from __future__ import annotations

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

import dill as pickle
import pytest
from inference.core.workflows.execution_engine.v1.compiler.graph_constructor import \
    establish_step_execution_dimensionality


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_no_inputs_no_control_flow_returns_zero():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that empty inputs and no control flow returns 0.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_no_inputs_no_control_flow_returns_zero', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_single_input_single_nonzero_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a single input having one non-zero dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_single_input_single_nonzero_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_multiple_inputs_single_dimensionality_each():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with multiple inputs each having one dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {3}, 'input2': {7}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_multiple_inputs_single_dimensionality_each', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_multiple_inputs_returns_minimum_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that the minimum non-zero dimensionality is returned.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {10}, 'input2': {2}, 'input3': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_multiple_inputs_returns_minimum_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_control_flow_lineage_single_branch():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that control flow lineage count is returned when no non-zero dimensionalities.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={}, control_flow_lineage_support=['branch1'], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_control_flow_lineage_single_branch', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_control_flow_lineage_multiple_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that multiple control flow branches are counted correctly.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={}, control_flow_lineage_support=['branch1', 'branch2', 'branch3'], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_control_flow_lineage_multiple_branches', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_only_zero_dimensionalities_no_control_flow():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when all dimensionalities are zero and no control flow.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {0}, 'input2': {0}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_only_zero_dimensionalities_no_control_flow', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_negative_output_offset_reduces_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that negative output offset reduces the resulting dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_negative_output_offset_reduces_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_positive_output_offset_preserves_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that positive output offset does not reduce dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_positive_output_offset_preserves_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_zero_output_offset_preserves_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that zero output offset preserves dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_zero_output_offset_preserves_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_multiple_dimensionalities_per_input():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with inputs having multiple dimensionalities in their sets.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {2, 4, 6}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_multiple_dimensionalities_per_input', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_mixed_zero_and_nonzero_dimensionalities():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a mix of zero and non-zero dimensionalities.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {0, 3}, 'input2': {0, 5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_mixed_zero_and_nonzero_dimensionalities', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_control_flow_ignored_when_nonzero_dimensionalities_exist():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that control flow lineage is ignored when non-zero dimensionalities exist.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {7}}, control_flow_lineage_support=['branch1', 'branch2'], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_control_flow_ignored_when_nonzero_dimensionalities_exist', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_negative_offset_with_dimensionality_one():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that negative offset on dimensionality 1 results in 0.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {1}}, control_flow_lineage_support=[], output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_negative_offset_with_dimensionality_one', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_very_large_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a very large dimensionality value.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {1000000}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_very_large_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_very_large_negative_offset():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a very large negative offset.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {1000}}, control_flow_lineage_support=[], output_dimensionality_offset=-500)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_very_large_negative_offset', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_input_name_variations():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    "Test that input names don't affect the result."
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'x': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_input_name_variations', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result1 = codeflash_output
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'very_long_input_name': {5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_input_name_variations', 'establish_step_execution_dimensionality', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result2 = codeflash_output

def test_empty_set_in_inputs_dimensionalities():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with an empty set as one of the input dimensionalities.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': set(), 'input2': {3}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_empty_set_in_inputs_dimensionalities', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_many_inputs_with_varying_dimensionalities():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with many inputs to ensure minimum is still found.'
    inputs = {f'input{i}': {i * 2} for i in range(1, 101)}
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_many_inputs_with_varying_dimensionalities', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_control_flow_with_many_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test control flow lineage with many branches.'
    branches = [f'branch_{i}' for i in range(50)]
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={}, control_flow_lineage_support=branches, output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_control_flow_with_many_branches', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_dimensionality_two_with_negative_offset():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test edge case where dimensionality 2 is reduced by negative offset.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {2}}, control_flow_lineage_support=[], output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_dimensionality_two_with_negative_offset', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_only_zero_dimensionalities_with_control_flow():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that control flow is used when dimensionalities are only zero.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {0, 0, 0}}, control_flow_lineage_support=['branch1', 'branch2'], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_only_zero_dimensionalities_with_control_flow', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_negative_offset_very_large_dimensionality():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test negative offset on very large dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {999999}}, control_flow_lineage_support=[], output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_negative_offset_very_large_dimensionality', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_duplicate_dimensionalities_in_set():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that duplicate values in a set are handled correctly (sets auto-deduplicate).'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {5, 5, 5}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_duplicate_dimensionalities_in_set', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_mixed_input_names_and_dimensionalities():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with various input names and multiple dimensionalities.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'weight': {3, 9}, 'bias': {3}, 'input_data': {3, 7, 11}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_mixed_input_names_and_dimensionalities', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_negative_offset_minimum_value():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test negative offset with minimum non-zero dimensionality.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {1, 2, 3}}, control_flow_lineage_support=[], output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_negative_offset_minimum_value', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_number_of_inputs():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with 1000 inputs to assess scalability.'
    inputs = {f'input_{i}': {i + 1} for i in range(1000)}
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_large_number_of_inputs', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_dimensionality_values():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with very large dimensionality values.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {1000000, 2000000, 3000000}}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_large_dimensionality_values', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_control_flow_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with 1000 control flow branches.'
    branches = [f'branch_{i}' for i in range(1000)]
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={}, control_flow_lineage_support=branches, output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_large_control_flow_branches', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_set_of_dimensionalities_per_input():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a large set of dimensionalities in a single input.'
    large_set = set(range(1, 1001))
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': large_set}, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_large_set_of_dimensionalities_per_input', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_many_inputs_with_many_dimensionalities():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with 100 inputs each having 10 dimensionalities.'
    inputs = {f'input_{i}': set(range(i * 10, i * 10 + 10)) for i in range(100)}
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_many_inputs_with_many_dimensionalities', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_large_negative_offset_still_reduces_by_one():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that even with huge dimensionality, negative offset reduces by 1.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {999999999}}, control_flow_lineage_support=[], output_dimensionality_offset=-999999)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_large_negative_offset_still_reduces_by_one', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_mixed_large_scale_scenario():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test a complex large-scale scenario with many inputs and control flow.'
    inputs = {f'input_{i}': {i % 100 + 1} for i in range(500)}
    branches = [f'branch_{i}' for i in range(50)]
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=branches, output_dimensionality_offset=-1)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_mixed_large_scale_scenario', 'establish_step_execution_dimensionality', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_performance_with_many_zero_and_nonzero():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test performance with many zero and non-zero dimensionalities mixed.'
    inputs = {}
    for i in range(500):
        if i % 2 == 0:
            inputs[f'input_{i}'] = {0}
        else:
            inputs[f'input_{i}'] = {i % 100 + 1}
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_performance_with_many_zero_and_nonzero', 'establish_step_execution_dimensionality', '3', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_boundary_case_dimensionality_equals_offset():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when dimensionality value is very close to offset magnitude.'
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities={'input1': {10}}, control_flow_lineage_support=[], output_dimensionality_offset=-9)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_boundary_case_dimensionality_equals_offset', 'establish_step_execution_dimensionality', '1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_all_dimensionalities_identical_large_scale():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test 500 inputs all with the same dimensionality.'
    inputs = {f'input_{i}': {42} for i in range(500)}
    _call__bound__arguments = inspect.signature(establish_step_execution_dimensionality).bind(inputs_dimensionalities=inputs, control_flow_lineage_support=[], output_dimensionality_offset=0)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(establish_step_execution_dimensionality, 'tests.inference.unit_tests.test_establish_step_execution_dimensionality__unit_test_1', None, 'test_all_dimensionalities_identical_large_scale', 'establish_step_execution_dimensionality', '2', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output