'''
Downloads models from Hugging Face to models/username_modelname.

Example:
python download-model.py facebook/opt-1.3b

'''

import argparse
import base64
import datetime
import hashlib
import json
import os
import re
import sys
from pathlib import Path

import requests
import tqdm
from requests.adapters import HTTPAdapter
from tqdm.contrib.concurrent import thread_map


class ModelDownloader:
    def __init__(self, max_retries = 5):
        self.s = requests.Session()
        if max_retries:
            self.s.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
            self.s.mount('https://huggingface.co', HTTPAdapter(max_retries=max_retries))
        if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
            self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
        if os.getenv('HF_TOKEN') is not None:
            self.s.headers = {'authorization': f'Bearer {os.getenv("HF_TOKEN")}'}

    def sanitize_model_and_branch_names(self, model, branch):
        if model[-1] == '/':
            model = model[:-1]

        if branch is None:
            branch = "main"
        else:
            pattern = re.compile(r"^[a-zA-Z0-9._-]+$")
            if not pattern.match(branch):
                raise ValueError(
                    "Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.")

        return model, branch

    def get_download_links_from_huggingface(self, model, branch, text_only=False):
        base = "https://huggingface.co"
        page = f"/api/models/{model}/tree/{branch}"
        cursor = b""

        links = []
        sha256 = []
        classifications = []
        has_pytorch = False
        has_pt = False
        # has_ggml = False
        has_safetensors = False
        is_lora = False
        while True:
            url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "")
            r = self.s.get(url, timeout=20)
            r.raise_for_status()
            content = r.content

            dict = json.loads(content)
            if len(dict) == 0:
                break

            for i in range(len(dict)):
                fname = dict[i]['path']
                if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')):
                    is_lora = True

                is_pytorch = re.match("(pytorch|adapter|gptq)_model.*\.bin", fname)
                is_safetensors = re.match(".*\.safetensors", fname)
                is_pt = re.match(".*\.pt", fname)
                is_ggml = re.match(".*ggml.*\.bin", fname)
                is_tokenizer = re.match("(tokenizer|ice|spiece).*\.model", fname)
                is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
                if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)):
                    if 'lfs' in dict[i]:
                        sha256.append([fname, dict[i]['lfs']['oid']])

                    if is_text:
                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
                        classifications.append('text')
                        continue

                    if not text_only:
                        links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
                        if is_safetensors:
                            has_safetensors = True
                            classifications.append('safetensors')
                        elif is_pytorch:
                            has_pytorch = True
                            classifications.append('pytorch')
                        elif is_pt:
                            has_pt = True
                            classifications.append('pt')
                        elif is_ggml:
                            # has_ggml = True
                            classifications.append('ggml')

            cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
            cursor = base64.b64encode(cursor)
            cursor = cursor.replace(b'=', b'%3D')

        # If both pytorch and safetensors are available, download safetensors only
        if (has_pytorch or has_pt) and has_safetensors:
            for i in range(len(classifications) - 1, -1, -1):
                if classifications[i] in ['pytorch', 'pt']:
                    links.pop(i)

        return links, sha256, is_lora

    def get_output_folder(self, model, branch, is_lora, base_folder=None):
        if base_folder is None:
            base_folder = 'models' if not is_lora else 'loras'

        output_folder = f"{'_'.join(model.split('/')[-2:])}"
        if branch != 'main':
            output_folder += f'_{branch}'

        output_folder = Path(base_folder) / output_folder
        return output_folder

    def get_single_file(self, url, output_folder, start_from_scratch=False):
        filename = Path(url.rsplit('/', 1)[1])
        output_path = output_folder / filename
        headers = {}
        mode = 'wb'
        if output_path.exists() and not start_from_scratch:

            # Check if the file has already been downloaded completely
            r = self.s.get(url, stream=True, timeout=20)
            total_size = int(r.headers.get('content-length', 0))
            if output_path.stat().st_size >= total_size:
                return

            # Otherwise, resume the download from where it left off
            headers = {'Range': f'bytes={output_path.stat().st_size}-'}
            mode = 'ab'

        with self.s.get(url, stream=True, headers=headers, timeout=20) as r:
            r.raise_for_status()  # Do not continue the download if the request was unsuccessful
            total_size = int(r.headers.get('content-length', 0))
            block_size = 1024 * 1024  # 1MB
            with open(output_path, mode) as f:
                with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
                    count = 0
                    for data in r.iter_content(block_size):
                        t.update(len(data))
                        f.write(data)
                        if total_size != 0 and self.progress_bar is not None:
                            count += len(data)
                            self.progress_bar(float(count) / float(total_size), f"Downloading {filename}")

    def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
        thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)

    def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1):
        self.progress_bar = progress_bar

        # Creating the folder and writing the metadata
        output_folder.mkdir(parents=True, exist_ok=True)
        metadata = f'url: https://huggingface.co/{model}\n' \
                   f'branch: {branch}\n' \
                   f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'

        sha256_str = '\n'.join([f'    {item[1]} {item[0]}' for item in sha256])
        if sha256_str:
            metadata += f'sha256sum:\n{sha256_str}'

        metadata += '\n'
        (output_folder / 'huggingface-metadata.txt').write_text(metadata)

        # Downloading the files
        print(f"Downloading the model to {output_folder}")
        self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)

    def check_model_files(self, model, branch, links, sha256, output_folder):
        # Validate the checksums
        validated = True
        for i in range(len(sha256)):
            fpath = (output_folder / sha256[i][0])

            if not fpath.exists():
                print(f"The following file is missing: {fpath}")
                validated = False
                continue

            with open(output_folder / sha256[i][0], "rb") as f:
                bytes = f.read()
                file_hash = hashlib.sha256(bytes).hexdigest()
                if file_hash != sha256[i][1]:
                    print(f'Checksum failed: {sha256[i][0]}  {sha256[i][1]}')
                    validated = False
                else:
                    print(f'Checksum validated: {sha256[i][0]}  {sha256[i][1]}')

        if validated:
            print('[+] Validated checksums of all model files!')
        else:
            print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.')


if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('MODEL', type=str, default=None, nargs='?')
    parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.')
    parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.')
    parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).')
    parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.')
    parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.')
    parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.')
    parser.add_argument('--max-retries', type=int, default=5, help='Max retries count when get error in download time.')
    args = parser.parse_args()

    branch = args.branch
    model = args.MODEL

    if model is None:
        print("Error: Please specify the model you'd like to download (e.g. 'python download-model.py facebook/opt-1.3b').")
        sys.exit()

    downloader = ModelDownloader(max_retries=args.max_retries)
    # Cleaning up the model/branch names
    try:
        model, branch = downloader.sanitize_model_and_branch_names(model, branch)
    except ValueError as err_branch:
        print(f"Error: {err_branch}")
        sys.exit()

    # Getting the download links from Hugging Face
    links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=args.text_only)

    # Getting the output folder
    output_folder = downloader.get_output_folder(model, branch, is_lora, base_folder=args.output)

    if args.check:
        # Check previously downloaded files
        downloader.check_model_files(model, branch, links, sha256, output_folder)
    else:
        # Download files
        downloader.download_model_files(model, branch, links, sha256, output_folder, threads=args.threads)