


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= '''ylacombe/bark-small'''
       snake_case__						= tempfile.mkdtemp()
       snake_case__						= '''en_speaker_1'''
       snake_case__						= '''This is a test string'''
       snake_case__						= '''speaker_embeddings_path.json'''
       snake_case__						= '''speaker_embeddings'''

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		**_a:List[Any]   ):
       return AutoTokenizer.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= self.get_tokenizer()

       snake_case__						= BarkProcessor(tokenizer=_a   )

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

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

     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= 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     ,		)

       snake_case__						= self.get_tokenizer(bos_token='''(BOS)'''     ,		eos_token='''(EOS)'''   )

       snake_case__						= 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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint     ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,		)

       snake_case__						= 35
       snake_case__						= 2
       snake_case__						= 8

       snake_case__						= {
           '''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
       snake_case__						= processor(text=self.input_string     ,		voice_preset=_a   )

       snake_case__						= 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
       snake_case__						= os.path.join(self.tmpdirname     ,		'''file.npz'''   )
       np.savez(_a     ,		**_a   )
       snake_case__						= processor(text=self.input_string     ,		voice_preset=_a   )
       snake_case__						= 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
       snake_case__						= processor(text=self.input_string     ,		voice_preset=self.voice_preset   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= self.get_tokenizer()

       snake_case__						= BarkProcessor(tokenizer=_a   )

       snake_case__						= processor(text=self.input_string   )

       snake_case__						= tokenizer(
           self.input_string     ,		padding='''max_length'''     ,		max_length=2_56     ,		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()   )





from copy import deepcopy
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:int     ,		_a:list[int] | None = None     ,		_a:int | None = None   ):

       if arr is None and size is not None:
         snake_case__						= size
         snake_case__						= [0] * size
       elif arr is not None:
         self.init(_a   )
       else:
         raise ValueError('''Either arr or size must be specified'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:list[int]   ):
       snake_case__						= len(_a   )
       snake_case__						= deepcopy(_a   )
       for i in range(1     ,		self.size   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           self.tree[j] += self.tree[i]

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.tree[:]
       for i in range(self.size - 1     ,		0     ,		-1   ):
         snake_case__						= self.next_(_a   )
         if j < self.size:
           arr[j] -= arr[i]
       return arr

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index + (index & (-index))

     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:int   ):
       return index - (index & (-index))

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:int     ,		_a:int   ):
       if index == 0:
         self.tree[0] += value
         return
       while index < self.size:
         self.tree[index] += value
         snake_case__						= self.next_(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       self.add(_a     ,		value - self.get(_a   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int   ):
       if right == 0:
         return 0
       snake_case__						= self.tree[0]
       right -= 1  # make right inclusive
       while right > 0:
         result += self.tree[right]
         snake_case__						= self.prev(_a   )
       return result

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:int   ):
       return self.prefix(_a   ) - self.prefix(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       return self.query(_a     ,		index + 1   )







     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int   ):
       value -= self.tree[0]
       if value < 0:
         return -1

       snake_case__						= 1  # Largest power of 2 <= size
       while j * 2 < self.size:
         j *= 2

       snake_case__						= 0

       while j > 0:
         if i + j < self.size and self.tree[i + j] <= value:
           value -= self.tree[i + j]
           i += j
         j //= 2
       return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()

