from dataclasses import asdict

  from typing import Any, Dict, List, Optional
  
  import numpy as np

  import pytest

  
  from config import (
      FunctionScoreConfig,
      IndexConfig,
      QueryConfig,

      RerankConfig,
      SPUConfig,
      SearchConfig,
  )
  from embeddings.text_encoder import TextEmbeddingEncoder

  from embeddings.image_encoder import CLIPImageEncoder

  from embeddings.text_embedding_tei import TEITextModel

  from embeddings.bf16 import encode_embedding_for_redis

  from embeddings.cache_keys import build_image_cache_key, build_text_cache_key

  from embeddings.config import CONFIG

  from query import QueryParser

  from context.request_context import create_request_context, set_current_request_context, clear_current_request_context

  pytestmark = [pytest.mark.embedding, pytest.mark.regression]
  

  
  class _FakeRedis:
      def __init__(self):
          self.store: Dict[str, bytes] = {}
  
      def ping(self):
          return True
  
      def get(self, key: str):
          return self.store.get(key)
  
      def setex(self, key: str, _expire, value: bytes):
          self.store[key] = value
          return True
  
      def expire(self, key: str, _expire):
          return key in self.store
  
      def delete(self, key: str):
          self.store.pop(key, None)
          return True
  
  
  class _FakeResponse:
      def __init__(self, payload: List[Optional[List[float]]]):
          self._payload = payload
  
      def raise_for_status(self):
          return None
  
      def json(self):
          return self._payload
  
  
  class _FakeTranslator:
      def translate(
          self,
          text: str,
          target_lang: str,
          source_lang: Optional[str] = None,
          prompt: Optional[str] = None,
      ) -> str:
          return f"{text}-{target_lang}"
  
  
  class _FakeQueryEncoder:

      def __init__(self):
          self.calls = []
  

      def encode(self, sentences, **kwargs):

          self.calls.append({"sentences": sentences, "kwargs": dict(kwargs)})

          if isinstance(sentences, str):
              sentences = [sentences]
          return np.array([np.array([0.11, 0.22, 0.33], dtype=np.float32) for _ in sentences], dtype=object)
  
  

  class _FakeClipTextEncoder:
      def __init__(self):
          self.calls = []
  
      def encode_clip_text(self, text, **kwargs):
          self.calls.append({"text": text, "kwargs": dict(kwargs)})
          return np.array([0.44, 0.55, 0.66], dtype=np.float32)
  
  

  def _tokenizer(text):
      return str(text).split()
  
  

  class _FakeEmbeddingCache:
      def __init__(self):
          self.store: Dict[str, np.ndarray] = {}
  
      def get(self, key: str):
          return self.store.get(key)
  
      def set(self, key: str, embedding: np.ndarray):
          self.store[key] = np.asarray(embedding, dtype=np.float32)
          return True
  
  

  def _build_test_config(*, image_embedding_field: Optional[str] = None) -> SearchConfig:

      return SearchConfig(
          field_boosts={"title.en": 3.0},
          indexes=[IndexConfig(name="default", label="default", fields=["title.en"], boost=1.0)],
          query_config=QueryConfig(
              supported_languages=["en", "zh"],
              default_language="en",
              enable_text_embedding=True,
              enable_query_rewrite=False,

              rewrite_dictionary={},

              text_embedding_field="title_embedding",

              image_embedding_field=image_embedding_field,

          ),

          function_score=FunctionScoreConfig(),

          rerank=RerankConfig(),
          spu_config=SPUConfig(enabled=True, spu_field="spu_id", inner_hits_size=3),
          es_index_name="test_products",

          es_settings={},

      )
  
  
  def test_text_embedding_encoder_response_alignment(monkeypatch):

      fake_cache = _FakeEmbeddingCache()
      monkeypatch.setattr("embeddings.text_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)

      def _fake_post(url, json, timeout, **kwargs):

          assert url.endswith("/embed/text")
          assert json == ["hello", "world"]

