from __future__ import annotations

import gc
import inspect
import os
import sys
import time
from collections import deque
from pathlib import Path
from threading import Lock
from unittest.mock import MagicMock, Mock, patch

import dill as pickle
import pytest
from inference.core.managers.decorators.fixed_size_cache import \
    WithFixedSizeCache


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

class MockModel:
    """Minimal mock of a Model for testing purposes."""
    pass

class MockModelManager:
    """Minimal mock of a ModelManager for testing purposes."""

    def __init__(self, models_dict=None):
        self._models = models_dict or {}

    def keys(self):
        return self._models.keys()

class TestMemoryPressureDetected:
    """Test suite for WithFixedSizeCache.memory_pressure_detected method."""

    def test_memory_pressure_detected_no_cuda(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when CUDA is not available.\n        \n        When torch.cuda.is_available() returns False, the method should\n        return False (default) since no GPU memory check can be performed.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=False):
            _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_no_cuda', 'memory_pressure_detected', '3_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_memory_pressure_detected_low_memory_pressure(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when memory usage is low.\n        \n        When available memory is above the threshold, the method should\n        return False, indicating no memory pressure.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(1000, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_low_memory_pressure', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_high_memory_pressure(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when memory usage is high.\n        \n        When available memory is below the threshold, the method should\n        return True, indicating memory pressure is detected.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(500, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_high_memory_pressure', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_return_type(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test that memory_pressure_detected always returns a boolean.\n        \n        The method should always return a bool type, never None or other types.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=False):
            _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_return_type', 'memory_pressure_detected', '3_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_memory_pressure_detected_zero_total_memory(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected with zero total memory (edge case).\n        \n        This tests the behavior when dividing by zero would occur.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(0, 0)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_zero_total_memory', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_torch_import_fails(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when torch import fails.\n        \n        If torch is not installed or import fails, the method should\n        catch the exception and return False.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        import builtins
        real_import = builtins.__import__

        def mock_import(name, *args, **kwargs):
            if name == 'torch':
                raise ImportError('torch not available')
            return real_import(name, *args, **kwargs)
        with patch('builtins.__import__', side_effect=mock_import):
            _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_torch_import_fails', 'memory_pressure_detected', '6_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

    def test_memory_pressure_detected_torch_cuda_mem_get_info_fails(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when torch.cuda.mem_get_info raises exception.\n        \n        If mem_get_info fails, the exception should be caught and False returned.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', side_effect=RuntimeError('CUDA error')):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_torch_cuda_mem_get_info_fails', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_exactly_at_threshold(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when memory ratio is exactly at threshold.\n        \n        When ratio equals threshold exactly (not less than), should return False.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(1000, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_exactly_at_threshold', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_very_high_pressure(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected with extremely low free memory.\n        \n        Test with nearly no free memory to verify high pressure detection.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(10, 100000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_very_high_pressure', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_very_large_memory_values(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected with very large memory values.\n        \n        Test with large integers representing GB-scale memory to check\n        for potential overflow or precision issues.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        large_total = 100 * 1024 * 1024 * 1024
        large_free = 15 * 1024 * 1024 * 1024
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(large_free, large_total)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_very_large_memory_values', 'memory_pressure_detected', '5_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_with_cache_clear_enabled(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected clears cache when USE_INFERENCE_MODELS is True.\n        \n        When pressure is detected and USE_INFERENCE_MODELS is True,\n        should call torch.cuda.empty_cache() before checking again.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(500, 10000)):
                with patch('torch.cuda.empty_cache') as mock_empty_cache:
                    _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                    _call__bound__arguments.apply_defaults()
                    codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_with_cache_clear_enabled', 'memory_pressure_detected', '3_0_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                    codeflash_output = codeflash_return_value
                    result = codeflash_output

    def test_memory_pressure_detected_cache_clear_resolves_pressure(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected when cache clear resolves memory pressure.\n        \n        When pressure is detected initially but cache clearing resolves it,\n        should return False after the second check.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        mem_values = [(500, 10000), (1500, 10000)]
        mem_call_count = [0]

