import torch
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

from transformers import BatchEncoding, PreTrainedTokenizerBase
from transformers.data.data_collator import (
    DataCollatorMixin,
    _torch_collate_batch,
)
from transformers.file_utils import PaddingStrategy

from typing import NewType
InputDataClass = NewType("InputDataClass", Any)

def pre_calc_rel_mat(segment_ids):
    valid_span = torch.zeros((segment_ids.shape[0], segment_ids.shape[1], segment_ids.shape[1]),
                             device=segment_ids.device, dtype=torch.bool)
    for i in range(segment_ids.shape[0]):
        for j in range(segment_ids.shape[1]):
            valid_span[i, j, :] = segment_ids[i, :] == segment_ids[i, j]

    return valid_span

@dataclass
class DataCollatorForKeyValueExtraction(DataCollatorMixin):
    """
    Data collator that will dynamically pad the inputs received, as well as the labels.
    Args:
        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
            The tokenizer used for encoding the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
        max_length (:obj:`int`, `optional`):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (:obj:`int`, `optional`):
            If set will pad the sequence to a multiple of the provided value.
            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
        label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
            The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
    """

    tokenizer: PreTrainedTokenizerBase
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None
    label_pad_token_id: int = -100

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None

        images = None
        if "images" in features[0]:
            images = torch.stack([torch.tensor(d.pop("images")) for d in features])
            IMAGE_LEN = int(images.shape[-1] / 16) * int(images.shape[-1] / 16) + 1

        batch = self.tokenizer.pad(
            features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            # Conversion to tensors will fail if we have labels as they are not of the same length yet.
            return_tensors="pt" if labels is None else None,
        )

        if images is not None:
            batch["images"] = images
            batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) and k == 'attention_mask' else v
                     for k, v in batch.items()}
            visual_attention_mask = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long)
            batch["attention_mask"] = torch.cat([batch['attention_mask'], visual_attention_mask], dim=1)

        if labels is None:
            return batch

        has_bbox_input = "bbox" in features[0]
        has_position_input = "position_ids" in features[0]
        padding_idx=self.tokenizer.pad_token_id
        sequence_length = torch.tensor(batch["input_ids"]).shape[1]
        padding_side = self.tokenizer.padding_side
        if padding_side == "right":
            batch["labels"] = [label + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels]
            if has_bbox_input:
                batch["bbox"] = [bbox + [[0, 0, 0, 0]] * (sequence_length - len(bbox)) for bbox in batch["bbox"]]
            if has_position_input:
                batch["position_ids"] = [position_id + [padding_idx] * (sequence_length - len(position_id))
                                          for position_id in batch["position_ids"]]

        else:
            batch["labels"] = [[self.label_pad_token_id] * (sequence_length - len(label)) + label for label in labels]
            if has_bbox_input:
                batch["bbox"] = [[[0, 0, 0, 0]] * (sequence_length - len(bbox)) + bbox for bbox in batch["bbox"]]
            if has_position_input:
                batch["position_ids"] = [[padding_idx] * (sequence_length - len(position_id))
                                          + position_id for position_id in batch["position_ids"]]

        if 'segment_ids' in batch:
            assert 'position_ids' in batch
            for i in range(len(batch['segment_ids'])):
                batch['segment_ids'][i] = batch['segment_ids'][i] + [batch['segment_ids'][i][-1] + 1] * (sequence_length - len(batch['segment_ids'][i])) + [
                    batch['segment_ids'][i][-1] + 2] * IMAGE_LEN

        batch = {k: torch.tensor(v, dtype=torch.int64) if isinstance(v[0], list) else v for k, v in batch.items()}

        if 'segment_ids' in batch:
            valid_span = pre_calc_rel_mat(
                segment_ids=batch['segment_ids']
            )
            batch['valid_span'] = valid_span
            del batch['segment_ids']

        if images is not None:
            visual_labels = torch.ones((len(batch['input_ids']), IMAGE_LEN), dtype=torch.long) * -100
            batch["labels"] = torch.cat([batch['labels'], visual_labels], dim=1)

        return batch