Utilities and Helpers

def upload_preset(uri: str, preset: str) -> None:
    """
    Upload a preset directory to a model hub.
    
    Supports uploading to Kaggle Models and other model hosting platforms.
    The URI format determines the target platform:
    - Kaggle: kaggle://<username>/<model>/<framework>/<variation>
    - HuggingFace: hf://<username>/<model>
    - Google Cloud Storage: gs://<bucket>/<path>
    
    Args:
        uri: The URI identifying where to upload the model.
             Format depends on the target platform.
        preset: Path to the local preset directory containing model files,
                configuration, and metadata.
                
    Raises:
        ValueError: If URI format is invalid or unsupported
        ConnectionError: If upload fails due to network issues
        AuthenticationError: If credentials are invalid or missing
        
    Example:
        # Upload to Kaggle Models
        keras_hub.upload_preset(
            "kaggle://username/bert-sentiment/keras/v1",
            "./my_bert_preset"
        )
    """
    ...

def imagenet_id_to_name(class_id: int) -> str:
    """
    Convert ImageNet class ID to human-readable class name.
    
    Args:
        class_id: Integer class ID (0-999 for ImageNet-1K)
        
    Returns:
        Human-readable class name string
        
    Raises:
        ValueError: If class_id is outside valid range
        
    Example:
        name = keras_hub.utils.imagenet_id_to_name(285)
        # Returns: "Egyptian cat"
    """
    ...

def imagenet_name_to_id(class_name: str) -> int:
    """
    Convert ImageNet class name to class ID.
    
    Args:
        class_name: Human-readable class name
        
    Returns:
        Integer class ID
        
    Raises:
        KeyError: If class_name not found in ImageNet classes
        
    Example:
        class_id = keras_hub.utils.imagenet_name_to_id("Egyptian cat")
        # Returns: 285
    """
    ...

def decode_imagenet_predictions(predictions, top: int = 5):
    """
    Decode ImageNet model predictions to human-readable format.
    
    Args:
        predictions: Model prediction array of shape (batch_size, 1000)
                    or (1000,) for single prediction
        top: Number of top predictions to return
        
    Returns:
        List of tuples (class_id, class_name, probability) for top predictions
        
    Example:
        # predictions shape: (1, 1000)
        decoded = keras_hub.utils.decode_imagenet_predictions(predictions, top=3)
        # Returns: [(285, "Egyptian cat", 0.85), (281, "tabby cat", 0.12), ...]
    """
    ...

def coco_id_to_name(class_id: int) -> str:
    """
    Convert COCO class ID to human-readable class name.
    
    Args:
        class_id: Integer class ID (1-80 for COCO)
        
    Returns:
        Human-readable class name string
        
    Raises:
        ValueError: If class_id is outside valid range
        
    Example:
        name = keras_hub.utils.coco_id_to_name(1)
        # Returns: "person"
    """
    ...

def coco_name_to_id(class_name: str) -> int:
    """
    Convert COCO class name to class ID.
    
    Args:
        class_name: Human-readable class name
        
    Returns:
        Integer class ID
        
    Raises:
        KeyError: If class_name not found in COCO classes
        
    Example:
        class_id = keras_hub.utils.coco_name_to_id("person")
        # Returns: 1
    """
    ...

def version() -> str:
    """
    Return the current Keras Hub version string.
    
    Returns:
        Version string in semantic versioning format
        
    Example:
        version_str = keras_hub.version()
        # Returns: "0.22.1"
    """
    ...

__version__: str = "0.22.1"
"""Current Keras Hub version as a string constant."""

import keras_hub
import numpy as np

# Load an ImageNet-trained model
model = keras_hub.models.ResNetImageClassifier.from_preset("resnet50_imagenet")

# Make predictions (example with random data)
images = np.random.random((2, 224, 224, 3))
predictions = model.predict(images)

# Decode predictions to human-readable format
for i, pred in enumerate(predictions):
    decoded = keras_hub.utils.decode_imagenet_predictions(pred, top=3)
    print(f"Image {i+1} predictions:")
    for class_id, class_name, probability in decoded:
        print(f"  {class_name} (ID: {class_id}): {probability:.3f}")
    print()

import keras_hub

# Convert ImageNet class ID to name
class_id = 285
class_name = keras_hub.utils.imagenet_id_to_name(class_id)
print(f"Class {class_id}: {class_name}")

# Convert back to ID
recovered_id = keras_hub.utils.imagenet_name_to_id(class_name)
print(f"Class '{class_name}': ID {recovered_id}")

