


'''simple docstring'''
import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

UpperCAmelCase_ :     Optional[int]								=      'Usage of script: script_name <size_of_canvas:int>'

UpperCAmelCase_ :     int								=      [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)


def 			_lowercase       (				UpperCamelCase__       :					int			):
 __A  :		Tuple       =			[[False for i in range(UpperCamelCase__			)] for j in range(UpperCamelCase__			)]
 return canvas


def 			_lowercase       (				UpperCamelCase__       :					list[list[bool]]			):
 for i, row in enumerate(UpperCamelCase__			):
  for j, _ in enumerate(UpperCamelCase__			):
   __A  :		Dict       =			bool(random.getrandbits(1			)			)


def 			_lowercase       (				UpperCamelCase__       :					list[list[bool]]			):
 __A  :		Tuple       =			np.array(UpperCamelCase__			)
 __A  :		Optional[int]       =			np.array(create_canvas(current_canvas.shape[0]			)			)
 for r, row in enumerate(UpperCamelCase__			):
  for c, pt in enumerate(UpperCamelCase__			):
   __A  :		Optional[Any]       =			__judge_point(
       UpperCamelCase__,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]			)

 __A  :		Dict       =			next_gen_canvas
 del next_gen_canvas  # cleaning memory as we move on.
 __A  :		list[list[bool]]       =			current_canvas.tolist()
 return return_canvas


def 			_lowercase       (				UpperCamelCase__       :					bool,				UpperCamelCase__       :					list[list[bool]]			):
 __A  :		Optional[Any]       =			0
 __A  :		Tuple       =			0
 # finding dead or alive neighbours count.
 for i in neighbours:
  for status in i:
   if status:
    alive += 1
   else:
    dead += 1

    # handling duplicate entry for focus pt.
 if pt:
  alive -= 1
 else:
  dead -= 1

 # running the rules of game here.
 __A  :		Tuple       =			pt
 if pt:
  if alive < 2:
   __A  :		Any       =			False
  elif alive == 2 or alive == 3:
   __A  :		Any       =			True
  elif alive > 3:
   __A  :		List[Any]       =			False
 else:
  if alive == 3:
   __A  :		List[str]       =			True

 return state


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise Exception(usage_doc)

 UpperCAmelCase_ :     int								=      int(sys.argv[1])
 # main working structure of this module.
 UpperCAmelCase_ :     Optional[Any]								=      create_canvas(canvas_size)
 seed(c)
 UpperCAmelCase_							,			UpperCAmelCase_ :     List[Any]								=      plt.subplots()
 fig.show()
 UpperCAmelCase_ :     List[str]								=      ListedColormap(['w', 'k'])
 try:
  while True:
   UpperCAmelCase_ :     int								=      run(c)
   ax.matshow(c, cmap=cmap)
   fig.canvas.draw()
   ax.cla()
 except KeyboardInterrupt:
  # do nothing.
  pass










'''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()






