from __future__ import annotations

import gc
import inspect
import os
import time
from pathlib import Path
from typing import Any, Dict, Set, Tuple
from unittest.mock import MagicMock, Mock

import dill as pickle
import pytest
from inference.core.workflows.execution_engine.v1.compiler.entities import \
    StepNode
from inference.core.workflows.execution_engine.v1.executor.execution_data_manager.branching_manager import \
    BranchingManager
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 (
    collect_inputs_dimensionalities,
    construct_mask_for_all_inputs_dimensionalities,
    get_masks_intersection_for_dimensions)


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_construct_mask_no_execution_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when step_node has no execution branches impacting inputs.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = []
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_no_execution_branches', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_with_scalar_false_mask():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when branching_manager contains a False mask (scalar).'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['branch1']
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={'branch1': False})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_with_scalar_false_mask', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_with_batch_oriented_branch():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a batch-oriented execution branch containing batch masks.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask_1 = {(0,), (1,)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask_1})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_with_batch_oriented_branch', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_with_unregistered_branch():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when execution branch is not registered in branching_manager.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['unregistered_branch']
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_with_unregistered_branch', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_with_true_scalar_mask():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when branching_manager contains a True mask (scalar).'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['branch1']
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={'branch1': True})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_with_true_scalar_mask', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_with_mixed_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with both batch-oriented and scalar branches.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch', 'scalar_branch']
    step_node.input_data = {}
    batch_mask = {(0,), (1,)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask, 'scalar_branch': True})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_with_mixed_branches', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_empty_batch_masks_list():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when there are no batch masks in the collection.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['scalar1', 'scalar2']
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={'scalar1': True, 'scalar2': True})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_empty_batch_masks_list', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_multiple_false_masks():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test when there are multiple False masks in non-batch set.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['unregistered1', 'unregistered2']
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_multiple_false_masks', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_single_element_batch_mask():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a batch mask containing a single element.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask = {(0,)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_single_element_batch_mask', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_multi_dimensional_batch_indices():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with batch masks containing multi-dimensional indices.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask = {(0, 0), (0, 1), (1, 0)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_multi_dimensional_batch_indices', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_empty_batch_mask():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with an empty batch mask set.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask = set()
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_empty_batch_mask', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_return_types():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that return types are correct tuple with dict and bool.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = []
    step_node.input_data = {}
    branching_manager = BranchingManager(masks={})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_return_types', 'construct_mask_for_all_inputs_dimensionalities', '5', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_get_masks_intersection_for_dimensions_empty_batch_masks():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with empty batch_masks list.'
    dimensions = {1, 2, 3}
    result = get_masks_intersection_for_dimensions(batch_masks=[], dimensions=dimensions)

def test_get_masks_intersection_for_dimensions_empty_dimensions():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with empty dimensions set.'
    batch_masks = [{(0,)}]
    result = get_masks_intersection_for_dimensions(batch_masks=batch_masks, dimensions=set())

def test_get_masks_intersection_for_dimensions_single_dimension():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with single dimension.'
    batch_masks = [{(0,), (1,), (2,)}]
    dimensions = {1}
    result = get_masks_intersection_for_dimensions(batch_masks=batch_masks, dimensions=dimensions)

def test_get_masks_intersection_for_dimensions_multiple_dimensions():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with multiple dimensions.'
    batch_masks = [{(0,), (1,)}, {(0, 0), (1, 1)}]
    dimensions = {1, 2}
    result = get_masks_intersection_for_dimensions(batch_masks=batch_masks, dimensions=dimensions)

def test_collect_inputs_dimensionalities_empty_input_data():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test collect_inputs_dimensionalities with empty input_data.'
    step_node = Mock(spec=StepNode)
    step_node.input_data = {}
    result = collect_inputs_dimensionalities(step_node=step_node)

def test_construct_mask_many_batch_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a large number of batch-oriented branches.'
    step_node = Mock(spec=StepNode)
    num_branches = 100
    step_node.execution_branches_impacting_inputs = [f'batch_branch_{i}' for i in range(num_branches)]
    step_node.input_data = {}
    masks = {}
    for i in range(num_branches):
        batch_mask = {(j,) for j in range(i, min(i + 10, 100))}
        masks[f'batch_branch_{i}'] = batch_mask
    branching_manager = BranchingManager(masks=masks)
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_many_batch_branches', 'construct_mask_for_all_inputs_dimensionalities', '8', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_many_scalar_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a large number of scalar branches.'
    step_node = Mock(spec=StepNode)
    num_branches = 100
    step_node.execution_branches_impacting_inputs = [f'scalar_branch_{i}' for i in range(num_branches)]
    step_node.input_data = {}
    masks = {f'scalar_branch_{i}': True for i in range(num_branches)}
    branching_manager = BranchingManager(masks=masks)
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_many_scalar_branches', 'construct_mask_for_all_inputs_dimensionalities', '7', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_large_batch_mask_set():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a batch mask containing many elements.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask = {(i,) for i in range(1000)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_large_batch_mask_set', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_construct_mask_highly_dimensional_indices():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with batch masks containing high-dimensional indices.'
    step_node = Mock(spec=StepNode)
    step_node.execution_branches_impacting_inputs = ['batch_branch']
    step_node.input_data = {}
    batch_mask = {tuple((j % 2 for j in range(10))) for j in range(100)}
    branching_manager = BranchingManager(masks={'batch_branch': batch_mask})
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_highly_dimensional_indices', 'construct_mask_for_all_inputs_dimensionalities', '6', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_get_masks_intersection_large_batch_masks():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with large batch masks.'
    batch_masks = [{(i,) for i in range(500)}, {(i,) for i in range(250, 750)}]
    dimensions = {1}
    result = get_masks_intersection_for_dimensions(batch_masks=batch_masks, dimensions=dimensions)

def test_construct_mask_mixed_many_branches():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test with a large mix of batch and scalar branches.'
    step_node = Mock(spec=StepNode)
    num_batch_branches = 50
    num_scalar_branches = 50
    step_node.execution_branches_impacting_inputs = [f'batch_branch_{i}' for i in range(num_batch_branches)] + [f'scalar_branch_{i}' for i in range(num_scalar_branches)]
    step_node.input_data = {}
    masks = {}
    for i in range(num_batch_branches):
        masks[f'batch_branch_{i}'] = {(j,) for j in range(i, min(i + 5, 100))}
    for i in range(num_scalar_branches):
        masks[f'scalar_branch_{i}'] = True
    branching_manager = BranchingManager(masks=masks)
    _call__bound__arguments = inspect.signature(construct_mask_for_all_inputs_dimensionalities).bind(step_node=step_node, branching_manager=branching_manager)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(construct_mask_for_all_inputs_dimensionalities, 'tests.inference.unit_tests.test_construct_mask_for_all_inputs_dimensionalities__unit_test_1', None, 'test_construct_mask_mixed_many_branches', 'construct_mask_for_all_inputs_dimensionalities', '10', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    result_masks, scalar_mask_contains_false = codeflash_return_value

def test_get_masks_intersection_many_dimensions():
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test get_masks_intersection_for_dimensions with many dimensions.'
    dimensions = set(range(1, 101))
    batch_masks = []
    for dim in range(1, 51):
        batch_mask = {tuple((j % 2 for j in range(dim))) for j in range(100)}
        batch_masks.append(batch_mask)
    result = get_masks_intersection_for_dimensions(batch_masks=batch_masks, dimensions=dimensions)