"""simple docstring"""




from collections import defaultdict
class 						__A							:






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     total  # total no of tasks (N)

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

       _lowerCAmelCase						:       Dict  			=     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
       _lowerCAmelCase						:       Union[str, Any]  			=     (1 << len(a__  )) - 1






     def     __A  (		self				,     a__				,     a__  ):
       # 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
       _lowerCAmelCase						:       Any  			=     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.
       _lowerCAmelCase						:       List[str]  			=     total_ways_util

       return self.dp[mask][task_no]






     def     __A  (		self				,     a__  ):
       # Store the list of persons for each task
       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__":
     _a      :		Any           =   5  # total no of tasks (the value of N)

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





"""simple docstring"""




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer.get_vocab()  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )
       processor.save_pretrained(
           self.tmpdirname				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     speaker_embeddings_directory=self.speaker_embeddings_directory				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor.from_pretrained(
           self.tmpdirname				,     self.speaker_embeddings_dict_path				,     bos_token="""(BOS)"""				,     eos_token="""(EOS)"""				,     )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer_add_kwargs.get_vocab()  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()  )




