


"""simple docstring"""
import numpy as np
from PIL import Image







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Tuple:
							snake_case_     							=		rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2989 * r + 0.5870 * g + 0.1140 * b







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Tuple:
							return (gray > 127) & (gray <= 255)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Any:
							snake_case_     							=		np.zeros_like(_A					)
							snake_case_     							=		np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)					)

							# Copy image to padded image
							snake_case_     							=		image

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


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

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







import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata

def  lowercase_						(						_A     :       str		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    min(re.search("^ +"       ,   _A		).end() if t.startswith(" "		) else 0 for t in string.splitlines()		)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A		)]		)


A :       Optional[int]						=   "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

A :       str						=   "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


A :       Optional[Any]						=   "\\n# Begin of markdown\n\nSome cool dataset card\n"







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             with tempfile.TemporaryDirectory() as tmp_dir:
                   lowerCamelCase__						:							List[str]       =    Path(__lowerCamelCase			) / "README.md"
                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Dict       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							Optional[Any]       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertDictEqual(__lowerCamelCase   , {}			)

                   with open(__lowerCamelCase   , "w+"			) as readme_file:
                         readme_file.write(__lowerCamelCase			)
                   lowerCamelCase__						:							int       =    DatasetMetadata.from_readme(__lowerCamelCase			)
                   self.assertEqual(__lowerCamelCase   , {}			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Dict       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "			)
             with self.assertRaises(__lowerCamelCase			):
                   DatasetMetadata.from_yaml_string(__lowerCamelCase			)

             lowerCamelCase__						:							Optional[Any]       =    _dedent(
                 "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "			)
             assert DatasetMetadata.from_yaml_string(__lowerCamelCase			)


