




from ... import PretrainedConfig


__a          =			{
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
    lowerCAmelCase        =				'''nezha'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=21_128       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-12       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=True       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[Any]:
         super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,bos_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      vocab_size
         UpperCAmelCase_      :							int       =      hidden_size
         UpperCAmelCase_      :							Optional[Any]       =      num_hidden_layers
         UpperCAmelCase_      :							List[Any]       =      num_attention_heads
         UpperCAmelCase_      :							int       =      hidden_act
         UpperCAmelCase_      :							List[Any]       =      intermediate_size
         UpperCAmelCase_      :							List[Any]       =      hidden_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Dict       =      max_position_embeddings
         UpperCAmelCase_      :							str       =      max_relative_position
         UpperCAmelCase_      :							Optional[int]       =      type_vocab_size
         UpperCAmelCase_      :							Dict       =      initializer_range
         UpperCAmelCase_      :							Tuple       =      layer_norm_eps
         UpperCAmelCase_      :							Any       =      classifier_dropout
         UpperCAmelCase_      :							List[str]       =      use_cache





from collections import defaultdict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      first_str.lower().strip()
     UpperCAmelCase_      :							Any       =      second_str.lower().strip()

     # Remove whitespace
     UpperCAmelCase_      :							Any       =      first_str.replace(''' '''			,						''''''				)
     UpperCAmelCase_      :							int       =      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          =			input('Enter the first string ').strip()
     __a          =			input('Enter the second string ').strip()

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