



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



def 	a			( ):
			'''simple docstring'''





			A_	:						List[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=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_	:						int	=     parser.parse_args()

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

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

			A_	:						Dict	=     []
			A_	:						Tuple	=     0
			A_	:						Optional[Any]	=     1_00_00
			A_	:						Optional[Any]	=     time.time()
			for text in data:
						A_	:						Tuple	=     f'{bos} {text.strip()} {sep}'
						A_	:						int	=     tokenizer.encode(lowerCamelCase__						,      add_special_tokens=lowerCamelCase__    )
						rslt.append(lowerCamelCase__    )

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

			A_	:						Union[str, Any]	=     f'{args.dump_file}.{args.tokenizer_name}.pickle'
			A_	:						Optional[int]	=     tokenizer.vocab_size
			if vocab_size < (1 << 16):
						A_	:						Union[str, Any]	=     [np.uintaa(lowerCamelCase__    ) for d in rslt]
			else:
						A_	:						Dict	=     [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()




'''simple docstring'''




class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	pass







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[Any]	=     [
				    [],
				    [],
				    [],
				]


	def     _a	(self       ,      lowercase       ,      lowercase							):
				try:
							if len(self.queues[priority]							) >= 100:
										raise OverflowError("""Maximum queue size is 100"""							)
							self.queues[priority].append(lowercase							)
				except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""							)


	def     _a	(self							):
				for queue in self.queues:
							if queue:
										return queue.pop(0							)
				raise UnderFlowError("""All queues are empty"""							)


	def __str__(self							):
				return "\n".join(F'Priority {i}: {q}' for i, q in enumerate(self.queues							)							)







class 	_lowerCAmelCase							:


	def __init__(self							):
				A_	:						List[str]	=     []


	def     _a	(self       ,      lowercase							):
				if len(self.queue							) == 100:
							raise OverFlowError("""Maximum queue size is 100"""							)
				self.queue.append(lowercase							)


	def     _a	(self							):
				if not self.queue:
							raise UnderFlowError("""The queue is empty"""							)
				else:
							A_	:						List[Any]	=     min(self.queue							)
							self.queue.remove(lowercase							)
							return data


	def __str__(self							):
				return str(self.queue							)






def 	a			( ):
			'''simple docstring'''





			A_	:						Optional[int]	=     FixedPriorityQueue()
			fpq.enqueue(0						,      10    )
			fpq.enqueue(1						,      70    )
			fpq.enqueue(0						,      1_00    )
			fpq.enqueue(2						,      1    )
			fpq.enqueue(2						,      5    )
			fpq.enqueue(1						,      7    )
			fpq.enqueue(2						,      4    )
			fpq.enqueue(1						,      64    )
			fpq.enqueue(0						,      1_28    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(lowerCamelCase__    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )
			print(fpq.dequeue()    )



def 	a			( ):
			'''simple docstring'''





			A_	:						int	=     ElementPriorityQueue()
			epq.enqueue(10    )
			epq.enqueue(70    )
			epq.enqueue(1_00    )
			epq.enqueue(1    )
			epq.enqueue(5    )
			epq.enqueue(7    )
			epq.enqueue(4    )
			epq.enqueue(64    )
			epq.enqueue(1_28    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(lowerCamelCase__    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )
			print(epq.dequeue()    )


if __name__ == "__main__":
						fixed_priority_queue()
						element_priority_queue()