






import unittest

import numpy as np

def    UpperCamelCase						(	_A,   _A,   _A,   _A = None,   ):






						"""simple docstring"""







						__magic_name__      :       Optional[Any]       						= np.shape(_A			)
						__magic_name__      :       Optional[Any]       						= np.shape(_A			)
						__magic_name__      :       Tuple       						= np.shape(_A			)

						if shape_a[0] != shape_b[0]:
												__magic_name__      :       Tuple       						= (
												    """Expected the same number of rows for A and B. """
												    f'Instead found A of size {shape_a} and B of size {shape_b}'
												)
												raise ValueError(_A			)

						if shape_b[1] != shape_c[1]:
												__magic_name__      :       List[Any]       						= (
												    """Expected the same number of columns for B and C. """
												    f'Instead found B of size {shape_b} and C of size {shape_c}'
												)
												raise ValueError(_A			)

						__magic_name__      :       List[str]       						= pseudo_inv
						if a_inv is None:
												try:
																		__magic_name__      :       List[Any]       						= np.linalg.inv(_A			)
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""			)

						return mat_c - mat_b.T @ a_inv @ mat_b





class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				None:
							__magic_name__      :       Any       						= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
							__magic_name__      :       List[Any]       						= np.array([[0, 3], [3, 0], [2, 3]]			)
							__magic_name__      :       Dict       						= np.array([[2, 1], [6, 3]]			)

							__magic_name__      :       int       						= schur_complement(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= np.block([[a, b], [b.T, c]]			)

							__magic_name__      :       int       						= np.linalg.det(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= np.linalg.det(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= np.linalg.det(lowerCAmelCase__			)

							self.assertAlmostEqual(lowerCAmelCase__      ,       det_a * det_s			)
	def       __magic_name__    (    self			)							->				None:
							__magic_name__      :       Dict       						= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
							__magic_name__      :       Optional[int]       						= np.array([[0, 3], [3, 0], [2, 3]]			)
							__magic_name__      :       Dict       						= np.array([[2, 1], [6, 3]]			)

							with self.assertRaises(lowerCAmelCase__			):
													schur_complement(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)







	def       __magic_name__    (    self			)							->				None:
							__magic_name__      :       Optional[Any]       						= np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
							__magic_name__      :       int       						= np.array([[0, 3], [3, 0], [2, 3]]			)
							__magic_name__      :       List[str]       						= np.array([[2, 1, 3], [6, 3, 5]]			)

							with self.assertRaises(lowerCAmelCase__			):
													schur_complement(lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		unittest.main()








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
)
__magic_name__:					str							=     logging.getLogger(__name__)

def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       int       						= argparse.ArgumentParser(
						    description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""			)
						parser.add_argument("""--file_path""",   type=_A,   default="""data/dump.txt""",   help="""The path to the data."""			)
						parser.add_argument("""--tokenizer_type""",   type=_A,   default="""bert""",   choices=["""bert""", """roberta""", """gpt2"""]			)
						parser.add_argument("""--tokenizer_name""",   type=_A,   default="""bert-base-uncased""",   help="""The tokenizer to use."""			)
						parser.add_argument("""--dump_file""",   type=_A,   default="""data/dump""",   help="""The dump file prefix."""			)
						__magic_name__      :       Dict       						= parser.parse_args()

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

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

						__magic_name__      :       List[Any]       						= []
						__magic_name__      :       str       						= 0
						__magic_name__      :       str       						= 10000
						__magic_name__      :       Dict       						= time.time()
						for text in data:
												__magic_name__      :       Tuple       						= f'{bos} {text.strip()} {sep}'
												__magic_name__      :       Optional[int]       						= tokenizer.encode(_A,   add_special_tokens=_A			)
												rslt.append(_A			)

												iter += 1
												if iter % interval == 0:
																		__magic_name__      :       Union[str, Any]       						= time.time()
																		logger.info(f'{iter} examples processed. - {(end-start):.2f}s/{interval}expl'			)
																		__magic_name__      :       Any       						= time.time()
						logger.info("""Finished binarization"""			)
						logger.info(f'{len(_A			)} examples processed.'			)

						__magic_name__      :       Tuple       						= f'{args.dump_file}.{args.tokenizer_name}.pickle'
						__magic_name__      :       Tuple       						= tokenizer.vocab_size
						if vocab_size < (1 << 16):
												__magic_name__      :       Optional[int]       						= [np.uintaa(_A			) for d in rslt]
						else:
												__magic_name__      :       str       						= [np.intaa(_A			) for d in rslt]
						random.shuffle(rslt_			)
						logger.info(f'Dump to {dp_file}'			)
						with open(_A,   """wb"""			) as handle:
												pickle.dump(rslt_,   _A,   protocol=pickle.HIGHEST_PROTOCOL			)


if __name__ == "__main__":
		main()