# COCO dataset utilities
coco_id = 1
coco_name = keras_hub.utils.coco_id_to_name(coco_id)
print(f"COCO class {coco_id}: {coco_name}")

coco_recovered_id = keras_hub.utils.coco_name_to_id(coco_name)
print(f"COCO class '{coco_name}': ID {coco_recovered_id}")

import keras_hub

# Train and save a custom model
model = keras_hub.models.BertTextClassifier.from_preset("bert_base_en", num_classes=2)

# Fine-tune the model on your data
# model.fit(train_data, epochs=3)

# Save as preset
model.save_to_preset("./my_custom_bert_sentiment")

# Upload to Kaggle Models
try:
    keras_hub.upload_preset(
        uri="kaggle://myusername/bert-sentiment-classifier/keras/v1",
        preset="./my_custom_bert_sentiment"
    )
    print("Successfully uploaded to Kaggle Models!")
except Exception as e:
    print(f"Upload failed: {e}")

import keras_hub

# Get version information
current_version = keras_hub.version()
print(f"Keras Hub version: {current_version}")

# Access version constant
version_constant = keras_hub.__version__
print(f"Version constant: {version_constant}")

# Check version compatibility
required_version = "0.22.0"
if keras_hub.__version__ >= required_version:
    print("Version requirements satisfied")
else:
    print(f"Please upgrade to version {required_version} or higher")

import keras_hub
import numpy as np

def process_imagenet_batch(model, images, top_k=5):
    """Process a batch of images and return decoded predictions."""
    predictions = model.predict(images)
    
    results = []
    for i, pred in enumerate(predictions):
        decoded = keras_hub.utils.decode_imagenet_predictions(pred, top=top_k)
        results.append({
            'image_index': i,
            'predictions': decoded
        })
    
    return results

# Example usage
model = keras_hub.models.EfficientNetImageClassifier.from_preset("efficientnet_b0_imagenet")
batch_images = np.random.random((4, 224, 224, 3))

results = process_imagenet_batch(model, batch_images, top_k=3)

for result in results:
    print(f"Image {result['image_index']}:")
    for class_id, class_name, prob in result['predictions']:
        print(f"  {class_name}: {prob:.3f}")
    print()

import keras_hub
import numpy as np

# Load object detection model
detector = keras_hub.models.RetinaNetObjectDetector.from_preset("retinanet_resnet50_pascalvoc")

# Make predictions (example with random data)
images = np.random.random((1, 512, 512, 3))
detections = detector.predict(images)

# Process detections (assuming COCO-format output)
def process_detections(detections, confidence_threshold=0.5):
    """Process detection results and convert class IDs to names."""
    processed = []
    
    # Extract detection components (format depends on model)
    boxes = detections['boxes'][0]  # Bounding boxes
    classes = detections['classes'][0]  # Class predictions
    scores = detections['scores'][0]  # Confidence scores
    
    # Filter by confidence
    valid_detections = scores > confidence_threshold
    
    for i in range(len(boxes)):
        if valid_detections[i]:
            class_id = int(classes[i])
            try:
                class_name = keras_hub.utils.coco_id_to_name(class_id)
            except ValueError:
                class_name = f"Class_{class_id}"
            
            processed.append({
                'box': boxes[i],
                'class_id': class_id,
                'class_name': class_name,
                'score': float(scores[i])
            })
    
    return processed

# Process results
results = process_detections(detections)
for detection in results:
    print(f"Detected {detection['class_name']} "
          f"(score: {detection['score']:.3f}) "
          f"at {detection['box']}")

import keras_hub
import os

def upload_model_safely(model, preset_name, upload_uri):
    """Safely upload a model with proper error handling."""
    
    # Save model to temporary preset directory
    temp_preset_dir = f"./temp_preset_{preset_name}"
    
    try:
        # Save model as preset
        print("Saving model as preset...")
        model.save_to_preset(temp_preset_dir)
        
        # Upload to model hub
        print(f"Uploading to {upload_uri}...")
        keras_hub.upload_preset(upload_uri, temp_preset_dir)
        
        print("Upload completed successfully!")
        return True
        
    except FileNotFoundError:
        print("Error: Model files not found")
        return False
    except ConnectionError:
        print("Error: Network connection failed")
        return False
    except PermissionError:
        print("Error: Authentication failed or insufficient permissions")
        return False
    except Exception as e:
        print(f"Unexpected error: {e}")
        return False
    finally:
        # Clean up temporary files
        if os.path.exists(temp_preset_dir):
            import shutil
            shutil.rmtree(temp_preset_dir)
            print("Cleaned up temporary files")

