





"""simple docstring"""


from collections import defaultdict



class      __SCREAMING_SNAKE_CASE    :







		'''simple docstring'''


		def __init__(		self   :       str					,   __a   :       List[str]					,   __a   :       Tuple	)		->				Optional[Any]:
								_UpperCamelCase      :	Optional[Any]						=       total  # total no of tasks (N)

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

								_UpperCamelCase      :	List[Any]						=       defaultdict(__a	)  # 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
								_UpperCamelCase      :	List[Any]						=       (1 << len(__a	)) - 1

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       Optional[int]					,   __a   :       Any	)		->				Optional[int]:
								# 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
								_UpperCamelCase      :	Optional[Any]						=       self.count_ways_until(__a					,   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.
								_UpperCamelCase      :	Union[str, Any]						=       total_ways_util

								return self.dp[mask][task_no]


		def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Tuple	)		->				List[str]:
								# Store the list of persons for each task
								for i in range(len(__a	)	):
														for j in task_performed[i]:
																				self.task[j].append(__a	)

        # 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__":
		lowerCamelCase__				  =   5  # total no of tasks (the value of N)

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







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Any						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Dict						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	str						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	List[str]						=       layer_norm_eps
        _UpperCamelCase      :	Tuple						=       image_size
        _UpperCamelCase      :	Tuple						=       patch_size
        _UpperCamelCase      :	Dict						=       num_channels
        _UpperCamelCase      :	Any						=       qkv_bias
        _UpperCamelCase      :	str						=       num_detection_tokens
        _UpperCamelCase      :	str						=       use_mid_position_embeddings
        _UpperCamelCase      :	List[str]						=       auxiliary_loss
        # Hungarian matcher
        _UpperCamelCase      :	List[Any]						=       class_cost
        _UpperCamelCase      :	int						=       bbox_cost
        _UpperCamelCase      :	Optional[int]						=       giou_cost
        # Loss coefficients
        _UpperCamelCase      :	List[Any]						=       bbox_loss_coefficient
        _UpperCamelCase      :	str						=       giou_loss_coefficient
        _UpperCamelCase      :	Dict						=       eos_coefficient





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
