






from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros


def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[int]					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:int     							=       image.shape
    UpperCamelCase	:int     							=       cols - block_size[1] + 1
    UpperCamelCase	:List[str]     							=       rows - block_size[0] + 1
    UpperCamelCase	:List[str]     							=       zeros((dst_height * dst_width, block_size[1] * block_size[0])	)
    UpperCamelCase	:Optional[int]     							=       0
    for i in range(0					,  SCREAMING_SNAKE_CASE__	):
        for j in range(0					,  SCREAMING_SNAKE_CASE__	):
            UpperCamelCase	:Optional[int]     							=       ravel(image[i : i + block_size[0], j : j + block_size[1]]	)
            UpperCamelCase	:Dict     							=       window
            row += 1

    return image_array


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:Optional[Any]     							=       image.shape[0], image.shape[1]
    UpperCamelCase	:Optional[Any]     							=       filter_kernel.shape[0]
    UpperCamelCase	:Union[str, Any]     							=       k_size // 2
    # Pads image with the edge values of array.
    UpperCamelCase	:Dict     							=       pad(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  mode='''edge'''	)

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    UpperCamelCase	:Dict     							=       imacol(SCREAMING_SNAKE_CASE__					,  (k_size, k_size)	)

    #  turn the kernel into shape(k*k, 1)
    UpperCamelCase	:List[Any]     							=       ravel(SCREAMING_SNAKE_CASE__	)
    # reshape and get the dst image
    UpperCamelCase	:Optional[Any]     							=       dot(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	).reshape(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    return dst


if __name__ == "__main__":
 # read original image
 __snake_case							 =  imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 __snake_case							 =  cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 __snake_case							 =  array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 __snake_case							 =  img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





