




import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 						A(		UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		LxmertTokenizer
 UpperCamelCase		     =		LxmertTokenizerFast
 UpperCamelCase		     =		True
 UpperCamelCase		     =		True

 def        a__				(       self      :						Any						)     ->			List[Any]:

       """simple docstring"""






       super().setUp()

       lowerCamelCase_	  =					[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       lowerCamelCase_	  =					os.path.join(self.tmpdirname		,							VOCAB_FILES_NAMES['vocab_file']						)
       with open(self.vocab_file		,							'w'		,							encoding='utf-8'						) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)

 def        a__				(       self      :						Any		,							A_      :						str						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					'UNwant\u00E9d,running'
       lowerCamelCase_	  =					'unwanted, running'
       return input_text, output_text

 def        a__				(       self      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.tokenizer_class(self.vocab_file						)

       lowerCamelCase_	  =					tokenizer.tokenize('UNwant\u00E9d,running'						)
       self.assertListEqual(A_		,							['un', '##want', '##ed', ',', 'runn', '##ing']						)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_						)		,							[7, 4, 5, 10, 8, 9]						)





 def        a__				(       self      :						Optional[Any]						)     ->			str:

       """simple docstring"""






       if not self.test_rust_tokenizer:
             return

       lowerCamelCase_	  =					self.get_tokenizer()
       lowerCamelCase_	  =					self.get_rust_tokenizer()

       lowerCamelCase_	  =					'I was born in 92000, and this is falsé.'

       lowerCamelCase_	  =					tokenizer.tokenize(A_						)
       lowerCamelCase_	  =					rust_tokenizer.tokenize(A_						)
       self.assertListEqual(A_		,							A_						)

       lowerCamelCase_	  =					tokenizer.encode(A_		,							add_special_tokens=A_						)
       lowerCamelCase_	  =					rust_tokenizer.encode(A_		,							add_special_tokens=A_						)
       self.assertListEqual(A_		,							A_						)

       lowerCamelCase_	  =					self.get_rust_tokenizer()
       lowerCamelCase_	  =					tokenizer.encode(A_						)
       lowerCamelCase_	  =					rust_tokenizer.encode(A_						)
       self.assertListEqual(A_		,							A_						)









import cva
import numpy as np







class 						A:



 '''simple docstring'''


 def __init__(       self      :						int		,							A_      :						float		,							A_      :						int						)     ->			List[Any]:

       """simple docstring"""






       if k in (0.04, 0.06):
             lowerCamelCase_	  =					k
             lowerCamelCase_	  =					window_size
       else:
             raise ValueError('invalid k value'						)

 def __str__(       self      :						str						)     ->			str:

       """simple docstring"""






       return str(self.k						)





 def        a__				(       self      :						Any		,							A_      :						str						)     ->			tuple[cva.Mat, list[list[int]]]:

       """simple docstring"""






       lowerCamelCase_	  =					cva.imread(A_		,							0						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					img.shape
       lowerCamelCase_	  =					[]
       lowerCamelCase_	  =					img.copy()
       lowerCamelCase_	  =					cva.cvtColor(A_		,							cva.COLOR_GRAY2RGB						)
       lowerCamelCase_  ,       lowerCamelCase_	  =					np.gradient(A_						)
       lowerCamelCase_	  =					dx**2
       lowerCamelCase_	  =					dy**2
       lowerCamelCase_	  =					dx * dy
       lowerCamelCase_	  =					0.04
       lowerCamelCase_	  =					self.window_size // 2
       for y in range(A_		,							h - offset						):
             for x in range(A_		,							w - offset						):
                   lowerCamelCase_	  =					ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   lowerCamelCase_	  =					ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   lowerCamelCase_	  =					(wxx * wyy) - (wxy**2)
                   lowerCamelCase_	  =					wxx + wyy
                   lowerCamelCase_	  =					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       :	Optional[int]									=     HarrisCorner(0.04, 3)
 lowerCamelCase					,					lowerCamelCase       :	Optional[int]									=     edge_detect.detect("path_to_image")
 cva.imwrite("detect.png", color_img)



