from abc import ABC, abstractmethod
from typing import List, Optional, Tuple, Union

import torch
from PIL import Image
from transformers import BatchEncoding, BatchFeature, ProcessorMixin

from .torch_utils import get_torch_device


class BaseVisualRetrieverProcessor(ABC, ProcessorMixin):
    """
    Base class for visual retriever processors.
    """

    @abstractmethod
    def process_images(
        self,
        images: List[Image.Image],
    ) -> Union[BatchFeature, BatchEncoding]:
        pass

    @abstractmethod
    def process_queries(
        self,
        queries: List[str],
        max_length: int = 50,
        suffix: Optional[str] = None,
    ) -> Union[BatchFeature, BatchEncoding]:
        pass

    @abstractmethod
    def score(
        self,
        qs: List[torch.Tensor],
        ps: List[torch.Tensor],
        device: Optional[Union[str, torch.device]] = None,
        **kwargs,
    ) -> torch.Tensor:
        pass

    @staticmethod
    def score_single_vector(
        qs: List[torch.Tensor],
        ps: List[torch.Tensor],
        device: Optional[Union[str, torch.device]] = None,
    ) -> torch.Tensor:
        """
        Compute the dot product score for the given single-vector query and passage embeddings.
        """
        device = device or get_torch_device("auto")

        if len(qs) == 0:
            raise ValueError("No queries provided")
        if len(ps) == 0:
            raise ValueError("No passages provided")

        qs_stacked = torch.stack(qs).to(device)
        ps_stacked = torch.stack(ps).to(device)

        scores = torch.einsum("bd,cd->bc", qs_stacked, ps_stacked)
        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"

        scores = scores.to(torch.float32)
        return scores

    @staticmethod
    def score_multi_vector(
        qs: Union[torch.Tensor, List[torch.Tensor]],
        ps: Union[torch.Tensor, List[torch.Tensor]],
        batch_size: int = 128,
        device: Optional[Union[str, torch.device]] = None,
    ) -> torch.Tensor:
        """
        Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector
        query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the
        image of a document page.

        Because the embedding tensors are multi-vector and can thus have different shapes, they
        should be fed as:
        (1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim)
        (2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually
            obtained by padding the list of tensors.

        Args:
            qs (`Union[torch.Tensor, List[torch.Tensor]`): Query embeddings.
            ps (`Union[torch.Tensor, List[torch.Tensor]`): Passage embeddings.
            batch_size (`int`, *optional*, defaults to 128): Batch size for computing scores.
            device (`Union[str, torch.device]`, *optional*): Device to use for computation. If not
                provided, uses `get_torch_device("auto")`.

        Returns:
            `torch.Tensor`: A tensor of shape `(n_queries, n_passages)` containing the scores. The score
            tensor is saved on the "cpu" device.
        """
        device = device or get_torch_device("auto")

        if len(qs) == 0:
            raise ValueError("No queries provided")
        if len(ps) == 0:
            raise ValueError("No passages provided")

        scores_list: List[torch.Tensor] = []

        for i in range(0, len(qs), batch_size):
            scores_batch = []
            # qs_batch = torch.nn.utils.rnn.pad_sequence(qs[i : i + batch_size], batch_first=True, padding_value=0).to(device)
            qs_batch = qs[i : i + batch_size].to(device)
            for j in range(0, len(ps), batch_size):
                ps_batch = torch.nn.utils.rnn.pad_sequence(
                    ps[j : j + batch_size], batch_first=True, padding_value=0
                ).to(device)
                scores_batch.append(torch.einsum("bnd,csd->bcns", qs_batch, ps_batch).max(dim=3)[0].sum(dim=2))
            scores_batch = torch.cat(scores_batch, dim=1).cpu()
            scores_list.append(scores_batch)

        scores = torch.cat(scores_list, dim=0)
        assert scores.shape[0] == len(qs), f"Expected {len(qs)} scores, got {scores.shape[0]}"

        scores = scores.to(torch.float32)
        return scores

    @abstractmethod
    def get_n_patches(
        self,
        image_size: Tuple[int, int],
        patch_size: int = 14,
        *args,
        **kwargs,
    ) -> Tuple[int, int]:
        """
        Get the number of patches (n_patches_x, n_patches_y) that will be used to process an
        image of size (height, width) with the given patch size.
        """
        pass