
"""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							):




      def __init__(							self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = "longest"				,    lowercase_ = True				,    lowercase_ = 5_1_2				,    lowercase_ = None				,    lowercase_ = True				,    lowercase_ = True				,    lowercase_ = True				,    **lowercase_				,    )  ->   List[Any]:
          super().__init__()
          if use_fast_bert_tokenizer:
              UpperCAmelCase       =   FastBertTokenizer(
                  lowercase_				,    token_out_type=tf.intaa				,    lower_case_nfd_strip_accents=lowercase_				,    **lowercase_							)
          else:
              UpperCAmelCase       =   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				,    )
              UpperCAmelCase       =   BertTokenizerLayer(
                  lowercase_				,    token_out_type=tf.intaa				,    lower_case=lowercase_				,    **lowercase_							)

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




      @classmethod
      def       a_ (							cls				,    lowercase_				,    **lowercase_							)  ->   Union[str, Any]:  # noqa: F821
          UpperCAmelCase       =   kwargs.pop('do_lower_case'				,    lowercase_							)
          UpperCAmelCase       =   tokenizer.do_lower_case if do_lower_case is None else do_lower_case
          UpperCAmelCase       =   kwargs.pop('cls_token_id'				,    lowercase_							)
          UpperCAmelCase       =   tokenizer.cls_token_id if cls_token_id is None else cls_token_id
          UpperCAmelCase       =   kwargs.pop('sep_token_id'				,    lowercase_							)
          UpperCAmelCase       =   tokenizer.sep_token_id if sep_token_id is None else sep_token_id
          UpperCAmelCase       =   kwargs.pop('pad_token_id'				,    lowercase_							)
          UpperCAmelCase       =   tokenizer.pad_token_id if pad_token_id is None else pad_token_id

          UpperCAmelCase       =   tokenizer.get_vocab()
          UpperCAmelCase       =   sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,    key=lambda lowercase_							: x[1]							)
          UpperCAmelCase       =   [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_							)  ->   Tuple:
          try:
              UpperCAmelCase       =   BertTokenizer.from_pretrained(lowercase_				,    *lowercase_				,    **lowercase_							)
          except:  # noqa: E722
              from .tokenization_bert_fast import BertTokenizerFast

              UpperCAmelCase       =   BertTokenizerFast.from_pretrained(lowercase_				,    *lowercase_				,    **lowercase_							)
          return cls.from_tokenizer(lowercase_				,    **lowercase_							)




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

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




      def       a_ (							self							)  ->   Optional[int]:
          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

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

SCREAMING_SNAKE_CASE_         =  180




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   k_size // 2
    UpperCAmelCase     ,							UpperCAmelCase       =   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    UpperCAmelCase       =   (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(lowerCAmelCase	) + np.square(lowerCAmelCase	)) / (2 * np.square(lowerCAmelCase	))	)
    )
    return g




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   np.zeros(lowerCAmelCase	)

    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            UpperCAmelCase       =   gradient_direction[row, col]

            if (
                0 <= direction < PI / 8
                or 15 * PI / 8 <= direction <= 2 * PI
                or 7 * PI / 8 <= direction <= 9 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row, col - 1]
                UpperCAmelCase       =   sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row + 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col]
                UpperCAmelCase       =   sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    UpperCAmelCase       =   sobel_grad[row, col]

    return destination




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   List[str]	)	-> Any:






    """simple docstring"""




    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            if destination[row, col] >= threshold_high:
                UpperCAmelCase       =   strong
            elif destination[row, col] <= threshold_low:
                UpperCAmelCase       =   0
            else:
                UpperCAmelCase       =   weak




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> List[str]:






    """simple docstring"""




    for row in range(1	,			image_shape[0]	):
        for col in range(1	,			image_shape[1]	):
            if destination[row, col] == weak:
                if 255 in (
                    destination[row, col + 1],
                    destination[row, col - 1],
                    destination[row - 1, col],
                    destination[row + 1, col],
                    destination[row - 1, col - 1],
                    destination[row + 1, col - 1],
                    destination[row - 1, col + 1],
                    destination[row + 1, col + 1],
                ):
                    UpperCAmelCase       =   strong
                else:
                    UpperCAmelCase       =   0




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int=15	,			lowerCAmelCase			:   Dict=30	,			lowerCAmelCase			:   str=128	,			lowerCAmelCase			:   Optional[Any]=255	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   img_convolve(lowerCAmelCase	,			gen_gaussian_kernel(9	,			sigma=1.4	)	)
    # get the gradient and degree by sobel_filter
    UpperCAmelCase     ,							UpperCAmelCase       =   sobel_filter(lowerCAmelCase	)
    UpperCAmelCase       =   PI + np.radadeg(lowerCAmelCase	)

    UpperCAmelCase       =   suppress_non_maximum(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	)

    detect_high_low_threshold(
        image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    track_edge(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    return destination


if __name__ == "__main__":
   # read original image in gray mode
   SCREAMING_SNAKE_CASE_         =  cva.imread(r'''../image_data/lena.jpg''', 0)
   # canny edge detection
   SCREAMING_SNAKE_CASE_         =  canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)



