


import numpy as np


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 1e-12    ,       lowercase__ = 100    ,       ):
						assert np.shape(lowercase__      )[0] == np.shape(lowercase__      )[1]
						# Ensure proper dimensionality.
						assert np.shape(lowercase__      )[0] == np.shape(lowercase__      )[0]
						# Ensure inputs are either both complex or both real
						assert np.iscomplexobj(lowercase__      ) == np.iscomplexobj(lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.iscomplexobj(lowercase__      )
						if is_complex:
												# Ensure complex input_matrix is Hermitian
												assert np.array_equal(lowercase__    ,       input_matrix.conj().T      )

						# Set convergence to False. Will define convergence when we exceed max_iterations
						# or when we have small changes from one iteration to next.

						__SCREAMING_SNAKE_CASE						:     Dict					=		False
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		0
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     Dict					=		1e12

						while not convergence:
												# Multiple matrix by the vector.
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.dot(lowercase__    ,       lowercase__      )
												# Normalize the resulting output vector.
												__SCREAMING_SNAKE_CASE						:     int					=		w / np.linalg.norm(lowercase__      )
												# Find rayleigh quotient
												# (faster than usual b/c we know vector is normalized already)
												__SCREAMING_SNAKE_CASE						:     str					=		vector.conj().T if is_complex else vector.T
												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		np.dot(lowercase__    ,       np.dot(lowercase__    ,       lowercase__      )      )

												# Check convergence.
												__SCREAMING_SNAKE_CASE						:     int					=		np.abs(lambda_ - lambda_previous      ) / lambda_
												iterations += 1

												if error <= error_tol or iterations >= max_iterations:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		True

												__SCREAMING_SNAKE_CASE						:     Tuple					=		lambda_

						if is_complex:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		np.real(lambda_      )

						return lambda_, vector


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		np.array([41, 4, 20]      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		real_input_matrix.astype(np.complexaaa      )
						__SCREAMING_SNAKE_CASE						:     str					=		np.triu(1J * complex_input_matrix    ,       1      )
						complex_input_matrix += imag_matrix
						complex_input_matrix += -1 * imag_matrix.T
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		np.array([41, 4, 20]      ).astype(np.complexaaa      )

						for problem_type in ["real", "complex"]:
												if problem_type == "real":
																		__SCREAMING_SNAKE_CASE						:     Any					=		real_input_matrix
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		real_vector
												elif problem_type == "complex":
																		__SCREAMING_SNAKE_CASE						:     List[str]					=		complex_input_matrix
																		__SCREAMING_SNAKE_CASE						:     str					=		complex_vector

												# Our implementation.
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     int					=		power_iteration(lowercase__    ,       lowercase__      )

												# Numpy implementation.

												# Get eigenvalues and eigenvectors using built-in numpy
												# eigh (eigh used for symmetric or hermetian matrices).
												__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.linalg.eigh(lowercase__      )
												# Last eigenvalue is the maximum one.
												__SCREAMING_SNAKE_CASE						:     List[str]					=		eigen_values[-1]
												# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		eigen_vectors[:, -1]

												# Check our implementation and numpy gives close answers.
												assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
												# Take absolute values element wise of each eigenvector.
												# as they are only unique to a minus sign.
												assert np.linalg.norm(np.abs(lowercase__      ) - np.abs(lowercase__      )      ) <= 1e-6


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						test_power_iteration()







"""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
)
a						: Union[str, Any]     	=			logging.getLogger(__name__)






def       _SCREAMING_SNAKE_CASE		(	) ->Tuple:






	'''simple docstring'''






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

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

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

	a      :		Optional[Any]	=				[]
	a      :		Optional[Any]	=				0
	a      :		int	=				1_0000
	a      :		Dict	=				time.time()
	for text in data:
		a      :		List[Any]	=				F"""{bos} {text.strip()} {sep}"""
		a      :		Optional[int]	=				tokenizer.encode(_lowercase    ,       add_special_tokens=_lowercase					)
		rslt.append(_lowercase					)

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

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


if __name__ == "__main__":
		main()


