

from collections import defaultdict

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str )					->				bool:
	A__							:   List[Any]					       =		first_str.lower().strip()
	A__							:   List[Any]					       =		second_str.lower().strip()

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

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

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

	# For each character in input strings,
	# increment count in the corresponding
	for i in range(len(lowercase_ ) ):
		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()
	A_	:			List[str]  	=      input('Enter the first string ').strip()
	A_	:			Dict  	=      input('Enter the second string ').strip()

	A_	:			Dict  	=      check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		args[1:]

						if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

						if audio is not None:
							A__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



