from __future__ import annotations

import gc
import inspect
import os
import time
from pathlib import Path
from typing import Dict, Set

import dill as pickle
import pytest
from inference.core.workflows.execution_engine.v1.executor.execution_data_manager.dynamic_batches_manager import \
    DynamicBatchIndex
from inference.core.workflows.execution_engine.v1.executor.execution_data_manager.step_input_assembler import \
    filter_to_valid_prefix_chains


def codeflash_wrap(codeflash_wrapped, codeflash_test_module_name, codeflash_test_class_name, codeflash_test_name, codeflash_function_name, codeflash_line_id, codeflash_loop_index, *args, **kwargs):
    test_id = f'{codeflash_test_module_name}:{codeflash_test_class_name}:{codeflash_test_name}:{codeflash_line_id}:{codeflash_loop_index}'
    if not hasattr(codeflash_wrap, 'index'):
        codeflash_wrap.index = {}
    if test_id in codeflash_wrap.index:
        codeflash_wrap.index[test_id] += 1
    else:
        codeflash_wrap.index[test_id] = 0
    codeflash_test_index = codeflash_wrap.index[test_id]
    invocation_id = f'{codeflash_line_id}_{codeflash_test_index}'
    test_stdout_tag = f"{codeflash_test_module_name}:{(codeflash_test_class_name + '.' if codeflash_test_class_name else '')}{codeflash_test_name}:{codeflash_function_name}:{codeflash_loop_index}:{invocation_id}"
    print(f'!$######{test_stdout_tag}######$!')
    exception = None
    gc.disable()
    try:
        counter = time.perf_counter_ns()
        return_value = codeflash_wrapped(*args, **kwargs)
        codeflash_duration = time.perf_counter_ns() - counter
    except Exception as e:
        codeflash_duration = time.perf_counter_ns() - counter
        exception = e
    gc.enable()
    print(f'!######{test_stdout_tag}######!')
    iteration = os.environ['CODEFLASH_TEST_ITERATION']
    with Path('{codeflash_run_tmp_dir_client_side}', f'test_return_values_{iteration}.bin').open('ab') as f:
        pickled_values = pickle.dumps((args, kwargs, exception)) if exception else pickle.dumps((args, kwargs, return_value))
        _test_name = f"{codeflash_test_module_name}:{(codeflash_test_class_name + '.' if codeflash_test_class_name else '')}{codeflash_test_name}:{codeflash_function_name}:{codeflash_line_id}".encode('ascii')
        f.write(len(_test_name).to_bytes(4, byteorder='big'))
        f.write(_test_name)
        f.write(codeflash_duration.to_bytes(8, byteorder='big'))
        f.write(len(pickled_values).to_bytes(4, byteorder='big'))
        f.write(pickled_values)
        f.write(codeflash_loop_index.to_bytes(8, byteorder='big'))
        f.write(len(invocation_id).to_bytes(4, byteorder='big'))
        f.write(invocation_id.encode('ascii'))
    if exception:
        raise exception
    return return_value

