




from dataclasses import dataclass

__A						:		Dict          =						"0.1"
__A						:		List[Any]          =						"Lucia Harcekova"

@dataclass
class 		lowercase_       :
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= 42




					def __repr__(   self:		Union[str, Any]):


									'''simple docstring'''


									return f'''({self.offset}, {self.length}, {self.indicator})'''





class 		lowercase_       :




					def __init__(   self:		Union[str, Any],     _lowercase:		int = 13,     _lowercase:		int = 6):


									'''simple docstring'''


									__lowerCAmelCase					=  window_size
									__lowerCAmelCase					=  lookahead_buffer_size
									__lowerCAmelCase					=  self.window_size - self.lookahead_buffer_size




					def     _lowercase						(   self:		Any,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  []
									__lowerCAmelCase					=  """"""

									# while there are still characters in text to compress
									while text:
													# find the next encoding phrase
													# - triplet with offset, length, indicator (the next encoding character)
													__lowerCAmelCase					=  self._find_encoding_token(_lowercase,     _lowercase)

													# update the search buffer:
													# - add new characters from text into it
													# - check if size exceed the max search buffer size, if so, drop the
													#   oldest elements
													search_buffer += text[: token.length + 1]
													if len(_lowercase) > self.search_buffer_size:
																	__lowerCAmelCase					=  search_buffer[-self.search_buffer_size :]

													# update the text
													__lowerCAmelCase					=  text[token.length + 1 :]

													# append the token to output
													output.append(_lowercase)

									return output




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		list[Token]):


									'''simple docstring'''


									__lowerCAmelCase					=  """"""

									for token in tokens:
													for _ in range(token.length):
																	output += output[-token.offset]
													output += token.indicator

									return output




					def     _lowercase						(   self:		Any,     _lowercase:		str,     _lowercase:		str):


									'''simple docstring'''


									if not text:
													raise ValueError("""We need some text to work with.""")

									# Initialise result parameters to default values
									__lowerCAmelCase	,				__lowerCAmelCase					=  0, 0

									if not search_buffer:
													return Token(_lowercase,     _lowercase,     text[length])

									for i, character in enumerate(_lowercase):
													__lowerCAmelCase					=  len(_lowercase) - i
													if character == text[0]:
																	__lowerCAmelCase					=  self._match_length_from_index(_lowercase,     _lowercase,     0,     _lowercase)
																	# if the found length is bigger than the current or if it's equal,
																	# which means it's offset is smaller: update offset and length
																	if found_length >= length:
																					__lowerCAmelCase	,				__lowerCAmelCase					=  found_offset, found_length

									return Token(_lowercase,     _lowercase,     text[length])







					def     _lowercase						(   self:		Dict,     _lowercase:		str,     _lowercase:		str,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									if not text or text[text_index] != window[window_index]:
													return 0
									return 1 + self._match_length_from_index(
									    _lowercase,     window + text[text_index],     text_index + 1,     window_index + 1)


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	# Initialize compressor class
	__A						:		Tuple          =						LZaaCompressor(window_size=13, lookahead_buffer_size=6)

	# Example
	__A						:		str          =						"cabracadabrarrarrad"
	__A						:		Optional[Any]          =						lzaa_compressor.compress(TEXT)
	print(lzaa_compressor.compress("ababcbababaa"))
	__A						:		Tuple          =						lzaa_compressor.decompress(compressed_text)
	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."











import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     str:







				'''simple docstring'''


				__lowerCAmelCase					=  min(re.search("""^ +"""						,			UpperCamelCase__   ).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(UpperCamelCase__   )]   )


__A						:		str          =						"\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

__A						:		int          =						"\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


__A						:		Any          =						"\\n# Begin of markdown\n\nSome cool dataset card\n"

class 		lowercase_       (	unittest.TestCase				):




					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


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

													with open(_lowercase,     """w+""") as readme_file:
																	readme_file.write(_lowercase)
													__lowerCAmelCase					=  DatasetMetadata.from_readme(_lowercase)
													self.assertDictEqual(_lowercase,     {})

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







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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

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





