



'''simple docstring'''




from collections import defaultdict



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    bool:
							UpperCAmelCase  :	Dict							  =	first_str.lower().strip()
							UpperCAmelCase  :	Union[str, Any]							  =	second_str.lower().strip()

							# Remove whitespace
							UpperCAmelCase  :	Optional[int]							  =	first_str.replace(""" """			,       """"""	)
							UpperCAmelCase  :	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
							UpperCAmelCase  :	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[Any]      						=					input("""Enter the first string """).strip()
							a     :    Union[str, Any]      						=					input("""Enter the second string """).strip()

							a     :    Any      						=					check_anagrams(input_a, input_b)
							print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

