
"""simple docstring"""




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      UpperCamelCase	(       tf.keras.layers.Layer      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = None		,	lowercase__ = "longest"		,	lowercase__ = True		,	lowercase__ = 512		,	lowercase__ = None		,	lowercase__ = True		,	lowercase__ = True		,	lowercase__ = True		,	**lowercase__		,	)    ->    Optional[int]:
									"""simple docstring"""







									super().__init__()
									if use_fast_bert_tokenizer:
														SCREAMING_SNAKE_CASE				=	FastBertTokenizer(
														    lowercase__		,	token_out_type=tf.intaa		,	lower_case_nfd_strip_accents=lowercase__		,	**lowercase__			)
									else:
														SCREAMING_SNAKE_CASE				=	tf.lookup.StaticVocabularyTable(
														    tf.lookup.KeyValueTensorInitializer(
														        keys=lowercase__		,	key_dtype=tf.string		,	values=tf.range(tf.size(lowercase__		,	out_type=tf.intaa			)		,	dtype=tf.intaa			)		,	value_dtype=tf.intaa		,	)		,	num_oov_buckets=1		,	)
														SCREAMING_SNAKE_CASE				=	BertTokenizerLayer(
														    lowercase__		,	token_out_type=tf.intaa		,	lower_case=lowercase__		,	**lowercase__			)

									SCREAMING_SNAKE_CASE				=	vocab_list
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	cls_token_id or vocab_list.index('[CLS]'			)
									SCREAMING_SNAKE_CASE				=	sep_token_id or vocab_list.index('[SEP]'			)
									SCREAMING_SNAKE_CASE				=	pad_token_id or vocab_list.index('[PAD]'			)
									SCREAMING_SNAKE_CASE				=	ShrinkLongestTrimmer(max_length - 3		,	axis=1			)  # Allow room for special tokens
									SCREAMING_SNAKE_CASE				=	max_length
									SCREAMING_SNAKE_CASE				=	padding
									SCREAMING_SNAKE_CASE				=	truncation
									SCREAMING_SNAKE_CASE				=	pad_to_multiple_of
									SCREAMING_SNAKE_CASE				=	return_token_type_ids
									SCREAMING_SNAKE_CASE				=	return_attention_mask


				@classmethod
				def 							A					(				cls		,	lowercase__		,	**lowercase__			)    ->    List[Any]:  # noqa: F821
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	kwargs.pop('do_lower_case'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									SCREAMING_SNAKE_CASE				=	kwargs.pop('cls_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									SCREAMING_SNAKE_CASE				=	kwargs.pop('sep_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									SCREAMING_SNAKE_CASE				=	kwargs.pop('pad_token_id'		,	lowercase__			)
									SCREAMING_SNAKE_CASE				=	tokenizer.pad_token_id if pad_token_id is None else pad_token_id

									SCREAMING_SNAKE_CASE				=	tokenizer.get_vocab()
									SCREAMING_SNAKE_CASE				=	sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]		,	key=lambda lowercase__			: x[1]			)
									SCREAMING_SNAKE_CASE				=	[entry[0] for entry in vocab]
									return cls(
									    vocab_list=lowercase__		,	do_lower_case=lowercase__		,	cls_token_id=lowercase__		,	sep_token_id=lowercase__		,	pad_token_id=lowercase__		,	**lowercase__		,	)


				@classmethod
				def 							A					(				cls		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									try:
														SCREAMING_SNAKE_CASE				=	BertTokenizer.from_pretrained(lowercase__		,	*lowercase__		,	**lowercase__			)
									except:  # noqa: E722
														from .tokenization_bert_fast import BertTokenizerFast

														SCREAMING_SNAKE_CASE				=	BertTokenizerFast.from_pretrained(lowercase__		,	*lowercase__		,	**lowercase__			)
									return cls.from_tokenizer(lowercase__		,	**lowercase__			)


				def 							A					(				self		,	lowercase__			)    ->    Tuple:
									"""simple docstring"""







									if self.do_lower_case:
														SCREAMING_SNAKE_CASE				=	case_fold_utfa(lowercase__			)
									SCREAMING_SNAKE_CASE				=	self.tf_tokenizer.tokenize(lowercase__			)
									return tokens.merge_dims(1		,	-1			)


				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	lowercase__=None		,	)    ->    List[Any]:
									"""simple docstring"""







									if padding is None:
														SCREAMING_SNAKE_CASE				=	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:
														SCREAMING_SNAKE_CASE				=	self.max_length
									if truncation is None:
														SCREAMING_SNAKE_CASE				=	self.truncation
									if pad_to_multiple_of is None:
														SCREAMING_SNAKE_CASE				=	self.pad_to_multiple_of
									if return_token_type_ids is None:
														SCREAMING_SNAKE_CASE				=	self.return_token_type_ids
									if return_attention_mask is None:
														SCREAMING_SNAKE_CASE				=	self.return_attention_mask
									if not isinstance(lowercase__		,	tf.Tensor			):
														SCREAMING_SNAKE_CASE				=	tf.convert_to_tensor(lowercase__			)
									if text_pair is not None and not isinstance(lowercase__		,	tf.Tensor			):
														SCREAMING_SNAKE_CASE				=	tf.convert_to_tensor(lowercase__			)
									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:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	text[:, 0], text[:, 1]
									SCREAMING_SNAKE_CASE				=	self.unpaired_tokenize(lowercase__			)
									if text_pair is None:  # Unpaired text
														if truncation:
																			SCREAMING_SNAKE_CASE				=	text[:, : max_length - 2]  # Allow room for special tokens
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	combine_segments(
														    (text,)		,	start_of_sequence_id=self.cls_token_id		,	end_of_segment_id=self.sep_token_id			)
									else:  # Paired text
														SCREAMING_SNAKE_CASE				=	self.unpaired_tokenize(lowercase__			)
														if truncation:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.paired_trimmer.trim([text, text_pair]			)
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	combine_segments(
														    (text, text_pair)		,	start_of_sequence_id=self.cls_token_id		,	end_of_segment_id=self.sep_token_id			)
									if padding == "longest":
														SCREAMING_SNAKE_CASE				=	input_ids.bounding_shape(axis=1			)
														if pad_to_multiple_of is not None:
																			# No ceiling division in tensorflow, so we negate floordiv instead
																			SCREAMING_SNAKE_CASE				=	pad_to_multiple_of * (-tf.math.floordiv(-pad_length		,	lowercase__			))
									else:
														SCREAMING_SNAKE_CASE				=	max_length

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	pad_model_inputs(lowercase__		,	max_seq_length=lowercase__		,	pad_value=self.pad_token_id			)
									SCREAMING_SNAKE_CASE				=	{'input_ids': input_ids}
									if return_attention_mask:
														SCREAMING_SNAKE_CASE				=	attention_mask
									if return_token_type_ids:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	pad_model_inputs(
														    lowercase__		,	max_seq_length=lowercase__		,	pad_value=self.pad_token_id			)
														SCREAMING_SNAKE_CASE				=	token_type_ids
									return output




				def 							A					(				self			)    ->    List[str]:
									"""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,
									}


"""simple docstring"""




from __future__ import annotations

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''			)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found'''			)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	[[2, 5, 7], [4, 8, 1_3], [9, 1_1, 1_5], [1_2, 1_7, 2_0]]
					SCREAMING_SNAKE_CASE				=	int(input('Enter the element to be searched:'			)			)
					print(SCREAMING_SNAKE_CASE_			)
					search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE_,     len(SCREAMING_SNAKE_CASE_			),     len(mat[0]			),     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