          assert kwargs["params"]["priority"] == 0

          return _FakeResponse([[0.1, 0.2], [0.3, 0.4]])

  
      monkeypatch.setattr("embeddings.text_encoder.requests.post", _fake_post)
  
      encoder = TextEmbeddingEncoder(service_url="http://127.0.0.1:6005")
      out = encoder.encode(["hello", "world"])
  
      assert len(out) == 2
      assert isinstance(out[0], np.ndarray)
      assert out[0].shape == (2,)

      assert isinstance(out[1], np.ndarray)
      assert out[1].shape == (2,)
  
  
  def test_text_embedding_encoder_raises_on_missing_vector(monkeypatch):

      fake_cache = _FakeEmbeddingCache()
      monkeypatch.setattr("embeddings.text_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)

      def _fake_post(url, json, timeout, **kwargs):

          return _FakeResponse([[0.1, 0.2], None])
  
      monkeypatch.setattr("embeddings.text_encoder.requests.post", _fake_post)
  
      encoder = TextEmbeddingEncoder(service_url="http://127.0.0.1:6005")
      with pytest.raises(ValueError):
          encoder.encode(["hello", "world"])

  
  
  def test_text_embedding_encoder_cache_hit(monkeypatch):

      fake_cache = _FakeEmbeddingCache()

      cached = np.array([0.9, 0.8], dtype=np.float32)

      fake_cache.store[build_text_cache_key("cached-text", normalize=True)] = cached
      monkeypatch.setattr("embeddings.text_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)

  
      calls = {"count": 0}
  

      def _fake_post(url, json, timeout, **kwargs):

          calls["count"] += 1
          return _FakeResponse([[0.3, 0.4]])
  
      monkeypatch.setattr("embeddings.text_encoder.requests.post", _fake_post)
  
      encoder = TextEmbeddingEncoder(service_url="http://127.0.0.1:6005")
      out = encoder.encode(["cached-text", "new-text"])
  
      assert calls["count"] == 1

      # encoder returns an object-dtype ndarray of 1-D float32 vectors; cast per-row
      # before numeric comparison.
      assert np.allclose(np.asarray(out[0], dtype=np.float32), cached)
      assert np.allclose(np.asarray(out[1], dtype=np.float32), np.array([0.3, 0.4], dtype=np.float32))

  def test_text_embedding_encoder_forwards_request_headers(monkeypatch):
      fake_cache = _FakeEmbeddingCache()
      monkeypatch.setattr("embeddings.text_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)
  
      captured = {}
  
      def _fake_post(url, json, timeout, **kwargs):
          captured["headers"] = dict(kwargs.get("headers") or {})
          return _FakeResponse([[0.1, 0.2]])
  
      monkeypatch.setattr("embeddings.text_encoder.requests.post", _fake_post)
  
      context = create_request_context(reqid="req-ctx-1", uid="user-ctx-1")
      set_current_request_context(context)
      try:
          encoder = TextEmbeddingEncoder(service_url="http://127.0.0.1:6005")
          encoder.encode(["hello"])
      finally:
          clear_current_request_context()
  
      assert captured["headers"]["X-Request-ID"] == "req-ctx-1"
      assert captured["headers"]["X-User-ID"] == "user-ctx-1"
  
  

  def test_image_embedding_encoder_cache_hit(monkeypatch):
      fake_cache = _FakeEmbeddingCache()
      cached = np.array([0.5, 0.6], dtype=np.float32)
      url = "https://example.com/a.jpg"

      fake_cache.store[
          build_image_cache_key(url, normalize=True, model_name=CONFIG.MULTIMODAL_MODEL_NAME)
      ] = cached

      monkeypatch.setattr("embeddings.image_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)
  
      calls = {"count": 0}
  
      def _fake_post(url, params, json, timeout, **kwargs):
          calls["count"] += 1

          assert params["priority"] == 0

          return _FakeResponse([[0.1, 0.2]])
  
      monkeypatch.setattr("embeddings.image_encoder.requests.post", _fake_post)
  
      encoder = CLIPImageEncoder(service_url="http://127.0.0.1:6008")
      out = encoder.encode_batch(["https://example.com/a.jpg", "https://example.com/b.jpg"])
  
      assert calls["count"] == 1
      assert np.allclose(out[0], cached)
      assert np.allclose(out[1], np.array([0.1, 0.2], dtype=np.float32))
  
