"""Token-usage metering — provider capture, normalization, engine plumbing.

Fakes follow the provider test convention: SimpleNamespace objects mimicking each
SDK's response surface, dict events for Bedrock's Converse stream.
"""

from __future__ import annotations

import asyncio
from contextlib import contextmanager
from types import SimpleNamespace

import aisuite as ai
from coworker.engine import TurnEngine
from coworker.events import EventType
from coworker.permissions import PermissionEngine
from coworker.providers import (
    AssistantTurn,
    ModelCapabilities,
    ProviderClient,
)
from coworker.providers.anthropic_provider import AnthropicProvider
from coworker.providers.base import TokenUsage
from coworker.providers.bedrock_provider import _BedrockConverseClient
from coworker.providers.gemini_provider import GeminiProvider
from coworker.providers.matrix import model_context_windows
from coworker.providers.openai_provider import OpenAIProvider
from coworker.tools import ToolRegistry


def _final_turn(chunks):
    return chunks[-1].turn


# -- TokenUsage ---------------------------------------------------------------------


def test_context_tokens_is_prompt_side_total():
    usage = TokenUsage(input=100, output=50, cache_read=300, cache_write=20)
    assert usage.context_tokens == 420
    assert usage.as_dict() == {
        "input": 100,
        "output": 50,
        "cache_read": 300,
        "cache_write": 20,
    }


# -- Anthropic ----------------------------------------------------------------------


class _FakeAnthropicClient:
    def __init__(self, events):
        def create(**kwargs):
            self.kwargs = kwargs
            return events

        @contextmanager
        def stream(**kwargs):
            self.kwargs = kwargs
            yield SimpleNamespace(get_final_message=lambda: events)

        self.messages = SimpleNamespace(create=create, stream=stream)
        self.beta = SimpleNamespace(messages=SimpleNamespace(create=create, stream=stream))


def test_anthropic_stream_captures_usage():
    events = [
        SimpleNamespace(
            type="message_start",
            message=SimpleNamespace(
                usage=SimpleNamespace(
                    input_tokens=7,
                    output_tokens=1,
                    cache_read_input_tokens=100,
                    cache_creation_input_tokens=25,
                )
            ),
        ),
        SimpleNamespace(
            type="content_block_start",
            index=0,
            content_block=SimpleNamespace(type="text"),
        ),
        SimpleNamespace(
            type="content_block_delta",
            index=0,
            delta=SimpleNamespace(type="text_delta", text="hi"),
        ),
        SimpleNamespace(
            type="message_delta",
            delta=SimpleNamespace(stop_reason="end_turn"),
            usage=SimpleNamespace(output_tokens=42),
        ),
        SimpleNamespace(type="message_stop"),
    ]
    provider = AnthropicProvider(client=_FakeAnthropicClient(events))
    turn = _final_turn(
        list(provider.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    assert turn.usage == TokenUsage(input=7, output=42, cache_read=100, cache_write=25)


def test_anthropic_complete_captures_usage():
    response = SimpleNamespace(
        content=[SimpleNamespace(type="text", text="hi")],
        stop_reason="end_turn",
        usage=SimpleNamespace(
            input_tokens=10,
            output_tokens=5,
            cache_read_input_tokens=0,
            cache_creation_input_tokens=0,
        ),
    )
    provider = AnthropicProvider(client=_FakeAnthropicClient(response))
    turn = provider.complete(model="m", messages=[{"role": "user", "content": "x"}])
    assert turn.usage == TokenUsage(input=10, output=5)


def test_anthropic_stream_without_usage_leaves_none():
    events = [
        SimpleNamespace(type="message_start"),  # no message/usage attrs
        SimpleNamespace(
            type="message_delta", delta=SimpleNamespace(stop_reason="end_turn")
        ),
    ]
    provider = AnthropicProvider(client=_FakeAnthropicClient(events))
    turn = _final_turn(
        list(provider.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    assert turn.usage is None


# -- OpenAI-compat ------------------------------------------------------------------


class _FakeOpenAIClient:
    def __init__(self, chunks, *, reject_stream_options=False):
        self.calls = []

        def create(**kwargs):
            self.calls.append(kwargs)
            if reject_stream_options and "stream_options" in kwargs:
                raise RuntimeError("unknown parameter: 'stream_options'")
            return chunks

        self.chat = SimpleNamespace(completions=SimpleNamespace(create=create))


def _openai_chunks():
    return [
        SimpleNamespace(
            choices=[
                SimpleNamespace(
                    delta=SimpleNamespace(content="hi", tool_calls=None),
                    finish_reason="stop",
                )
            ],
            usage=None,
        ),
        # Usage arrives on a final empty-choices chunk (include_usage contract).
        SimpleNamespace(
            choices=[],
            usage=SimpleNamespace(
                prompt_tokens=140,
                completion_tokens=9,
                prompt_tokens_details=SimpleNamespace(cached_tokens=40),
            ),
        ),
    ]


def test_openai_stream_requests_and_captures_usage():
    fake = _FakeOpenAIClient(_openai_chunks())
    provider = OpenAIProvider(client=fake)
    turn = _final_turn(
        list(provider.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    assert fake.calls[0]["stream_options"] == {"include_usage": True}
    # Cached share is carved out of prompt_tokens into cache_read.
    assert turn.usage == TokenUsage(input=100, output=9, cache_read=40)


