from __future__ import annotations

import gc
import os
import time
from unittest.mock import MagicMock, Mock, patch
from uuid import uuid4

import numpy as np
import pytest
import torch
from inference.core.workflows.execution_engine.entities.base import (
    ImageParentMetadata, WorkflowImageData)
from inference.models.depth_anything_v3.depth_anything_v3_inference_models import \
    InferenceModelsDepthAnythingV3Adapter


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}:{codeflash_duration}######!')
    if exception:
        raise exception
    return return_value

class TestInferenceModelsDepthAnythingV3AdapterPredict:
    """Test suite for InferenceModelsDepthAnythingV3Adapter.predict function"""

    def test_predict_basic_valid_input(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with a basic valid numpy array input'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        mock_model = Mock()
        depth_tensor = torch.randn(1, 480, 640)
        mock_model.return_value = [depth_tensor]
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (480, 640, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_basic_valid_input', 'predict', '7', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_returns_dictionary_with_required_keys(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        "Test that predict returns a dictionary with 'image' and 'normalized_depth' keys"
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_returns_dictionary_with_required_keys', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_normalized_depth_values_in_valid_range(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that normalized depth values are between 0 and 1'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_values = np.array([[10.0, 20.0], [30.0, 40.0]])
        depth_tensor = torch.from_numpy(depth_values).float()
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_normalized_depth_values_in_valid_range', 'predict', '7', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

    def test_predict_image_output_is_workflow_image_data(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that the output image is a WorkflowImageData instance'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[5.0, 10.0], [15.0, 20.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_image_output_is_workflow_image_data', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_colored_depth_has_correct_shape(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that the colored depth image maintains the spatial dimensions'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(384, 512)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (384, 512, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_colored_depth_has_correct_shape', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_image = result[0]['image'].numpy_image

    def test_predict_colored_depth_uint8_dtype(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that the colored depth image has uint8 data type'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_colored_depth_uint8_dtype', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_image = result[0]['image'].numpy_image

    def test_predict_with_different_input_sizes(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with various input image dimensions'
        sizes = [(100, 100), (320, 240), (640, 480), (1024, 768)]
        for height, width in sizes:
            adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
            depth_tensor = torch.randn(height, width)
            mock_model = Mock(return_value=[depth_tensor])
            adapter._model = mock_model
            input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
            codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_different_input_sizes', 'predict', '2_5', codeflash_loop_index, adapter, input_image)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_raises_error_when_depth_min_equals_max(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that predict raises ValueError when depth map has no variation'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        constant_value = 5.0
        depth_tensor = torch.full((10, 10), constant_value)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (10, 10, 3), dtype=np.uint8)
        with pytest.raises(ValueError, match='Depth map has no variation'):
            codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_raises_error_when_depth_min_equals_max', 'predict', '7_0', codeflash_loop_index, adapter, input_image)

    def test_predict_with_very_small_input(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with minimal size input (1x1 pixel)'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.array([[[128, 128, 128]]], dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_very_small_input', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_very_small_depth_range(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with minimal variation in depth values'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0, 1.0001], [1.0002, 1.0003]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_very_small_depth_range', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_with_large_depth_range(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with very large range of depth values'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[0.001, 1000.0], [500.0, 999.9]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_large_depth_range', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_with_negative_depth_values(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with negative depth values'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[-10.0, -5.0], [5.0, 10.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_negative_depth_values', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_with_extremely_large_values(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with very large depth values'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1000000.0, 10000000.0], [100000000.0, 1000000000.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_extremely_large_values', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_with_single_channel_output(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict when model returns single channel depth map'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(100, 100)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_single_channel_output', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_preserves_aspect_ratio(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that predict preserves aspect ratio of input'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        height, width = (480, 720)
        aspect_ratio = height / width
        depth_tensor = torch.randn(height, width)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_preserves_aspect_ratio', 'predict', '8', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        output_height, output_width = result[0]['image'].numpy_image.shape[:2]
        output_aspect_ratio = output_height / output_width

