


'''simple docstring'''







import cva
import numpy as np
class 	UpperCamelCase__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            if k in (0.04, 0.06):
                   _lowerCAmelCase    :							int   			=       k
                   _lowerCAmelCase    :							Optional[int]   			=       window_size
            else:
                   raise ValueError('invalid k value'   )







     def __str__( self   ):

            '''simple docstring'''

            return str(self.k   )







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       cva.imread(snake_case__      ,	0   )
            _lowerCAmelCase    :							Optional[Any]   			=       img.shape
            _lowerCAmelCase    :							list[list[int]]   			=       []
            _lowerCAmelCase    :							Optional[int]   			=       img.copy()
            _lowerCAmelCase    :							str   			=       cva.cvtColor(snake_case__      ,	cva.COLOR_GRAY2RGB   )
            _lowerCAmelCase    :							Optional[Any]   			=       np.gradient(snake_case__   )
            _lowerCAmelCase    :							Any   			=       dx**2
            _lowerCAmelCase    :							Tuple   			=       dy**2
            _lowerCAmelCase    :							str   			=       dx * dy
            _lowerCAmelCase    :							Union[str, Any]   			=       0.04
            _lowerCAmelCase    :							List[str]   			=       self.window_size // 2
            for y in range(snake_case__      ,	h - offset   ):
                   for x in range(snake_case__      ,	w - offset   ):
                          _lowerCAmelCase    :							Union[str, Any]   			=       ixx[
                              y - offset : y + offset + 1, x - offset : x + offset + 1
                          ].sum()
                          _lowerCAmelCase    :							Dict   			=       iyy[
                              y - offset : y + offset + 1, x - offset : x + offset + 1
                          ].sum()
                          _lowerCAmelCase    :							int   			=       ixy[
                              y - offset : y + offset + 1, x - offset : x + offset + 1
                          ].sum()

                          _lowerCAmelCase    :							Optional[Any]   			=       (wxx * wyy) - (wxy**2)
                          _lowerCAmelCase    :							Optional[Any]   			=       wxx + wyy
                          _lowerCAmelCase    :							Optional[Any]   			=       det - k * (trace**2)
                          # Can change the value
                          if r > 0.5:
                                 corner_list.append([x, y, r]   )
                                 color_img.itemset((y, x, 0)      ,	0   )
                                 color_img.itemset((y, x, 1)      ,	0   )
                                 color_img.itemset((y, x, 2)      ,	255   )
            return color_img, corner_list


if __name__ == "__main__":
       lowerCAmelCase    :     Tuple					=						HarrisCorner(0.04, 3)
       lowerCAmelCase	,						lowerCAmelCase    :     List[Any]					=						edge_detect.detect("""path_to_image""")
       cva.imwrite("""detect.png""", color_img)





'''simple docstring'''







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None   ):

            '''simple docstring'''

            super().__init__()
            _lowerCAmelCase    :							Union[str, Any]   			=       pad_token_id
            _lowerCAmelCase    :							List[Any]   			=       max_length
            _lowerCAmelCase    :							Tuple   			=       vocab
            _lowerCAmelCase    :							str   			=       merges
            _lowerCAmelCase    :							List[str]   			=       BytePairTokenizer(snake_case__      ,	snake_case__      ,	sequence_length=snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       [' '.join(snake_case__   ) for m in tokenizer.bpe_ranks.keys()]
            _lowerCAmelCase    :							Any   			=       tokenizer.get_vocab()
            return cls(snake_case__      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       GPTaTokenizer.from_pretrained(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            return cls.from_tokenizer(snake_case__      ,	*snake_case__      ,	**snake_case__   )







     @classmethod
     def a	( cls      ,	snake_case__   ):

            '''simple docstring'''

            return cls(**snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return {
                "vocab": self.vocab,
                "merges": self.merges,
                "max_length": self.max_length,
                "pad_token_id": self.pad_token_id,
            }







     def a	( self      ,	snake_case__      ,	snake_case__ = None   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.tf_tokenizer(snake_case__   )
            _lowerCAmelCase    :							str   			=       tf.ones_like(snake_case__   )

            if self.pad_token_id is not None:
                   # pad the tokens up to max length
                   _lowerCAmelCase    :							Optional[int]   			=       max_length if max_length is not None else self.max_length

                   if max_length is not None:
                          _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       pad_model_inputs(
                              snake_case__      ,	max_seq_length=snake_case__      ,	pad_value=self.pad_token_id   )

            return {"attention_mask": attention_mask, "input_ids": input_ids}

