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   :				int     			=						logging.getLogger(__name__)





def 		__lowerCamelCase							():
					SCREAMING_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=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."					)
					SCREAMING_SNAKE_CASE           =    parser.parse_args()

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

					logger.info("Start encoding"					)
					logger.info(F"{len(UpperCAmelCase__					)} examples to process."					)

					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    1_0_0_0_0
					SCREAMING_SNAKE_CASE           =    time.time()
					for text in data:
										SCREAMING_SNAKE_CASE           =    F"{bos} {text.strip()} {sep}"
										SCREAMING_SNAKE_CASE           =    tokenizer.encode(UpperCAmelCase__ ,	add_special_tokens=UpperCAmelCase__					)
										rslt.append(UpperCAmelCase__					)

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

					SCREAMING_SNAKE_CASE           =    F"{args.dump_file}.{args.tokenizer_name}.pickle"
					SCREAMING_SNAKE_CASE           =    tokenizer.vocab_size
					if vocab_size < (1 << 1_6):
										SCREAMING_SNAKE_CASE           =    [np.uintaa(UpperCAmelCase__					) for d in rslt]
					else:
										SCREAMING_SNAKE_CASE           =    [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()





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




