
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer





class 						lowercase       (  tf.keras.layers.Layer					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = "longest" ,	SCREAMING_SNAKE_CASE__ = True ,	SCREAMING_SNAKE_CASE__ = 512 ,	SCREAMING_SNAKE_CASE__ = None ,	SCREAMING_SNAKE_CASE__ = True ,	SCREAMING_SNAKE_CASE__ = True ,	SCREAMING_SNAKE_CASE__ = True ,	**SCREAMING_SNAKE_CASE__ ,	):
								"""simple docstring"""







								super().__init__()
								if use_fast_bert_tokenizer:
											lowerCAmelCase__ :    Optional[Any]				      =			FastBertTokenizer(
											    _lowerCAmelCase ,	token_out_type=tf.intaa ,	lower_case_nfd_strip_accents=_lowerCAmelCase ,	**_lowerCAmelCase    )
								else:
											lowerCAmelCase__ :    Union[str, Any]				      =			tf.lookup.StaticVocabularyTable(
											    tf.lookup.KeyValueTensorInitializer(
											        keys=_lowerCAmelCase ,	key_dtype=tf.string ,	values=tf.range(tf.size(_lowerCAmelCase ,	out_type=tf.intaa    ) ,	dtype=tf.intaa    ) ,	value_dtype=tf.intaa ,	) ,	num_oov_buckets=1 ,	)
											lowerCAmelCase__ :    int				      =			BertTokenizerLayer(
											    _lowerCAmelCase ,	token_out_type=tf.intaa ,	lower_case=_lowerCAmelCase ,	**_lowerCAmelCase    )

								lowerCAmelCase__ :    Tuple				      =			vocab_list
								lowerCAmelCase__ :    Any				      =			do_lower_case
								lowerCAmelCase__ :    Optional[Any]				      =			cls_token_id or vocab_list.index('''[CLS]'''    )
								lowerCAmelCase__ :    List[str]				      =			sep_token_id or vocab_list.index('''[SEP]'''    )
								lowerCAmelCase__ :    int				      =			pad_token_id or vocab_list.index('''[PAD]'''    )
								lowerCAmelCase__ :    Optional[Any]				      =			ShrinkLongestTrimmer(max_length - 3 ,	axis=1    )  # Allow room for special tokens
								lowerCAmelCase__ :    Any				      =			max_length
								lowerCAmelCase__ :    Optional[int]				      =			padding
								lowerCAmelCase__ :    List[Any]				      =			truncation
								lowerCAmelCase__ :    str				      =			pad_to_multiple_of
								lowerCAmelCase__ :    int				      =			return_token_type_ids
								lowerCAmelCase__ :    Tuple				      =			return_attention_mask
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__ ,	**SCREAMING_SNAKE_CASE__    ):  # noqa: F821
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			kwargs.pop('''do_lower_case''' ,	_lowerCAmelCase    )
								lowerCAmelCase__ :    List[str]				      =			tokenizer.do_lower_case if do_lower_case is None else do_lower_case
								lowerCAmelCase__ :    Dict				      =			kwargs.pop('''cls_token_id''' ,	_lowerCAmelCase    )
								lowerCAmelCase__ :    Union[str, Any]				      =			tokenizer.cls_token_id if cls_token_id is None else cls_token_id
								lowerCAmelCase__ :    int				      =			kwargs.pop('''sep_token_id''' ,	_lowerCAmelCase    )
								lowerCAmelCase__ :    Optional[Any]				      =			tokenizer.sep_token_id if sep_token_id is None else sep_token_id
								lowerCAmelCase__ :    Union[str, Any]				      =			kwargs.pop('''pad_token_id''' ,	_lowerCAmelCase    )
								lowerCAmelCase__ :    int				      =			tokenizer.pad_token_id if pad_token_id is None else pad_token_id

								lowerCAmelCase__ :    str				      =			tokenizer.get_vocab()
								lowerCAmelCase__ :    Union[str, Any]				      =			sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,	key=lambda SCREAMING_SNAKE_CASE__    : x[1]    )
								lowerCAmelCase__ :    Dict				      =			[entry[0] for entry in vocab]
								return cls(
								    vocab_list=_lowerCAmelCase ,	do_lower_case=_lowerCAmelCase ,	cls_token_id=_lowerCAmelCase ,	sep_token_id=_lowerCAmelCase ,	pad_token_id=_lowerCAmelCase ,	**_lowerCAmelCase ,	)
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__ ,	*SCREAMING_SNAKE_CASE__ ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								try:
											lowerCAmelCase__ :    Tuple				      =			BertTokenizer.from_pretrained(_lowerCAmelCase ,	*_lowerCAmelCase ,	**_lowerCAmelCase    )
								except:  # noqa: E722
											from .tokenization_bert_fast import BertTokenizerFast