        def get_mem_info():
            result = mem_values[mem_call_count[0]]
            mem_call_count[0] += 1
            return result
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', side_effect=get_mem_info):
                with patch('torch.cuda.empty_cache'):
                    _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                    _call__bound__arguments.apply_defaults()
                    codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_cache_clear_resolves_pressure', 'memory_pressure_detected', '6_0_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                    codeflash_output = codeflash_return_value
                    result = codeflash_output

    def test_memory_pressure_detected_with_many_models_in_queue(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        "Test memory_pressure_detected with many models tracked in queue.\n        \n        Test with large number of models to verify the method doesn't\n        iterate over models (it shouldn't) and remains performant.\n        "
        models_dict = {f'model_{i}': MockModel() for i in range(1000)}
        mock_manager = MockModelManager(models_dict)
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(1000, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_with_many_models_in_queue', 'memory_pressure_detected', '4_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output

    def test_memory_pressure_detected_repeated_calls_consistency(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected returns consistent results on repeated calls.\n        \n        When called multiple times with same memory state, should return\n        consistent results (no unexpected state changes).\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(500, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_repeated_calls_consistency', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result1 = codeflash_output
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_repeated_calls_consistency', 'memory_pressure_detected', '3_0_5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result2 = codeflash_output
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_repeated_calls_consistency', 'memory_pressure_detected', '3_0_10', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result3 = codeflash_output

    def test_memory_pressure_detected_with_varying_memory_states(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected with rapidly changing memory states.\n        \n        Simulate varying memory conditions to verify method handles\n        dynamic memory pressure correctly.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        memory_states = [(1500, 10000), (300, 10000), (2000, 10000), (400, 10000), (1000, 10000)]
        expected_results = [False, True, False, True, False]
        with patch('torch.cuda.is_available', return_value=True):
            for mem_state, expected in zip(memory_states, expected_results):
                with patch('torch.cuda.mem_get_info', return_value=mem_state):
                    _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                    _call__bound__arguments.apply_defaults()
                    codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_with_varying_memory_states', 'memory_pressure_detected', '5_0_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                    codeflash_output = codeflash_return_value
                    result = codeflash_output

    def test_memory_pressure_detected_stress_test_many_calls(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Stress test memory_pressure_detected with 100 rapid calls.\n        \n        Test that method can handle high call frequency without\n        performance degradation or resource leaks.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(500, 10000)):
                results = []
                for _ in range(100):
                    _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                    _call__bound__arguments.apply_defaults()
                    codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_stress_test_many_calls', 'memory_pressure_detected', '3_0_1_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                    codeflash_output = codeflash_return_value
                    result = codeflash_output
                    results.append(result)

    def test_memory_pressure_detected_with_extreme_ratio_values(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        'Test memory_pressure_detected with extreme but valid memory ratios.\n        \n        Test with ratios near 0 and near 1 to verify boundary handling.\n        '
        mock_manager = MockModelManager({})
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(1, 1000000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_with_extreme_ratio_values', 'memory_pressure_detected', '3_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result_near_zero = codeflash_output
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(999000, 1000000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_with_extreme_ratio_values', 'memory_pressure_detected', '4_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result_near_one = codeflash_output

    def test_memory_pressure_detected_thread_safety_mock(self):
        codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
        "Test that memory_pressure_detected doesn't cause race conditions.\n        \n        Verify internal lock is properly initialized and method can be\n        called from multiple contexts without deadlock.\n        "
        models_dict = {f'model_{i}': MockModel() for i in range(100)}
        mock_manager = MockModelManager(models_dict)
        cache = WithFixedSizeCache(mock_manager)
        with patch('torch.cuda.is_available', return_value=True):
            with patch('torch.cuda.mem_get_info', return_value=(1000, 10000)):
                _call__bound__arguments = inspect.signature(cache.memory_pressure_detected).bind()
                _call__bound__arguments.apply_defaults()
                codeflash_return_value = codeflash_wrap(cache.memory_pressure_detected, 'tests.inference.unit_tests.test_memory_pressure_detected__unit_test_1', 'TestMemoryPressureDetected', 'test_memory_pressure_detected_thread_safety_mock', 'memory_pressure_detected', '4_0_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
                codeflash_output = codeflash_return_value
                result = codeflash_output