import io
import fitz
from docx import Document

def process_pdf(file_stream: io.BytesIO):
    doc = fitz.open(stream=file_stream, filetype="pdf")
    full_text = ""
    char_map = []
    for page_num, page in enumerate(doc):
        words = page.get_text("words")
        for word in words:
            x0, y0, x1, y1, word_text = word[:5]
            location_info = {"page": page_num, "bbox": (x0, y0, x1, y1)}
            for char in word_text:
                char_map.append(location_info)
            char_map.extend([None] * len(" "))
            full_text += word_text + " "
    doc.close()
    return full_text.strip(), char_map

def process_docx(file_stream: io.BytesIO):
    doc = Document(file_stream)
    full_text = ""
    char_map = []
    for p_idx, para in enumerate(doc.paragraphs):
        for r_idx, run in enumerate(para.runs):
            location_info = {"p_idx": p_idx, "r_idx": r_idx}
            for _ in run.text:
                char_map.append(location_info)
            full_text += run.text
        char_map.append(None)
        full_text += "\n"
    return full_text.strip(), char_map

def map_ner_results(ner_results, char_map, Entity):
    entities = []
    for result in ner_results:
        start, end = result['start'], result['end']
        unique_locations = {}
        for i in range(start, end):
            if i < len(char_map) and char_map[i]:
                loc_tuple = tuple(sorted(char_map[i].items()))
                if loc_tuple not in unique_locations:
                    unique_locations[loc_tuple] = char_map[i]
        if unique_locations:
            entities.append(Entity(
                text=result['word'],
                label=result['entity_group'],
                location=list(unique_locations.values())
            ))
    return entities