


'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=2    ,   UpperCamelCase_=3    ,   UpperCamelCase_=64    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[int]												= np.random.default_rng(UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= length
							UpperCamelCase__   :Optional[int]												= rng.normal(size=(length,)							).astype(np.floataa							)
							UpperCamelCase__   :str												= a * self.x + b + rng.normal(scale=0.1    ,   size=(length,)							).astype(np.floataa							)




		def __len__(		self							):

							'''simple docstring'''


							return self.length




		def __getitem__(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							return {"x": self.x[i], "y": self.y[i]}



class    lowercase	(							torch.nn.Module					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=0    ,   UpperCamelCase_=0    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :Tuple												= torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
							UpperCamelCase__   :List[str]												= torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
							UpperCamelCase__   :Tuple												= True




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
												UpperCamelCase__   :Tuple												= False
							return x * self.a[0] + self.b[0]



class    lowercase	(							torch.nn.Module					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_=0    ,   UpperCamelCase_=0    ,   UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()
							UpperCamelCase__   :List[str]												= torch.nn.Parameter(torch.tensor(UpperCamelCase_							).float()							)
							UpperCamelCase__   :List[Any]												= torch.nn.Parameter(torch.tensor(UpperCamelCase_							).float()							)
							UpperCamelCase__   :str												= True




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_=None							):

							'''simple docstring'''


							if self.first_batch:
												print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''							)
												UpperCamelCase__   :Optional[int]												= False
							return x * self.a + self.b



def 			a      (			__a ,					__a = 16   )  ->	Union[str, Any]:



					'''simple docstring'''






					from datasets import load_dataset
					from transformers import AutoTokenizer

					UpperCamelCase__   :Any												= AutoTokenizer.from_pretrained('''bert-base-cased'''   )
					UpperCamelCase__   :Tuple												= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
					UpperCamelCase__   :Dict												= load_dataset('''csv''' ,					data_files=__a   )
					UpperCamelCase__   :int												= datasets['''train'''].unique('''label'''   )

					UpperCamelCase__   :List[str]												= {v: i for i, v in enumerate(__a   )}

					def tokenize_function(__a   ):
										# max_length=None => use the model max length (it's actually the default)
										UpperCamelCase__   :Any												= tokenizer(
										    examples['''sentence1'''] ,					examples['''sentence2'''] ,					truncation=__a ,					max_length=__a ,					padding='''max_length'''   )
										if "label" in examples:
															UpperCamelCase__   :str												= [label_to_id[l] for l in examples['''label''']]
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					UpperCamelCase__   :str												= datasets.map(
					    __a ,					batched=__a ,					remove_columns=['''sentence1''', '''sentence2''', '''label'''] ,					)

					def collate_fn(__a   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(__a ,					padding='''max_length''' ,					max_length=128 ,					return_tensors='''pt'''   )
										return tokenizer.pad(__a ,					padding='''longest''' ,					return_tensors='''pt'''   )

					# Instantiate dataloaders.
					UpperCamelCase__   :int												= DataLoader(tokenized_datasets['''train'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=2   )
					UpperCamelCase__   :Dict												= DataLoader(tokenized_datasets['''validation'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=1   )

					return train_dataloader, eval_dataloader



'''simple docstring'''
from collections import defaultdict
class    lowercase	:

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :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
							UpperCamelCase__   :Union[str, Any]												= [
							    [-1 for i in range(total + 1							)] for j in range(2 ** len(UpperCamelCase_							)							)
							]

							UpperCamelCase__   :str												= 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
							UpperCamelCase__   :Optional[int]												= (1 << len(UpperCamelCase_							)) - 1




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							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__   :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.
							UpperCamelCase__   :Optional[int]												= total_ways_util

							return self.dp[mask][task_no]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


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

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