




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class 	lowerCamelCase_							(				unittest.TestCase ):


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_     						=      [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    '的',
							    '价',
							    '格',
							    '是',
							    '15',
							    '便',
							    'alex',
							    '##andra',
							    '，',
							    '。',
							    '-',
							    't',
							    'shirt',
							]
							SCREAMING_SNAKE_CASE_     						=      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]						)						)

							SCREAMING_SNAKE_CASE_     						=      {
							    'do_resize': True,
							    'size': {'height': 224, 'width': 224},
							    'do_center_crop': True,
							    'crop_size': {'height': 18, 'width': 18},
							    'do_normalize': True,
							    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
							    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
							    'do_convert_rgb': True,
							}
							SCREAMING_SNAKE_CASE_     						=      os.path.join(self.tmpdirname    ,    _lowerCAmelCase						)
							with open(self.image_processor_file    ,    'w'    ,    encoding='utf-8'						) as fp:
													json.dump(_lowerCAmelCase    ,    _lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    **_lowerCAmelCase  :		str						):
							return BertTokenizer.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Dict    ,    **_lowerCAmelCase  :		Optional[int]						):
							return BertTokenizerFast.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    **_lowerCAmelCase  :		List[Any]						):
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname    ,    **_lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
							shutil.rmtree(self.tmpdirname						)



	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      [np.random.randint(255    ,    size=(3, 30, 400)    ,    dtype=np.uinta						)]

							SCREAMING_SNAKE_CASE_     						=      [Image.fromarray(np.moveaxis(_lowerCAmelCase    ,    0    ,    -1						)						) for x in image_inputs]

							return image_inputs



	def 				lowerCAmelCase_  (							self  :		Any						):
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)
							processor_slow.save_pretrained(self.tmpdirname						)
							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname    ,    use_fast=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)
							processor_fast.save_pretrained(self.tmpdirname						)
							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor.from_pretrained(self.tmpdirname						)

							self.assertEqual(processor_slow.tokenizer.get_vocab()    ,    tokenizer_slow.get_vocab()						)
							self.assertEqual(processor_fast.tokenizer.get_vocab()    ,    tokenizer_fast.get_vocab()						)
							self.assertEqual(tokenizer_slow.get_vocab()    ,    tokenizer_fast.get_vocab()						)
							self.assertIsInstance(processor_slow.tokenizer    ,    _lowerCAmelCase						)
							self.assertIsInstance(processor_fast.tokenizer    ,    _lowerCAmelCase						)

							self.assertEqual(processor_slow.image_processor.to_json_string()    ,    image_processor.to_json_string()						)
							self.assertEqual(processor_fast.image_processor.to_json_string()    ,    image_processor.to_json_string()						)
							self.assertIsInstance(processor_slow.image_processor    ,    _lowerCAmelCase						)
							self.assertIsInstance(processor_fast.image_processor    ,    _lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=self.get_tokenizer()    ,    image_processor=self.get_image_processor()						)
							processor.save_pretrained(self.tmpdirname						)

							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer(cls_token='(CLS)'    ,    sep_token='(SEP)'						)
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor(do_normalize=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname    ,    cls_token='(CLS)'    ,    sep_token='(SEP)'    ,    do_normalize=_lowerCAmelCase						)

							self.assertEqual(processor.tokenizer.get_vocab()    ,    tokenizer_add_kwargs.get_vocab()						)
							self.assertIsInstance(processor.tokenizer    ,    _lowerCAmelCase						)

							self.assertEqual(processor.image_processor.to_json_string()    ,    image_processor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.image_processor    ,    _lowerCAmelCase						)



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      image_processor(_lowerCAmelCase    ,    return_tensors='np'						)
							SCREAMING_SNAKE_CASE_     						=      processor(images=_lowerCAmelCase    ,    return_tensors='np'						)

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()    ,    input_processor[key].sum()    ,    delta=1E-2						)



	def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'Alexandra，T-shirt的价格是15便士。'

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      tokenizer(_lowerCAmelCase						)

							for key in encoded_tok.keys():
													self.assertListEqual(encoded_tok[key]    ,    encoded_processor[key]						)



	def 				lowerCAmelCase_  (							self  :		Tuple						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'Alexandra，T-shirt的价格是15便士。'
							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase    ,    images=_lowerCAmelCase						)

							self.assertListEqual(list(inputs.keys()						)    ,    ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']						)

							# test if it raises when no input is passed
							with pytest.raises(_lowerCAmelCase						):
													processor()



	def 				lowerCAmelCase_  (							self  :		Optional[int]						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_     						=      processor.batch_decode(_lowerCAmelCase						)
							SCREAMING_SNAKE_CASE_     						=      tokenizer.batch_decode(_lowerCAmelCase						)

							self.assertListEqual(_lowerCAmelCase    ,    _lowerCAmelCase						)




	def 				lowerCAmelCase_  (							self  :		str						):
							SCREAMING_SNAKE_CASE_     						=      self.get_image_processor()
							SCREAMING_SNAKE_CASE_     						=      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_     						=      ChineseCLIPProcessor(tokenizer=_lowerCAmelCase    ,    image_processor=_lowerCAmelCase						)

							SCREAMING_SNAKE_CASE_     						=      'Alexandra，T-shirt的价格是15便士。'
							SCREAMING_SNAKE_CASE_     						=      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_     						=      processor(text=_lowerCAmelCase    ,    images=_lowerCAmelCase						)

							self.assertListEqual(list(inputs.keys()						)    ,    processor.model_input_names						)





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







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

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



 def __str__(							self  :		Tuple						):
       return str(self.k						)




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

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      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__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)