(num_calls):
            test_image = Image.new('RGB', (64, 64))
            _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_stress_test_rapid_calls', 'caption', '1_5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_caption_with_different_color_modes():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with images in different color modes'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Color mode test']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        color_modes = ['RGB', 'RGBA', 'L']
        for mode in color_modes:
            if mode == 'L':
                test_image = Image.new(mode, (50, 50), color=128)
            else:
                test_image = Image.new(mode, (50, 50), color=(255, 0, 0, 255) if mode == 'RGBA' else (255, 0, 0))
            _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_different_color_modes', 'caption', '1_5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_caption_with_complex_image_content():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test caption with images containing complex content and patterns'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Complex pattern detected']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_array = np.zeros((128, 128, 3), dtype=np.uint8)
        test_array[::2, ::2] = [255, 0, 0]
        test_array[1::2, 1::2] = [0, 0, 255]
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_array)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_with_complex_image_content', 'caption', '1_7', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_caption_kwargs_passthrough():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that caption function accepts and handles additional kwargs'
    with patch('inference.models.moondream2.moondream2_inference_models.AutoModel.from_pretrained') as mock_from_pretrained:
        mock_model_instance = MagicMock()
        mock_model_instance.caption.return_value = ['Kwargs test']
        mock_from_pretrained.return_value = mock_model_instance
        adapter = InferenceModelsMoondream2Adapter(model_id='test_model')
        test_image = Image.new('RGB', (50, 50))
        _call__bound__arguments = inspect.signature(adapter.caption).bind(test_image, some_param='value', another_param=123)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(adapter.caption, 'tests.inference.unit_tests.test_caption__unit_test_1', None, 'test_caption_kwargs_passthrough', 'caption', '1_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output