

'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			DebertaTokenizer
  SCREAMING_SNAKE_CASE    :  str							=			True
  SCREAMING_SNAKE_CASE    :  List[Any]							=			DebertaTokenizerFast






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowercase							  = [
       '''l''',
       '''o''',
       '''w''',
       '''e''',
       '''r''',
       '''s''',
       '''t''',
       '''i''',
       '''d''',
       '''n''',
       '''\u0120''',
       '''\u0120l''',
       '''\u0120n''',
       '''\u0120lo''',
       '''\u0120low''',
       '''er''',
       '''\u0120lowest''',
       '''\u0120newer''',
       '''\u0120wider''',
       '''[UNK]''',
   ]
   __lowercase							  = dict(zip(lowercase__					,range(len(lowercase__			)			)			)			)
   __lowercase							  = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
   __lowercase							  = {'''unk_token''': '''[UNK]'''}

   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''vocab_file''']			)
   __lowercase							  = os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['''merges_file''']			)
   with open(self.vocab_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write(json.dumps(lowercase__			) + '''\n'''			)
   with open(self.merges_file					,'''w'''					,encoding='''utf-8'''			) as fp:
    fp.write('''\n'''.join(lowercase__			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,**lowercase__   :     Any			):
   kwargs.update(self.special_tokens_map			)
   return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     int			):
   __lowercase							  = '''lower newer'''
   __lowercase							  = '''lower newer'''
   return input_text, output_text






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_tokenizer()
   __lowercase							  = '''lower newer'''
   __lowercase							  = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
   __lowercase							  = tokenizer.tokenize(lowercase__			)
   self.assertListEqual(lowercase__					,lowercase__			)

   __lowercase							  = tokens + [tokenizer.unk_token]
   __lowercase							  = [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__			)					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = self.get_tokenizer()
   __lowercase							  = tokenizer('''Hello'''					,'''World'''			)
   __lowercase							  = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
   self.assertListEqual(tokd['''token_type_ids''']					,lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = tokenizer.encode('''sequence builders'''					,add_special_tokens=lowercase__			)
   __lowercase							  = tokenizer.encode('''multi-sequence build'''					,add_special_tokens=lowercase__			)

   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)
   __lowercase							  = tokenizer.encode(
       '''sequence builders'''					,'''multi-sequence build'''					,add_special_tokens=lowercase__					,add_prefix_space=lowercase__			)

   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__			)
   __lowercase							  = tokenizer.build_inputs_with_special_tokens(lowercase__					,lowercase__			)

   assert encoded_sentence == encoded_text_from_decode
   assert encoded_pair == encoded_pair_from_decode






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = [self.tokenizer_class]
   if self.test_rust_tokenizer:
    tokenizer_classes.append(self.rust_tokenizer_class			)

   for tokenizer_class in tokenizer_classes:
    __lowercase							  = tokenizer_class.from_pretrained('''microsoft/deberta-base'''			)

    __lowercase							  = [
        '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
        '''ALBERT incorporates two parameter reduction techniques''',
        '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
        ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
        ''' vocabulary embedding.''',
    ]

    __lowercase							  = tokenizer(lowercase__					,padding=lowercase__			)
    __lowercase							  = [tokenizer.decode(lowercase__					,skip_special_tokens=lowercase__			) for seq in encoding['''input_ids''']]

    # fmt: off
    __lowercase							  = {
        '''input_ids''': [
            [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 4, 2]
        ],
        '''token_type_ids''': [
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        ],
        '''attention_mask''': [
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        ]
    }
    # fmt: on

    __lowercase							  = [
        '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
        '''ALBERT incorporates two parameter reduction techniques''',
        '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
        ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
        ''' vocabulary embedding.''',
    ]

    self.assertDictEqual(encoding.data					,lowercase__			)

    for expected, decoded in zip(lowercase__					,lowercase__			):
     self.assertEqual(lowercase__					,lowercase__			)









import cva
import numpy as np
class     a    :



      """simple docstring"""


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

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




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

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	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__":
       a		:       int			     = HarrisCorner(0.04, 3)
       a     ,a		:       Any			     = edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)


