
---

### **1. Install Dependencies**

To begin, you'll need to install the required libraries. Use the following commands to install them:
```bash
pip install -q transformers datasets
pip install 'git+https://github.com/facebookresearch/detectron2.git'
pip install torchvision
sudo apt install tesseract-ocr
pip install -q pytesseract
```
After running these commands, restart your runtime to ensure that the dependencies are properly loaded.

### **2. Login to Hugging Face**

To interact with the Hugging Face Hub, you need to log in to your Hugging Face account. Execute the following Python code:
```python
from huggingface_hub import notebook_login
notebook_login()
```

### **3. Define Global Variables**

Set up key global variables, including the model checkpoint and batch size. These values help in managing the model's training process:
```python
model_checkpoint = "microsoft/layoutlmv2-base-uncased"
batch_size = 4
```

### **4. Load Dataset**

We'll be using a preprocessed version of the DocVQA dataset, which is available on Hugging Face. You can load it with the following code:
```python
from datasets import load_dataset

dataset = load_dataset("nielsr/docvqa_1200_examples")
```

### **5. Data Preprocessing**

Here, we handle different preprocessing steps to ensure the dataset is formatted correctly for the model.

#### **Filtering English Questions**:
Filter out non-English questions and select the first answer for each example:
```python
updated_dataset = dataset.map(lambda example: {"question": example["query"]["en"]}, remove_columns=["query"])
updated_dataset = updated_dataset.map(lambda example: {"answer": example["answers"][0]}, remove_columns=["answer", "answers"])
```

#### **Removing Long Examples**:
Since LayoutLMv2 has a maximum position embedding length of 512 tokens, we remove examples where the total length exceeds this limit:
```python
updated_dataset = updated_dataset.filter(lambda x: len(x["words"]) + len(x["question"].split()) < 512)
```

#### **Removing OCR Columns**:
Remove unnecessary OCR-related columns like "words" and "bounding_boxes" from the dataset:
```python
updated_dataset = updated_dataset.remove_columns("words")
updated_dataset = updated_dataset.remove_columns("bounding_boxes")
```

### **6. Preprocess Document Images**

LayoutLMv2 requires document images to be processed along with their associated OCR data. We use the `LayoutLMv2Processor` to preprocess the images:
```python
from transformers import AutoProcessor

processor = AutoProcessor.from_pretrained(model_checkpoint)
image_processor = processor.image_processor

def get_ocr_words_and_boxes(examples):
    images = [image.convert("RGB") for image in examples["image"]]
    encoded_inputs = image_processor(images)
    examples["image"] = encoded_inputs.pixel_values
    examples["words"] = encoded_inputs.words
    examples["boxes"] = encoded_inputs.boxes
    return examples

dataset_with_ocr = updated_dataset.map(get_ocr_words_and_boxes, batched=True, batch_size=2)
```

### **7. Preprocess Text Data**

Next, we preprocess the text data (questions and answers). This is necessary to ensure that the question-answer pairs are tokenized and aligned with the OCR data:
```python
tokenizer = processor.tokenizer

def subfinder(words_list, answer_list):
    matches = []
    start_indices = []
    end_indices = []
    for idx, i in enumerate(range(len(words_list))):
        if words_list[i] == answer_list[0] and words_list[i : i + len(answer_list)] == answer_list:
            matches.append(answer_list)
            start_indices.append(idx)
            end_indices.append(idx + len(answer_list) - 1)
    if matches:
        return matches[0], start_indices[0], end_indices[0]
    else:
        return None, 0, 0
```

### **8. Encoding and Tokenizing Dataset**

We need to encode the dataset and calculate the positions for the start and end tokens of the answer. This also involves adjusting token positions based on `token_type_ids`:
```python
def encode_dataset(examples, max_length=512):
    questions = examples["question"]
    words = examples["words"]
    boxes = examples["boxes"]
    answers = examples["answer"]

    encoding = tokenizer(questions, words, boxes, max_length=max_length, padding="max_length", truncation=True)
    start_positions = []
    end_positions = []

    for i in range(len(questions)):
        cls_index = encoding["input_ids"][i].index(tokenizer.cls_token_id)
        words_example = [word.lower() for word in words[i]]
        answer = answers[i]
        match, word_idx_start, word_idx_end = subfinder(words_example, answer.lower().split())

        if match:
            token_type_ids = encoding["token_type_ids"][i]
            token_start_index = 0
            while token_type_ids[token_start_index] != 1:
                token_start_index += 1

            token_end_index = len(encoding["input_ids"][i]) - 1
            while token_type_ids[token_end_index] != 1:
                token_end_index -= 1

            word_ids = encoding.word_ids(i)[token_start_index : token_end_index + 1]
            start_position = cls_index
            end_position = cls_index

            # Identify the start and end positions in the tokenized input
            while word_ids[start_position] != word_idx_start:
                start_position += 1
            while word_ids[end_position] != word_idx_end:
                end_position += 1

            start_positions.append(start_position)
            end_positions.append(end_position)
    
    encoding["start_positions"] = start_positions
    encoding["end_positions"] = end_positions
    return encoding
```

### **9. Model Training and Fine-Tuning**

After preparing the data, the next step is to fine-tune the LayoutLMv2 model with the processed dataset. You'll need to configure the model and set up the training loop using Hugging Face's `Trainer`. Let me know if you want more details on this part of the process!

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