import argparse
import dataclasses
import os
import parser
from typing import List

import tqdm
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from huggingface_hub import hf_hub_download, snapshot_download
from transformers import AutoConfig, AutoTokenizer, BloomConfig, BloomTokenizerFast, GPT2Config, GPT2Tokenizer


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)

    def download_all(self):
        file_path = snapshot_download(self.repo_id)


def test_init(model: str, dir_path: str):
    if model == "gpt2":
        config = GPT2Config.from_pretrained(dir_path)
        actor = GPTActor(config=config)
        critic = GPTCritic(config=config)
        reward_model = GPTRM(config=config)
        tokenizer = GPT2Tokenizer.from_pretrained(dir_path)
    elif model == "bloom":
        config = BloomConfig.from_pretrained(dir_path)
        actor = BLOOMActor(config=config)
        critic = BLOOMCritic(config=config)
        reward_model = BLOOMRM(config=config)
        tokenizer = BloomTokenizerFast.from_pretrained(dir_path)
    elif model == "opt":
        config = AutoConfig.from_pretrained(dir_path)
        actor = OPTActor(config=config)
        critic = OPTCritic(config=config)
        reward_model = OPTRM(config=config)
        tokenizer = AutoTokenizer.from_pretrained(dir_path)
    else:
        raise NotImplementedError(f"Model {model} not implemented")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-dir", type=str, default="test_models")
    parser.add_argument("--config-only", default=False, action="store_true")
    args = parser.parse_args()

    if os.path.exists(args.model_dir):
        print(f"[INFO]: {args.model_dir} already exists")
        exit(0)

    repo_list = {
        "gpt2": HFRepoFiles(
            repo_id="gpt2",
            files=["config.json", "tokenizer.json", "vocab.json", "merges.txt"]
        ),
        "bloom": HFRepoFiles(
            repo_id="bigscience/bloom-560m",
            files=["config.json", "tokenizer.json", "tokenizer_config.json"]
        ),
        "opt": HFRepoFiles(
            repo_id="facebook/opt-350m",
            files=["config.json", "tokenizer_config.json", "vocab.json", "merges.txt"]
        ),
    }

    os.mkdir(args.model_dir)
    for model_name in tqdm.tqdm(repo_list):
        dir_path = os.path.join(args.model_dir, model_name)
        if args.config_only:
            os.mkdir(dir_path)
            repo_list[model_name].download(dir_path)
        else:
            repo_list[model_name].download_all()
        test_init(model_name, dir_path)

2ed644a61cab28fad726aae40a5a876045143c6e
<mask token>


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)
    <mask token>


<mask token>

<mask token>


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)

    def download_all(self):
        file_path = snapshot_download(self.repo_id)


<mask token>

<mask token>


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)

    def download_all(self):
        file_path = snapshot_download(self.repo_id)


def test_init(model: str, dir_path: str):
    if model == 'gpt2':
        config = GPT2Config.from_pretrained(dir_path)
        actor = GPTActor(config=config)
        critic = GPTCritic(config=config)
        reward_model = GPTRM(config=config)
        tokenizer = GPT2Tokenizer.from_pretrained(dir_path)
    elif model == 'bloom':
        config = BloomConfig.from_pretrained(dir_path)
        actor = BLOOMActor(config=config)
        critic = BLOOMCritic(config=config)
        reward_model = BLOOMRM(config=config)
        tokenizer = BloomTokenizerFast.from_pretrained(dir_path)
    elif model == 'opt':
        config = AutoConfig.from_pretrained(dir_path)
        actor = OPTActor(config=config)
        critic = OPTCritic(config=config)
        reward_model = OPTRM(config=config)
        tokenizer = AutoTokenizer.from_pretrained(dir_path)
    else:
        raise NotImplementedError(f'Model {model} not implemented')


<mask token>

import argparse
import dataclasses
import os
import parser
from typing import List
import tqdm
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from huggingface_hub import hf_hub_download, snapshot_download
from transformers import AutoConfig, AutoTokenizer, BloomConfig, BloomTokenizerFast, GPT2Config, GPT2Tokenizer


