

class       __snake_case  :  # Public class to implement a graph

		def __init__(					self						,		_A						,		_A						,		_A):
						SCREAMING_SNAKE_CASE_									=						row
						SCREAMING_SNAKE_CASE_									=						col
						SCREAMING_SNAKE_CASE_									=						graph

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
						return (
						    0 <= i < self.ROW
						    and 0 <= j < self.COL
						    and not visited[i][j]
						    and self.graph[i][j]
						)

		def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
						# Checking all 8 elements surrounding nth element
						SCREAMING_SNAKE_CASE_									=						[-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
						SCREAMING_SNAKE_CASE_									=						[-1, 0, 1, -1, 1, -1, 0, 1]
						SCREAMING_SNAKE_CASE_									=						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 						lowerCAmelCase__     (					self):  # And finally, count all islands.
						SCREAMING_SNAKE_CASE_									=						[[False for j in range(self.COL)] for i in range(self.ROW)]
						SCREAMING_SNAKE_CASE_									=						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





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


UpperCamelCase__  :      List[Any]			=    logging.get_logger(__name__)

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



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Any						 =  'biogpt'

  def __init__(					self						,		_A=42384						,		_A=1024						,		_A=24						,		_A=16						,		_A=4096						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=1024						,		_A=0.0_2						,		_A=1E-12						,		_A=True						,		_A=True						,		_A=0.0						,		_A=0.0						,		_A=1						,		_A=0						,		_A=2						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						scale_embedding
      SCREAMING_SNAKE_CASE_									=						use_cache
      SCREAMING_SNAKE_CASE_									=						layerdrop
      SCREAMING_SNAKE_CASE_									=						activation_dropout
      super().__init__(pad_token_id=_A						,		bos_token_id=_A						,		eos_token_id=_A						,		**_A)


