
"""simple docstring"""






from collections import defaultdict





def __A						(   a_ :							str		,		a_ :							str     )->							bool:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= first_str.lower().strip()
		SCREAMING_SNAKE_CASE       :     int				= second_str.lower().strip()

		# Remove whitespace
		SCREAMING_SNAKE_CASE       :     Optional[int]				= first_str.replace(''' '''		,		''''''     )
		SCREAMING_SNAKE_CASE       :     str				= second_str.replace(''' '''		,		''''''     )

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

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

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(a_     )     ):
				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()
					lowerCamelCase__ :       Tuple						     =			input("Enter the first string ").strip()
					lowerCamelCase__ :       List[str]						     =			input("Enter the second string ").strip()

					lowerCamelCase__ :       Optional[int]						     =			check_anagrams(input_a, input_b)
					print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





