from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :		Optional[int]							=	logging.get_logger(__name__)

lowerCamelCase_    :		Dict							=	{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class 						a__							(       a__		):
			A__    :       Union[str, Any]        =      """canine"""



			def __init__(						self				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=1_6_3_8_4				,						UpperCAmelCase=1_6				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=0				,						UpperCAmelCase=0Xe000				,						UpperCAmelCase=0Xe001				,						UpperCAmelCase=4				,						UpperCAmelCase=4				,						UpperCAmelCase=8				,						UpperCAmelCase=1_6_3_8_4				,						UpperCAmelCase=1_2_8				,						**UpperCAmelCase				,						)			->   List[str]:
								super().__init__(pad_token_id=lowerCAmelCase__				,						bos_token_id=lowerCAmelCase__				,						eos_token_id=lowerCAmelCase__				,						**lowerCAmelCase__   )

								__a		 =       max_position_embeddings
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       intermediate_size
								__a		 =       hidden_act
								__a		 =       hidden_dropout_prob
								__a		 =       attention_probs_dropout_prob
								__a		 =       initializer_range
								__a		 =       type_vocab_size
								__a		 =       layer_norm_eps

								# Character config:
								__a		 =       downsampling_rate
								__a		 =       upsampling_kernel_size
								__a		 =       num_hash_functions
								__a		 =       num_hash_buckets
								__a		 =       local_transformer_stride






from collections import defaultdict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       first_str.lower().strip()
					__a		 =       second_str.lower().strip()

					# Remove whitespace
					__a		 =       first_str.replace(' '				,    ''						)
					__a		 =       second_str.replace(' '				,    ''						)

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

					# Default values for count should be 0
					__a		 =       defaultdict(__lowerCamelCase						)

					# For each character in input strings,
					# increment count in the corresponding
					for i in range(len(__lowerCamelCase						)						):
										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_    :		List[str]							=	input("""Enter the first string """).strip()
	lowerCamelCase_    :		Optional[Any]							=	input("""Enter the second string """).strip()

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




