





from collections import defaultdict

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						total  # total no of tasks (N)

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

            _UpperCamelCase              =						defaultdict(A      )  # 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              =						(1 << len(A      )) - 1

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """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              =						self.count_ways_until(A,       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              =						total_ways_util

            return self.dp[mask][task_no]



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

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

        # 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    : Union[str, Any]    	=  5  # total no of tasks (the value of N)

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













import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers.generation import (
           MaxLengthCriteria,
           MaxNewTokensCriteria,
           MaxTimeCriteria,
           StoppingCriteriaList,
           validate_stopping_criteria,
       )

@require_torch
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

            with self.assertWarns(A      ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       11      )

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






