






'''simple docstring'''







from collections import defaultdict

class _lowercase     :




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Dict					):
														lowerCamelCase__ :     List[str]	  =	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(UpperCamelCase__					)					)
														]

														lowerCamelCase__ :     List[Any]	  =	defaultdict(UpperCamelCase__					)  # 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__ :     Optional[Any]	  =	(1 << len(UpperCamelCase__					)) - 1




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    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
														lowerCamelCase__ :     str	  =	self.count_ways_until(UpperCamelCase__      ,							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__ :     Optional[Any]	  =	total_ways_util

														return self.dp[mask][task_no]




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Optional[int]					):
														# Store the list of persons for each task
														for i in range(len(UpperCamelCase__					)					):
																					for j in task_performed[i]:
																												self.task[j].append(UpperCamelCase__					)

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

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









'''simple docstring'''







import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
			_A     :      Optional[int]   					=pd.read_csv('''sample_data.csv''', header=None)
			_A     :      Any   					=df.shape[:1][0]
			# If you're using some other dataset input the target column
			_A     :      List[str]   					=df.iloc[:, 1:2]
			_A     :      int   					=actual_data.values.reshape(len_data, 1)
			_A     :      Union[str, Any]   					=MinMaxScaler().fit_transform(actual_data)
			_A     :      Optional[int]   					=10
			_A     :      Union[str, Any]   					=5
			_A     :      Union[str, Any]   					=20
			_A     :      str   					=len_data - periods * look_back
			_A     :      List[Any]   					=actual_data[:division]
			_A     :      Optional[Any]   					=actual_data[division - look_back :]
			_A					,				_A     :      Tuple   					=[], []
			_A					,				_A     :      List[str]   					=[], []

			for i in range(0, len(train_data) - forward_days - look_back + 1):
						train_x.append(train_data[i : i + look_back])
						train_y.append(train_data[i + look_back : i + look_back + forward_days])
			for i in range(0, len(test_data) - forward_days - look_back + 1):
						test_x.append(test_data[i : i + look_back])
						test_y.append(test_data[i + look_back : i + look_back + forward_days])
			_A     :      List[Any]   					=np.array(train_x)
			_A     :      str   					=np.array(test_x)
			_A     :      List[Any]   					=np.array([list(i.ravel()) for i in train_y])
			_A     :      Any   					=np.array([list(i.ravel()) for i in test_y])

			_A     :      Optional[Any]   					=Sequential()
			model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
			model.add(LSTM(64, input_shape=(128, 1)))
			model.add(Dense(forward_days))
			model.compile(loss='''mean_squared_error''', optimizer='''adam''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

