"""Deterministic mock embedding client for testing and local development.

No network calls are made.  Vectors are generated from a seeded hash of the
input text so identical inputs always produce identical vectors.
"""

import hashlib
import math

from app.embeddings.base import EmbeddingClient

_MOCK_DIM = 1536


class MockEmbeddingClient(EmbeddingClient):
    """Return deterministic pseudo-random vectors without calling any API.

    The vector for each text is derived from ``sha256(text)``.  This ensures
    tests that check vector similarity behave consistently across runs.

    Example::

        client = MockEmbeddingClient()
        v = client.get_embedding("hello")
        assert len(v) == 1536
    """

    @property
    def dimension(self) -> int:
        return _MOCK_DIM

    def get_embedding(self, text: str) -> list[float]:
        """Return a deterministic unit vector derived from ``sha256(text)``.

        Args:
            text: Input text.

        Returns:
            Normalised float vector of length :attr:`dimension`.
        """
        seed = int(hashlib.sha256(text.encode()).hexdigest(), 16)
        vector: list[float] = []
        for _i in range(_MOCK_DIM):
            seed = (seed * 6364136223846793005 + 1442695040888963407) & 0xFFFFFFFFFFFFFFFF
            val = (seed >> 33) / (2**31) - 1.0
            vector.append(val)

        # Normalise to unit length
        norm = math.sqrt(sum(v * v for v in vector)) or 1.0
        return [v / norm for v in vector]

    def get_embeddings(self, texts: list[str]) -> list[list[float]]:
        """Embed a batch of texts using the deterministic mock.

        Args:
            texts: List of input strings.

        Returns:
            List of unit vectors in the same order as ``texts``.
        """
        return [self.get_embedding(t) for t in texts]