


'''simple docstring'''
class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase = ""      ,      __lowercase = False						):

  """simple docstring"""







  __A  :		dict[str, RadixNode]       =			{}

  # A node will be a leaf if the tree contains its word
  __A  :		str       =			is_leaf

  __A  :		Union[str, Any]       =			prefix


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			0
  for q, w in zip(self.prefix      ,      __lowercase						):
   if q != w:
    break

   x += 1

  return self.prefix[:x], self.prefix[x:], word[x:]


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  for word in words:
   self.insert(__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if self.prefix == word:
   __A  :		Any       =			True

  # Case 2: The node has no edges that have a prefix to the word
  # Solution: We create an edge from the current node to a new one
  # containing the word
  elif word[0] not in self.nodes:
   __A  :		List[Any]       =			RadixNode(prefix=__lowercase      ,      is_leaf=__lowercase						)

  else:
   __A  :		Union[str, Any]       =			self.nodes[word[0]]
   __A     ,__A     ,__A  :		List[Any]       =			incoming_node.match(
       __lowercase						)

   # Case 3: The node prefix is equal to the matching
   # Solution: We insert remaining word on the next node
   if remaining_prefix == "":
    self.nodes[matching_string[0]].insert(__lowercase						)

   # Case 4: The word is greater equal to the matching
   # Solution: Create a node in between both nodes, change
   # prefixes and add the new node for the remaining word
   else:
    __A  :		Tuple       =			remaining_prefix

    __A  :		Optional[int]       =			self.nodes[matching_string[0]]
    __A  :		str       =			RadixNode(__lowercase      ,      __lowercase						)
    __A  :		Union[str, Any]       =			aux_node

    if remaining_word == "":
     __A  :		Any       =			True
    else:
     self.nodes[matching_string[0]].insert(__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.nodes.get(word[0]      ,      __lowercase						)
  if not incoming_node:
   return False
  else:
   __A     ,__A     ,__A  :		List[str]       =			incoming_node.match(
       __lowercase						)
   # If there is remaining prefix, the word can't be on the tree
   if remaining_prefix != "":
    return False
   # This applies when the word and the prefix are equal
   elif remaining_word == "":
    return incoming_node.is_leaf
   # We have word remaining so we check the next node
   else:
    return incoming_node.find(__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			self.nodes.get(word[0]      ,      __lowercase						)
  if not incoming_node:
   return False
  else:
   __A     ,__A     ,__A  :		Dict       =			incoming_node.match(
       __lowercase						)
   # If there is remaining prefix, the word can't be on the tree
   if remaining_prefix != "":
    return False
   # We have word remaining so we check the next node
   elif remaining_word != "":
    return incoming_node.delete(__lowercase						)
   else:
    # If it is not a leaf, we don't have to delete
    if not incoming_node.is_leaf:
     return False
    else:
     # We delete the nodes if no edges go from it
     if len(incoming_node.nodes						) == 0:
      del self.nodes[word[0]]
      # We merge the current node with its only child
      if len(self.nodes						) == 1 and not self.is_leaf:
       __A  :		List[Any]       =			list(self.nodes.values()						)[0]
       __A  :		Union[str, Any]       =			merging_node.is_leaf
       self.prefix += merging_node.prefix
       __A  :		int       =			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
     elif len(incoming_node.nodes						) > 1:
      __A  :		Optional[int]       =			False
     # If there is 1 edge, we merge it with its child
     else:
      __A  :		str       =			list(incoming_node.nodes.values()						)[0]
      __A  :		List[str]       =			merging_node.is_leaf
      incoming_node.prefix += merging_node.prefix
      __A  :		Dict       =			merging_node.nodes

     return True







 def  snake_case__   (      self      ,      __lowercase = 0						):

  """simple docstring"""







  if self.prefix != "":
   print('-' * height      ,      self.prefix      ,      '  (leaf)' if self.is_leaf else ''						)

  for value in self.nodes.values():
   value.print_tree(height + 1						)


def 			_lowercase       (				):
 __A  :		Tuple       =			'banana bananas bandana band apple all beast'.split()
 __A  :		Dict       =			RadixNode()
 root.insert_many(UpperCamelCase__			)

 assert all(root.find(UpperCamelCase__			) for word in words			)
 assert not root.find('bandanas'			)
 assert not root.find('apps'			)
 root.delete('all'			)
 assert not root.find('all'			)
 root.delete('banana'			)
 assert not root.find('banana'			)
 assert root.find('bananas'			)

 return True


def 			_lowercase       (				):
 assert test_trie()


def 			_lowercase       (				):
 __A  :		Optional[int]       =			RadixNode()
 __A  :		str       =			'banana bananas bandanas bandana band apple all beast'.split()
 root.insert_many(UpperCamelCase__			)

 print('Words:',				UpperCamelCase__			)
 print('Tree:'			)
 root.print_tree()


if __name__ == "__main__":
 main()










'''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
)
UpperCAmelCase_ :     Any								=      logging.getLogger(__name__)


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

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

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

 __A  :		int       =			[]
 __A  :		List[str]       =			0
 __A  :		Tuple       =			10000
 __A  :		List[str]       =			time.time()
 for text in data:
  __A  :		int       =			f"""{bos} {text.strip()} {sep}"""
  __A  :		List[Any]       =			tokenizer.encode(UpperCamelCase__,				add_special_tokens=UpperCamelCase__			)
  rslt.append(UpperCamelCase__			)

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

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


if __name__ == "__main__":
 main()






