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_v2.depth_anything_v2_inference_models import \
    InferenceModelsDepthAnythingV2Adapter


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

def test_predict_basic_valid_input_2d():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test basic functionality with a valid 2D input image.\n    This ensures the core prediction pipeline works correctly.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.ones((480, 640), dtype=torch.float16)
        mock_depth_tensor[100:200, 100:200] = 0.5
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(480, 640, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_basic_valid_input_2d', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        result_dict = result[0]
        normalized_depth = result_dict['normalized_depth']

def test_predict_returns_tuple_of_dict():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that predict returns a tuple containing a single dictionary.\n    This verifies the return type structure matches expectations.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.full((480, 640), 2.5, dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(360, 480, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_returns_tuple_of_dict', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_predict_normalized_depth_within_bounds():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that normalized_depth values are properly bounded between 0 and 1.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]], dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_normalized_depth_within_bounds', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_colored_depth_output_shape():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that the image output has correct shape (height, width, 3 for RGB).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (256, 512)
        mock_depth_tensor = torch.rand((height, width), dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_colored_depth_output_shape', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_image = result[0]['image']

def test_predict_image_is_workflow_image_data():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that the returned image is a WorkflowImageData object with proper metadata.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.ones((128, 128), dtype=torch.float32) * 3.5
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(128, 128, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_image_is_workflow_image_data', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        image = result[0]['image']

def test_predict_raises_error_on_constant_depth_map():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that predict raises ValueError when depth map has no variation\n    (all values equal).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.full((100, 100), 5.0, dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(100, 100, 3).astype(np.uint8)
        with pytest.raises(ValueError, match='Depth map has no variation'):
            codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_raises_error_on_constant_depth_map', 'predict', '1_6_0', codeflash_loop_index, input_image)

def test_predict_with_very_small_variation():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with minimal depth variation (very close min and max).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        depth_values = np.full((50, 50), 1.0)
        depth_values[0, 0] = 1.0001
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(50, 50, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_very_small_variation', 'predict', '1_8', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_negative_depth_values():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with negative depth values (should normalize correctly).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        depth_values = np.array([[-5.0, 0.0], [5.0, 10.0]])
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_negative_depth_values', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_float16_depth_tensor():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict correctly converts float16 tensors to float32.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.tensor([[1.5, 2.5], [3.5, 4.5]], dtype=torch.float16)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_float16_depth_tensor', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_very_large_depth_values():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with extremely large depth values.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        depth_values = np.array([[1000000.0, 2000000.0], [3000000.0, 4000000.0]])
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_very_large_depth_values', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_very_small_depth_values():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with very small (close to zero) depth values.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        depth_values = np.array([[1e-10, 2e-10], [3e-10, 4e-10]])
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_very_small_depth_values', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_minimum_image_size():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with the smallest possible valid image (1x1 pixel).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.tensor([[5.0]], dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(1, 1, 3).astype(np.uint8)
        with pytest.raises(ValueError, match='Depth map has no variation'):
            codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_minimum_image_size', 'predict', '1_6_0', codeflash_loop_index, input_image)

def test_predict_with_two_pixel_variation():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with minimal 2-pixel image with variation.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.tensor([[1.0, 2.0]], dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(1, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_two_pixel_variation', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_colored_depth_is_uint8():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that the colored depth image has uint8 dtype.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        mock_depth_tensor = torch.tensor([[0.1, 0.5], [0.9, 0.2]], dtype=torch.float32)
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(2, 2, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_colored_depth_is_uint8', 'predict', '1_6', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_depth = result[0]['image'].numpy_image

def test_predict_colored_depth_values_in_valid_range():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test that the colored depth visualization has values in valid uint8 range.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        depth_values = np.linspace(0, 100, 100).reshape(10, 10)
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(10, 10, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_colored_depth_values_in_valid_range', 'predict', '1_7', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        colored_depth = result[0]['image'].numpy_image

