# ! rm -r unilm
# ! pip install unilm# ! wget https://guillaumejaume.github.io/FUNSD/dataset.zip
# ! unzip dataset.zip && mv dataset data && rm -rf dataset.zip __MACOSXfrom PIL import Image, ImageDraw, ImageFont
import os

base_path = "/home/sourab/temp/data/dataset"

image = Image.open(os.path.join(base_path, "training_data/images/0000971160.png"))
image = image.convert("RGB")
imageimport json

with open(os.path.join(base_path, "training_data/annotations/0000971160.json")) as f:
    data = json.load(f)

for annotation in data["form"]:
    print(annotation)draw = ImageDraw.Draw(image, "RGBA")

font = ImageFont.load_default()

label2color = {"question": "blue", "answer": "green", "header": "orange", "other": "violet"}

for annotation in data["form"]:
    label = annotation["label"]
    general_box = annotation["box"]
    draw.rectangle(general_box, outline=label2color[label], width=2)
    draw.text((general_box[0] + 10, general_box[1] - 10), label, fill=label2color[label], font=font)
    words = annotation["words"]
    for word in words:
        box = word["box"]
        draw.rectangle(box, outline=label2color[label], width=1)

image# ! python unilm/layoutlm/examples/seq_labeling/preprocess.py --data_dir data/dataset/training_data/annotations \
#                                                       --data_split train \
#                                                       --output_dir data \
#                                                       --model_name_or_path microsoft/layoutlm-base-uncased \
#                                                       --max_len 510

# ! python unilm/layoutlm/examples/seq_labeling/preprocess.py --data_dir data/dataset/testing_data/annotations \
#                                                       --data_split test \
#                                                       --output_dir data \
#                                                       --model_name_or_path microsoft/layoutlm-base-uncased \
#                                                       --max_len 510# ! cat data/train.txt | cut -d$'\t' -f 2 | grep -v "^$"| sort | uniq > data/labels.txtfrom torch.nn import CrossEntropyLoss


def get_labels(path):
    with open(path, "r") as f:
        labels = f.read().splitlines()
    if "O" not in labels:
        labels = ["O"] + labels
    return labels


labels = get_labels("data/labels.txt")
num_labels = len(labels)
label_map = {i: label for i, label in enumerate(labels)}
# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
pad_token_label_id = CrossEntropyLoss().ignore_indexprint(labels)import logging
import os

import torch
from torch.utils.data import Dataset

logger = logging.getLogger(__name__)


class FunsdDataset(Dataset):
    def __init__(self, args, tokenizer, labels, pad_token_label_id, mode):
        if args.local_rank not in [-1, 0] and mode == "train":
            torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

        # Load data features from cache or dataset file
        cached_features_file = os.path.join(
            args.data_dir,
            "cached_{}_{}_{}".format(
                mode,
                list(filter(None, args.model_name_or_path.split("/"))).pop(),
                str(args.max_seq_length),
            ),
        )
        if os.path.exists(cached_features_file) and not args.overwrite_cache:
            logger.info("Loading features from cached file %s", cached_features_file)
            features = torch.load(cached_features_file)
        else:
            logger.info("Creating features from dataset file at %s", args.data_dir)
            examples = read_examples_from_file(args.data_dir, mode)
            features = convert_examples_to_features(
                examples,
                labels,
                args.max_seq_length,
                tokenizer,
                cls_token_at_end=bool(args.model_type in ["xlnet"]),
                # xlnet has a cls token at the end
                cls_token=tokenizer.cls_token,
                cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0,
                sep_token=tokenizer.sep_token,
                sep_token_extra=bool(args.model_type in ["roberta"]),
                # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
                pad_on_left=bool(args.model_type in ["xlnet"]),
                # pad on the left for xlnet
                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0,
                pad_token_label_id=pad_token_label_id,
            )
            # if args.local_rank in [-1, 0]:
            # logger.info("Saving features into cached file %s", cached_features_file)
            # torch.save(features, cached_features_file)

        if args.local_rank == 0 and mode == "train":
            torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache

        self.features = features
        # Convert to Tensors and build dataset
        self.all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
        self.all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
        self.all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
        self.all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long)
        self.all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long)

    def __len__(self):
        return len(self.features)

    def __getitem__(self, index):
        return (
            self.all_input_ids[index],
            self.all_input_mask[index],
            self.all_segment_ids[index],
            self.all_label_ids[index],
            self.all_bboxes[index],
        )


class InputExample(object):
    """A single training/test example for token classification."""

