

'''simple docstring'''
from pathlib import Path

import numpy as np
from PIL import Image







def  _A		(     snake_case					)      ->     np.ndarray:
   _lowercase      :				List[str]      					=       rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2989 * r + 0.5870 * g + 0.1140 * b







def  _A		(     snake_case					)      ->     np.ndarray:
   return (gray > 1_27) & (gray <= 2_55)







def  _A		(     snake_case		,							snake_case					)      ->     np.ndarray:
   _lowercase      :				Dict      					=       np.zeros_like(snake_case					)
   _lowercase      :				Union[str, Any]      					=       np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

   # Copy image to padded image
   _lowercase      :				str      					=       image

   # Iterate over image & apply kernel
   for x in range(image.shape[1]					):
      for y in range(image.shape[0]					):
         _lowercase      :				Dict      					=       (
             kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
         ).sum()
         _lowercase      :				List[Any]      					=       int(summation > 0					)
   return output


if __name__ == "__main__":
   # read original image
   _snake_case         =  Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
   _snake_case         =  np.array(Image.open(lena_path))
   # kernel to be applied
   _snake_case         =  np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
   _snake_case         =  dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
   # Save the output image
   _snake_case         =  Image.fromarray(output).convert('RGB')
   pil_img.save('result_dilation.png')









'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


_snake_case         =  logging.getLogger()


@unittest.skip('Temporarily disable the doc tests.'  )
@require_torch
@require_tf
@slow
class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       [file for file in os.listdir(_UpperCamelCase  ) if os.path.isfile(os.path.join(_UpperCamelCase      ,    _UpperCamelCase  )  )]

      if identifier is not None:
         _lowercase      :				str      					=       [file for file in files if identifier in file]

      if n_identifier is not None:
         if isinstance(_UpperCamelCase      ,    _UpperCamelCase  ):
            for n_ in n_identifier:
               _lowercase      :				Dict      					=       [file for file in files if n_ not in file]
         else:
            _lowercase      :				Optional[Any]      					=       [file for file in files if n_identifier not in file]

      _lowercase      :				Dict      					=       ignore_files or []
      ignore_files.append("__init__.py"  )
      _lowercase      :				List[str]      					=       [file for file in files if file not in ignore_files]

      for file in files:
         # Open all files
         print("Testing"      ,    _UpperCamelCase  )

         if only_modules:
            _lowercase      :				Optional[Any]      					=       file.split("."  )[0]
            try:
               _lowercase      :				Union[str, Any]      					=       getattr(_UpperCamelCase      ,    _UpperCamelCase  )
               _lowercase      :				Optional[int]      					=       doctest.DocTestSuite(_UpperCamelCase  )
               _lowercase      :				Tuple      					=       unittest.TextTestRunner().run(_UpperCamelCase  )
               self.assertIs(len(result.failures  )      ,    0  )
            except AttributeError:
               logger.info(f'''{module_identifier} is not a module.'''  )
         else:
            _lowercase      :				Optional[Any]      					=       doctest.testfile(str(".." / directory / file  )      ,    optionflags=doctest.ELLIPSIS  )
            self.assertIs(result.failed      ,    0  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       Path("src/transformers"  )
      _lowercase      :				str      					=       "modeling"
      _lowercase      :				Tuple      					=       [
          "modeling_ctrl.py",
          "modeling_tf_ctrl.py",
      ]
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase      ,    ignore_files=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       Path("src/transformers"  )
      _lowercase      :				Any      					=       "tokenization"
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       Path("src/transformers"  )
      _lowercase      :				Optional[Any]      					=       "configuration"
      self.analyze_directory(_UpperCamelCase      ,    identifier=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       Path("src/transformers"  )
      _lowercase      :				List[Any]      					=       ["configuration", "modeling", "tokenization"]
      self.analyze_directory(_UpperCamelCase      ,    n_identifier=_UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       Path("docs/source"  )
      _lowercase      :				int      					=       ["favicon.ico"]
      self.analyze_directory(_UpperCamelCase      ,    ignore_files=_UpperCamelCase      ,    only_modules=_UpperCamelCase  )






