




"""simple docstring"""


from pathlib import Path

import numpy as np
from PIL import Image


def  UpperCAmelCase__	(snake_case__							: np.ndarray   ):
  """simple docstring"""

  _snake_case		, _snake_case		, _snake_case							:		Optional[int]								=  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b


def  UpperCAmelCase__	(snake_case__							: np.ndarray   ):
  """simple docstring"""

  return (gray > 1_27) & (gray <= 2_55)


def  UpperCAmelCase__	(snake_case__							: np.ndarray		,       snake_case__							: np.ndarray   ):
  """simple docstring"""

  _snake_case							:		List[str]								=  np.zeros_like(SCREAMING_SNAKE_CASE_   )
  _snake_case							:		str								=  np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)   )

  # Copy image to padded image
  _snake_case							:		int								=  image

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


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









"""simple docstring"""


import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


A_      =    '''<<<<<<< This should probably be modified because it mentions: '''

A_      =    '''=======
>>>>>>>
'''

A_      =    [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

A_      =    [
    # (pattern, replacement)
    # Order is important here for some replacements
    (r'''tfds\.core''', r'''datasets'''),
    (r'''tf\.io\.gfile\.GFile''', r'''open'''),
    (r'''tf\.([\w\d]+)''', r'''datasets.Value(\'\1\')'''),
    (r'''tfds\.features\.Text\(\)''', r'''datasets.Value(\'string\')'''),
    (r'''tfds\.features\.Text\(''', r'''datasets.Value(\'string\'),'''),
    (r'''features\s*=\s*tfds.features.FeaturesDict\(''', r'''features=datasets.Features('''),
    (r'''tfds\.features\.FeaturesDict\(''', r'''dict('''),
    (r'''The TensorFlow Datasets Authors''', r'''The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'''),
    (r'''tfds\.''', r'''datasets.'''),
    (r'''dl_manager\.manual_dir''', r'''self.config.data_dir'''),
    (r'''self\.builder_config''', r'''self.config'''),
]


def  UpperCAmelCase__	(snake_case__							: Namespace   ):
  """simple docstring"""

  return ConvertCommand(args.tfds_path		,       args.datasets_directory   )






class  lowercase(					__a			):







 '''simple docstring'''





 @staticmethod
 def 			UpperCamelCase_       (    a_:		ArgumentParser		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  parser.add_parser(
       """convert""",				help="""Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.""",				)
   train_parser.add_argument(
       """--tfds_path""",				type=a_,				required=a_,				help="""Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.""",				)
   train_parser.add_argument(
       """--datasets_directory""",				type=a_,				required=a_,				help="""Path to the HuggingFace Datasets folder."""		)
   train_parser.set_defaults(func=a_		)


 def __init__(    self:		List[str],				a_:		str,				a_:		str,				*a_:		str		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  get_logger("""datasets-cli/converting"""		)

   _snake_case							:		Any								=  tfds_path
   _snake_case							:		Optional[Any]								=  datasets_directory


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   if os.path.isdir(self._tfds_path		):
     _snake_case							:		int								=  os.path.abspath(self._tfds_path		)
   elif os.path.isfile(self._tfds_path		):
     _snake_case							:		Any								=  os.path.dirname(self._tfds_path		)
   else:
     raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""		)

   _snake_case							:		Union[str, Any]								=  os.path.abspath(self._datasets_directory		)

   self._logger.info(f"Converting datasets from {abs_tfds_path} to {abs_datasets_path}"		)

