



"""simple docstring"""






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


_lowercase			:			List[str]										=      logging.get_logger(__name__)

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

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'biogpt'



					def __init__(	self							:  Dict,  lowerCamelCase							:  int=4_2384,  lowerCamelCase							:  List[str]=1024,  lowerCamelCase							:  Tuple=24,  lowerCamelCase							:  str=16,  lowerCamelCase							:  Tuple=4096,  lowerCamelCase							:  List[str]="gelu",  lowerCamelCase							:  Dict=0.1,  lowerCamelCase							:  Optional[Any]=0.1,  lowerCamelCase							:  List[Any]=1024,  lowerCamelCase							:  Union[str, Any]=0.02,  lowerCamelCase							:  Any=1E-12,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  str=0.0,  lowerCamelCase							:  Union[str, Any]=1,  lowerCamelCase							:  Dict=0,  lowerCamelCase							:  Optional[Any]=2,  **lowerCamelCase							:  Optional[Any],  )-> int:
								lowerCamelCase__		:		List[str]		     =vocab_size
								lowerCamelCase__		:		Optional[int]		     =max_position_embeddings
								lowerCamelCase__		:		Tuple		     =hidden_size
								lowerCamelCase__		:		Union[str, Any]		     =num_hidden_layers
								lowerCamelCase__		:		int		     =num_attention_heads
								lowerCamelCase__		:		str		     =intermediate_size
								lowerCamelCase__		:		Tuple		     =hidden_act
								lowerCamelCase__		:		Dict		     =hidden_dropout_prob
								lowerCamelCase__		:		int		     =attention_probs_dropout_prob
								lowerCamelCase__		:		List[Any]		     =initializer_range
								lowerCamelCase__		:		Dict		     =layer_norm_eps
								lowerCamelCase__		:		List[str]		     =scale_embedding
								lowerCamelCase__		:		Any		     =use_cache
								lowerCamelCase__		:		int		     =layerdrop
								lowerCamelCase__		:		str		     =activation_dropout
								super().__init__(pad_token_id=lowerCamelCase,  bos_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  **lowerCamelCase					)









"""simple docstring"""






from collections import defaultdict

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  Union[str, Any],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		List[Any]		     =total  # total no of tasks (N)

								# DP table will have a dimension of (2^M)*N
								# initially all values are set to -1
								lowerCamelCase__		:		Optional[Any]		     =[
								    [-1 for i in range(total + 1					)] for j in range(2 ** len(lowerCamelCase					)					)
								]

								lowerCamelCase__		:		Any		     =defaultdict(lowerCamelCase					)  # stores the list of persons for each task

								# final_mask is used to check if all persons are included by setting all bits
								# to 1
								lowerCamelCase__		:		List[Any]		     =(1 << len(lowerCamelCase					)) - 1



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  str,  lowerCamelCase							:  Any					)-> Any:
								# if mask == self.finalmask all persons are distributed tasks, return 1
								if mask == self.final_mask:
											return 1

								# if not everyone gets the task and no more tasks are available, return 0
								if task_no > self.total_tasks:
											return 0

								# if case already considered
								if self.dp[mask][task_no] != -1:
											return self.dp[mask][task_no]

								# Number of ways when we don't this task in the arrangement
								lowerCamelCase__		:		Optional[int]		     =self.count_ways_until(lowerCamelCase,  task_no + 1					)

								# now assign the tasks one by one to all possible persons and recursively
								# assign for the remaining tasks.
								if task_no in self.task:
											for p in self.task[task_no]:
														# if p is already given a task
														if mask & (1 << p):
																	continue

														# assign this task to p and change the mask value. And recursively
														# assign tasks with the new mask value.
														total_ways_util += self.count_ways_until(mask | (1 << p),  task_no + 1					)

        # save the value.
								lowerCamelCase__		:		int		     =total_ways_util

								return self.dp[mask][task_no]




					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  Dict					)-> int:
								# Store the list of persons for each task
								for i in range(len(lowerCamelCase					)					):
											for j in task_performed[i]:
														self.task[j].append(lowerCamelCase					)

        # call the function to fill the DP table, final answer is stored in dp[0][1]
								return self.count_ways_until(0,  1					)


if __name__ == "__main__":
		_lowercase			:			Tuple										=						5  # total no of tasks (the value of N)

		# the list of tasks that can be done by M persons.
		_lowercase			:			Dict										=						[[1, 3, 4], [1, 2, 5], [3, 4]]
		print(
		    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
		        task_performed
		    )
		)




