code: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert (
             c % greatest_common_divisor(a,		a					) == 0
         )  # greatest_common_divisor(a,b) function implemented below
         ((_a)   , (_a)   , (_a))          =				extended_gcd(a,		a					)  # extended_gcd(a,b) function implemented below
         _a          =				c / d
         return (r * x, r * y)
  
  
  
  def 						__a      (  a,		a,		a,		a = 2					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         ((_a)   , (_a))          =				diophantine(a,		a,		a					)  # Initial value
         _a          =				greatest_common_divisor(a,		a					)
         _a          =				a // d
         _a          =				b // d
  
         for i in range(a					):
                _a          =				xa + i * q
                _a          =				ya - i * p
                print(a,		a					)
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         if a < b:
                _a   , _a          =				b, a
  
         while a % b != 0:
                _a   , _a          =				b, a % b
  
         return b
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert a >= 0 and b >= 0
  
         if b == 0:
                _a   , _a   , _a          =				a, 1, 0
         else:
                ((_a)   , (_a)   , (_a))          =				extended_gcd(a,		a % b					)
                _a          =				q
                _a          =				p - q * (a // b)
  
         assert a % d == 0 and b % d == 0
         assert d == a * x + b * y
  
         return (d, x, y)
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod(name="""diophantine""", verbose=True)
     testmod(name="""diophantine_all_soln""", verbose=True)
     testmod(name="""extended_gcd""", verbose=True)
     testmod(name="""greatest_common_divisor""", verbose=True)
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
      # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			'qdqbert'
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:List[Any]=30_522				,  UpperCamelCase__							:Optional[int]=768				,  UpperCamelCase__							:str=12				,  UpperCamelCase__							:Tuple=12				,  UpperCamelCase__							:Any=3_072				,  UpperCamelCase__							:Union[str, Any]="gelu"				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:Any=0.1				,  UpperCamelCase__							:Dict=512				,  UpperCamelCase__							:Optional[int]=2				,  UpperCamelCase__							:Any=0.02				,  UpperCamelCase__							:Optional[int]=1E-12				,  UpperCamelCase__							:str=True				,  UpperCamelCase__							:str=1				,  UpperCamelCase__							:List[Any]=0				,  UpperCamelCase__							:List[str]=2				,  **UpperCamelCase__							:Optional[Any]				,  ):
            super().__init__(pad_token_id=UpperCamelCase__				,  bos_token_id=UpperCamelCase__				,  eos_token_id=UpperCamelCase__				,  **UpperCamelCase__       )
  
            _a          =				vocab_size
            _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
            _a          =				use_cache
  