    def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            words: list. The words of the sequence.
            labels: (Optional) list. The labels for each word of the sequence. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.words = words
        self.labels = labels
        self.boxes = boxes
        self.actual_bboxes = actual_bboxes
        self.file_name = file_name
        self.page_size = page_size


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(
        self,
        input_ids,
        input_mask,
        segment_ids,
        label_ids,
        boxes,
        actual_bboxes,
        file_name,
        page_size,
    ):
        assert (
            0 <= all(boxes) <= 1000
        ), "Error with input bbox ({}): the coordinate value is not between 0 and 1000".format(boxes)
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_ids = label_ids
        self.boxes = boxes
        self.actual_bboxes = actual_bboxes
        self.file_name = file_name
        self.page_size = page_size


def read_examples_from_file(data_dir, mode):
    file_path = os.path.join(data_dir, "{}.txt".format(mode))
    box_file_path = os.path.join(data_dir, "{}_box.txt".format(mode))
    image_file_path = os.path.join(data_dir, "{}_image.txt".format(mode))
    guid_index = 1
    examples = []
    with open(file_path, encoding="utf-8") as f, open(box_file_path, encoding="utf-8") as fb, open(
        image_file_path, encoding="utf-8"
    ) as fi:
        words = []
        boxes = []
        actual_bboxes = []
        file_name = None
        page_size = None
        labels = []
        for line, bline, iline in zip(f, fb, fi):
            if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                if words:
                    examples.append(
                        InputExample(
                            guid="{}-{}".format(mode, guid_index),
                            words=words,
                            labels=labels,
                            boxes=boxes,
                            actual_bboxes=actual_bboxes,
                            file_name=file_name,
                            page_size=page_size,
                        )
                    )
                    guid_index += 1
                    words = []
                    boxes = []
                    actual_bboxes = []
                    file_name = None
                    page_size = None
                    labels = []
            else:
                splits = line.split("\t")
                bsplits = bline.split("\t")
                isplits = iline.split("\t")
                assert len(splits) == 2
                assert len(bsplits) == 2
                assert len(isplits) == 4
                assert splits[0] == bsplits[0]
                words.append(splits[0])
                if len(splits) > 1:
                    labels.append(splits[-1].replace("\n", ""))
                    box = bsplits[-1].replace("\n", "")
                    box = [int(b) for b in box.split()]
                    boxes.append(box)
                    actual_bbox = [int(b) for b in isplits[1].split()]
                    actual_bboxes.append(actual_bbox)
                    page_size = [int(i) for i in isplits[2].split()]
                    file_name = isplits[3].strip()
                else:
                    # Examples could have no label for mode = "test"
                    labels.append("O")
        if words:
            examples.append(
                InputExample(
                    guid="%s-%d".format(mode, guid_index),
                    words=words,
                    labels=labels,
                    boxes=boxes,
                    actual_bboxes=actual_bboxes,
                    file_name=file_name,
                    page_size=page_size,
                )
            )
    return examples


def convert_examples_to_features(
    examples,
    label_list,
    max_seq_length,
    tokenizer,
    cls_token_at_end=False,
    cls_token="[CLS]",
    cls_token_segment_id=1,
    sep_token="[SEP]",
    sep_token_extra=False,
    pad_on_left=False,
    pad_token=0,
    cls_token_box=[0, 0, 0, 0],
    sep_token_box=[1000, 1000, 1000, 1000],
    pad_token_box=[0, 0, 0, 0],
    pad_token_segment_id=0,
    pad_token_label_id=-1,
    sequence_a_segment_id=0,
    mask_padding_with_zero=True,
):
    """Loads a data file into a list of `InputBatch`s
    `cls_token_at_end` define the location of the CLS token:
        - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
        - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
    `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for ex_index, example in enumerate(examples):
        file_name = example.file_name
        page_size = example.page_size
        width, height = page_size
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d", ex_index, len(examples))

