from __future__ import annotations

import gc
import inspect
import os
import time
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from unittest.mock import MagicMock, Mock, patch

import dill as pickle
import numpy as np
import pytest
import supervision as sv
from fastapi import BackgroundTasks
from inference.core.active_learning.entities import ImageDimensions
from inference.core.cache.base import BaseCache
from inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1 import \
    RoboflowDatasetUploadBlockV1
from inference.core.workflows.execution_engine.entities.base import (
    Batch, ImageParentMetadata, WorkflowImageData)


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 InMemoryCache(BaseCache):
    """Simple in-memory cache implementation for testing."""

    def __init__(self):
        self._store = {}

    def get(self, key: str):
        return self._store.get(key)

    def set(self, key: str, value: str, expire: float=None):
        self._store[key] = value

    def zadd(self, key: str, value: str, score: float, expire: float=None):
        if key not in self._store:
            self._store[key] = []
        self._store[key].append((value, score))

    def zrangebyscore(self, key: str, min: float=-1, max: float=float('inf'), withscores: bool=False):
        if key not in self._store:
            return []
        return self._store.get(key, [])

    def zremrangebyscore(self, key: str, start: int=-1, stop: float=float('inf')):
        if key in self._store:
            del self._store[key]

    def acquire_lock(self, key: str, expire: float=None):
        lock = Mock()
        lock.release = Mock()
        return lock

    def set_numpy(self, key: str, value, expire: float=None):
        self._store[key] = value

    def get_numpy(self, key: str):
        return self._store.get(key)

def create_test_workflow_image_data(width: int=100, height: int=100, numpy_image: np.ndarray=None) -> WorkflowImageData:
    """Helper function to create a real WorkflowImageData instance."""
    parent_metadata = ImageParentMetadata(parent_id='test_image_id')
    if numpy_image is None:
        numpy_image = np.zeros((height, width, 3), dtype=np.uint8)
    return WorkflowImageData(parent_metadata=parent_metadata, numpy_image=numpy_image)

def create_test_batch(size: int, width: int=100, height: int=100) -> Batch[WorkflowImageData]:
    """Helper function to create a real Batch of WorkflowImageData."""
    images = [create_test_workflow_image_data(width=width, height=height) for _ in range(size)]
    indices = [(i,) for i in range(size)]
    return Batch(content=images, indices=indices)

def test_run_with_disable_sink_true():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that run returns early when disable_sink is True.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=3)
    _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=['tag1'], disable_sink=True, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
    _call__bound__arguments.apply_defaults()
    codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_disable_sink_true', 'run', '4', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
    codeflash_output = codeflash_return_value
    result = codeflash_output

def test_run_without_api_key_raises_error():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that run raises ValueError when api_key is None.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key=None, background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with pytest.raises(ValueError, match='cannot run without Roboflow API key'):
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=['tag1'], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_without_api_key_raises_error', 'run', '4_0', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)

def test_run_returns_list_of_dicts():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that run returns a list with dict entries for each image.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=2)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Test message')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=['tag1'], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_returns_list_of_dicts', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    for entry in result:
        pass

def test_run_with_single_image():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test basic run with a single image.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=['tag1'], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_single_image', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_passes_correct_parameters_to_register():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that run passes correct parameters to register_datapoint_at_roboflow.'
    cache = InMemoryCache()
    api_key = 'test_api_key_123'
    target_project = 'my_project'
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key=api_key, background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project=target_project, usage_quota_name='quota1', minutely_usage_limit=5, persist_predictions=True, hourly_usage_limit=50, daily_usage_limit=500, max_image_size=(800, 600), compression_level=90, registration_tags=['tag1', 'tag2'], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='batch_', labeling_batches_recreation_frequency='weekly', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_passes_correct_parameters_to_register', 'run', '6_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    mock_register.assert_called_once()
    call_kwargs = mock_register.call_args.kwargs

def test_run_with_predictions_batch():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a predictions batch.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=2)
    predictions_batch = Batch(content=[None, None], indices=[(0,), (1,)])
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=predictions_batch, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_predictions_batch', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_image_names_batch():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with custom image names batch.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=2)
    image_names_batch = Batch(content=['image1.jpg', 'image2.jpg'], indices=[(0,), (1,)])
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=image_names_batch)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_image_names_batch', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    calls = mock_register.call_args_list

def test_run_with_empty_registration_tags():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with empty registration tags list.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_empty_registration_tags', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_zero_usage_limits():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with zero usage limits.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=0, persist_predictions=False, hourly_usage_limit=0, daily_usage_limit=0, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_zero_usage_limits', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_maximum_compression_level():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with maximum compression level (100).'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=100, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_maximum_compression_level', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_minimum_compression_level():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with minimum compression level (0).'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=0, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_minimum_compression_level', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_very_small_max_image_size():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with very small max image size.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(1, 1), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_very_small_max_image_size', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_very_large_max_image_size():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with very large max image size.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(4096, 4096), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_very_large_max_image_size', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_persist_predictions_false():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with persist_predictions set to False.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_persist_predictions_false', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    call_kwargs = mock_register.call_args.kwargs