    def test_predict_with_float32_depth_tensor(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict when depth tensor is float32'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(50, 50, dtype=torch.float32)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_float32_depth_tensor', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_float64_depth_tensor(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict when depth tensor is float64'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(50, 50, dtype=torch.float64)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_float64_depth_tensor', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_normalized_depth_min_value_is_zero(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that minimum value in normalized depth is 0'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0, 5.0], [10.0, 20.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_normalized_depth_min_value_is_zero', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_normalized_depth_max_value_is_one(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that maximum value in normalized depth is 1'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.tensor([[1.0, 5.0], [10.0, 20.0]])
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (2, 2, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_normalized_depth_max_value_is_one', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized = result[0]['normalized_depth']

    def test_predict_with_large_resolution_image(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with large resolution input (1920x1440)'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        height, width = (1080, 1920)
        depth_tensor = torch.randn(height, width)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_large_resolution_image', 'predict', '7', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_maintains_performance_with_medium_batch(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict performance with medium-sized depth maps'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        height, width = (720, 1280)
        depth_tensor = torch.randn(height, width)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_maintains_performance_with_medium_batch', 'predict', '7', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_multiple_predictions_sequence(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict called multiple times in sequence'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        height, width = (480, 640)
        for i in range(10):
            depth_tensor = torch.randn(height, width)
            mock_model = Mock(return_value=[depth_tensor])
            adapter._model = mock_model
            input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
            codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_multiple_predictions_sequence', 'predict', '3_4', codeflash_loop_index, adapter, input_image)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_with_varying_aspect_ratios(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with multiple different aspect ratios'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        aspect_ratios = [(100, 100), (100, 200), (200, 100), (480, 640), (640, 480)]
        for height, width in aspect_ratios:
            depth_tensor = torch.randn(height, width)
            mock_model = Mock(return_value=[depth_tensor])
            adapter._model = mock_model
            input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
            codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_varying_aspect_ratios', 'predict', '3_4', codeflash_loop_index, adapter, input_image)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_predict_output_values_consistency(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that the same input produces consistent normalized output'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        height, width = (256, 256)
        depth_values = np.random.rand(height, width) * 100
        depth_tensor = torch.from_numpy(depth_values).float()
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (height, width, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_output_values_consistency', 'predict', '8', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result1 = codeflash_output
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_output_values_consistency', 'predict', '12', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result2 = codeflash_output

    def test_predict_with_all_zero_input_image(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with all-black input image'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(100, 100)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.zeros((100, 100, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_all_zero_input_image', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_all_white_input_image(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with all-white input image'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(100, 100)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.full((100, 100, 3), 255, dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_all_white_input_image', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_with_uniform_gray_input_image(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test predict with uniform gray input image'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(100, 100)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.full((100, 100, 3), 128, dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_with_uniform_gray_input_image', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_colored_depth_values_in_uint8_range(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that colored depth visualization values are in valid uint8 range'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(100, 100)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_colored_depth_values_in_uint8_range', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_image = result[0]['image'].numpy_image

    def test_predict_returns_tuple_not_list(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that predict returns a tuple, not a list'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(50, 50)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_returns_tuple_not_list', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_return_tuple_length_is_one(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that predict returns tuple with exactly one element'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(50, 50)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_return_tuple_length_is_one', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

    def test_predict_parent_metadata_is_unique(self):
        np.random.seed(42)
        torch.manual_seed(42)
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that each prediction creates unique parent metadata'
        adapter = Mock(spec=InferenceModelsDepthAnythingV3Adapter)
        depth_tensor = torch.randn(50, 50)
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        input_image = np.random.randint(0, 256, (50, 50, 3), dtype=np.uint8)
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_parent_metadata_is_unique', 'predict', '6', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result1 = codeflash_output
        mock_model = Mock(return_value=[depth_tensor])
        adapter._model = mock_model
        codeflash_return_value = codeflash_wrap(InferenceModelsDepthAnythingV3Adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', 'TestInferenceModelsDepthAnythingV3AdapterPredict', 'test_predict_parent_metadata_is_unique', 'predict', '10', codeflash_loop_index, adapter, input_image)
        codeflash_output = codeflash_return_value
        result2 = codeflash_output
        parent_id_1 = result1[0]['image'].parent_metadata.parent_id
        parent_id_2 = result2[0]['image'].parent_metadata.parent_id