






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase					  =   min(re.search('^ +'			,							__SCREAMING_SNAKE_CASE							).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(__SCREAMING_SNAKE_CASE							)]							)


UpperCAmelCase     				=			'''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

UpperCAmelCase     				=			'''\
---
---
# Begin of markdown

Some cool dataset card
'''


UpperCAmelCase     				=			'''\
# Begin of markdown

Some cool dataset card
'''







class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


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

			with open(snake_case		,   'w+'	) as readme_file:
				readme_file.write(snake_case	)
			lowercase					  =   DatasetMetadata.from_readme(snake_case	)
			self.assertDictEqual(snake_case		,   {}	)

			with open(snake_case		,   'w+'	) as readme_file:
				readme_file.write(snake_case	)
			lowercase					  =   DatasetMetadata.from_readme(snake_case	)
			self.assertEqual(snake_case		,   {}	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   _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(snake_case	)

		lowercase					  =   _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(snake_case	):
			DatasetMetadata.from_yaml_string(snake_case	)

		lowercase					  =   _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(snake_case	)



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

