concatenation

raise ValueError("Invalid key size. Key must be 32 bytes long for AES-256.") # Generate a random 128-bit IV iv = os.urandom(16) # Create a new AES-CBC cipher object cipher = Cipher(algorithms.AES(key), modes.CBC(iv…

encrypted_data = encryptor.update(padded_data) + encryptor.finalize() return encrypted_data # Function to decrypt data def decrypt_data(encrypted_data, key, iv): cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=defau…

- Look for operations involving array or tensor manipulations, such as concatenation, addition, or multiplication. 2. **Check Array Dimensions:** - Ensure that all arrays or tensors involved in operations have compatible dimensions. …

# Extract synonyms for each token synonyms = [] for token in tokens: # Use WordNet to get synonyms synsets = nltk.corpus.wordnet.synsets(token) for synset in synsets: for lemma in synset.lemma…

if __name__ == '__main__': app.run(debug=True) ``` ### Explanation 1. **Setup Flask and Flask-Caching**: - Import necessary modules and initialize Flask and Flask-Caching. - Configure caching to use Redis. 2. **Define the API E…

#### Step 3: Implement the Main Search Endpoint Combine the results from both services and handle errors appropriately. ```python @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): try: s…

logger = logging.getLogger(__name__) @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): try: sparse_results = call_sparse_retrieval(query) except HTTPException as e: logger.err…

dense_results = call_dense_retrieval(query) except HTTPException as e: dense_results = {"results": [], "total_results": 0} return JSONResponse(content={"error_code": e.status_code, "message": e.detail}, status_co…

query = ''.join(np.random.choice(list(string.ascii_letters + string.digits), size=query_length)) test_queries.append(query) # Simulate complexity calculation and resizing complexity = len(query) / 20…

all_resized_queries.append(resized_batch) # Concatenate all resized queries resized_queries = torch.cat(all_resized_queries, dim=0) # Print the shape of the resized queries to verify print(resized_queries.shape) ``` ### Explanation …

# Identify sparse and dense documents def is_sparse(document): # Define a threshold to determine sparsity threshold = 10 # Example threshold return len(document.split()) < threshold df['is_sparse'] = df['text'].apply(is_sparse…

Here's an updated version of your code with proper handling of padding and IV: ```python import os from cryptography.hazmat.primitives import padding from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes from cryptog…