




from typing import Any

import numpy as np




def     __magic_name__       (				A	:      np.ndarray     ):






			'''simple docstring'''







			return np.array_equal(A,						matrix.conjugate().T     )




def     __magic_name__       (				A	:      np.ndarray,						A	:      np.ndarray     ):






			'''simple docstring'''







			a					=    v.conjugate().T
			a					=    v_star.dot(A     )
			assert isinstance(A,						np.ndarray     )
			return (v_star_dot.dot(A     )) / (v_star.dot(A     ))




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]     )
			a					=    np.array([[1], [2], [3]]     )
			assert is_hermitian(A     ), F"""{a} is not hermitian."""
			print(rayleigh_quotient(A,						A     )     )

			a					=    np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]     )
			assert is_hermitian(A     ), F"""{a} is not hermitian."""
			assert rayleigh_quotient(A,						A     ) == float(3     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		tests()












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