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)

    def download_all(self):
        file_path = snapshot_download(self.repo_id)


def test_init(model: str, dir_path: str):
    if model == 'gpt2':
        config = GPT2Config.from_pretrained(dir_path)
        actor = GPTActor(config=config)
        critic = GPTCritic(config=config)
        reward_model = GPTRM(config=config)
        tokenizer = GPT2Tokenizer.from_pretrained(dir_path)
    elif model == 'bloom':
        config = BloomConfig.from_pretrained(dir_path)
        actor = BLOOMActor(config=config)
        critic = BLOOMCritic(config=config)
        reward_model = BLOOMRM(config=config)
        tokenizer = BloomTokenizerFast.from_pretrained(dir_path)
    elif model == 'opt':
        config = AutoConfig.from_pretrained(dir_path)
        actor = OPTActor(config=config)
        critic = OPTCritic(config=config)
        reward_model = OPTRM(config=config)
        tokenizer = AutoTokenizer.from_pretrained(dir_path)
    else:
        raise NotImplementedError(f'Model {model} not implemented')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--model-dir', type=str, default='test_models')
    parser.add_argument('--config-only', default=False, action='store_true')
    args = parser.parse_args()
    if os.path.exists(args.model_dir):
        print(f'[INFO]: {args.model_dir} already exists')
        exit(0)
    repo_list = {'gpt2': HFRepoFiles(repo_id='gpt2', files=['config.json',
        'tokenizer.json', 'vocab.json', 'merges.txt']), 'bloom':
        HFRepoFiles(repo_id='bigscience/bloom-560m', files=['config.json',
        'tokenizer.json', 'tokenizer_config.json']), 'opt': HFRepoFiles(
        repo_id='facebook/opt-350m', files=['config.json',
        'tokenizer_config.json', 'vocab.json', 'merges.txt'])}
    os.mkdir(args.model_dir)
    for model_name in tqdm.tqdm(repo_list):
        dir_path = os.path.join(args.model_dir, model_name)
        if args.config_only:
            os.mkdir(dir_path)
            repo_list[model_name].download(dir_path)
        else:
            repo_list[model_name].download_all()
        test_init(model_name, dir_path)

import argparse
import dataclasses
import os
import parser
from typing import List

import tqdm
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from huggingface_hub import hf_hub_download, snapshot_download
from transformers import AutoConfig, AutoTokenizer, BloomConfig, BloomTokenizerFast, GPT2Config, GPT2Tokenizer


@dataclasses.dataclass
class HFRepoFiles:
    repo_id: str
    files: List[str]

    def download(self, dir_path: str):
        for file in self.files:
            file_path = hf_hub_download(self.repo_id, file, local_dir=dir_path)

    def download_all(self):
        file_path = snapshot_download(self.repo_id)


def test_init(model: str, dir_path: str):
    if model == "gpt2":
        config = GPT2Config.from_pretrained(dir_path)
        actor = GPTActor(config=config)
        critic = GPTCritic(config=config)
        reward_model = GPTRM(config=config)
        tokenizer = GPT2Tokenizer.from_pretrained(dir_path)
    elif model == "bloom":
        config = BloomConfig.from_pretrained(dir_path)
        actor = BLOOMActor(config=config)
        critic = BLOOMCritic(config=config)
        reward_model = BLOOMRM(config=config)
        tokenizer = BloomTokenizerFast.from_pretrained(dir_path)
    elif model == "opt":
        config = AutoConfig.from_pretrained(dir_path)
        actor = OPTActor(config=config)
        critic = OPTCritic(config=config)
        reward_model = OPTRM(config=config)
        tokenizer = AutoTokenizer.from_pretrained(dir_path)
    else:
        raise NotImplementedError(f"Model {model} not implemented")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-dir", type=str, default="test_models")
    parser.add_argument("--config-only", default=False, action="store_true")
    args = parser.parse_args()

    if os.path.exists(args.model_dir):
        print(f"[INFO]: {args.model_dir} already exists")
        exit(0)

    repo_list = {
        "gpt2": HFRepoFiles(
            repo_id="gpt2",
            files=["config.json", "tokenizer.json", "vocab.json", "merges.txt"]
        ),
        "bloom": HFRepoFiles(
            repo_id="bigscience/bloom-560m",
            files=["config.json", "tokenizer.json", "tokenizer_config.json"]
        ),
        "opt": HFRepoFiles(
            repo_id="facebook/opt-350m",
            files=["config.json", "tokenizer_config.json", "vocab.json", "merges.txt"]
        ),
    }

    os.mkdir(args.model_dir)
    for model_name in tqdm.tqdm(repo_list):
        dir_path = os.path.join(args.model_dir, model_name)
        if args.config_only:
            os.mkdir(dir_path)
            repo_list[model_name].download(dir_path)
        else:
            repo_list[model_name].download_all()
        test_init(model_name, dir_path)
