




'''simple docstring'''







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
						from .tokenization_deberta_va import DebertaVaTokenizer
else:
						_lowerCamelCase				:		Optional[int]				      =    None

_lowerCamelCase				:		Optional[int]				      =    logging.get_logger(__name__)

_lowerCamelCase				:		Union[str, Any]				      =    {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase				:		List[Any]				      =    {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

_lowerCamelCase				:		str				      =    {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

_lowerCamelCase				:		Union[str, Any]				      =    {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					VOCAB_FILES_NAMES
					lowercase__       =					PRETRAINED_VOCAB_FILES_MAP
					lowercase__       =					PRETRAINED_INIT_CONFIGURATION
					lowercase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__       =					DebertaVaTokenizer






					def __init__(       self,    __UpperCamelCase=None,    __UpperCamelCase=None,    __UpperCamelCase=False,    __UpperCamelCase=False,    __UpperCamelCase="[CLS]",    __UpperCamelCase="[SEP]",    __UpperCamelCase="[UNK]",    __UpperCamelCase="[SEP]",    __UpperCamelCase="[PAD]",    __UpperCamelCase="[CLS]",    __UpperCamelCase="[MASK]",    **__UpperCamelCase,    ):
							super().__init__(
							    __UpperCamelCase,    tokenizer_file=__UpperCamelCase,    do_lower_case=__UpperCamelCase,    bos_token=__UpperCamelCase,    eos_token=__UpperCamelCase,    unk_token=__UpperCamelCase,    sep_token=__UpperCamelCase,    pad_token=__UpperCamelCase,    cls_token=__UpperCamelCase,    mask_token=__UpperCamelCase,    split_by_punct=__UpperCamelCase,    **__UpperCamelCase,    )

							_snake_case      			=			do_lower_case
							_snake_case      			=			split_by_punct
							_snake_case      			=			vocab_file
							_snake_case      			=			False if not self.vocab_file else True






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None					):

							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_snake_case      			=			[self.cls_token_id]
							_snake_case      			=			[self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None,    __UpperCamelCase=False					):

							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__UpperCamelCase,    token_ids_a=__UpperCamelCase,    already_has_special_tokens=__UpperCamelCase					)

							if token_ids_a is not None:
									return [1] + ([0] * len(__UpperCamelCase					)) + [1] + ([0] * len(__UpperCamelCase					)) + [1]
							return [1] + ([0] * len(__UpperCamelCase					)) + [1]






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None					):
							_snake_case      			=			[self.sep_token_id]
							_snake_case      			=			[self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep					) * [0]
							return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
									    "tokenizer."					)

							if not os.path.isdir(__UpperCamelCase					):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
									return
							_snake_case      			=			os.path.join(
							    __UpperCamelCase,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCamelCase					):
									copyfile(self.vocab_file,    __UpperCamelCase					)

							return (out_vocab_file,)











'''simple docstring'''







import numpy as np






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		_snake_case			,       _snake_case      			=			a.shape
		_snake_case      			=			min(lowercase_		,				lowercase_    )
		_snake_case      			=			np.eye(lowercase_    )
		_snake_case      			=			a.copy()

		for k in range(t - 1    ):
				# select a column of modified matrix A':
				_snake_case      			=			r[k:, [k]]
				# construct first basis vector
				_snake_case      			=			np.zeros_like(lowercase_    )
				_snake_case      			=			1.0
				# determine scaling factor
				_snake_case      			=			np.linalg.norm(lowercase_    )
				# construct vector v for Householder reflection
				_snake_case      			=			x + np.sign(x[0]    ) * alpha * ea
				v /= np.linalg.norm(lowercase_    )

				# construct the Householder matrix
				_snake_case      			=			np.eye(m - k    ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				_snake_case      			=			np.block([[np.eye(lowercase_    ), np.zeros((k, m - k)    )], [np.zeros((m - k, k)    ), q_k]]    )

				_snake_case      			=			q @ q_k.T
				_snake_case      			=			q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()





