






"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       0
				A__							 =       len(lowerCAmelCase__				) - 1

				while left <= right:
								# avoid divided by 0 during interpolation
								if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																return left
												else:
																return None

								A__							 =       left + ((item - sorted_collection[left]) * (right - left)) // (
								    sorted_collection[right] - sorted_collection[left]
								)

								# out of range check
								if point < 0 or point >= len(lowerCAmelCase__				):
												return None

								A__							 =       sorted_collection[point]
								if current_item == item:
												return point
								else:
												if point < left:
																A__							 =       left
																A__							 =       point
												elif point > right:
																A__							 =       right
																A__							 =       point
												else:
																if item < current_item:
																				A__							 =       point - 1
																else:
																				A__							 =       point + 1
				return None



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):

				# avoid divided by 0 during interpolation
				if sorted_collection[left] == sorted_collection[right]:
								if sorted_collection[left] == item:
												return left
								else:
												return None

				A__							 =       left + ((item - sorted_collection[left]) * (right - left)) // (
				    sorted_collection[right] - sorted_collection[left]
				)

				# out of range check
				if point < 0 or point >= len(lowerCAmelCase__				):
								return None

				if sorted_collection[point] == item:
								return point
				elif point < left:
								return interpolation_search_by_recursion(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				elif point > right:
								return interpolation_search_by_recursion(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
				else:
								if sorted_collection[point] > item:
												return interpolation_search_by_recursion(
												    lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,point - 1				)
								else:
												return interpolation_search_by_recursion(
												    lowerCAmelCase__       ,lowerCAmelCase__       ,point + 1       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__				):
				if collection != sorted(lowerCAmelCase__				):
								raise ValueError('Collection must be ascending sorted'				)
				return True


if __name__ == "__main__":
					import sys
					SCREAMING_SNAKE_CASE						:						str					   =       0
					if debug == 1:
										SCREAMING_SNAKE_CASE						:						List[Any]					   =       [10, 30, 40, 45, 50, 66, 77, 93]
										try:
															__assert_sorted(collection)
										except ValueError:
															sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
										SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       67

					SCREAMING_SNAKE_CASE						:						Dict					   =       interpolation_search(collection, target)
					if result is not None:
										print(f'''{target} found at positions: {result}''')
					else:
										print('''Not found''')










"""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
)
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       logging.getLogger(__name__)



def  __lowerCamelCase					(					):
				A__							 =       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.'				)
				A__							 =       parser.parse_args()

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

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

				A__							 =       []
				A__							 =       0
				A__							 =       1_0000
				A__							 =       time.time()
				for text in data:
								A__							 =       f'''{bos} {text.strip()} {sep}'''
								A__							 =       tokenizer.encode(lowerCAmelCase__       ,add_special_tokens=lowerCAmelCase__				)
								rslt.append(lowerCAmelCase__				)

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

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