def test_predict_large_4k_image():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with a large 4K resolution image (3840x2160).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (2160, 3840)
        depth_values = np.random.rand(height, width) * 100
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_large_4k_image', 'predict', '1_8', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_predict_very_wide_image():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with an extremely wide image (10000 x 100 pixels).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (100, 10000)
        depth_values = np.linspace(0, 1, height * width).reshape(height, width)
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_very_wide_image', 'predict', '1_8', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_very_tall_image():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with an extremely tall image (100 x 10000 pixels).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (10000, 100)
        depth_values = np.linspace(0, 1, height * width).reshape(height, width)
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_very_tall_image', 'predict', '1_8', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_many_unique_depth_values():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with a large image containing many unique depth values.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (500, 500)
        x = np.linspace(0, 100, width)
        y = np.linspace(0, 100, height)
        depth_values = np.outer(y, x)
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_many_unique_depth_values', 'predict', '1_10', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_extreme_depth_range():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with depth values spanning a very large range (1e-5 to 1e5).\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (100, 100)
        depth_values = np.logspace(-5, 5, height * width).reshape(height, width)
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_extreme_depth_range', 'predict', '1_8', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_performance_multiple_predictions():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test performance of making multiple predictions in sequence.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (256, 256)

        def create_depth_tensor(*args, **kwargs):
            depth_values = np.random.rand(height, width) * 100
            return (torch.from_numpy(depth_values).float(),)
        mock_model_instance.side_effect = create_depth_tensor
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        for _ in range(10):
            input_image = np.random.rand(height, width, 3).astype(np.uint8)
            codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_performance_multiple_predictions', 'predict', '1_6_1', codeflash_loop_index, input_image)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_predict_high_dynamic_range_depth():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with depth values having high dynamic range variation.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (200, 200)
        depth_values = np.zeros((height, width))
        depth_values[0:100, 0:100] = 1.0
        depth_values[100:200, 0:100] = np.linspace(1.0, 1000.0, 10000).reshape(100, 100)
        depth_values[0:100, 100:200] = 10000.0
        depth_values[100:200, 100:200] = 0.1
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_high_dynamic_range_depth', 'predict', '1_12', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']

def test_predict_with_sparse_depth_values():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with mostly uniform depth with few sparse high/low values.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (300, 300)
        depth_values = np.ones((height, width)) * 50.0
        depth_values[0, 0] = 0.0
        depth_values[height - 1, width - 1] = 100.0
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_with_sparse_depth_values', 'predict', '1_10', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']
        middle_count = np.sum((normalized_depth > 0.4) & (normalized_depth < 0.6))

def test_predict_checkerboard_depth_pattern():
    np.random.seed(42)
    torch.manual_seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    '\n    Test predict with alternating high/low depth pattern.\n    '
    with patch('inference.models.depth_anything_v2.depth_anything_v2_inference_models.AutoModel.from_pretrained') as mock_auto_model:
        mock_model_instance = MagicMock()
        height, width = (100, 100)
        depth_values = np.zeros((height, width))
        for i in range(height):
            for j in range(width):
                if (i + j) % 2 == 0:
                    depth_values[i, j] = 1.0
                else:
                    depth_values[i, j] = 100.0
        mock_depth_tensor = torch.from_numpy(depth_values).float()
        mock_model_instance.return_value = (mock_depth_tensor,)
        mock_auto_model.return_value = mock_model_instance
        adapter = InferenceModelsDepthAnythingV2Adapter(model_id='test-model')
        input_image = np.random.rand(height, width, 3).astype(np.uint8)
        codeflash_return_value = codeflash_wrap(adapter.predict, 'tests.inference.unit_tests.test_predict__unit_test_1', None, 'test_predict_checkerboard_depth_pattern', 'predict', '1_9', codeflash_loop_index, input_image)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        normalized_depth = result[0]['normalized_depth']