        tokens = []
        token_boxes = []
        actual_bboxes = []
        label_ids = []
        for word, label, box, actual_bbox in zip(example.words, example.labels, example.boxes, example.actual_bboxes):
            word_tokens = tokenizer.tokenize(word)
            tokens.extend(word_tokens)
            token_boxes.extend([box] * len(word_tokens))
            actual_bboxes.extend([actual_bbox] * len(word_tokens))
            # Use the real label id for the first token of the word, and padding ids for the remaining tokens
            label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))

        # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        special_tokens_count = 3 if sep_token_extra else 2
        if len(tokens) > max_seq_length - special_tokens_count:
            tokens = tokens[: (max_seq_length - special_tokens_count)]
            token_boxes = token_boxes[: (max_seq_length - special_tokens_count)]
            actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)]
            label_ids = label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        token_boxes += [sep_token_box]
        actual_bboxes += [[0, 0, width, height]]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
            # roberta uses an extra separator b/w pairs of sentences
            tokens += [sep_token]
            token_boxes += [sep_token_box]
            actual_bboxes += [[0, 0, width, height]]
            label_ids += [pad_token_label_id]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if cls_token_at_end:
            tokens += [cls_token]
            token_boxes += [cls_token_box]
            actual_bboxes += [[0, 0, width, height]]
            label_ids += [pad_token_label_id]
            segment_ids += [cls_token_segment_id]
        else:
            tokens = [cls_token] + tokens
            token_boxes = [cls_token_box] + token_boxes
            actual_bboxes = [[0, 0, width, height]] + actual_bboxes
            label_ids = [pad_token_label_id] + label_ids
            segment_ids = [cls_token_segment_id] + segment_ids

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
            label_ids = ([pad_token_label_id] * padding_length) + label_ids
            token_boxes = ([pad_token_box] * padding_length) + token_boxes
        else:
            input_ids += [pad_token] * padding_length
            input_mask += [0 if mask_padding_with_zero else 1] * padding_length
            segment_ids += [pad_token_segment_id] * padding_length
            label_ids += [pad_token_label_id] * padding_length
            token_boxes += [pad_token_box] * padding_length

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length
        assert len(label_ids) == max_seq_length
        assert len(token_boxes) == max_seq_length

        if ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s", example.guid)
            logger.info("tokens: %s", " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s", " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s", " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids]))
            logger.info("label_ids: %s", " ".join([str(x) for x in label_ids]))
            logger.info("boxes: %s", " ".join([str(x) for x in token_boxes]))
            logger.info("actual_bboxes: %s", " ".join([str(x) for x in actual_bboxes]))

        features.append(
            InputFeatures(
                input_ids=input_ids,
                input_mask=input_mask,
                segment_ids=segment_ids,
                label_ids=label_ids,
                boxes=token_boxes,
                actual_bboxes=actual_bboxes,
                file_name=file_name,
                page_size=page_size,
            )
        )
    return featuresfrom transformers import LayoutLMTokenizer

# from .unilm.layoutlm.data.funsd import FunsdDataset, InputFeatures
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler

batch_size = 16
args = {
    "local_rank": -1,
    "overwrite_cache": True,
    "data_dir": "/home/sourab/temp/data/",
    "model_name_or_path": "microsoft/layoutlm-base-uncased",
    "max_seq_length": 512,
    "model_type": "layoutlm",
}


# class to turn the keys of a dict into attributes (thanks Stackoverflow)
class AttrDict(dict):
    def __init__(self, *args, **kwargs):
        super(AttrDict, self).__init__(*args, **kwargs)
        self.__dict__ = self


args = AttrDict(args)

tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")

