




import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     def        __snake_case      (			self     :      Tuple	) ->				Tuple:
           lowerCamelCase__					     = {
               'model_details': {
                   'Organization': 'testing',
                   'Model date': 'today',
                   'Model version': 'v2.1, Developed by Test Corp in 2019.',
                   'Architecture': 'Convolutional Neural Network.',
               },
               'metrics': 'BLEU and ROUGE-1',
               'evaluation_data': {
                   'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
                   'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
               },
               'training_data': {
                   'Dataset': 'English Wikipedia dump dated 2018-12-01',
                   'Preprocessing': (
                       'Using SentencePiece vocabulary of size 52k tokens. See details on'
                       ' https://arxiv.org/pdf/1810.03993.pdf'
                   ),
               },
               'quantitative_analyses': {'BLEU': 55.1, 'ROUGE-1': 7_6},
           }




     def        __snake_case      (			self     :      str	) ->				List[str]:
           lowerCamelCase__					     = ModelCard.from_dict(self.inputs_dict	)
           self.assertTrue(hasattr(A__		,     'model_details'	)	)
           self.assertTrue(hasattr(A__		,     'intended_use'	)	)
           self.assertTrue(hasattr(A__		,     'factors'	)	)
           self.assertTrue(hasattr(A__		,     'metrics'	)	)
           self.assertTrue(hasattr(A__		,     'evaluation_data'	)	)
           self.assertTrue(hasattr(A__		,     'training_data'	)	)
           self.assertTrue(hasattr(A__		,     'quantitative_analyses'	)	)
           self.assertTrue(hasattr(A__		,     'ethical_considerations'	)	)
           self.assertTrue(hasattr(A__		,     'caveats_and_recommendations'	)	)




     def        __snake_case      (			self     :      List[str]	) ->				List[str]:
           lowerCamelCase__					     = ModelCard.from_dict(self.inputs_dict	)
           lowerCamelCase__					     = json.loads(modelcard.to_json_string()	)
           for key, value in self.inputs_dict.items():
                 self.assertEqual(obj[key]		,     A__	)




     def        __snake_case      (			self     :      Dict	) ->				Any:
           lowerCamelCase__					     = ModelCard.from_dict(self.inputs_dict	)

           with tempfile.TemporaryDirectory() as tmpdirname:
                 lowerCamelCase__					     = os.path.join(A__		,     'modelcard.json'	)
                 model_card_first.to_json_file(A__	)
                 lowerCamelCase__					     = ModelCard.from_json_file(A__	)

           self.assertEqual(model_card_second.to_dict()		,     model_card_first.to_dict()	)




     def        __snake_case      (			self     :      List[Any]	) ->				int:
           lowerCamelCase__					     = ModelCard.from_dict(self.inputs_dict	)

           with tempfile.TemporaryDirectory() as tmpdirname:
                 model_card_first.save_pretrained(A__	)
                 lowerCamelCase__					     = ModelCard.from_pretrained(A__	)

           self.assertEqual(model_card_second.to_dict()		,     model_card_first.to_dict()	)
import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCamelCase__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase				=  k_size // 2
  _UpperCamelCase	,		_UpperCamelCase				=  np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _UpperCamelCase				=  (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      _UpperCamelCase				=  gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row, col - 1]
        _UpperCamelCase				=  sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row + 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col]
        _UpperCamelCase				=  sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _UpperCamelCase				=  sobel_grad[row, col]

  return destination


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      if destination[row, col] >= threshold_high:
        _UpperCamelCase				=  strong
      elif destination[row, col] <= threshold_low:
        _UpperCamelCase				=  0
      else:
        _UpperCamelCase				=  weak


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0]      ):
    for col in range(1  ,			image_shape[1]      ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _UpperCamelCase				=  strong
        else:
          _UpperCamelCase				=  0


def 							lowerCAmelCase__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  return destination


if __name__ == "__main__":
   # read original image in gray mode
   lowerCamelCase__   = cva.imread(R'''../image_data/lena.jpg''', 0)
   # canny edge detection
   lowerCamelCase__   = canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)




