






from collections import defaultdict
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Tuple							)						->	Dict:


         """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(lowerCamelCase							)							)
         ]

         _UpperCAmelCase							=    defaultdict(lowerCamelCase							)  # 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(lowerCamelCase							)) - 1


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			Any							)						->	List[Any]:


         """simple docstring"""
         # 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
         _UpperCAmelCase							=    self.count_ways_until(lowerCamelCase       ,				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  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			Tuple							)						->	Optional[int]:


         """simple docstring"""
         # Store the list of persons for each task
         for i in range(len(lowerCamelCase							)							):
                for j in task_performed[i]:
                       self.task[j].append(lowerCamelCase							)

        # 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:     Tuple   					=					5  # total no of tasks (the value of N)

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






import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_UpperCAmelCase							:			Optional[Any]        =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
             for file in itertools.chain.from_iterable(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      pa.Table:
     if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[Any]   					=						self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
             raise ValueError(
                 F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""      )
     for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						pa.Table.from_batches([record_batch]      )
                     # Uncomment for debugging (will print the Arrow table size and elements)
                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                     yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





