
"""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
)
A_    							=					logging.getLogger(__name__)






def  lowercase						(	):
			lowerCamelCase_					=  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.'''						)
			lowerCamelCase_					=  parser.parse_args()

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

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

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

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

			lowerCamelCase_					=  f"{args.dump_file}.{args.tokenizer_name}.pickle"
			lowerCamelCase_					=  tokenizer.vocab_size
			if vocab_size < (1 << 16):
						lowerCamelCase_					=  [np.uintaa(lowerCAmelCase__						) for d in rslt]
			else:
						lowerCamelCase_					=  [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()






from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