def test_single_dimension_returns_input():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that single dimension returns the input indices as-is.'
    per_dim_sets = {1: {(0,), (1,), (2,)}}
    dimensions = {1}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_single_dimension_returns_input', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_empty_dimensions_returns_empty():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that empty dimensions set returns empty dict.'
    per_dim_sets = {}
    dimensions = set()
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_empty_dimensions_returns_empty', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_two_dimensions_valid_chains():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test valid parent-child chains across two dimensions.'
    per_dim_sets = {1: {(0,), (1,)}, 2: {(0, 0), (0, 1), (1, 0)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_two_dimensions_valid_chains', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_two_dimensions_broken_chain():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that orphaned indices are filtered out.'
    per_dim_sets = {1: {(0,), (1,)}, 2: {(0, 0), (2, 0)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_two_dimensions_broken_chain', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_three_dimensions_valid_chains():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test valid parent-child chains across three dimensions.'
    per_dim_sets = {1: {(0,), (1,)}, 2: {(0, 0), (0, 1)}, 3: {(0, 0, 0), (0, 0, 1), (1, 0, 0)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_three_dimensions_valid_chains', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_no_valid_chains_returns_empty_sets():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that when no chains exist, all dimensions return empty sets.'
    per_dim_sets = {1: {(0,)}, 2: {(1, 0)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_no_valid_chains_returns_empty_sets', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_dimension_not_in_per_dim_sets():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that missing dimensions are treated as empty sets.'
    per_dim_sets = {1: {(0,), (1,)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_dimension_not_in_per_dim_sets', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_empty_per_dim_sets():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with empty per_dim_sets but non-empty dimensions.'
    per_dim_sets = {}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_empty_per_dim_sets', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_single_root_multiple_children():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test single root with multiple children at different levels.'
    per_dim_sets = {1: {(0,)}, 2: {(0, 0), (0, 1)}, 3: {(0, 0, 0), (0, 0, 1), (0, 1, 0)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_single_root_multiple_children', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_unsorted_dimensions_ordering():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that dimension ordering is handled correctly internally.'
    per_dim_sets = {3: {(0, 0, 0)}, 2: {(0, 0)}, 1: {(0,)}}
    dimensions = {3, 1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_unsorted_dimensions_ordering', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_all_dimensions_have_empty_sets():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when all dimensions have empty index sets.'
    per_dim_sets = {1: set(), 2: set(), 3: set()}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_all_dimensions_have_empty_sets', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_tuple_depths():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with deeply nested tuples.'
    per_dim_sets = {1: {(0,)}, 2: {(0, 0)}, 3: {(0, 0, 0)}, 4: {(0, 0, 0, 0)}, 5: {(0, 0, 0, 0, 0)}}
    dimensions = {1, 2, 3, 4, 5}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_large_tuple_depths', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_non_sequential_dimensions():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with non-sequential dimension numbers.'
    per_dim_sets = {1: {(0,)}, 5: {(0, 0)}, 10: {(0, 0, 0)}}
    dimensions = {1, 5, 10}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_non_sequential_dimensions', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_multiple_orphaned_indices():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test filtering multiple orphaned indices.'
    per_dim_sets = {1: {(0,)}, 2: {(1, 0), (2, 0), (2, 1)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_multiple_orphaned_indices', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_mixed_valid_and_invalid_chains():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test mixture of valid and invalid chains.'
    per_dim_sets = {1: {(0,), (1,), (2,)}, 2: {(0, 0), (1, 1), (2, 0), (3, 0)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_mixed_valid_and_invalid_chains', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_single_index_all_dimensions():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with exactly one index per dimension.'
    per_dim_sets = {1: {(0,)}, 2: {(0, 0)}, 3: {(0, 0, 0)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_single_index_all_dimensions', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_number_of_indices_per_dimension():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with 1000 indices per dimension maintaining valid chains.'
    per_dim_sets = {1: {(i,) for i in range(100)}, 2: {(i, j) for i in range(100) for j in range(10)}, 3: {(i, j, k) for i in range(100) for j in range(10) for k in range(10)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_large_number_of_indices_per_dimension', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_many_dimensions_simple_chain():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with many dimensions (1-10) in a simple chain.'
    per_dim_sets = {}
    for dim in range(1, 11):
        per_dim_sets[dim] = {tuple([0] * dim)}
    dimensions = set(range(1, 11))
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_many_dimensions_simple_chain', 'filter_to_valid_prefix_chains', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output
    for dim in range(1, 11):
        pass

def test_wide_branching_tree():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with wide branching (many children per parent).'
    per_dim_sets = {1: {(i,) for i in range(50)}, 2: {(i, j) for i in range(50) for j in range(50)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_wide_branching_tree', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_deep_chain_many_indices():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test deep chain with multiple indices at each level.'
    per_dim_sets = {}
    for dim in range(1, 6):
        if dim == 1:
            per_dim_sets[dim] = {(i,) for i in range(10)}
        else:
            indices = set()
            for parent_i in range(10 ** (dim - 1)):
                parent_tuple = tuple((int(d) for d in str(parent_i).zfill(dim - 1)))
                for j in range(10):
                    child_tuple = parent_tuple + (j,)
                    indices.add(child_tuple)
            per_dim_sets[dim] = indices
    dimensions = {1, 2, 3, 4, 5}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_deep_chain_many_indices', 'filter_to_valid_prefix_chains', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_many_orphaned_indices():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test filtering performance with many orphaned indices.'
    per_dim_sets = {1: {(i,) for i in range(100)}, 2: {(i, j) for i in range(100) for j in range(10)} | {(1000 + i, j) for i in range(90) for j in range(10)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_many_orphaned_indices', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_branching_then_pruning():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test scenario where tree branches then some branches are pruned.'
    per_dim_sets = {1: {(i,) for i in range(5)}, 2: {(i, j) for i in range(5) for j in range(3)}, 3: {(i, j, k) for i in range(3) for j in range(3) for k in range(5)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_branching_then_pruning', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_performance_with_sparse_chains():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test performance with mostly broken chains.'
    per_dim_sets = {1: {(i,) for i in range(100)}, 2: {(0, j) for j in range(100)}, 3: {(0, 0, k) for k in range(100)}}
    dimensions = {1, 2, 3}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_performance_with_sparse_chains', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_indices_values():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with large index values.'
    per_dim_sets = {1: {(999999,), (1000000,)}, 2: {(999999, 999999), (1000000, 1000000)}}
    dimensions = {1, 2}
    _call__bound__arguments = inspect.signature(filter_to_valid_prefix_chains).bind(per_dim_sets, dimensions)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(filter_to_valid_prefix_chains, 'tests.inference.unit_tests.test_filter_to_valid_prefix_chains__unit_test_1', None, 'test_large_indices_values', 'filter_to_valid_prefix_chains', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output