





'''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  lowerCAmelCase_		(		unittest.TestCase		):




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   "ylacombe/bark-small"
     _lowerCAmelCase						      =   tempfile.mkdtemp()
     _lowerCAmelCase						      =   "en_speaker_1"
     _lowerCAmelCase						      =   "This is a test string"
     _lowerCAmelCase						      =   "speaker_embeddings_path.json"
     _lowerCAmelCase						      =   "speaker_embeddings"




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				str:
     return AutoTokenizer.from_pretrained(self.checkpoint      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     shutil.rmtree(self.tmpdirname		)




 def 				_snake_case (						self		)     ->				str:
     _lowerCAmelCase						      =   self.get_tokenizer()

     _lowerCAmelCase						      =   BarkProcessor(tokenizer=_lowerCAmelCase		)

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

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




 @slow
 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   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						      =   self.get_tokenizer(bos_token="(BOS)"      ,  eos_token="(EOS)"		)

     _lowerCAmelCase						      =   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 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   BarkProcessor.from_pretrained(
         pretrained_processor_name_or_path=self.checkpoint      ,  speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,  )

     _lowerCAmelCase						      =   35
     _lowerCAmelCase						      =   2
     _lowerCAmelCase						      =   8

     _lowerCAmelCase						      =   {
         "semantic_prompt": np.ones(_lowerCAmelCase		),
         "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						      =   processor(text=self.input_string      ,  voice_preset=_lowerCAmelCase		)

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

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

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

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





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.get_tokenizer()

     _lowerCAmelCase						      =   BarkProcessor(tokenizer=_lowerCAmelCase		)

     _lowerCAmelCase						      =   processor(text=self.input_string		)

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

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









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


