


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	str    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Tuple													=    tempfile.mkdtemp()

          # fmt: off
          __UpperCAmelCase			:				Union[str, Any]													=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
          # fmt: on
          __UpperCAmelCase			:				Tuple													=    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]    )    )

          __UpperCAmelCase			:				str													=    {
              '''do_resize''': True,
              '''size''': {'''height''': 18, '''width''': 18},
              '''do_normalize''': True,
              '''image_mean''': [0.5, 0.5, 0.5],
              '''image_std''': [0.5, 0.5, 0.5],
          }
          __UpperCAmelCase			:				List[str]													=    os.path.join(self.tmpdirname							,				snake_case    )
          with open(self.image_processor_file							,				'''w'''							,				encoding='''utf-8'''    ) as fp:
               json.dump(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	str							,				**snake_case  :	Any    )		->							Optional[Any]:
          return BertTokenizer.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Tuple							,				**snake_case  :	List[Any]    )		->							Optional[int]:
          return ViTImageProcessor.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Optional[int]:
          shutil.rmtree(self.tmpdirname    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Optional[int]													=    [np.random.randint(255							,				size=(3, 30, 400)							,				dtype=np.uinta    )]

          __UpperCAmelCase			:				List[Any]													=    [Image.fromarray(np.moveaxis(snake_case							,				0							,				-1    )    ) for x in image_inputs]

          return image_inputs

     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[Any]:
          __UpperCAmelCase			:				Tuple													=    self.get_tokenizer()
          __UpperCAmelCase			:				Optional[Any]													=    self.get_image_processor()

          __UpperCAmelCase			:				Tuple													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          processor.save_pretrained(self.tmpdirname    )
          __UpperCAmelCase			:				Union[str, Any]													=    VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname    )

          self.assertEqual(processor.tokenizer.get_vocab()							,				tokenizer.get_vocab()    )
          self.assertIsInstance(processor.tokenizer							,				(BertTokenizer, BertTokenizerFast)    )

          self.assertEqual(processor.image_processor.to_json_string()							,				image_processor.to_json_string()    )
          self.assertIsInstance(processor.image_processor							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Dict													=    VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()							,				image_processor=self.get_image_processor()    )
          processor.save_pretrained(self.tmpdirname    )

          __UpperCAmelCase			:				Dict													=    self.get_tokenizer(bos_token='''(BOS)'''							,				eos_token='''(EOS)'''    )
          __UpperCAmelCase			:				int													=    self.get_image_processor(do_normalize=snake_case							,				padding_value=1.0    )

          __UpperCAmelCase			:				List[str]													=    VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname							,				bos_token='''(BOS)'''							,				eos_token='''(EOS)'''							,				do_normalize=snake_case							,				padding_value=1.0    )

          self.assertEqual(processor.tokenizer.get_vocab()							,				tokenizer_add_kwargs.get_vocab()    )
          self.assertIsInstance(processor.tokenizer							,				(BertTokenizer, BertTokenizerFast)    )

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

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				Any													=    self.get_image_processor()
          __UpperCAmelCase			:				Dict													=    self.get_tokenizer()

          __UpperCAmelCase			:				Any													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				str													=    self.prepare_image_inputs()

          __UpperCAmelCase			:				List[str]													=    image_processor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				Optional[int]													=    processor(images=snake_case							,				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  :	int    )		->							int:
          __UpperCAmelCase			:				List[Any]													=    self.get_image_processor()
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_tokenizer()

          __UpperCAmelCase			:				Dict													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				List[Any]													=    '''lower newer'''

          __UpperCAmelCase			:				str													=    processor(text=snake_case    )

          __UpperCAmelCase			:				str													=    tokenizer(snake_case    )

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

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				Optional[Any]													=    self.get_image_processor()
          __UpperCAmelCase			:				Any													=    self.get_tokenizer()

          __UpperCAmelCase			:				Optional[int]													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				Any													=    '''lower newer'''
          __UpperCAmelCase			:				List[str]													=    self.prepare_image_inputs()

          __UpperCAmelCase			:				Any													=    processor(text=snake_case							,				images=snake_case    )

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

          # test if it raises when no input is passed
          with self.assertRaises(snake_case    ):
               processor()

     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Optional[int]:
          __UpperCAmelCase			:				Any													=    self.get_image_processor()
          __UpperCAmelCase			:				int													=    self.get_tokenizer()

          __UpperCAmelCase			:				Any													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				Tuple													=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCAmelCase			:				Tuple													=    processor.batch_decode(snake_case    )
          __UpperCAmelCase			:				int													=    tokenizer.batch_decode(snake_case    )

          self.assertListEqual(snake_case							,				snake_case    )






     def 				lowerCamelCase__				(       self  :	str    )		->							List[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_image_processor()
          __UpperCAmelCase			:				Tuple													=    self.get_tokenizer()

          __UpperCAmelCase			:				List[str]													=    VisionTextDualEncoderProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				Any													=    '''lower newer'''
          __UpperCAmelCase			:				List[Any]													=    self.prepare_image_inputs()

          __UpperCAmelCase			:				Union[str, Any]													=    processor(text=snake_case							,				images=snake_case    )

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



'''simple docstring'''



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __UpperCAmelCase			:				Optional[int]													=    zeros((dst_height * dst_width, k_size * k_size)						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     for i, j in product(range(_lowercase						)    ,      range(_lowercase						)						):
          __UpperCAmelCase			:				List[Any]													=    ravel(image[i : i + k_size, j : j + k_size]						)
          __UpperCAmelCase			:				Optional[int]													=    window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

     # reshape and get the dst image
     __UpperCAmelCase			:				Optional[int]													=    dot(_lowercase    ,      _lowercase						).reshape(_lowercase    ,      _lowercase						).astype(_lowercase						)

     return dst


if __name__ == "__main__":
      # read original image
      __UpperCAmelCase    :Dict				       =     imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      __UpperCAmelCase    :Tuple				       =     cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[Any]				       =     gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()