import argparse
import glob
import json
import os
import random
from concurrent.futures import ProcessPoolExecutor, as_completed
from typing import Generator
from urllib.parse import urlparse

import boto3
from pypdf import PdfReader
from tqdm import tqdm

from olmocr.data.renderpdf import render_pdf_to_base64png
from olmocr.filter import PdfFilter
from olmocr.prompts import (
    build_openai_silver_data_prompt,
    openai_response_format_schema,
)
from olmocr.prompts.anchor import get_anchor_text

TARGET_IMAGE_DIM = 2048


pdf_filter = PdfFilter()


def build_page_query(local_pdf_path: str, pretty_pdf_path: str, page: int) -> dict:
    image_base64 = render_pdf_to_base64png(local_pdf_path, page, TARGET_IMAGE_DIM)
    anchor_text = get_anchor_text(local_pdf_path, page, pdf_engine="pdfreport")

    # DEBUG crappy temporary code here that does the actual api call live so I can debug it a bit
    # from openai import OpenAI
    # client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

    # response = client.chat.completions.create(
    #     model="gpt-4o-2024-08-06",
    #     messages= [
    #             {
    #                 "role": "user",
    #                 "content": [
    #                     {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
    #                     {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}
    #                 ],
    #             }
    #         ],
    #     temperature=0.1,
    #     max_tokens=3000,
    #     logprobs=True,
    #     top_logprobs=5,
    #     response_format=openai_response_format_schema()
    # )
    # print(response)

    # Construct OpenAI Batch API request format#
    # There are a few tricks to know when doing data processing with OpenAI's apis
    # First off, use the batch query system, it's 1/2 the price and exactly the same performance
    # Second off, use structured outputs. If your application is not an actual chatbot, use structured outputs!
    # Even if the last 10 queries you ran with the regular chat api returned exactly what you wanted without extra "LLM fluff text", that doesn't mean this will hold across 1000's of queries
    # Also, structured outputs let you cheat, because the order in which fields are in your schema, is the order in which the model will answer them, so you can have it answer some "preperatory" or "chain of thought" style questions first before going into the meat of your response, which is going to give better answers
    # Check your prompt for typos, it makes a performance difference!
    # Ask for logprobs, it's not any more expensive and you can use them later to help identify problematic responses
    return {
        "custom_id": f"{pretty_pdf_path}-{page}",
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "gpt-4o-2024-08-06",
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": build_openai_silver_data_prompt(anchor_text)},
                        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
                    ],
                }
            ],
            "temperature": 0.1,
            "max_tokens": 6000,
            "logprobs": True,
            "top_logprobs": 5,
            "response_format": openai_response_format_schema(),
        },
    }


def sample_pdf_pages(num_pages: int, first_n_pages: int, max_sample_pages: int) -> list:
    if num_pages <= first_n_pages:
        return list(range(1, num_pages + 1))  # Return all pages if fewer than first_n_pages
    sample_pages = list(range(1, first_n_pages + 1))  # Always get the first_n_pages
    remaining_pages = list(range(first_n_pages + 1, num_pages + 1))
    if remaining_pages:
        sample_pages += random.sample(remaining_pages, min(max_sample_pages - first_n_pages, len(remaining_pages)))
    return sample_pages


def fetch_s3_file(s3_url: str, local_path: str) -> str:
    parsed = urlparse(s3_url)
    bucket_name = parsed.netloc
    key = parsed.path.lstrip("/")

    s3 = boto3.client("s3")
    s3.download_file(bucket_name, key, local_path)
    return local_path


def process_pdf(pdf_path: str, first_n_pages: int, max_sample_pages: int, no_filter: bool) -> Generator[dict, None, None]:
    if pdf_path.startswith("s3://"):
        local_pdf_path = os.path.join("/tmp", os.path.basename(pdf_path))
        fetch_s3_file(pdf_path, local_pdf_path)
    else:
        local_pdf_path = pdf_path

    if (not no_filter) and pdf_filter.filter_out_pdf(local_pdf_path):
        print(f"Skipping {local_pdf_path} due to common filter")
        return []

    pretty_pdf_path = pdf_path

    pdf = PdfReader(local_pdf_path)
    num_pages = len(pdf.pages)

    sample_pages = sample_pdf_pages(num_pages, first_n_pages, max_sample_pages)

    result = []
    for page in sample_pages:
        try:
            query = build_page_query(local_pdf_path, pretty_pdf_path, page)
            result.append(query)
        except Exception as e:
            print(f"Error processing page {page} of {pdf_path}: {e}")

    return result


def main():
    parser = argparse.ArgumentParser(description="Sample PDFs and create requests for GPT-4o.")
    parser.add_argument("--glob_path", type=str, help="Local or S3 path glob (e.g., *.pdf or s3://bucket/pdfs/*.pdf).")
    parser.add_argument("--path_list", type=str, help="Path to a file containing paths to PDFs, one per line.")
    parser.add_argument("--no_filter", action="store_true", help="Disables the basic spam/language filtering so that ALL pdfs listed are used")
    parser.add_argument("--num_sample_docs", type=int, default=5000, help="Number of PDF documents to sample.")
    parser.add_argument("--first_n_pages", type=int, default=0, help="Always sample the first N pages of each PDF.")
    parser.add_argument("--max_sample_pages", type=int, default=15, help="Max number of pages to sample per PDF.")
    parser.add_argument("--output", type=str, default="openai_batch_data", help="Output destination")
    parser.add_argument("--reservoir_size", type=int, default=None, help="Size of the reservoir for sampling paths. Defaults to 10x num_sample_docs.")
    args = parser.parse_args()

