


"""simple docstring"""
def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Optional[Any]:
							return len_board * len_board_column * row + column







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			)     ->					Any:




							if board[row][column] != word[word_index]:
														return False

							if word_index == len(_A					) - 1:
														return True

							snake_case_     							=		[(0, 1), (0, -1), (-1, 0), (1, 0)]
							snake_case_     							=		len(_A					)
							snake_case_     							=		len(board[0]					)
							for direction in traverts_directions:
														snake_case_     							=		row + direction[0]
														snake_case_     							=		column + direction[1]
														if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																					continue

														snake_case_     							=		get_point_key(_A					,			_A					,			_A					,			_A					)
														if key in visited_points_set:
																					continue

														visited_points_set.add(_A					)
														if exits_word(_A					,			_A					,			_A					,			_A					,			word_index + 1					,			_A					):
																					return True

														visited_points_set.remove(_A					)

							return False







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					List[str]:
							snake_case_     							=		(
							    "The board should be a non empty matrix of single chars strings."
							)

							snake_case_     							=		len(_A					)
							if not isinstance(_A					,			_A					) or len(_A					) == 0:
														raise ValueError(_A					)

							for row in board:
														if not isinstance(_A					,			_A					) or len(_A					) == 0:
																					raise ValueError(_A					)

														for item in row:
																					if not isinstance(_A					,			_A					) or len(_A					) != 1:
																												raise ValueError(_A					)

    # Validate word
							if not isinstance(_A					,			_A					) or len(_A					) == 0:
														raise ValueError(
														    """The word parameter should be a string of length greater than 0."""					)

							snake_case_     							=		len(board[0]					)
							for i in range(_A					):
														for j in range(_A					):
																					if exits_word(
																					    _A					,			_A					,			_A					,			_A					,			0					,			{get_point_key(_A					,			_A					,			_A					,			_A					)}					):
																												return True

							return False


if __name__ == "__main__":
				import doctest

				doctest.testmod()







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			)


