





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Union[str, Any]      ,   snake_case							:List[Any]=2      ,   snake_case							:Tuple=3      ,   snake_case							:Tuple=64      ,   snake_case							:List[str]=None	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      np.random.default_rng(snake_case	)
        A_       :					Optional[int]        =      length
        A_       :					Any        =      rng.normal(size=(length,)	).astype(np.floataa	)
        A_       :					Optional[int]        =      a * self.x + b + rng.normal(scale=0.1      ,   size=(length,)	).astype(np.floataa	)



     def __len__(				self							:List[Any]	):





        '''simple docstring'''




        return self.length





     def __getitem__(				self							:List[Any]      ,   snake_case							:str	):





        '''simple docstring'''




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




class        __magic_name__	(				torch.nn.Module				):





     """simple docstring"""



     def __init__(				self							:Optional[Any]      ,   snake_case							:str=0      ,   snake_case							:List[str]=0      ,   snake_case							:Optional[int]=False	):





        '''simple docstring'''




        super().__init__()
        A_       :					Optional[int]        =      torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
        A_       :					Optional[int]        =      torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
        A_       :					Optional[Any]        =      True





     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Any=None	):





        '''simple docstring'''




        if self.first_batch:
           print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"	)
           A_       :					Optional[int]        =      False
        return x * self.a[0] + self.b[0]




class        __magic_name__	(				torch.nn.Module				):





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Dict=0      ,   snake_case							:List[str]=0      ,   snake_case							:int=False	):





        '''simple docstring'''




        super().__init__()
        A_       :					int        =      torch.nn.Parameter(torch.tensor(snake_case	).float()	)
        A_       :					List[str]        =      torch.nn.Parameter(torch.tensor(snake_case	).float()	)
        A_       :					Optional[int]        =      True





     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:Optional[Any]=None	):





        '''simple docstring'''




        if self.first_batch:
           print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"	)
           A_       :					int        =      False
        return x * self.a + self.b




def   __snake_case    (       _lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			int = 16						)			->    Optional[Any]:
   from datasets import load_dataset
   from transformers import AutoTokenizer

   A_       :					Tuple        =      AutoTokenizer.from_pretrained("bert-base-cased"						)
   A_       :					Tuple        =      {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
   A_       :					Union[str, Any]        =      load_dataset("csv"					,					data_files=_lowerCAmelCase						)
   A_       :					Tuple        =      datasets["train"].unique("label"						)

   A_       :					Optional[int]        =      {v: i for i, v in enumerate(_lowerCAmelCase						)}

   def tokenize_function(_lowerCAmelCase			:			str						):
      # max_length=None => use the model max length (it's actually the default)
      A_       :					int        =      tokenizer(
          examples["sentence1"]					,					examples["sentence2"]					,					truncation=_lowerCAmelCase					,					max_length=_lowerCAmelCase					,					padding="max_length"						)
      if "label" in examples:
         A_       :					Any        =      [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
   A_       :					Dict        =      datasets.map(
       _lowerCAmelCase					,					batched=_lowerCAmelCase					,					remove_columns=["sentence1", "sentence2", "label"]					,					)

   def collate_fn(_lowerCAmelCase			:			List[Any]						):
      # 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(_lowerCAmelCase					,					padding="max_length"					,					max_length=128					,					return_tensors="pt"						)
      return tokenizer.pad(_lowerCAmelCase					,					padding="longest"					,					return_tensors="pt"						)

   # Instantiate dataloaders.
   A_       :					Dict        =      DataLoader(tokenized_datasets["train"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=2						)
   A_       :					Optional[int]        =      DataLoader(tokenized_datasets["validation"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=1						)

   return train_dataloader, eval_dataloader







from collections import defaultdict


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:int      ,   snake_case							:Dict      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      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(snake_case	)	)
        ]

        A_       :					Optional[int]        =      defaultdict(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_       :					Dict        =      (1 << len(snake_case	)) - 1



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Union[str, Any]      ,   snake_case							:str	):





        '''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_       :					Tuple        =      self.count_ways_until(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_       :					Any        =      total_ways_util

        return self.dp[mask][task_no]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[str]	):





        '''simple docstring'''




        for i in range(len(snake_case	)	):
           for j in task_performed[i]:
              self.task[j].append(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__":
 _lowerCAmelCase				:   Dict						       =						5  # total no of tasks (the value of N)

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