





def      A__       (    SCREAMING_SNAKE_CASE_   :      list[int]				,	SCREAMING_SNAKE_CASE_   :      list[int]				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   return not any(
       neighbour == 1 and colored_vertices[i] == color
       for i, neighbour in enumerate(SCREAMING_SNAKE_CASE_						)						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      list[list[int]]				,	SCREAMING_SNAKE_CASE_   :      int				,	SCREAMING_SNAKE_CASE_   :      list[int]				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if index == len(SCREAMING_SNAKE_CASE_						):
      return True

   # Recursive Step
   for i in range(SCREAMING_SNAKE_CASE_						):
      if valid_coloring(graph[index]				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
         # Color current vertex
         _UpperCAmelCase           =   i
         # Validate coloring
         if util_color(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	index + 1						):
            return True
         # Backtrack
         _UpperCAmelCase           =   -1
   return False
def      A__       (    SCREAMING_SNAKE_CASE_   :      list[list[int]]				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  list[int]:






   """simple docstring"""







   _UpperCAmelCase           =   [-1] * len(SCREAMING_SNAKE_CASE_						)

   if util_color(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	0						):
      return colored_vertices

   return []






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


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   """biogpt"""





     def __init__(     self				,							_UpperCamelCase=42384				,							_UpperCamelCase=1024				,							_UpperCamelCase=24				,							_UpperCamelCase=16				,							_UpperCamelCase=4096				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=1024				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-12				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.0				,							_UpperCamelCase=1				,							_UpperCamelCase=0				,							_UpperCamelCase=2				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   scale_embedding
        _UpperCAmelCase           =   use_cache
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   activation_dropout
        super().__init__(pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)