											lowerCAmelCase__ :    Any				      =			BertTokenizerFast.from_pretrained(_lowerCAmelCase ,	*_lowerCAmelCase ,	**_lowerCAmelCase    )
								return cls.from_tokenizer(_lowerCAmelCase ,	**_lowerCAmelCase    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								if self.do_lower_case:
											lowerCAmelCase__ :    Union[str, Any]				      =			case_fold_utfa(_lowerCAmelCase    )
								lowerCAmelCase__ :    Tuple				      =			self.tf_tokenizer.tokenize(_lowerCAmelCase    )
								return tokens.merge_dims(1 ,	-1    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__=None ,	):
								"""simple docstring"""







								if padding is None:
											lowerCAmelCase__ :    List[Any]				      =			self.padding
								if padding not in ("longest", "max_length"):
											raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''    )
								if max_length is not None and text_pair is not None:
											# Because we have to instantiate a Trimmer to do it properly
											raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''    )
								if max_length is None:
											lowerCAmelCase__ :    int				      =			self.max_length
								if truncation is None:
											lowerCAmelCase__ :    Optional[int]				      =			self.truncation
								if pad_to_multiple_of is None:
											lowerCAmelCase__ :    Optional[Any]				      =			self.pad_to_multiple_of
								if return_token_type_ids is None:
											lowerCAmelCase__ :    Union[str, Any]				      =			self.return_token_type_ids
								if return_attention_mask is None:
											lowerCAmelCase__ :    Optional[Any]				      =			self.return_attention_mask
								if not isinstance(_lowerCAmelCase ,	tf.Tensor    ):
											lowerCAmelCase__ :    Any				      =			tf.convert_to_tensor(_lowerCAmelCase    )
								if text_pair is not None and not isinstance(_lowerCAmelCase ,	tf.Tensor    ):
											lowerCAmelCase__ :    str				      =			tf.convert_to_tensor(_lowerCAmelCase    )
								if text_pair is not None:
											if text.shape.rank > 1:
														raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''    )
											if text_pair.shape.rank > 1:
														raise ValueError('''text_pair should not be multidimensional!'''    )
								if text.shape.rank == 2:
											lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			text[:, 0], text[:, 1]
								lowerCAmelCase__ :    str				      =			self.unpaired_tokenize(_lowerCAmelCase    )
								if text_pair is None:  # Unpaired text
											if truncation:
														lowerCAmelCase__ :    Optional[int]				      =			text[:, : max_length - 2]  # Allow room for special tokens
											lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[int]				      =			combine_segments(
											    (text,) ,	start_of_sequence_id=self.cls_token_id ,	end_of_segment_id=self.sep_token_id    )
								else:  # Paired text
											lowerCAmelCase__ :    Any				      =			self.unpaired_tokenize(_lowerCAmelCase    )
											if truncation:
														lowerCAmelCase__      ,				lowerCAmelCase__ :    List[Any]				      =			self.paired_trimmer.trim([text, text_pair]    )
											lowerCAmelCase__      ,				lowerCAmelCase__ :    List[Any]				      =			combine_segments(
											    (text, text_pair) ,	start_of_sequence_id=self.cls_token_id ,	end_of_segment_id=self.sep_token_id    )
								if padding == "longest":
											lowerCAmelCase__ :    List[str]				      =			input_ids.bounding_shape(axis=1    )
											if pad_to_multiple_of is not None:
														# No ceiling division in tensorflow, so we negate floordiv instead
														lowerCAmelCase__ :    List[str]				      =			pad_to_multiple_of * (-tf.math.floordiv(-pad_length ,	_lowerCAmelCase    ))
								else:
											lowerCAmelCase__ :    List[str]				      =			max_length

								lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			pad_model_inputs(_lowerCAmelCase ,	max_seq_length=_lowerCAmelCase ,	pad_value=self.pad_token_id    )
								lowerCAmelCase__ :    Dict				      =			{'''input_ids''': input_ids}
								if return_attention_mask:
											lowerCAmelCase__ :    List[str]				      =			attention_mask
								if return_token_type_ids:
											lowerCAmelCase__      ,				lowerCAmelCase__ :    Union[str, Any]				      =			pad_model_inputs(
											    _lowerCAmelCase ,	max_seq_length=_lowerCAmelCase ,	pad_value=self.pad_token_id    )
											lowerCAmelCase__ :    str				      =			token_type_ids
								return output
					def lowercase_			(		self    ):
								"""simple docstring"""







								return {
								    "vocab_list": self.vocab_list,
								    "do_lower_case": self.do_lower_case,
								    "cls_token_id": self.cls_token_id,
								    "sep_token_id": self.sep_token_id,
								    "pad_token_id": self.pad_token_id,
								}





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