def test_run_with_persist_predictions_true():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with persist_predictions set to True.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=True, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_persist_predictions_true', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    call_kwargs = mock_register.call_args.kwargs

def test_run_with_all_batch_creation_frequencies():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with each possible batch creation frequency.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    frequencies = ['never', 'daily', 'weekly', 'monthly']
    for frequency in frequencies:
        images_batch = create_test_batch(size=1)
        with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
            mock_register.return_value = (False, 'Success')
            _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency=frequency, image_name=None)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_all_batch_creation_frequencies', 'run', '4_1_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_run_with_error_status_true():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run when register returns error_status=True.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (True, 'Error occurred')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_error_status_true', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_mixed_image_names_some_none():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with some image names as None.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=3)
    image_names_batch = Batch(content=['image1.jpg', None, 'image3.jpg'], indices=[(0,), (1,), (2,)])
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=image_names_batch)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_mixed_image_names_some_none', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    calls = mock_register.call_args_list

def test_run_with_special_characters_in_target_project():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with special characters in target_project.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test-project_123', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_special_characters_in_target_project', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_special_characters_in_api_key():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with special characters in api_key.'
    cache = InMemoryCache()
    api_key = 'api_key-with_special.chars+123'
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key=api_key, background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_special_characters_in_api_key', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    call_kwargs = mock_register.call_args.kwargs

def test_run_with_many_registration_tags():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with many registration tags.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1)
    tags = [f'tag_{i}' for i in range(20)]
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=tags, disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_many_registration_tags', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output
    call_kwargs = mock_register.call_args.kwargs

def test_run_with_large_batch_of_images():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a large batch of images (100 images).'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=100)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_large_batch_of_images', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_very_large_batch_of_images():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a very large batch of images (500 images).'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=500)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_very_large_batch_of_images', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_maximum_scale_batch():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with maximum scale batch (1000 images).'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=1000)
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_maximum_scale_batch', 'run', '4_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_large_batch_and_predictions():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a large batch and corresponding predictions.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=200)
    predictions_batch = Batch(content=[None] * 200, indices=[(i,) for i in range(200)])
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=predictions_batch, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_large_batch_and_predictions', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_large_batch_and_image_names():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a large batch and corresponding image names.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=150)
    image_names_batch = Batch(content=[f'image_{i}.jpg' for i in range(150)], indices=[(i,) for i in range(150)])
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=image_names_batch)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_large_batch_and_image_names', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_large_batch_mixed_success_and_error():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with large batch returning mixed success/error statuses.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=100)

    def side_effect(*args, **kwargs):
        call_number = len(mock_register.call_args_list)
        if call_number % 5 == 0:
            return (True, 'Error')
        return (False, 'Success')
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.side_effect = side_effect
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_large_batch_mixed_success_and_error', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_with_large_registration_tags_list():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test run with a large list of registration tags.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    images_batch = create_test_batch(size=10)
    tags = [f'tag_{i}' for i in range(100)]
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.return_value = (False, 'Success')
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=tags, disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_with_large_registration_tags_list', 'run', '5_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output

def test_run_result_structure_consistency():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that result structure is consistent across all entries.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    for batch_size in [1, 5, 10, 50]:
        images_batch = create_test_batch(size=batch_size)
        with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
            mock_register.return_value = (False, 'Success')
            _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
            _call__bound__arguments.apply_defaults()
            codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_result_structure_consistency', 'run', '3_1_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
            codeflash_output = codeflash_return_value
            result = codeflash_output

def test_run_iterates_through_all_images_in_batch():
    np.random.seed(42)
    codeflash_loop_index = int(os.environ['CODEFLASH_LOOP_INDEX'])
    'Test that run iterates through all images without skipping.'
    cache = InMemoryCache()
    block = RoboflowDatasetUploadBlockV1(cache=cache, api_key='test_api_key', background_tasks=None, thread_pool_executor=None)
    batch_size = 250
    images_batch = create_test_batch(size=batch_size)
    call_counter = {'count': 0}

    def count_calls(*args, **kwargs):
        call_counter['count'] += 1
        return (False, 'Success')
    with patch('inference.core.workflows.core_steps.sinks.roboflow.dataset_upload.v1.register_datapoint_at_roboflow') as mock_register:
        mock_register.side_effect = count_calls
        _call__bound__arguments = inspect.signature(block.run).bind(images=images_batch, predictions=None, target_project='test_project', usage_quota_name='test_quota', minutely_usage_limit=10, persist_predictions=False, hourly_usage_limit=100, daily_usage_limit=1000, max_image_size=(640, 480), compression_level=95, registration_tags=[], disable_sink=False, fire_and_forget=False, labeling_batch_prefix='test_batch', labeling_batches_recreation_frequency='daily', image_name=None)
        _call__bound__arguments.apply_defaults()
        codeflash_return_value = codeflash_wrap(block.run, 'tests.inference.unit_tests.test_run__unit_test_1', None, 'test_run_iterates_through_all_images_in_batch', 'run', '7_1', codeflash_loop_index, *_call__bound__arguments.args, **_call__bound__arguments.kwargs)
        codeflash_output = codeflash_return_value
        result = codeflash_output