    # Set default reservoir_size if not provided
    if args.reservoir_size is None:
        args.reservoir_size = 10 * args.num_sample_docs

    # Initialize reservoir sampling variables
    pdf_paths = []
    n = 0  # Total number of items seen

    # Load PDF paths from glob or path_list using reservoir sampling
    if args.glob_path:
        if args.glob_path.startswith("s3://"):
            # Handle S3 globbing using boto3 with pagination
            parsed = urlparse(args.glob_path)
            s3 = boto3.client("s3")
            bucket_name = parsed.netloc
            prefix = os.path.dirname(parsed.path.lstrip("/")) + "/"
            paginator = s3.get_paginator("list_objects_v2")
            page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=prefix)

            for page in page_iterator:
                for obj in page.get("Contents", []):
                    if obj["Key"].endswith(".pdf"):
                        n += 1
                        path = f"s3://{bucket_name}/{obj['Key']}"
                        if len(pdf_paths) < args.reservoir_size:
                            pdf_paths.append(path)
                        else:
                            s = random.randint(1, n)
                            if s <= args.reservoir_size:
                                pdf_paths[s - 1] = path
        else:
            # Handle local globbing using glob.iglob()
            for path in glob.iglob(args.glob_path, recursive=True):
                n += 1
                if len(pdf_paths) < args.reservoir_size:
                    pdf_paths.append(path)
                else:
                    s = random.randint(1, n)
                    if s <= args.reservoir_size:
                        pdf_paths[s - 1] = path
    elif args.path_list:
        with open(args.path_list, "r") as f:
            for line in f:
                n += 1
                path = line.strip()
                if len(pdf_paths) < args.reservoir_size:
                    pdf_paths.append(path)
                else:
                    s = random.randint(1, n)
                    if s <= args.reservoir_size:
                        pdf_paths[s - 1] = path

    # Shuffle the reservoir
    random.shuffle(pdf_paths)

    print(f"Loaded and shuffled {len(pdf_paths)} paths to use.")

    # Rest of the code remains the same
    cur_file_num = 0
    output_dir = args.output
    max_file_size = 99 * 1024 * 1024  # 99MB in bytes
    cur_file_size = 0
    cur_file_path = os.path.join(output_dir, f"output_{cur_file_num}.jsonl")

    # Ensure output directory exists
    os.makedirs(output_dir, exist_ok=True)

    # Open the first file for writing
    cur_file = open(cur_file_path, "w")

    # Counter to track PDFs that produce at least one output
    pdfs_with_output = 0

    # Using ThreadPoolExecutor to process files concurrently
    with ProcessPoolExecutor() as executor:
        futures = []

        with tqdm(desc="Processing PDFs", leave=False, total=args.num_sample_docs) as pb:
            for pdf_path in pdf_paths:
                futures.append(executor.submit(process_pdf, pdf_path, args.first_n_pages, args.max_sample_pages, args.no_filter))

            for future in as_completed(futures):
                has_output = False  # Track if the current PDF produces at least one request
                try:
                    request_results = future.result()  # Get the result from the thread

                    for request_obj in request_results:
                        request_json = json.dumps(request_obj)
                        request_size = len(request_json.encode("utf-8"))  # Calculate size in bytes

                        # Check if the current request can fit in the current file
                        if cur_file_size + request_size > max_file_size:
                            # Close the current file and create a new one
                            cur_file.close()
                            cur_file_num += 1
                            cur_file_path = os.path.join(output_dir, f"output_{cur_file_num}.jsonl")
                            cur_file = open(cur_file_path, "w")
                            cur_file_size = 0  # Reset file size

                        # Write the JSON entry to the file
                        cur_file.write(request_json)
                        cur_file.write("\n")
                        cur_file_size += request_size

                        has_output = True  # At least one request object was generated

                    if has_output:
                        pdfs_with_output += 1
                        pb.update(1)

                        if pdfs_with_output >= args.num_sample_docs:
                            executor.shutdown(cancel_futures=True)
                            break

                except Exception as e:
                    print(f"Error processing {pdf_path}: {str(e)}")

    # Close the last open file
    cur_file.close()

    # Print or log the number of PDFs that resulted in at least one output
    print(f"Number of sampled PDFs that produced at least one output: {pdfs_with_output}")


if __name__ == "__main__":
    main()
