
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       :				List[str]       =			logging.getLogger(__name__)






def        a_		(       ) ->  Optional[int]:
			_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=__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.' )
			_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(__a )} 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(__a       ,					add_special_tokens=__a )
						rslt.append(__a )

						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(__a )} 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(__a ) for d in rslt]
			else:
						_snake_case		=			[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()







"""simple docstring"""






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


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : Any   			= {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "openai-gpt"
      __UpperCamelCase	 =  {
          "max_position_embeddings": "n_positions",
          "hidden_size": "n_embd",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }
      def __init__(						self     :  List[str]						,	lowercase_     :  List[str]=40478						,	lowercase_     :  List[str]=512						,	lowercase_     :  Optional[Any]=768						,	lowercase_     :  Tuple=12						,	lowercase_     :  Tuple=12						,	lowercase_     :  Union[str, Any]="gelu"						,	lowercase_     :  List[Any]=0.1						,	lowercase_     :  Tuple=0.1						,	lowercase_     :  List[Any]=0.1						,	lowercase_     :  List[Any]=1e-5						,	lowercase_     :  int=0.02						,	lowercase_     :  Optional[int]="cls_index"						,	lowercase_     :  Any=True						,	lowercase_     :  List[Any]=None						,	lowercase_     :  List[str]=True						,	lowercase_     :  Optional[Any]=0.1						,	**lowercase_     :  List[str]						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		vocab_size
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		n_positions
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		n_embd
          SCREAMING_SNAKE_CASE_      :  Dict    						=		n_layer
          SCREAMING_SNAKE_CASE_      :  Any    						=		n_head
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		afn
          SCREAMING_SNAKE_CASE_      :  int    						=		resid_pdrop
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		embd_pdrop
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		attn_pdrop
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		layer_norm_epsilon
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		summary_type
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		summary_use_proj
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		summary_activation
          SCREAMING_SNAKE_CASE_      :  Any    						=		summary_first_dropout
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		summary_proj_to_labels
          super().__init__(**lowercase_)


