Utilities

class Tasks:
    """
    Comprehensive task type constants for different AI domains.
    """
    
    # Computer Vision Tasks
    class CVTasks:
        IMAGE_CLASSIFICATION = 'image-classification'
        OBJECT_DETECTION = 'object-detection'
        SEMANTIC_SEGMENTATION = 'semantic-segmentation'
        INSTANCE_SEGMENTATION = 'instance-segmentation'
        FACE_DETECTION = 'face-detection'
        FACE_RECOGNITION = 'face-recognition'
        IMAGE_GENERATION = 'image-generation'
        IMAGE_INPAINTING = 'image-inpainting'
        IMAGE_DENOISING = 'image-denoising'
        IMAGE_SUPER_RESOLUTION = 'image-super-resolution'
        # Additional CV task constants available
    
    # Natural Language Processing Tasks
    class NLPTasks:
        TEXT_CLASSIFICATION = 'text-classification'
        TOKEN_CLASSIFICATION = 'token-classification'
        TEXT_GENERATION = 'text-generation'
        TRANSLATION = 'translation'
        SUMMARIZATION = 'summarization'
        QUESTION_ANSWERING = 'question-answering'
        SENTIMENT_ANALYSIS = 'sentiment-analysis'
        NAMED_ENTITY_RECOGNITION = 'named-entity-recognition'
        TEXT_RANKING = 'text-ranking'
        FILL_MASK = 'fill-mask'
        # Additional NLP task constants available
    
    # Audio Processing Tasks
    class AudioTasks:
        AUTOMATIC_SPEECH_RECOGNITION = 'automatic-speech-recognition'
        TEXT_TO_SPEECH = 'text-to-speech'
        AUDIO_CLASSIFICATION = 'audio-classification'
        SPEAKER_RECOGNITION = 'speaker-recognition'
        AUDIO_SEPARATION = 'audio-separation'
        # Additional audio task constants available
    
    # Multi-Modal Tasks
    class MultiModalTasks:
        IMAGE_CAPTIONING = 'image-captioning'
        VISUAL_QUESTION_ANSWERING = 'visual-question-answering'
        TEXT_TO_IMAGE_SYNTHESIS = 'text-to-image-synthesis'
        IMAGE_TEXT_MATCHING = 'image-text-matching'
        # Additional multi-modal task constants available
    
    # Scientific Computing Tasks
    class ScienceTasks:
        PROTEIN_STRUCTURE_PREDICTION = 'protein-structure-prediction'
        MOLECULAR_PROPERTY_PREDICTION = 'molecular-property-prediction'
        # Additional science task constants available

def get_logger(
    log_file: str = None,
    log_level: int = INFO,
    file_mode: str = 'w',
    **kwargs
) -> Logger:
    """
    Create and configure logger for ModelScope applications.
    
    Parameters:
    - log_file: Path to log file (optional, logs to console if None)
    - log_level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
    - file_mode: File mode for log file ('w' for write, 'a' for append)
    - **kwargs: Additional logging configuration options
    
    Returns:
    Configured logger instance
    """

def read_config(path: str, **kwargs) -> dict:
    """
    Read configuration from file (JSON, YAML, or Python).
    
    Parameters:
    - path: Path to configuration file
    - **kwargs: Additional configuration loading options
    
    Returns:
    Configuration dictionary
    """

def create_model_if_not_exist(
    model_name: str,
    model_type: str = 'model',
    **kwargs
):
    """
    Create model repository on ModelScope Hub if it doesn't exist.
    
    Parameters:
    - model_name: Name of the model repository
    - model_type: Type of repository ('model', 'dataset')
    - **kwargs: Additional repository creation options
    """

from modelscope import Tasks, pipeline

# Use task constants for pipeline creation
classifier = pipeline(Tasks.CVTasks.IMAGE_CLASSIFICATION, model='model_name')
detector = pipeline(Tasks.CVTasks.OBJECT_DETECTION, model='detection_model')

# NLP tasks
text_classifier = pipeline(Tasks.NLPTasks.TEXT_CLASSIFICATION, model='nlp_model')
generator = pipeline(Tasks.NLPTasks.TEXT_GENERATION, model='generation_model')

# Audio tasks
asr = pipeline(Tasks.AudioTasks.AUTOMATIC_SPEECH_RECOGNITION, model='speech_model')
tts = pipeline(Tasks.AudioTasks.TEXT_TO_SPEECH, model='tts_model')

# Multi-modal tasks
captioner = pipeline(Tasks.MultiModalTasks.IMAGE_CAPTIONING, model='caption_model')
vqa = pipeline(Tasks.MultiModalTasks.VISUAL_QUESTION_ANSWERING, model='vqa_model')

# List all available CV tasks
cv_tasks = [attr for attr in dir(Tasks.CVTasks) if not attr.startswith('_')]
print(f"Available CV tasks: {cv_tasks}")

from modelscope import get_logger
import logging

# Basic logger
logger = get_logger()
logger.info("Basic logging message")

# Logger with file output
file_logger = get_logger(
    log_file='modelscope.log',
    log_level=logging.DEBUG,
    file_mode='a'
)

file_logger.debug("Debug message")
file_logger.info("Info message")
file_logger.warning("Warning message")
file_logger.error("Error message")

