
from collections import defaultdict

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	total  # total no of tasks (N)

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

						__lowerCamelCase    					=	defaultdict(SCREAMING_SNAKE_CASE__  )  # 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    					=	(1 << len(SCREAMING_SNAKE_CASE__  )) - 1






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							str:
						# 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    					=	self.count_ways_until(SCREAMING_SNAKE_CASE__							,			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    					=	total_ways_util

						return self.dp[mask][task_no]







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

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

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Any												=    _LazyModule(__name__, globals()["__file__"], _import_structure)



