


import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_snake_case					:		Any      =    logging.getLogger(__name__)







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[int]										=       argparse.ArgumentParser(
							    description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."     )
							parser.add_argument("--file_path"							,     type=__lowerCamelCase							,     default="data/dump.txt"							,     help="The path to the data."     )
							parser.add_argument("--tokenizer_type"							,     type=__lowerCamelCase							,     default="bert"							,     choices=["bert", "roberta", "gpt2"]     )
							parser.add_argument("--tokenizer_name"							,     type=__lowerCamelCase							,     default="bert-base-uncased"							,     help="The tokenizer to use."     )
							parser.add_argument("--dump_file"							,     type=__lowerCamelCase							,     default="data/dump"							,     help="The dump file prefix."     )
							__snake_case		:      List[Any]										=       parser.parse_args()

							logger.info(F'Loading Tokenizer ({args.tokenizer_name})'     )
							if args.tokenizer_type == "bert":
														__snake_case		:      Tuple										=       BertTokenizer.from_pretrained(args.tokenizer_name     )
														__snake_case		:      List[str]										=       tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
														__snake_case		:      Optional[int]										=       tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
							elif args.tokenizer_type == "roberta":
														__snake_case		:      str										=       RobertaTokenizer.from_pretrained(args.tokenizer_name     )
														__snake_case		:      Union[str, Any]										=       tokenizer.special_tokens_map['''cls_token''']  # `<s>`
														__snake_case		:      Any										=       tokenizer.special_tokens_map['''sep_token''']  # `</s>`
							elif args.tokenizer_type == "gpt2":
														__snake_case		:      List[str]										=       GPTaTokenizer.from_pretrained(args.tokenizer_name     )
														__snake_case		:      List[Any]										=       tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
														__snake_case		:      Dict										=       tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

							logger.info(F'Loading text from {args.file_path}'     )
							with open(args.file_path							,     "r"							,     encoding="utf8"     ) as fp:
														__snake_case		:      Dict										=       fp.readlines()

							logger.info("Start encoding"     )
							logger.info(F'{len(__lowerCamelCase     )} examples to process.'     )

							__snake_case		:      List[str]										=       []
							__snake_case		:      Optional[int]										=       0
							__snake_case		:      Optional[int]										=       1_0_0_0_0
							__snake_case		:      Any										=       time.time()
							for text in data:
														__snake_case		:      Optional[Any]										=       F'{bos} {text.strip()} {sep}'
														__snake_case		:      Dict										=       tokenizer.encode(__lowerCamelCase							,     add_special_tokens=__lowerCamelCase     )
														rslt.append(__lowerCamelCase     )

														iter += 1
														if iter % interval == 0:
																					__snake_case		:      str										=       time.time()
																					logger.info(F'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'     )
																					__snake_case		:      str										=       time.time()
							logger.info("Finished binarization"     )
							logger.info(F'{len(__lowerCamelCase     )} examples processed.'     )

							__snake_case		:      Union[str, Any]										=       F'{args.dump_file}.{args.tokenizer_name}.pickle'
							__snake_case		:      Union[str, Any]										=       tokenizer.vocab_size
							if vocab_size < (1 << 1_6):
														__snake_case		:      Dict										=       [np.uintaa(__lowerCamelCase     ) for d in rslt]
							else:
														__snake_case		:      Tuple										=       [np.intaa(__lowerCamelCase     ) for d in rslt]
							random.shuffle(rslt_     )
							logger.info(F'Dump to {dp_file}'     )
							with open(__lowerCamelCase							,     "wb"     ) as handle:
														pickle.dump(rslt_							,     __lowerCamelCase							,     protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
			main()





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

_snake_case					:		List[Any]      =    "lm_head.decoder.weight"
_snake_case					:		Optional[Any]      =    "lm_head.weight"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


if __name__ == "__main__":
			_snake_case					:		Dict      =    argparse.ArgumentParser()
			parser.add_argument("--dialogpt_path", default=".", type=str)
			_snake_case					:		Any      =    parser.parse_args()
			for MODEL in DIALOGPT_MODELS:
						_snake_case					:		Dict      =    os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
						_snake_case					:		List[str]      =    f'''./DialoGPT-{MODEL}'''
						convert_dialogpt_checkpoint(
						    checkpoint_path,
						    pytorch_dump_folder_path,
						)

