






"""simple docstring"""






from manim import *





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       Rectangle(height=0.5  ,					width=0.5						)
					A__							 =       Rectangle(height=0.46  ,					width=0.46						).set_stroke(width=0						)

					A__							 =       [mem.copy() for i in range(6						)]
					A__							 =       [mem.copy() for i in range(6						)]
					A__							 =       VGroup(*__a						).arrange(__a  ,					buff=0						)
					A__							 =       VGroup(*__a						).arrange(__a  ,					buff=0						)
					A__							 =       VGroup(__a  ,					__a						).arrange(__a  ,					buff=0						)
					A__							 =       Text('CPU'  ,					font_size=24						)
					A__							 =       Group(__a  ,					__a						).arrange(__a  ,					buff=0.5  ,					aligned_edge=__a						)
					cpu.move_to([-2.5, -0.5, 0]						)
					self.add(__a						)

					A__							 =       [mem.copy() for i in range(1						)]
					A__							 =       VGroup(*__a						).arrange(__a  ,					buff=0						)
					A__							 =       Text('GPU'  ,					font_size=24						)
					A__							 =       Group(__a  ,					__a						).arrange(__a  ,					buff=0.5  ,					aligned_edge=__a						)
					gpu.align_to(__a  ,					__a						)
					gpu.set_x(gpu.get_x() - 1						)

					self.add(__a						)

					A__							 =       [mem.copy() for i in range(6						)]
					A__							 =       VGroup(*__a						).arrange(__a  ,					buff=0						)

					A__							 =       Text('Model'  ,					font_size=24						)
					A__							 =       Group(__a  ,					__a						).arrange(__a  ,					buff=0.5  ,					aligned_edge=__a						)
					model.move_to([3, -1.0, 0]						)

					self.play(
					    Create(__a  ,					run_time=1						)  ,					Create(__a  ,					run_time=1						)  ,					Create(__a  ,					run_time=1						)  ,					)

					A__							 =       MarkupText(
					    f'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''  ,					font_size=24  ,					)

					A__							 =       Square(side_length=2.2						)
					key.move_to([-5, 2, 0]						)

					A__							 =       MarkupText(
					    f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''  ,					font_size=18  ,					)

					key_text.move_to([-5, 2.4, 0]						)

					step_a.move_to([2, 2, 0]						)
					self.play(Write(__a  ,					run_time=2.5						)  ,					Write(__a						)  ,					Write(__a						)						)

					self.add(__a						)

					A__							 =       []
					A__							 =       []
					A__							 =       []
					for i, rect in enumerate(__a						):
									A__							 =       Rectangle(height=0.46  ,					width=0.46						).set_stroke(width=0.0						).set_fill(__a  ,					opacity=0.7						)
									cpu_target.move_to(__a						)
									cpu_target.generate_target()
									A__							 =       0.46 / 4
									A__							 =       0.46 / 3

									if i == 0:
													cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)  ,					buff=0.02  ,					direction=__a						)
													cpu_target.target.set_x(cpu_target.target.get_x() + 0.1						)
									elif i == 3:
													cpu_target.target.next_to(cpu_targs[0].target  ,					direction=__a  ,					buff=0.0						)
									else:
													cpu_target.target.next_to(cpu_targs[i - 1].target  ,					direction=__a  ,					buff=0.0						)
									cpu_targs.append(__a						)

									first_animations.append(rect.animate(run_time=0.5						).set_stroke(__a						)						)
									second_animations.append(MoveToTarget(__a  ,					run_time=1.5						)						)

					self.play(*__a						)
					self.play(*__a						)

					self.wait()










"""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()


