
"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''swinv2'''

		__lowercase							:  str						   =		{
		    '''num_attention_heads''': '''num_heads''',
		    '''num_hidden_layers''': '''num_layers''',
		}




		def __init__(     self							,  lowerCAmelCase__=2_2_4							,  lowerCAmelCase__=4							,  lowerCAmelCase__=3							,  lowerCAmelCase__=9_6							,  lowerCAmelCase__=[2, 2, 6, 2]							,  lowerCAmelCase__=[3, 6, 1_2, 2_4]							,  lowerCAmelCase__=7							,  lowerCAmelCase__=4.0							,  lowerCAmelCase__=True							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=False							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=1E-5							,  lowerCAmelCase__=3_2							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = image_size
				__SCREAMING_SNAKE_CASE            = patch_size
				__SCREAMING_SNAKE_CASE            = num_channels
				__SCREAMING_SNAKE_CASE            = embed_dim
				__SCREAMING_SNAKE_CASE            = depths
				__SCREAMING_SNAKE_CASE            = len(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = num_heads
				__SCREAMING_SNAKE_CASE            = window_size
				__SCREAMING_SNAKE_CASE            = mlp_ratio
				__SCREAMING_SNAKE_CASE            = qkv_bias
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = drop_path_rate
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = use_absolute_embeddings
				__SCREAMING_SNAKE_CASE            = layer_norm_eps
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = encoder_stride
				# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				__SCREAMING_SNAKE_CASE            = int(embed_dim * 2 ** (len(lowerCAmelCase__) - 1))
				__SCREAMING_SNAKE_CASE            = (0, 0, 0, 0)






"""simple docstring"""





from collections import defaultdict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = first_str.lower().strip()
		__SCREAMING_SNAKE_CASE            = second_str.lower().strip()

		# Remove whitespace
		__SCREAMING_SNAKE_CASE            = first_str.replace(""" """  ,				""""""				)
		__SCREAMING_SNAKE_CASE            = second_str.replace(""" """  ,				""""""				)

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

		# Default values for count should be 0
		__SCREAMING_SNAKE_CASE            = defaultdict(UpperCamelCase_				)

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(UpperCamelCase_				)				):
				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()
							__magic_name__       =			input("Enter the first string ").strip()
							__magic_name__       =			input("Enter the second string ").strip()

							__magic_name__       =			check_anagrams(input_a, input_b)
							print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")




