



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = ['''image_processor''', '''tokenizer''']
							_a			      = '''CLIPImageProcessor'''
							_a			      = ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')




							def __init__(       self     :       Optional[Any]				,				A_     :       Union[str, Any]=None				,				A_     :       List[Any]=None				,				**A_     :       int):
									lowerCAmelCase_			:     Union[str, Any]									= None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,				A_				,				)
											lowerCAmelCase_			:     str									= kwargs.pop('''feature_extractor''')

									lowerCAmelCase_			:     List[str]									= image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.''')
									if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.''')

									super().__init__(A_				,				A_)




							def __call__(       self     :       Optional[Any]				,				A_     :       Dict=None				,				A_     :       int=None				,				A_     :       str=None				,				**A_     :       List[str]):

									if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.''')

									if text is not None:
											lowerCAmelCase_			:     str									= self.tokenizer(A_				,				return_tensors=A_				,				**A_)

									if images is not None:
											lowerCAmelCase_			:     str									= self.image_processor(A_				,				return_tensors=A_				,				**A_)

									if text is not None and images is not None:
											lowerCAmelCase_			:     str									= image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**A_)				,				tensor_type=A_)




							def    UpperCAmelCase__			(       self     :       Any				,				*A_     :       Tuple				,				**A_     :       List[str]):
									return self.tokenizer.batch_decode(*A_				,				**A_)




							def    UpperCAmelCase__			(       self     :       str				,				*A_     :       List[str]				,				**A_     :       Tuple):
									return self.tokenizer.decode(*A_				,				**A_)







							@property
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Tuple									= self.tokenizer.model_input_names
									lowerCAmelCase_			:     Union[str, Any]									= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))













"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

lowercase_				     =     imread(R'digital_image_processing/image_data/lena_small.jpg')
lowercase_				     =     cvtColor(img, COLOR_BGR2GRAY)



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= cn.convert_to_negative(__UpperCamelCase )
  # assert negative_img array for at least one True
  assert negative_img.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  with Image.open('''digital_image_processing/image_data/lena_small.jpg''' ) as img:
    # Work around assertion for response
    assert str(cc.change_contrast(__UpperCamelCase		,       1_1_0 ) ).startswith(
        '''<PIL.Image.Image image mode=RGB size=100x100 at''' )



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= canny.gen_gaussian_kernel(9		,       sigma=1.4 )
  # Assert ambiguous array
  assert resp.all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= imread('''digital_image_processing/image_data/lena_small.jpg'''		,       0 )
  # assert ambiguous array for all == True
  assert canny_img.all()
  __A									= canny.canny(__UpperCamelCase )
  # assert canny array for at least one True
  assert canny_array.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert gg.gaussian_filter(__UpperCamelCase		,       5		,       sigma=0.9 ).all()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
  __A									= conv.img_convolve(__UpperCamelCase		,       __UpperCamelCase ).astype(__UpperCamelCase )
  assert res.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  assert med.median_filter(__UpperCamelCase		,       3 ).any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A			,    __A									= sob.sobel_filter(__UpperCamelCase )
  assert grad.any() and theta.any()



def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= sp.make_sepia(__UpperCamelCase		,       2_0 )
  assert sepia.all()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ):




  """simple docstring"""







  __A									= bs.Burkes(imread(__UpperCamelCase		,       1 )		,       1_2_0 )
  burkes.process()
  assert burkes.output_img.any()



def 					lowerCAmelCase				(    __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"		,       ):




  """simple docstring"""







  __A									= rs.NearestNeighbour(imread(__UpperCamelCase		,       1 )		,       4_0_0		,       2_0_0 )
  nn.process()
  assert nn.output.any()





def 					lowerCAmelCase				(    ):




  """simple docstring"""







  __A									= '''digital_image_processing/image_data/lena.jpg'''

  # Reading the image and converting it to grayscale.
  __A									= imread(__UpperCamelCase		,       0 )

  # Test for get_neighbors_pixel function() return not None
  __A									= 0
  __A									= 0
  __A									= image[x_coordinate][y_coordinate]

  __A									= lbp.get_neighbors_pixel(
      __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert neighbors_pixels is not None

  # Test for local_binary_pattern function()
  # Create a numpy array as the same height and width of read image
  __A									= np.zeros((image.shape[0], image.shape[1]) )

  # Iterating through the image and calculating the local binary pattern value
  # for each pixel.
  for i in range(0		,       image.shape[0] ):
    for j in range(0		,       image.shape[1] ):
      __A									= lbp.local_binary_value(__UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase )

  assert lbp_image.any()




