from __future__ import annotations

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

import numpy as np
import pytest
from inference.core.models.base import BaseInference
from inference.core.models.types import PreprocessReturnMetadata


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

class ConcreteInference(BaseInference):
    """Concrete implementation of BaseInference for testing purposes."""

    def preprocess(self, image, **kwargs):
        """Convert input to numpy array and return metadata."""
        if isinstance(image, np.ndarray):
            processed = image
        else:
            processed = np.array([[image]])
        metadata = PreprocessReturnMetadata()
        return (processed, metadata)

    def predict(self, img_in, **kwargs):
        """Run a simple prediction (identity or sum)."""
        return (img_in,)

    def postprocess(self, predictions, preprocess_return_metadata, **kwargs):
        """Return the first prediction as-is."""
        return predictions[0]

def test_infer_with_numpy_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test basic inference with a numpy array input.'
    inference = ConcreteInference()
    input_array = np.array([[1, 2], [3, 4]], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_numpy_array', 'infer', '3', codeflash_loop_index, input_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_scalar_input():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a scalar value as input.'
    inference = ConcreteInference()
    scalar_value = 42
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_scalar_input', 'infer', '3', codeflash_loop_index, scalar_value)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_list_input():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a list input.'
    inference = ConcreteInference()
    list_input = [1, 2, 3, 4]
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_list_input', 'infer', '3', codeflash_loop_index, list_input)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_returns_postprocessed_output():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that infer returns the postprocessed output.'
    inference = ConcreteInference()
    input_array = np.array([[1.0, 2.0], [3.0, 4.0]])
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_returns_postprocessed_output', 'infer', '3', codeflash_loop_index, input_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_kwargs():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that infer properly passes kwargs through pipeline.'

    class KwargsTrackingInference(BaseInference):

        def preprocess(self, image, **kwargs):
            self.preprocess_kwargs = kwargs
            return (np.array([1]), PreprocessReturnMetadata())

        def predict(self, img_in, **kwargs):
            self.predict_kwargs = kwargs
            return (img_in,)

        def postprocess(self, predictions, preprocess_return_metadata, **kwargs):
            self.postprocess_kwargs = kwargs
            return predictions[0]
    inference = KwargsTrackingInference()
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_kwargs', 'infer', '3', codeflash_loop_index, np.array([1]), custom_param='test_value', another_param=123)

def test_infer_with_empty_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with an empty numpy array.'
    inference = ConcreteInference()
    empty_array = np.array([], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_empty_array', 'infer', '3', codeflash_loop_index, empty_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_single_element_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a single-element array.'
    inference = ConcreteInference()
    single_element = np.array([42.0])
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_single_element_array', 'infer', '3', codeflash_loop_index, single_element)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_3d_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a 3D numpy array (e.g., image data).'
    inference = ConcreteInference()
    image_array = np.random.rand(10, 10, 3).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_3d_array', 'infer', '3', codeflash_loop_index, image_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_4d_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a 4D numpy array (batch of images).'
    inference = ConcreteInference()
    batch_array = np.random.rand(2, 5, 5, 3).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_4d_array', 'infer', '3', codeflash_loop_index, batch_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_different_dtypes():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with arrays of different data types.'
    inference = ConcreteInference()
    dtypes = [np.float32, np.float64, np.int32, np.int64, np.uint8]
    for dtype in dtypes:
        input_array = np.array([[1, 2], [3, 4]], dtype=dtype)
        codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_different_dtypes', 'infer', '3_1', codeflash_loop_index, input_array)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_infer_with_zero_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with an array of all zeros.'
    inference = ConcreteInference()
    zero_array = np.zeros((3, 3), dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_zero_array', 'infer', '3', codeflash_loop_index, zero_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_ones_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with an array of all ones.'
    inference = ConcreteInference()
    ones_array = np.ones((4, 4), dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_ones_array', 'infer', '3', codeflash_loop_index, ones_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_negative_values():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with negative values in input.'
    inference = ConcreteInference()
    neg_array = np.array([[-1, -2], [-3, -4]], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_negative_values', 'infer', '3', codeflash_loop_index, neg_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_very_large_values():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with very large values.'
    inference = ConcreteInference()
    large_array = np.array([[1000000.0, 10000000.0], [100000000.0, 1000000000.0]], dtype=np.float64)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_very_large_values', 'infer', '3', codeflash_loop_index, large_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_very_small_values():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with very small values.'
    inference = ConcreteInference()
    small_array = np.array([[1e-06, 1e-07], [1e-08, 1e-09]], dtype=np.float64)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_very_small_values', 'infer', '3', codeflash_loop_index, small_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_nan_values():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with NaN values.'
    inference = ConcreteInference()
    nan_array = np.array([[1.0, np.nan], [np.nan, 4.0]], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_nan_values', 'infer', '3', codeflash_loop_index, nan_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_inf_values():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with infinity values.'
    inference = ConcreteInference()
    inf_array = np.array([[np.inf, -np.inf], [1.0, 2.0]], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_inf_values', 'infer', '3', codeflash_loop_index, inf_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_no_kwargs():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with no additional keyword arguments.'
    inference = ConcreteInference()
    input_array = np.array([[1, 2], [3, 4]], dtype=np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_no_kwargs', 'infer', '3', codeflash_loop_index, input_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_preserves_input():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that infer does not modify the original input.'
    inference = ConcreteInference()
    input_array = np.array([[1, 2], [3, 4]], dtype=np.float32)
    input_copy = input_array.copy()
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_preserves_input', 'infer', '4', codeflash_loop_index, input_array)