  

  def test_image_embedding_encoder_passes_priority(monkeypatch):
      fake_cache = _FakeEmbeddingCache()
      monkeypatch.setattr("embeddings.image_encoder.RedisEmbeddingCache", lambda **kwargs: fake_cache)
  
      def _fake_post(url, params, json, timeout, **kwargs):
          assert params["priority"] == 1
          return _FakeResponse([[0.1, 0.2]])
  
      monkeypatch.setattr("embeddings.image_encoder.requests.post", _fake_post)
  
      encoder = CLIPImageEncoder(service_url="http://127.0.0.1:6008")
      out = encoder.encode_batch(["https://example.com/a.jpg"], priority=1)
      assert len(out) == 1
      assert np.allclose(out[0], np.array([0.1, 0.2], dtype=np.float32))
  
  

  def test_query_parser_generates_query_vector_with_encoder():

      encoder = _FakeQueryEncoder()

      parser = QueryParser(
          config=_build_test_config(),

          text_encoder=encoder,

          translator=_FakeTranslator(),

          tokenizer=_tokenizer,

      )
  
      parsed = parser.parse("red dress", tenant_id="162", generate_vector=True)
      assert parsed.query_vector is not None
      assert parsed.query_vector.shape == (3,)

      assert encoder.calls
      assert encoder.calls[0]["kwargs"]["priority"] == 1

  def test_query_parser_generates_image_query_vector_with_clip_text_encoder():
      text_encoder = _FakeQueryEncoder()
      image_encoder = _FakeClipTextEncoder()
      parser = QueryParser(
          config=_build_test_config(image_embedding_field="image_embedding.vector"),
          text_encoder=text_encoder,
          image_encoder=image_encoder,
          translator=_FakeTranslator(),
          tokenizer=_tokenizer,
      )
  
      parsed = parser.parse("red dress", tenant_id="162", generate_vector=True)
      assert parsed.query_vector is not None
      assert parsed.image_query_vector is not None
      assert parsed.image_query_vector.shape == (3,)
      assert image_encoder.calls
      assert image_encoder.calls[0]["text"] == "red dress"
      assert image_encoder.calls[0]["kwargs"]["priority"] == 1
  
  

  def test_query_parser_skips_query_vector_when_disabled():
      parser = QueryParser(
          config=_build_test_config(),
          text_encoder=_FakeQueryEncoder(),
          translator=_FakeTranslator(),

          tokenizer=_tokenizer,

      )
  
      parsed = parser.parse("red dress", tenant_id="162", generate_vector=False)
      assert parsed.query_vector is None

      assert parsed.image_query_vector is None

  
  
  def test_tei_text_model_splits_batches_over_client_limit(monkeypatch):
      monkeypatch.setattr(TEITextModel, "_health_check", lambda self: None)
      calls = []
  
      class _Response:
          def __init__(self, payload):
              self._payload = payload
  
          def raise_for_status(self):
              return None
  
          def json(self):
              return self._payload
  
      def _fake_post(url, json, timeout):
          inputs = list(json["inputs"])
          calls.append(inputs)
          return _Response([[float(idx)] for idx, _ in enumerate(inputs, start=1)])
  
      monkeypatch.setattr("embeddings.text_embedding_tei.requests.post", _fake_post)
  
      model = TEITextModel(
          base_url="http://127.0.0.1:8080",
          timeout_sec=20,
          max_client_batch_size=24,
      )
      vectors = model.encode([f"text-{idx}" for idx in range(25)], normalize_embeddings=False)
  
      assert len(calls) == 2
      assert len(calls[0]) == 24
      assert len(calls[1]) == 1
      assert len(vectors) == 25