'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[str] ,   __UpperCAmelCase  :					str			)							->	List[str]:
  if name != "training_function" and name != "main":
    raise ValueError(f"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""			)
  lowercase				,      lowercase				:							int									=   [], False
  for line in lines:
    if not found_start and f"""def {name}""" in line:
      lowercase				:							List[Any]									=   True
      good_lines.append(__UpperCAmelCase			)
      continue
    if found_start:
      if name == "training_function" and "def main" in line:
        return good_lines
      if name == "main" and "if __name__" in line:
        return good_lines
      good_lines.append(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[str]			)							->	Union[str, Any]:
  return [line for line in lines if not line.lstrip().startswith('''#'''			) and line != "\n"]







def 				__UpperCamelCase (					__UpperCAmelCase  :					str ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					bool ,   __UpperCAmelCase  :					str = None			)							->	Union[str, Any]:
  with open(__UpperCAmelCase ,   '''r'''			) as f:
    lowercase				:							Optional[int]									=   f.readlines()
  with open(os.path.abspath(os.path.join('''examples''' ,   '''nlp_example.py'''			)			) ,   '''r'''			) as f:
    lowercase				:							List[Any]									=   f.readlines()
  with open(__UpperCAmelCase ,   '''r'''			) as f:
    lowercase				:							Union[str, Any]									=   f.readlines()
  if secondary_filename is not None:
    with open(__UpperCAmelCase ,   '''r'''			) as f:
      lowercase				:							Union[str, Any]									=   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
  if parser_only:
    lowercase				:							Union[str, Any]									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''main'''			)			)
    lowercase				:							Any									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''main'''			)			)
    lowercase				:							Any									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''main'''			)			)
    if secondary_filename is not None:
      lowercase				:							Dict									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''main'''			)			)
  else:
    lowercase				:							Optional[Any]									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''training_function'''			)			)
    lowercase				:							Optional[Any]									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''training_function'''			)			)
    lowercase				:							Optional[Any]									=   clean_lines(get_function_contents_by_name(__UpperCAmelCase ,   '''training_function'''			)			)
    if secondary_filename is not None:
      lowercase				:							Dict									=   clean_lines(
          get_function_contents_by_name(__UpperCAmelCase ,   '''training_function'''			)			)

  lowercase				:							int									=   '''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

  # Specific code in our script that differs from the full version, aka what is new
  lowercase				:							Optional[int]									=   []
  lowercase				:							Union[str, Any]									=   []  # We keep track of the idxs just in case it's a repeated statement
  lowercase				:							Optional[int]									=   iter(__UpperCAmelCase			)
  for i in range(len(__UpperCAmelCase			) - 1			):
    if i not in passed_idxs:
      lowercase				:							Optional[Any]									=   next(__UpperCAmelCase			)
      if (line not in full_file_func) and (line.lstrip() != _dl_line):
        if "TESTING_MOCKED_DATALOADERS" not in line:
          new_feature_code.append(__UpperCAmelCase			)
          passed_idxs.append(__UpperCAmelCase			)
        else:
          # Skip over the `config['num_epochs'] = 2` statement
          lowercase				:							Dict									=   next(__UpperCAmelCase			)

    # Extract out just the new parts from the full_file_training_func
  lowercase				:							str									=   []
  lowercase				:							Optional[Any]									=   []  # We keep track of the idxs just in case it's a repeated statement
  for i, line in enumerate(__UpperCAmelCase			):
    if i not in passed_idxs:
      if (line not in full_file_func) and (line.lstrip() != _dl_line):
        if "TESTING_MOCKED_DATALOADERS" not in line:
          new_full_example_parts.append(__UpperCAmelCase			)
          passed_idxs.append(__UpperCAmelCase			)

    # Finally, get the overall diff
  lowercase				:							Optional[int]									=   [line for line in new_feature_code if line not in new_full_example_parts]
  if secondary_filename is not None:
    lowercase				:							Optional[Any]									=   [line for line in full_file_contents if line not in secondary_file_func]
    lowercase				:							int									=   [line for line in diff_from_example if line not in diff_from_two]

  return diff_from_example
'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]			):
    for y in range(image.shape[0]			):
      lowercase				:							str									=   (
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      lowercase				:							Tuple									=   int(summation == 5			)
  return output


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
 # read original image
 _A		:      Optional[int]						   =np.array(Image.open(r'''..\image_data\lena.jpg'''))
 # Apply erosion operation to a binary image
 _A		:      Optional[int]						   =erosion(grayabinary(rgbagray(image)), structuring_element)
 # Save the output image
 _A		:      int						   =Image.fromarray(output).convert('''RGB''')
 pil_img.save('''result_erosion.png''')