





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


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Optional[Any]				      = 'xglm'
   _UpperCamelCase					:	str				      = ['past_key_values']

   _UpperCamelCase					:	Union[str, Any]				      = {
       'num_attention_heads': 'attention_heads',
       'hidden_size': 'd_model',
       'num_hidden_layers': 'num_layers',
   }


   def __init__(					self    :       Any    ,  a    :       List[Any]=256_008    ,  a    :       List[str]=2_048    ,  a    :       List[str]=1_024    ,  a    :       int=4_096    ,  a    :       int=24    ,  a    :       List[str]=16    ,  a    :       Dict="gelu"    ,  a    :       Optional[int]=0.1    ,  a    :       Optional[Any]=0.1    ,  a    :       Optional[Any]=0.0    ,  a    :       int=0.0    ,  a    :       Optional[int]=0.02    ,  a    :       Union[str, Any]=True    ,  a    :       List[Any]=True    ,  a    :       Any=2    ,  a    :       Dict=1    ,  a    :       str=0    ,  a    :       Tuple=2    ,  **a    :       Optional[Any]    ,  )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						vocab_size
     lowercase__			    =						max_position_embeddings
     lowercase__			    =						d_model
     lowercase__			    =						ffn_dim
     lowercase__			    =						num_layers
     lowercase__			    =						attention_heads
     lowercase__			    =						activation_function
     lowercase__			    =						dropout
     lowercase__			    =						attention_dropout
     lowercase__			    =						activation_dropout
     lowercase__			    =						layerdrop
     lowercase__			    =						init_std
     lowercase__			    =						scale_embedding  # scale factor will be sqrt(d_model) if True
     lowercase__			    =						use_cache

     super().__init__(
         pad_token_id=a    ,  bos_token_id=a    ,  eos_token_id=a    ,  decoder_start_token_id=a    ,  **a    ,  )









class        SCREAMING_SNAKE_CASE     :  # Public class to implement a graph


   def __init__(					self    :       int    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						row
     lowercase__			    =						col
     lowercase__			    =						graph


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     bool:





     """simple docstring"""







     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       list[list[bool]] )->     None:





     """simple docstring"""







     lowercase__			    =						[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     lowercase__			    =						[-1, 0, 1, -1, 1, -1, 0, 1]
     lowercase__			    =						True  # Make those cells visited
     for k in range(8 ):
       if self.is_safe(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a ):
         self.diffs(i + row_nbr[k]    ,  j + col_nbr[k]    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     int:  # And finally, count all islands.





     """simple docstring"""







     lowercase__			    =						[[False for j in range(self.COL )] for i in range(self.ROW )]
     lowercase__			    =						0
     for i in range(self.ROW ):
       for j in range(self.COL ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(a    ,  a    ,  a )
           count += 1
     return count


