

from collections import defaultdict

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			total  # total no of tasks (N)

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

							A__        =			defaultdict(lowercase       )  # 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
							A__        =			(1 << len(lowercase       )) - 1


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Any:
							'''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
							A__        =			self.count_ways_until(lowercase      ,      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.
							A__        =			total_ways_util

							return self.dp[mask][task_no]


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						str:
							'''simple docstring'''






							for i in range(len(lowercase       )       ):
											for j in task_performed[i]:
															self.task[j].append(lowercase       )

        # 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
	    )
	)




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
	import numpy as np
	import tensorflow as tf

	from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			@slow
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			TFCamembertModel.from_pretrained("jplu/tf-camembert-base"       )

							A__        =			tf.convert_to_tensor(
							    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,      dtype=tf.intaa      ,      )  # J'aime le camembert !"

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			tf.TensorShape((1, 10, 768)       )
							self.assertEqual(output.shape      ,      lowercase       )
							# compare the actual values for a slice.
							A__        =			tf.convert_to_tensor(
							    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      dtype=tf.floataa      ,      )
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(np.allclose(output[:, :3, :3].numpy()      ,      expected_slice.numpy()      ,      atol=1e-4       )       )

