from unittest.mock import MagicMock
import pytest
from src.openai_compatible import (
 NormalizedResponse,
 OpenAICompatibleRequest,
 send_openai_compatible,
)
from src.vendor_capabilities import VendorCapabilities, register

@pytest.fixture
def caps() -> VendorCapabilities:
 return VendorCapabilities(vendor="test", model="test-model", context_window=8192, cost_input_per_mtok=1.0, cost_output_per_mtok=2.0)

def _mock_completion(text: str = "hello", tool_calls=None, usage_input: int = 10, usage_output: int = 5):
 m = MagicMock()
 m.choices = [MagicMock()]
 m.choices[0].message.content = text
 m.choices[0].message.tool_calls = tool_calls or []
 m.usage.prompt_tokens = usage_input
 m.usage.completion_tokens = usage_output
 m.usage.prompt_tokens_details = None
 m.usage.completion_tokens_details = None
 return m

def test_send_non_streaming_returns_normalized_response(caps: VendorCapabilities) -> None:
 client = MagicMock()
 client.chat.completions.create.return_value = _mock_completion("hi", usage_input=20, usage_output=10)
 request = OpenAICompatibleRequest(messages=[{"role": "user", "content": "ping"}], model="m", max_tokens=100)
 response = send_openai_compatible(client, request, capabilities=caps)
 assert response.text == "hi"
 assert response.tool_calls == []
 assert response.usage_input_tokens == 20
 assert response.usage_output_tokens == 10

def test_send_streaming_aggregates_chunks(caps: VendorCapabilities) -> None:
 client = MagicMock()
 chunks = [
  MagicMock(choices=[MagicMock(delta=MagicMock(content="hel", tool_calls=None))]),
  MagicMock(choices=[MagicMock(delta=MagicMock(content="lo", tool_calls=None))]),
  MagicMock(choices=[MagicMock(delta=MagicMock(content="", tool_calls=None))], usage=MagicMock(prompt_tokens=15, completion_tokens=5)),
 ]
 client.chat.completions.create.return_value = iter(chunks)
 received: list = []
 request = OpenAICompatibleRequest(messages=[{"role": "user", "content": "ping"}], model="m", stream=True, stream_callback=received.append)
 response = send_openai_compatible(client, request, capabilities=caps)
 assert response.text == "hello"
 assert received == ["hel", "lo"]
 assert response.usage_input_tokens == 15

def test_tool_call_detection_in_response(caps: VendorCapabilities) -> None:
 tool_call = MagicMock()
 tool_call.id = "call_1"
 tool_call.function.name = "read_file"
 tool_call.function.arguments = '{"path": "/tmp/x"}'
 completion = _mock_completion(text="", tool_calls=[tool_call])
 client = MagicMock()
 client.chat.completions.create.return_value = completion
 request = OpenAICompatibleRequest(messages=[{"role": "user", "content": "ping"}], model="m")
 response = send_openai_compatible(client, request, capabilities=caps)
 assert len(response.tool_calls) == 1
 assert response.tool_calls[0]["function"]["name"] == "read_file"
 assert response.tool_calls[0]["id"] == "call_1"

def test_vision_multimodal_message(caps: VendorCapabilities) -> None:
 client = MagicMock()
 client.chat.completions.create.return_value = _mock_completion("looks like a cat")
 messages = [{"role": "user", "content": [{"type": "text", "text": "what is this?"}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}]}]
 request = OpenAICompatibleRequest(messages=messages, model="m")
 response = send_openai_compatible(client, request, capabilities=caps)
 sent_messages = client.chat.completions.create.call_args.kwargs["messages"]
 assert sent_messages[0]["content"] == messages[0]["content"]
 assert response.text == "looks like a cat"

def test_error_classification_429_to_rate_limit(caps: VendorCapabilities) -> None:
 from openai import RateLimitError
 from src.ai_client import ProviderError
 client = MagicMock()
 client.chat.completions.create.side_effect = RateLimitError("rate limited", response=MagicMock(status_code=429), body=None)
 request = OpenAICompatibleRequest(messages=[{"role": "user", "content": "ping"}], model="m")
 with pytest.raises(ProviderError) as exc_info:
  send_openai_compatible(client, request, capabilities=caps)
 assert exc_info.value.kind == "rate_limit"

def test_normalized_response_is_frozen_dataclass() -> None:
 from dataclasses import FrozenInstanceError
 r = NormalizedResponse(text="x", tool_calls=[], usage_input_tokens=0, usage_output_tokens=0, usage_cache_read_tokens=0, usage_cache_creation_tokens=0, raw_response=None)
 with pytest.raises(FrozenInstanceError):
  r.text = "y"