# the LayoutLM authors already defined a specific FunsdDataset, so we are going to use this here
train_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode="train")
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=batch_size)

eval_dataset = FunsdDataset(args, tokenizer, labels, pad_token_label_id, mode="test")
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=batch_size)len(train_dataloader)len(eval_dataloader)batch = next(iter(train_dataloader))
input_ids = batch[0][0]
tokenizer.decode(input_ids)from peft import get_peft_config, PeftModel, get_peft_model, LoraConfig, TaskType

peft_config = LoraConfig(
    task_type=TaskType.TOKEN_CLS, inference_mode=False, r=16, lora_alpha=16, lora_dropout=0.1, bias="all"
)
peft_configfrom transformers import LayoutLMForTokenClassification
import torch
from transformers import set_seed

seed = 100
set_seed(seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=num_labels)
model = get_peft_model(model, peft_config)
model.to(device)print(model.model.layoutlm.encoder.layer[0].attention.self.query.weight)
print(model.model.layoutlm.encoder.layer[0].attention.self.query.lora_A.weight)
print(model.model.classifier.weight)from transformers import AdamW, get_linear_schedule_with_warmup
from tqdm import tqdm

num_train_epochs = 100

optimizer = torch.optim.AdamW(model.parameters(), lr=3e-3)
lr_scheduler = get_linear_schedule_with_warmup(
    optimizer=optimizer,
    num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),
    num_training_steps=(len(train_dataloader) * num_train_epochs),
)


global_step = 0

t_total = len(train_dataloader) * num_train_epochs  # total number of training steps

# put the model in training mode
model.train()
for epoch in range(num_train_epochs):
    for batch in tqdm(train_dataloader, desc="Training"):
        input_ids = batch[0].to(device)
        bbox = batch[4].to(device)
        attention_mask = batch[1].to(device)
        token_type_ids = batch[2].to(device)
        labels = batch[3].to(device)

        # forward pass
        outputs = model(
            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
        )
        loss = outputs.loss

        # print loss every 100 steps
        if global_step % 10 == 0:
            print(f"Loss after {global_step} steps: {loss.item()}")

        # backward pass to get the gradients
        loss.backward()

        # print("Gradients on classification head:")
        # print(model.classifier.weight.grad[6,:].sum())

        # update
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        global_step += 1import numpy as np
from seqeval.metrics import (
    classification_report,
    f1_score,
    precision_score,
    recall_score,
)

eval_loss = 0.0
nb_eval_steps = 0
preds = None
out_label_ids = None

# put model in evaluation mode
model.eval()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
    with torch.no_grad():
        input_ids = batch[0].to(device)
        bbox = batch[4].to(device)
        attention_mask = batch[1].to(device)
        token_type_ids = batch[2].to(device)
        labels = batch[3].to(device)

        # forward pass
        outputs = model(
            input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
        )
        # get the loss and logits
        tmp_eval_loss = outputs.loss
        logits = outputs.logits

        eval_loss += tmp_eval_loss.item()
        nb_eval_steps += 1

        # compute the predictions
        if preds is None:
            preds = logits.detach().cpu().numpy()
            out_label_ids = labels.detach().cpu().numpy()
        else:
            preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
            out_label_ids = np.append(out_label_ids, labels.detach().cpu().numpy(), axis=0)

# compute average evaluation loss
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2)

out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_list = [[] for _ in range(out_label_ids.shape[0])]

for i in range(out_label_ids.shape[0]):
    for j in range(out_label_ids.shape[1]):
        if out_label_ids[i, j] != pad_token_label_id:
            out_label_list[i].append(label_map[out_label_ids[i][j]])
            preds_list[i].append(label_map[preds[i][j]])

results = {
    "loss": eval_loss,
    "precision": precision_score(out_label_list, preds_list),
    "recall": recall_score(out_label_list, preds_list),
    "f1": f1_score(out_label_list, preds_list),
}
print(results)model.print_trainable_parameters()model.save_pretrained("peft_layoutlm")