


import cva
import numpy as np



class 				_UpperCamelCase			:

   '''simple docstring'''







   def __init__(	self	:			Optional[Any]							,	_lowerCamelCase	:			float							,	_lowerCamelCase	:			int				):






         '''simple docstring'''



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

   def __str__(	self	:			Optional[int]				):






         '''simple docstring'''



         return str(self.k				)


   def 	_snake_case     (	self	:			List[str]							,	_lowerCamelCase	:			str				):






         '''simple docstring'''



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

                     __lowerCamelCase  :   Tuple							    =    (wxx * wyy) - (wxy**2)
                     __lowerCamelCase  :   List[Any]							    =    wxx + wyy
                     __lowerCamelCase  :   Union[str, 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)							,	2_5_5				)
         return color_img, corner_list


if __name__ == "__main__":
    __UpperCamelCase     :    Dict 	=				HarrisCorner(0.04, 3)
    __UpperCamelCase	,				__UpperCamelCase     :    Optional[Any] 	=				edge_detect.detect('path_to_image')
    cva.imwrite('detect.png', color_img)







"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__					=get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__					={"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__					=">>zh<<"
UpperCAmelCase__					="Helsinki-NLP/"

if is_torch_available():
       UpperCAmelCase__					="pt"
elif is_tf_available():
       UpperCAmelCase__					="tf"
else:
       UpperCAmelCase__					="jax"





@require_sentencepiece
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Dict           =		MarianTokenizer
      a			:  Optional[int]           =		False
      a			:  Any           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       super().setUp()
       __lowercase						=						["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase						=						dict(zip(A_   ,							range(len(A_				)				)				)				)
       __lowercase						=						Path(self.tmpdirname				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

       __lowercase						=						MarianTokenizer.from_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							**A_						:						Any				):






       '''simple docstring'''


       return MarianTokenizer.from_pretrained(self.tmpdirname   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						"""</s>"""
       __lowercase						=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""</s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<unk>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<pad>"""				)
       self.assertEqual(len(A_				)   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''				)
       __lowercase						=						en_de_tokenizer(["""I am a small frog"""]   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       __lowercase						=						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(A_   ,							batch.input_ids[0]				)

       __lowercase						=						tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_				)
       __lowercase						=						[x.name for x in Path(A_				).glob("""*"""				)]
       self.assertIn("""source.spm"""   ,							A_				)
       MarianTokenizer.from_pretrained(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()

       __lowercase						=						tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]   ,							padding=A_   ,							truncation=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch.input_ids.shape   ,							(2, 5_1_2)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()
       __lowercase						=						tok(["""I am a tiny frog""", """I am a small frog"""]   ,							padding=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch_smaller.input_ids.shape   ,							(2, 1_0)				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """attention_mask""": [[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, 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, 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, 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], [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, 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]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""Helsinki-NLP/opus-mt-en-de"""   ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,							decode_kwargs={"""use_source_tokenizer""": True}   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

       __lowercase						=						"""Tämä on testi"""
       __lowercase						=						"""This is a test"""

       __lowercase						=						[7_6, 7, 2_0_4_7, 2]
       __lowercase						=						[6_9, 1_2, 1_1, 9_4_0, 2]

       __lowercase						=						tokenizer(A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer(text_target=A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.decode(A_   ,							skip_special_tokens=A_				)
       self.assertEqual(A_   ,							A_				)
