
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 	a_		(	__lowercase       :     Any			)			->  List[Any]:
			if "model" in orig_key:
						_snake_case          =      orig_key.replace('model.'	, ''			)
			if "norm1" in orig_key:
						_snake_case          =      orig_key.replace('norm1'	, 'attention.output.LayerNorm'			)
			if "norm2" in orig_key:
						_snake_case          =      orig_key.replace('norm2'	, 'output.LayerNorm'			)
			if "norm" in orig_key:
						_snake_case          =      orig_key.replace('norm'	, 'LayerNorm'			)
			if "transformer" in orig_key:
						_snake_case          =      orig_key.split('.'			)[0].split('_'			)[-1]
						_snake_case          =      orig_key.replace(f'''transformer_{layer_num}'''	, f'''encoder.layer.{layer_num}'''			)
			if "mha.attn" in orig_key:
						_snake_case          =      orig_key.replace('mha.attn'	, 'attention.self'			)
			if "mha" in orig_key:
						_snake_case          =      orig_key.replace('mha'	, 'attention'			)
			if "W_q" in orig_key:
						_snake_case          =      orig_key.replace('W_q'	, 'self.query'			)
			if "W_k" in orig_key:
						_snake_case          =      orig_key.replace('W_k'	, 'self.key'			)
			if "W_v" in orig_key:
						_snake_case          =      orig_key.replace('W_v'	, 'self.value'			)
			if "ff1" in orig_key:
						_snake_case          =      orig_key.replace('ff1'	, 'intermediate.dense'			)
			if "ff2" in orig_key:
						_snake_case          =      orig_key.replace('ff2'	, 'output.dense'			)
			if "ff" in orig_key:
						_snake_case          =      orig_key.replace('ff'	, 'output.dense'			)
			if "mlm_class" in orig_key:
						_snake_case          =      orig_key.replace('mlm.mlm_class'	, 'cls.predictions.decoder'			)
			if "mlm" in orig_key:
						_snake_case          =      orig_key.replace('mlm'	, 'cls.predictions.transform'			)
			if "cls" not in orig_key:
						_snake_case          =      'yoso.' + orig_key

			return orig_key






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Union[str, Any]			)			->  List[Any]:
			for key in orig_state_dict.copy().keys():
						_snake_case          =      orig_state_dict.pop(__lowercase			)

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									_snake_case          =      val

			_snake_case          =      orig_state_dict['cls.predictions.decoder.bias']
			_snake_case          =      torch.arange(__lowercase			).expand((1, -1)			) + 2

			return orig_state_dict






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     Tuple	, __lowercase       :     Optional[int]			)			->  Tuple:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model_state_dict']
			_snake_case          =      YosoConfig.from_json_file(__lowercase			)
			_snake_case          =      YosoForMaskedLM(__lowercase			)

			_snake_case          =      convert_checkpoint_helper(config.max_position_embeddings	, __lowercase			)

			print(model.load_state_dict(__lowercase			)			)
			model.eval()
			model.save_pretrained(__lowercase			)

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''			)


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The json file for YOSO model config.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase	:    List[Any]          =      parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

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
)
_lowerCamelCase	:    Tuple          =      logging.getLogger(__name__)






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

			logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''			)
			if args.tokenizer_type == "bert":
						_snake_case          =      BertTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `[CLS]`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `[SEP]`
			elif args.tokenizer_type == "roberta":
						_snake_case          =      RobertaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['cls_token']  # `<s>`
						_snake_case          =      tokenizer.special_tokens_map['sep_token']  # `</s>`
			elif args.tokenizer_type == "gpt2":
						_snake_case          =      GPTaTokenizer.from_pretrained(args.tokenizer_name			)
						_snake_case          =      tokenizer.special_tokens_map['bos_token']  # `<|endoftext|>`
						_snake_case          =      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          =      fp.readlines()

			logger.info('Start encoding'			)
			logger.info(f'''{len(__lowercase			)} examples to process.'''			)

			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      10_000
			_snake_case          =      time.time()
			for text in data:
						_snake_case          =      f'''{bos} {text.strip()} {sep}'''
						_snake_case          =      tokenizer.encode(__lowercase	, add_special_tokens=__lowercase			)
						rslt.append(__lowercase			)

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

			_snake_case          =      f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
			_snake_case          =      tokenizer.vocab_size
			if vocab_size < (1 << 16):
						_snake_case          =      [np.uintaa(__lowercase			) for d in rslt]
			else:
						_snake_case          =      [np.intaa(__lowercase			) for d in rslt]
			random.shuffle(rslt_			)
			logger.info(f'''Dump to {dp_file}'''			)
			with open(__lowercase	, 'wb'			) as handle:
						pickle.dump(rslt_	, __lowercase	, protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
			main()