def test_openai_stream_retries_without_stream_options_when_rejected():
    fake = _FakeOpenAIClient(_openai_chunks(), reject_stream_options=True)
    provider = OpenAIProvider(client=fake)
    turn = _final_turn(
        list(provider.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    assert "stream_options" not in fake.calls[-1]
    assert turn.text == "hi"  # the turn still completes; only metering is lost


def test_openai_complete_captures_usage_without_cache_details():
    response = SimpleNamespace(
        choices=[
            SimpleNamespace(
                message=SimpleNamespace(content="hi", tool_calls=None),
                finish_reason="stop",
            )
        ],
        usage=SimpleNamespace(
            prompt_tokens=30, completion_tokens=4, prompt_tokens_details=None
        ),
    )
    fake = _FakeOpenAIClient(response)
    provider = OpenAIProvider(client=fake)
    turn = provider.complete(model="m", messages=[{"role": "user", "content": "x"}])
    assert turn.usage == TokenUsage(input=30, output=4)


# -- Gemini -------------------------------------------------------------------------


class _FakeGeminiClient:
    def __init__(self, responses):
        def generate_content_stream(**kwargs):
            self.kwargs = kwargs
            return iter(responses)

        def generate_content(**kwargs):
            self.kwargs = kwargs
            return responses[0]

        self.models = SimpleNamespace(
            generate_content=generate_content,
            generate_content_stream=generate_content_stream,
        )


def _gemini_response(text, usage_metadata=None):
    return SimpleNamespace(
        candidates=[
            SimpleNamespace(
                content=SimpleNamespace(
                    parts=[SimpleNamespace(text=text, function_call=None)]
                ),
                finish_reason=SimpleNamespace(name="STOP"),
            )
        ],
        usage_metadata=usage_metadata,
    )


def test_gemini_stream_keeps_last_usage_metadata():
    responses = [
        _gemini_response(
            "he",
            SimpleNamespace(
                prompt_token_count=90,
                candidates_token_count=1,
                cached_content_token_count=50,
                thoughts_token_count=0,
            ),
        ),
        _gemini_response(
            "y",
            # Cumulative — the last chunk carries the final totals.
            SimpleNamespace(
                prompt_token_count=90,
                candidates_token_count=12,
                cached_content_token_count=50,
                thoughts_token_count=6,
            ),
        ),
    ]
    provider = GeminiProvider(client=_FakeGeminiClient(responses))
    turn = _final_turn(
        list(provider.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    # input = prompt minus cached; thinking tokens fold into output.
    assert turn.usage == TokenUsage(input=40, output=18, cache_read=50)


# -- Bedrock (Converse) -------------------------------------------------------------


def test_bedrock_converse_stream_captures_metadata_usage():
    fake = SimpleNamespace(
        converse_stream=lambda **kwargs: {
            "stream": [
                {"contentBlockDelta": {"delta": {"text": "hi"}, "contentBlockIndex": 0}},
                {"messageStop": {"stopReason": "end_turn"}},
                {
                    "metadata": {
                        "usage": {
                            "inputTokens": 11,
                            "outputTokens": 3,
                            "cacheReadInputTokens": 8,
                            "cacheWriteInputTokens": 2,
                        }
                    }
                },
            ]
        }
    )
    client = _BedrockConverseClient(client=fake)
    turn = _final_turn(
        list(client.stream(model="m", messages=[{"role": "user", "content": "x"}]))
    )
    assert turn.usage == TokenUsage(input=11, output=3, cache_read=8, cache_write=2)


# -- engine plumbing ----------------------------------------------------------------


class _UsageProvider(ProviderClient):
    def complete(self, *, model, messages, tools=None, **settings):
        return AssistantTurn(
            text="done",
            finish_reason="stop",
            usage=TokenUsage(input=100, output=20, cache_read=5),
        )

    def capabilities(self, model):
        return ModelCapabilities()


def _run_engine(tmp_path):
    registry = ToolRegistry()
    registry.register_all(ai.toolkits.files(root=str(tmp_path), allow_write=True))
    engine = TurnEngine(
        provider=_UsageProvider(),
        registry=registry,
        permissions=PermissionEngine(workspace_root=tmp_path),
        model="gpt-5.5",
    )

    async def _collect():
        return [ev async for ev in engine.run("hello")]

    return engine, asyncio.run(_collect())


def test_engine_attaches_usage_to_event_and_message(tmp_path):
    engine, events = _run_engine(tmp_path)
    assistant = next(ev for ev in events if ev.type == EventType.ASSISTANT_MESSAGE)
    expected = {
        "model": "gpt-5.5",
        "input": 100,
        "output": 20,
        "cache_read": 5,
        "cache_write": 0,
    }
    assert assistant.data["usage"] == expected
    persisted = next(m for m in engine.messages if m.get("role") == "assistant")
    assert persisted["usage"] == expected


def test_outbound_messages_strip_usage_sidecar(tmp_path):
    engine, _ = _run_engine(tmp_path)
    assert all("usage" not in m for m in engine._outbound_messages())


# -- matrix -------------------------------------------------------------------------


def test_model_context_windows_covers_verified_entries_only():
    windows = model_context_windows()
    assert windows["anthropic:claude-fable-5"] == 1_000_000
    assert "together:thinkingmachines/Inkling" not in windows  # unverified stays absent
    assert all(isinstance(v, int) and v > 0 for v in windows.values())