


'''simple docstring'''


from collections import defaultdict







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       str     		=      first_str.lower().strip()
				_SCREAMING_SNAKE_CASE :       Dict     		=      second_str.lower().strip()

				# Remove whitespace
				_SCREAMING_SNAKE_CASE :       List[str]     		=      first_str.replace(""" """							,      """"""     )
				_SCREAMING_SNAKE_CASE :       int     		=      second_str.replace(""" """							,      """"""     )

				# Strings of different lengths are not anagrams
				if len(SCREAMING_SNAKE_CASE__     ) != len(SCREAMING_SNAKE_CASE__     ):
								return False

				# Default values for count should be 0
				_SCREAMING_SNAKE_CASE :       defaultdict[str, int]     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				# For each character in input strings,
				# increment count in the corresponding
				for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
								count[first_str[i]] += 1
								count[second_str[i]] -= 1

				return all(_count == 0 for _count in count.values()     )


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

				testmod()
				UpperCAmelCase_		:   List[Any]							   =		input('Enter the first string ').strip()
				UpperCAmelCase_		:   int							   =		input('Enter the second string ').strip()

				UpperCAmelCase_		:   List[Any]							   =		check_anagrams(input_a, input_b)
				print(F"{input_a} and {input_b} are {'' if status else 'not '}anagrams.")








'''simple docstring'''


UpperCAmelCase_		:   Union[str, Any]							   =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_		:   Dict							   =		[{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_		:   Any							   =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