# Custom logger configuration
custom_logger = get_logger(
    log_file='custom.log',
    log_level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)

custom_logger.info("Custom formatted log message")

from modelscope import read_config

# Read JSON configuration
json_config = read_config('config.json')
print(f"JSON config: {json_config}")

# Read YAML configuration
yaml_config = read_config('config.yaml')
print(f"YAML config: {yaml_config}")

# Read Python configuration file
py_config = read_config('config.py')
print(f"Python config: {py_config}")

# Use configuration in model training
training_config = read_config('training_config.json')

from modelscope import EpochBasedTrainer, TrainingArgs

training_args = TrainingArgs(
    output_dir=training_config['output_dir'],
    max_epochs=training_config['max_epochs'],
    learning_rate=training_config['learning_rate'],
    train_batch_size=training_config['batch_size']
)

from modelscope import create_model_if_not_exist, HubApi

# Create model repository if it doesn't exist
create_model_if_not_exist(
    model_name='my-custom-model',
    model_type='model',
    visibility=1,  # Public repository
    license='Apache-2.0'
)

# Use with Hub API
api = HubApi()
api.login('your_token')

# Check if model exists before creating
try:
    model_info = api.get_model_info('my-custom-model')
    print(f"Model exists: {model_info['name']}")
except:
    # Model doesn't exist, create it
    create_model_if_not_exist('my-custom-model')
    print("Model repository created")

from modelscope.utils.device import get_device, is_gpu_available

# Get optimal device
device = get_device()
print(f"Selected device: {device}")

# Check GPU availability
if is_gpu_available():
    print("GPU is available for acceleration")
    device = 'cuda'
else:
    print("Using CPU for computation")
    device = 'cpu'

# Use device in model loading
from modelscope import Model
model = Model.from_pretrained('model_name', device=device)

from modelscope.utils.file_utils import get_cache_dir, makedirs

# Get cache directory
cache_dir = get_cache_dir()
print(f"Cache directory: {cache_dir}")

# Create directories safely
output_dir = './output/experiments/run_1'
makedirs(output_dir, exist_ok=True)
print(f"Created directory: {output_dir}")

# File path utilities
from modelscope.utils.file_utils import is_local_path, get_file_extension

path1 = '/local/path/to/file.json'
path2 = 'https://remote.com/file.json'

print(f"Is local path: {is_local_path(path1)}")  # True
print(f"Is local path: {is_local_path(path2)}")  # False

print(f"File extension: {get_file_extension(path1)}")  # .json

from modelscope.utils.import_utils import (
    is_torch_available,
    is_transformers_available,
    is_opencv_available
)

# Check for optional dependencies
if is_torch_available():
    print("PyTorch is available")
    from modelscope import TorchModel
    # Use PyTorch-specific functionality

if is_transformers_available():
    print("Transformers library is available")
    # Use HuggingFace transformers integration

if is_opencv_available():
    print("OpenCV is available")
    # Use computer vision functionality

# Conditional imports based on availability
def get_model_class():
    if is_torch_available():
        from modelscope import TorchModel
        return TorchModel
    else:
        from modelscope import Model
        return Model

ModelClass = get_model_class()
model = ModelClass.from_pretrained('model_name')

from modelscope import read_config, get_logger

# Create configuration template
def create_training_config(
    model_name: str,
    output_dir: str,
    max_epochs: int = 10,
    learning_rate: float = 1e-5
) -> dict:
    """Create standardized training configuration."""
    
    config = {
        'model': {
            'name': model_name,
            'type': 'classification'
        },
        'training': {
            'output_dir': output_dir,
            'max_epochs': max_epochs,
            'learning_rate': learning_rate,
            'batch_size': 32,
            'eval_strategy': 'epoch',
            'save_strategy': 'epoch',
            'logging_steps': 100
        },
        'data': {
            'train_file': 'train.json',
            'eval_file': 'eval.json',
            'test_file': 'test.json'
        }
    }
    
    return config

# Use configuration template
config = create_training_config(
    model_name='damo/nlp_structbert_base_chinese',
    output_dir='./output',
    max_epochs=5,
    learning_rate=2e-5
)

# Save configuration
import json
with open('training_config.json', 'w') as f:
    json.dump(config, f, indent=2)

# Load and use configuration
loaded_config = read_config('training_config.json')
logger = get_logger('training.log')
logger.info(f"Training configuration loaded: {loaded_config['model']['name']}")

from modelscope import get_logger, read_config, Tasks

def setup_experiment(config_path: str, log_file: str = None):
    """
    Set up complete experiment environment.
    
    Parameters:
    - config_path: Path to experiment configuration
    - log_file: Optional log file path
    
    Returns:
    Tuple of (config, logger, task_type)
    """
    
    # Load configuration
    config = read_config(config_path)
    
    # Setup logging
    logger = get_logger(
        log_file=log_file,
        log_level=config.get('log_level', 'INFO')
    )
    
    # Determine task type
    task_type = config.get('task_type', Tasks.NLPTasks.TEXT_CLASSIFICATION)
    
    logger.info(f"Experiment setup complete")
    logger.info(f"Task type: {task_type}")
    logger.info(f"Configuration loaded from: {config_path}")
    
    return config, logger, task_type

# Use utility composition
config, logger, task = setup_experiment(
    'experiment_config.json',
    'experiment.log'
)

# Proceed with experiment using setup
from modelscope import pipeline
pipe = pipeline(task, model=config['model_name'])