





from collections import defaultdict





class 					A__       :




   """simple docstring"""



   def __init__(   self   ,    lowercase   ,    lowercase)     ->		List[Any]:







          '''simple docstring'''

          a__		:      Tuple					=							total  # total no of tasks (N)

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

          a__		:      Tuple					=							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
          a__		:      Tuple					=							(1 << len(SCREAMING_SNAKE_CASE_)) - 1

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase)     ->		Dict:







          '''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__		:      str					=							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.
          a__		:      Tuple					=							total_ways_util

          return self.dp[mask][task_no]

   def 				__lowercase						(   self   ,    lowercase)     ->		List[Any]:







          '''simple docstring'''

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

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




"""simple docstring"""




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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							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     )     )