"""Test fixtures for ZeroGPU OpenCode Provider tests."""

import pytest
from unittest.mock import MagicMock, patch


@pytest.fixture
def mock_tokenizer():
    """Create a mock tokenizer for testing."""
    tokenizer = MagicMock()
    tokenizer.pad_token = None
    tokenizer.eos_token = "</s>"
    tokenizer.pad_token_id = 0
    tokenizer.eos_token_id = 2
    tokenizer.model_max_length = 4096

    def mock_apply_chat_template(messages, tokenize=False, add_generation_prompt=True):
        parts = []
        for msg in messages:
            role = msg.get("role", msg.role if hasattr(msg, "role") else "user")
            content = msg.get("content", msg.content if hasattr(msg, "content") else "")
            if role == "system":
                parts.append(f"<|system|>{content}</s>")
            elif role == "user":
                parts.append(f"<|user|>{content}</s>")
            elif role == "assistant":
                parts.append(f"<|assistant|>{content}</s>")
        if add_generation_prompt:
            parts.append("<|assistant|>")
        return "".join(parts)

    tokenizer.apply_chat_template = mock_apply_chat_template

    def mock_call(text, return_tensors=None, truncation=True, max_length=None):
        import torch
        # Simple mock: return input_ids based on text length
        token_count = max(1, len(text) // 4)
        return {
            "input_ids": torch.ones((1, token_count), dtype=torch.long),
            "attention_mask": torch.ones((1, token_count), dtype=torch.long),
        }

    tokenizer.__call__ = mock_call
    tokenizer.return_value = mock_call("test")

    def mock_decode(tokens, skip_special_tokens=True):
        return "This is a test response."

    tokenizer.decode = mock_decode

    return tokenizer


@pytest.fixture
def mock_model():
    """Create a mock model for testing."""
    import torch

    model = MagicMock()

    def mock_generate(**kwargs):
        input_ids = kwargs.get("input_ids", torch.ones((1, 10), dtype=torch.long))
        input_length = input_ids.shape[1]
        # Generate some tokens
        generated = torch.ones((1, input_length + 20), dtype=torch.long)
        return generated

    model.generate = mock_generate
    model.device = "cpu"

    return model


@pytest.fixture
def sample_messages():
    """Sample chat messages for testing."""
    return [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"},
    ]


@pytest.fixture
def sample_request_data():
    """Sample request data for OpenAI-compatible endpoint."""
    return {
        "model": "meta-llama/Llama-3.1-8B-Instruct",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello!"},
        ],
        "temperature": 0.7,
        "max_tokens": 512,
        "stream": False,
    }


@pytest.fixture
def sample_streaming_request_data():
    """Sample streaming request data."""
    return {
        "model": "meta-llama/Llama-3.1-8B-Instruct",
        "messages": [
            {"role": "user", "content": "Tell me a joke."},
        ],
        "temperature": 0.7,
        "max_tokens": 256,
        "stream": True,
    }


@pytest.fixture(autouse=True)
def mock_torch_cuda():
    """Mock CUDA availability for tests."""
    with patch("torch.cuda.is_available", return_value=False):
        yield