


'''simple docstring'''



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor



@require_torch
class 			a       (	unittest.TestCase ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Any    )		->							Any:
          __UpperCAmelCase			:				Dict													=    '''ZinengTang/tvlt-base'''
          __UpperCAmelCase			:				Tuple													=    tempfile.mkdtemp()

     def 				lowerCamelCase__				(       self  :	int							,				**snake_case  :	str    )		->							List[Any]:
          return TvltImageProcessor.from_pretrained(self.checkpoint							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				**snake_case  :	str    )		->							Tuple:
          return TvltFeatureExtractor.from_pretrained(self.checkpoint							,				**snake_case    )

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

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

          __UpperCAmelCase			:				List[str]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )
          processor.save_pretrained(self.tmpdirname    )
          __UpperCAmelCase			:				Optional[Any]													=    TvltProcessor.from_pretrained(self.tmpdirname    )

          self.assertIsInstance(processor.feature_extractor							,				snake_case    )
          self.assertIsInstance(processor.image_processor							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Any    )		->							Union[str, Any]:
          __UpperCAmelCase			:				int													=    self.get_image_processor()
          __UpperCAmelCase			:				Optional[int]													=    self.get_feature_extractor()

          __UpperCAmelCase			:				Optional[int]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    np.ones([1_2000]    )

          __UpperCAmelCase			:				Dict													=    feature_extractor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				int													=    processor(audio=snake_case							,				return_tensors='''np'''    )

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

     def 				lowerCamelCase__				(       self  :	int    )		->							Tuple:
          __UpperCAmelCase			:				int													=    self.get_image_processor()
          __UpperCAmelCase			:				Any													=    self.get_feature_extractor()

          __UpperCAmelCase			:				Union[str, Any]													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Dict													=    np.ones([3, 224, 224]    )

          __UpperCAmelCase			:				Any													=    image_processor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				Union[str, Any]													=    processor(images=snake_case							,				return_tensors='''np'''    )

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

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

          __UpperCAmelCase			:				Dict													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          __UpperCAmelCase			:				Optional[int]													=    np.ones([1_2000]    )
          __UpperCAmelCase			:				Dict													=    np.ones([3, 224, 224]    )

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

          self.assertListEqual(list(inputs.keys()    )							,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']    )

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






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

          __UpperCAmelCase			:				str													=    TvltProcessor(image_processor=snake_case							,				feature_extractor=snake_case    )

          self.assertListEqual(
              processor.model_input_names							,				image_processor.model_input_names + feature_extractor.model_input_names							,				msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''							,				)



'''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()