"""Code for serving bloom blocks via hivemind-server"""
from queue import Empty
from typing import Sequence, Tuple, Optional

import torch
from hivemind import use_hivemind_log_handler
from hivemind.moe.server.module_backend import ModuleBackend
from hivemind.moe.server.task_pool import TaskPool
from hivemind.utils import InvalidStateError, get_logger

from src.bloom.from_pretrained import BloomBlock
from src.server.cache import MemoryCache

use_hivemind_log_handler("in_root_logger")
logger = get_logger(__file__)

MAX_LENGTH = 2048


class InferenceTaskPool(TaskPool):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        assert self.min_batch_size == 1, "min_batch_size in InferenceTaskPool cannot be greater 1"

    def iterate_minibatches(self, *args, **kwargs):
        """Form minibatches by grouping one or more tasks together up to self.max_batch_size"""

        while True:
            try:
                logger.debug(f"{self.name} getting next task")
                task = self.tasks.get(timeout=self.timeout)
            except Empty:
                logger.warning(f"Timeout reached but batch doesn't contain >={self.min_batch_size} elements yet")
                continue

            try:
                if task.future.set_running_or_notify_cancel():
                    yield [task]
            except InvalidStateError as e:
                logger.debug(f"Failed to add task to batch: {task.future} raised {e}")


class TransformerBackend(ModuleBackend):
    """A wrapper for BloomBlock that can process requests for bloom layer forward, forward_incremental, and backward"""

    def __init__(self, *args, memory_cache: MemoryCache, backend_dtype: Optional[torch.dtype] = None, **kwargs):
        super().__init__(*args, **kwargs)
        assert isinstance(self.module, BloomBlock)
        self.memory_cache = memory_cache
        for name, param in self.module.named_parameters():
            assert not param.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does"
        for name, buf in self.module.named_buffers():
            assert not buf.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does"

        self.inference_pool = InferenceTaskPool(
            self.inference_step, max_batch_size=self.forward_pool.max_batch_size, name=f"{self.name}_inference"
        )
        self.dtype = backend_dtype if backend_dtype else self.module.input_layernorm.weight.dtype

    def inference_step(self, cache_metadata: torch.IntTensor, *inputs: torch.Tensor) -> Tuple[torch.Tensor, ...]:
        with torch.inference_mode():
            attention_cache_handle = int(cache_metadata[0, 0].item())
            prefix_length = int(cache_metadata[0, 1].item())
            hidden_states = inputs[0]  # todo: in future, it would be best to support attention mask here
            assert (
                hidden_states.ndim == 3
            ), "expected hidden states to be 3-dimensional: [batch_size, seq_len, hid_size]"

            with self.memory_cache.use_cache(attention_cache_handle) as cache:
                assert isinstance(self.module, BloomBlock) and cache.shape[0] == 2 and cache.ndim == 5
                layer_past = past_k, past_v = cache[0, :, :prefix_length], cache[1, :, :prefix_length]
                print("METADATA:", cache_metadata, past_k.shape, past_v.shape)
                hidden_states, (new_k, new_v) = self.module.forward(
                    hidden_states, layer_past=layer_past, use_cache=True
                )

                # todo remove these asserts once we pass all tests
                new_length = new_v.shape[1]
                assert new_length > prefix_length
                assert new_k.shape[0] == past_k.shape[0] and new_v.shape[0] == past_v.shape[0]
                assert new_k.shape[1] == new_length and new_v.shape[1] == new_length
                assert new_k.shape[2:] == past_k.shape[2:] and new_v.shape[2:] == past_v.shape[2:]
                assert torch.allclose(new_v[:, : past_v.shape[1]], past_v)
                assert torch.allclose(new_k[:, : past_k.shape[1]], past_k)
                cache[0, :, prefix_length:new_length, :] = new_k[:, prefix_length:new_length]
                cache[1, :, prefix_length:new_length, :] = new_v[:, prefix_length:new_length]
                return (hidden_states,)

    def get_pools(self) -> Sequence[TaskPool]:
        return self.forward_pool, self.backward_pool, self.inference_pool