   _snake_case							:		Tuple								=  []
   _snake_case							:		Dict								=  []
   _snake_case							:		Optional[Any]								=  {}

   if os.path.isdir(self._tfds_path		):
     _snake_case							:		List[str]								=  os.listdir(a_		)
   else:
     _snake_case							:		int								=  [os.path.basename(self._tfds_path		)]

   for f_name in file_names:
     self._logger.info(f"Looking at file {f_name}"		)
     _snake_case							:		Dict								=  os.path.join(a_,				a_		)
     _snake_case							:		Union[str, Any]								=  os.path.join(a_,				a_		)

     if not os.path.isfile(a_		) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
       self._logger.info("""Skipping file"""		)
       continue

     with open(a_,				encoding="""utf-8"""		) as f:
       _snake_case							:		str								=  f.readlines()

     _snake_case							:		List[str]								=  []
     _snake_case							:		Any								=  False
     _snake_case							:		Union[str, Any]								=  False
     _snake_case							:		Optional[Any]								=  []
     for line in lines:
       _snake_case							:		Optional[int]								=  line

       # Convert imports
       if "import tensorflow.compat.v2 as tf" in out_line:
         continue
       elif "@tfds.core" in out_line:
         continue
       elif "builder=self" in out_line:
         continue
       elif "import tensorflow_datasets.public_api as tfds" in out_line:
         _snake_case							:		Optional[Any]								=  """import datasets\n"""
       elif "import tensorflow" in out_line:
         # order is important here
         _snake_case							:		Optional[int]								=  """"""
         continue
       elif "from absl import logging" in out_line:
         _snake_case							:		int								=  """from datasets import logging\n"""
       elif "getLogger" in out_line:
         _snake_case							:		Any								=  out_line.replace("""getLogger""",				"""get_logger"""		)
       elif any(expression in out_line for expression in TO_HIGHLIGHT		):
         _snake_case							:		Union[str, Any]								=  True
         _snake_case							:		Optional[Any]								=  list(filter(lambda a_		: e in out_line,				a_		)		)
         out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(a_		) + """\n"""		)
         out_lines.append(a_		)
         out_lines.append(a_		)
         continue
       else:
         for pattern, replacement in TO_CONVERT:
           _snake_case							:		List[str]								=  re.sub(a_,				a_,				a_		)

                # Take care of saving utilities (to later move them together with main script)
       if "tensorflow_datasets" in out_line:
         _snake_case							:		Dict								=  re.match(r"""from\stensorflow_datasets.*import\s([^\.\r\n]+)""",				a_		)
         tfds_imports.extend(imp.strip() for imp in match.group(1		).split(""","""		)		)
         _snake_case							:		Optional[Any]								=  """from . import """ + match.group(1		)

       # Check we have not forget anything
       if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
         raise ValueError(f"Error converting {out_line.strip()}"		)

       if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
         _snake_case							:		Tuple								=  True
       out_lines.append(a_		)

     if is_builder or "wmt" in f_name:
       # We create a new directory for each dataset
       _snake_case							:		List[str]								=  f_name.replace(""".py""",				""""""		)
       _snake_case							:		str								=  os.path.join(a_,				a_		)
       _snake_case							:		str								=  os.path.join(a_,				a_		)
       os.makedirs(a_,				exist_ok=a_		)
       self._logger.info(f"Adding directory {output_dir}"		)
       imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}		)
     else:
       # Utilities will be moved at the end
       utils_files.append(a_		)

     if needs_manual_update:
       with_manual_update.append(a_		)

     with open(a_,				"""w""",				encoding="""utf-8"""		) as f:
       f.writelines(a_		)
     self._logger.info(f"Converted in {output_file}"		)

   for utils_file in utils_files:
     try:
       _snake_case							:		Optional[int]								=  os.path.basename(a_		)
       _snake_case							:		Optional[Any]								=  imports_to_builder_map[f_name.replace(""".py""",				""""""		)]
       self._logger.info(f"Moving {dest_folder} to {utils_file}"		)
       shutil.copy(a_,				a_		)
     except KeyError:
       self._logger.error(f"Cannot find destination folder for {utils_file}. Please copy manually."		)

   if with_manual_update:
     for file_path in with_manual_update:
       self._logger.warning(
           f"You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."		)


