





"""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 					A_			(  tf.keras.layers.Layer     ):



							"""simple docstring"""






							def __init__(						self						:Any  ,							lowerCamelCase_						:List  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:int = None  ,							lowerCamelCase_						:int = None  ,							lowerCamelCase_						:int = None  ,							lowerCamelCase_						:str = "longest"  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:int = 512  ,							lowerCamelCase_						:int = None  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:bool = True  ,							lowerCamelCase_						:bool = True  ,							**lowerCamelCase_						:Any  ,							):





										"""simple docstring"""

										super().__init__()
										if use_fast_bert_tokenizer:
													lowerCamelCase__			:  Optional[int]  =FastBertTokenizer(
													    lowerCamelCase_  ,							token_out_type=tf.intaa  ,							lower_case_nfd_strip_accents=lowerCamelCase_  ,							**lowerCamelCase_ )
										else:
													lowerCamelCase__			:  str  =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__			:  List[str]  =BertTokenizerLayer(
													    lowerCamelCase_  ,							token_out_type=tf.intaa  ,							lower_case=lowerCamelCase_  ,							**lowerCamelCase_ )

										lowerCamelCase__			:  int  =vocab_list
										lowerCamelCase__			:  int  =do_lower_case
										lowerCamelCase__			:  str  =cls_token_id or vocab_list.index('[CLS]' )
										lowerCamelCase__			:  Optional[Any]  =sep_token_id or vocab_list.index('[SEP]' )
										lowerCamelCase__			:  Union[str, Any]  =pad_token_id or vocab_list.index('[PAD]' )
										lowerCamelCase__			:  List[Any]  =ShrinkLongestTrimmer(max_length - 3  ,							axis=1 )  # Allow room for special tokens
										lowerCamelCase__			:  Tuple  =max_length
										lowerCamelCase__			:  Dict  =padding
										lowerCamelCase__			:  List[Any]  =truncation
										lowerCamelCase__			:  Dict  =pad_to_multiple_of
										lowerCamelCase__			:  Tuple  =return_token_type_ids
										lowerCamelCase__			:  Optional[int]  =return_attention_mask





							@classmethod
							def    UpperCAmelCase__							(						cls						:Dict  ,							lowerCamelCase_						:"PreTrainedTokenizerBase"  ,							**lowerCamelCase_						:List[Any] ):  # noqa: F821





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =kwargs.pop('do_lower_case'  ,							lowerCamelCase_ )
										lowerCamelCase__			:  int  =tokenizer.do_lower_case if do_lower_case is None else do_lower_case
										lowerCamelCase__			:  Dict  =kwargs.pop('cls_token_id'  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[int]  =tokenizer.cls_token_id if cls_token_id is None else cls_token_id
										lowerCamelCase__			:  Any  =kwargs.pop('sep_token_id'  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[Any]  =tokenizer.sep_token_id if sep_token_id is None else sep_token_id
										lowerCamelCase__			:  Dict  =kwargs.pop('pad_token_id'  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[int]  =tokenizer.pad_token_id if pad_token_id is None else pad_token_id

										lowerCamelCase__			:  List[str]  =tokenizer.get_vocab()
										lowerCamelCase__			:  List[Any]  =sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  ,							key=lambda lowerCamelCase_ : x[1] )
										lowerCamelCase__			:  List[str]  =[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    UpperCAmelCase__							(						cls						:List[Any]  ,							lowerCamelCase_						:Union[str, os.PathLike]  ,							*lowerCamelCase_						:Dict  ,							**lowerCamelCase_						:Union[str, Any] ):





										"""simple docstring"""

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

													lowerCamelCase__			:  List[str]  =BertTokenizerFast.from_pretrained(lowerCamelCase_  ,							*lowerCamelCase_  ,							**lowerCamelCase_ )
										return cls.from_tokenizer(lowerCamelCase_  ,							**lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Union[str, Any]  ,							lowerCamelCase_						:List[str] ):





										"""simple docstring"""

										if self.do_lower_case:
													lowerCamelCase__			:  Any  =case_fold_utfa(lowerCamelCase_ )
										lowerCamelCase__			:  Union[str, Any]  =self.tf_tokenizer.tokenize(lowerCamelCase_ )
										return tokens.merge_dims(1  ,							-1 )





							def    UpperCAmelCase__							(						self						:str  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:Any=None  ,							lowerCamelCase_						:Tuple=None  ,							lowerCamelCase_						:int=None  ,							lowerCamelCase_						:Any=None  ,							lowerCamelCase_						:Optional[int]=None  ,							lowerCamelCase_						:Dict=None  ,							lowerCamelCase_						:str=None  ,							):