def test_infer_with_large_2d_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a large 2D array (1000x1000).'
    inference = ConcreteInference()
    large_array = np.random.rand(1000, 1000).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_large_2d_array', 'infer', '3', codeflash_loop_index, large_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_large_3d_image_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a large 3D array (500x500x3).'
    inference = ConcreteInference()
    large_image = np.random.rand(500, 500, 3).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_large_3d_image_array', 'infer', '3', codeflash_loop_index, large_image)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_many_sequential_calls():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with many sequential calls.'
    inference = ConcreteInference()
    test_array = np.array([[1, 2], [3, 4]], dtype=np.float32)
    results = []
    for i in range(100):
        codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_many_sequential_calls', 'infer', '4_0', codeflash_loop_index, test_array)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        results.append(result)
    for result in results:
        pass

def test_infer_with_batch_of_large_arrays():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference on multiple large arrays.'
    inference = ConcreteInference()
    arrays = [np.random.rand(100, 100).astype(np.float32) for _ in range(10)]
    results = [inference.infer(arr) for arr in arrays]
    for result, original in zip(results, arrays):
        pass

def test_infer_with_high_dimensional_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a high-dimensional array (5D).'
    inference = ConcreteInference()
    high_dim_array = np.random.rand(2, 3, 4, 5, 6).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_high_dimensional_array', 'infer', '3', codeflash_loop_index, high_dim_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_very_wide_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a very wide array (1x1000).'
    inference = ConcreteInference()
    wide_array = np.random.rand(1, 1000).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_very_wide_array', 'infer', '3', codeflash_loop_index, wide_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_very_tall_array():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with a very tall array (1000x1).'
    inference = ConcreteInference()
    tall_array = np.random.rand(1000, 1).astype(np.float32)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_very_tall_array', 'infer', '3', codeflash_loop_index, tall_array)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_repeated_calls_same_data():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that repeated calls with same data produce same results.'
    inference = ConcreteInference()
    input_array = np.array([[1.5, 2.5], [3.5, 4.5]], dtype=np.float32)
    results = []
    for _ in range(50):
        codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_repeated_calls_same_data', 'infer', '4_0', codeflash_loop_index, input_array)
        codeflash_output = codeflash_return_value
        result = codeflash_output
        results.append(result)
    for i in range(1, len(results)):
        pass

def test_infer_with_mixed_size_arrays():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with arrays of varying sizes.'
    inference = ConcreteInference()
    sizes = [(10, 10), (100, 100), (50, 50), (200, 200)]
    for size in sizes:
        input_array = np.random.rand(*size).astype(np.float32)
        codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_mixed_size_arrays', 'infer', '3_1', codeflash_loop_index, input_array)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_infer_with_many_kwargs():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with many keyword arguments.'

    class MultiKwargsInference(BaseInference):

        def preprocess(self, image, **kwargs):
            self.kwargs_count = len(kwargs)
            return (np.array([1]), PreprocessReturnMetadata())

        def predict(self, img_in, **kwargs):
            return (img_in,)

        def postprocess(self, predictions, preprocess_return_metadata, **kwargs):
            return predictions[0]
    inference = MultiKwargsInference()
    kwargs = {f'param_{i}': i for i in range(100)}
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_many_kwargs', 'infer', '4', codeflash_loop_index, np.array([1]), **kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_performance_large_dataset():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference performance with large dataset processing.'
    inference = ConcreteInference()
    for i in range(50):
        large_array = np.random.rand(200, 200).astype(np.float32)
        codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_performance_large_dataset', 'infer', '2_1', codeflash_loop_index, large_array)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_infer_with_concatenated_large_arrays():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with very large concatenated arrays.'
    inference = ConcreteInference()
    arrays = [np.random.rand(100, 100) for _ in range(10)]
    concatenated = np.concatenate(arrays, axis=0)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_concatenated_large_arrays', 'infer', '4', codeflash_loop_index, concatenated)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_infer_with_stacked_large_arrays():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test inference with stacked large arrays.'
    inference = ConcreteInference()
    arrays = [np.random.rand(50, 50) for _ in range(20)]
    stacked = np.stack(arrays)
    codeflash_return_value = codeflash_wrap(inference.infer, 'tests.inference.unit_tests.test_infer__unit_test_1', None, 'test_infer_with_stacked_large_arrays', 'infer', '4', codeflash_loop_index, stacked)
    codeflash_output = codeflash_return_value
    result = codeflash_output