# Example usage
model = keras_hub.models.GPT2CausalLM.from_preset("gpt2_base_en")
success = upload_model_safely(
    model, 
    "my_gpt2_model",
    "kaggle://username/my-gpt2/keras/v1"
)

if success:
    print("Model is now available on Kaggle Models!")

def compute_sentence_piece_proto(
    data: list,
    vocab_size: int,
    model_type: str = "unigram",
    pad_id: int = 0,
    eos_id: int = 1,
    unk_id: int = 2,
    bos_id: int = -1,
    **kwargs
) -> bytes:
    """
    Compute a SentencePiece model proto from training data.
    
    Creates a SentencePiece model protocol buffer that can be used
    to initialize SentencePiece tokenizers.
    
    Args:
        data: List of strings to train the tokenizer on
        vocab_size: Target vocabulary size
        model_type: SentencePiece model type ("unigram", "bpe", "word", "char")
        pad_id: ID for padding token
        eos_id: ID for end-of-sequence token  
        unk_id: ID for unknown token
        bos_id: ID for beginning-of-sequence token (-1 to disable)
        **kwargs: Additional SentencePiece training parameters
        
    Returns:
        bytes: Serialized SentencePiece model proto
        
    Example:
        training_data = ["Hello world", "How are you", "Fine thanks"]
        proto = keras_hub.tokenizers.compute_sentence_piece_proto(
            data=training_data,
            vocab_size=1000,
            model_type="unigram"
        )
    """
    ...

def compute_word_piece_vocabulary(
    data: list,
    vocab_size: int,
    reserved_tokens: list = None,
    **kwargs
) -> dict:
    """
    Compute a WordPiece vocabulary from training data.
    
    Creates a WordPiece vocabulary dictionary suitable for use with
    WordPieceTokenizer.
    
    Args:
        data: List of strings to train the vocabulary on
        vocab_size: Target vocabulary size
        reserved_tokens: List of tokens to reserve (e.g., ["[PAD]", "[UNK]"])
        **kwargs: Additional vocabulary computation parameters
        
    Returns:
        dict: Vocabulary mapping from token to ID
        
    Example:
        training_data = ["Hello world", "How are you", "Fine thanks"] 
        vocab = keras_hub.tokenizers.compute_word_piece_vocabulary(
            data=training_data,
            vocab_size=1000,
            reserved_tokens=["[PAD]", "[UNK]", "[CLS]", "[SEP]"]
        )
    """
    ...

import keras_hub
import numpy as np

def analyze_image_predictions(model, images, detailed=True):
    """Comprehensive analysis of image classification predictions."""
    
    # Get predictions
    predictions = model.predict(images)
    
    analysis = {
        'total_images': len(images),
        'model_version': keras_hub.version(),
        'predictions': []
    }
    
    for i, pred in enumerate(predictions):
        # Get top predictions
        top_preds = keras_hub.utils.decode_imagenet_predictions(pred, top=5)
        
        # Calculate prediction confidence
        top_prob = top_preds[0][2]
        entropy = -np.sum(pred * np.log(pred + 1e-10))
        
        image_analysis = {
            'image_index': i,
            'top_prediction': {
                'class_id': top_preds[0][0],
                'class_name': top_preds[0][1],
                'probability': float(top_prob)
            },
            'confidence_level': 'high' if top_prob > 0.8 else 'medium' if top_prob > 0.5 else 'low',
            'entropy': float(entropy)
        }
        
        if detailed:
            image_analysis['all_top_predictions'] = [
                {
                    'class_id': int(cid),
                    'class_name': cname,
                    'probability': float(prob)
                }
                for cid, cname, prob in top_preds
            ]
        
        analysis['predictions'].append(image_analysis)
    
    return analysis

# Example usage
model = keras_hub.models.ViTImageClassifier.from_preset("vit_base_patch16_224")
test_images = np.random.random((3, 224, 224, 3))

analysis = analyze_image_predictions(model, test_images, detailed=True)

print(f"Analysis using Keras Hub {analysis['model_version']}")
print(f"Processed {analysis['total_images']} images")

for pred in analysis['predictions']:
    top_pred = pred['top_prediction']
    print(f"\nImage {pred['image_index']}:")
    print(f"  Top prediction: {top_pred['class_name']} ({top_pred['probability']:.3f})")
    print(f"  Confidence: {pred['confidence_level']}")
    print(f"  Entropy: {pred['entropy']:.3f}")