										"""simple docstring"""

										if padding is None:
													lowerCamelCase__			:  List[str]  =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__			:  Tuple  =self.max_length
										if truncation is None:
													lowerCamelCase__			:  Tuple  =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__			:  Optional[int]  =self.return_token_type_ids
										if return_attention_mask is None:
													lowerCamelCase__			:  Union[str, 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__			:  Dict  =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__			:  List[str]  =text[:, 0], text[:, 1]
										lowerCamelCase__			:  int  =self.unpaired_tokenize(lowerCamelCase_ )
										if text_pair is None:  # Unpaired text
													if truncation:
																lowerCamelCase__			:  Dict  =text[:, : max_length - 2]  # Allow room for special tokens
													lowerCamelCase__			:  Any  =combine_segments(
													    (text,)  ,							start_of_sequence_id=self.cls_token_id  ,							end_of_segment_id=self.sep_token_id )
										else:  # Paired text
													lowerCamelCase__			:  List[str]  =self.unpaired_tokenize(lowerCamelCase_ )
													if truncation:
																lowerCamelCase__			:  str  =self.paired_trimmer.trim([text, text_pair] )
													lowerCamelCase__			:  List[str]  =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__			:  Optional[int]  =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__			:  Tuple  =pad_to_multiple_of * (-tf.math.floordiv(-pad_length  ,							lowerCamelCase_ ))
										else:
													lowerCamelCase__			:  Union[str, Any]  =max_length

										lowerCamelCase__			:  Dict  =pad_model_inputs(lowerCamelCase_  ,							max_seq_length=lowerCamelCase_  ,							pad_value=self.pad_token_id )
										lowerCamelCase__			:  int  ={'input_ids': input_ids}
										if return_attention_mask:
													lowerCamelCase__			:  List[str]  =attention_mask
										if return_token_type_ids:
													lowerCamelCase__			:  Any  =pad_model_inputs(
													    lowerCamelCase_  ,							max_seq_length=lowerCamelCase_  ,							pad_value=self.pad_token_id )
													lowerCamelCase__			:  Any  =token_type_ids
										return output





							def    UpperCAmelCase__							(						self						:int ):





										"""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"""


import cva
import numpy as np






def      lowerCAmelCase_     (	snake_case_	:    np.ndarray							,					snake_case_	:    int							,					snake_case_	:    int							,					snake_case_	:    int    )     ->int:

			try:
						return int(image[x_coordinate][y_coordinate] >= center    )
			except (IndexError, TypeError):
						return 0






def      lowerCAmelCase_     (	snake_case_	:    np.ndarray							,					snake_case_	:    int							,					snake_case_	:    int    )     ->int:
			lowerCamelCase__			:  Optional[int]  =image[x_coordinate][y_coordinate]
			lowerCamelCase__			:  int  =[1, 2, 4, 8, 1_6, 3_2, 6_4, 1_2_8]

			# skip get_neighbors_pixel if center is null
			if center is None:
						return 0

			# Starting from the top right, assigning value to pixels clockwise
			lowerCamelCase__			:  int  =[
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					snake_case_							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					snake_case_							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					y_coordinate + 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					snake_case_							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate + 1							,					y_coordinate - 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					snake_case_							,					y_coordinate - 1							,					snake_case_    ),
			    get_neighbors_pixel(snake_case_							,					x_coordinate - 1							,					y_coordinate - 1							,					snake_case_    ),
			]

			# Converting the binary value to decimal.
			return sum(
			    binary_value * power for binary_value, power in zip(snake_case_							,					snake_case_    )    )


if __name__ == "__main__":
							# Reading the image and converting it to grayscale.
							lowerCAmelCase			=		cva.imread(
							    """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
							)

							# Create a numpy array as the same height and width of read image
							lowerCAmelCase			=		np.zeros((image.shape[0], image.shape[1]))

							# Iterating through the image and calculating the
							# local binary pattern value for each pixel.
							for i in range(0, image.shape[0]):
														for j in range(0, image.shape[1]):
																					lowerCAmelCase			=		local_binary_value(image, i, j)

							cva.imshow("""local binary pattern""", lbp_image)
							cva.waitKey(0)
							cva.destroyAllWindows()