






"""simple docstring"""
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_							      = logging.getLogger(__name__)

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

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

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

			UpperCAmelCase_	:					List[Any]     	=   []
			UpperCAmelCase_	:					Any     	=   0
			UpperCAmelCase_	:					Optional[int]     	=   1_00_00
			UpperCAmelCase_	:					Dict     	=   time.time()
			for text in data:
						UpperCAmelCase_	:					Union[str, Any]     	=   F"""{bos} {text.strip()} {sep}"""
						UpperCAmelCase_	:					Optional[Any]     	=   tokenizer.encode(A__      ,add_special_tokens=A__		)
						rslt.append(A__		)

						iter += 1
						if iter % interval == 0:
									UpperCAmelCase_	:					Optional[int]     	=   time.time()
									logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""		)
									UpperCAmelCase_	:					str     	=   time.time()
			logger.info("Finished binarization"		)
			logger.info(F"""{len(A__		)} examples processed."""		)

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


if __name__ == "__main__":
			main()










from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''rwkv'''
	__UpperCamelCase   =		{'''max_position_embeddings''': '''context_length'''}




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Tuple=50_277					,      __lowerCamelCase							:     List[str]=1_024					,      __lowerCamelCase							:     List[Any]=4_096					,      __lowerCamelCase							:     Tuple=32					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[Any]=1E-5					,      __lowerCamelCase							:     Tuple=0					,      __lowerCamelCase							:     int=0					,      __lowerCamelCase							:     Dict=6					,      __lowerCamelCase							:     List[Any]=False					,      __lowerCamelCase							:     Tuple=True					,      **__lowerCamelCase							:     Optional[int]					,      )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     context_length
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     attention_hidden_size if attention_hidden_size is not None else hidden_size
			__lowercase											=     intermediate_size if intermediate_size is not None else 4 * hidden_size
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     rescale_every
			__lowercase											=     use_cache

			__lowercase											=     bos_token_id
			__lowercase											=     eos_token_id

			super().__init__(
			    tie_word_embeddings=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase   )




