'''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 					__magic_name__		(					unittest.TestCase):







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
          lowercase_      :    List[Any]       		= """ylacombe/bark-small"""
          lowercase_      :    List[str]       		= tempfile.mkdtemp()
          lowercase_      :    Tuple       		= """en_speaker_1"""
          lowercase_      :    Union[str, Any]       		= """This is a test string"""
          lowercase_      :    int       		= """speaker_embeddings_path.json"""
          lowercase_      :    Any       		= """speaker_embeddings"""







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    **lowercase_       :						Optional[int]					):
          return AutoTokenizer.from_pretrained(self.checkpoint       ,    **lowercase_					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
          shutil.rmtree(self.tmpdirname					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]					):
          lowercase_      :    Any       		= self.get_tokenizer()

          lowercase_      :    Optional[Any]       		= BarkProcessor(tokenizer=lowercase_					)

          processor.save_pretrained(self.tmpdirname					)
          lowercase_      :    Union[str, Any]       		= BarkProcessor.from_pretrained(self.tmpdirname					)

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







   @slow
   def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
          lowercase_      :    Any       		= 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       ,    )

          lowercase_      :    List[str]       		= self.get_tokenizer(bos_token="""(BOS)"""       ,    eos_token="""(EOS)"""					)

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

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

          lowercase_      :    Union[str, Any]       		= {
              """semantic_prompt""": np.ones(lowercase_					),
              """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)					),
              """fine_prompt""": np.ones((nb_codebooks_total, seq_len)					),
          }

          # test providing already loaded voice_preset
          lowercase_      :    str       		= processor(text=self.input_string       ,    voice_preset=lowercase_					)

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

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

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

          # test loading voice preset from the hub
          lowercase_      :    Union[str, Any]       		= processor(text=self.input_string       ,    voice_preset=self.voice_preset					)







   def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple					):
          lowercase_      :    List[str]       		= self.get_tokenizer()

          lowercase_      :    int       		= BarkProcessor(tokenizer=lowercase_					)

          lowercase_      :    Any       		= processor(text=self.input_string					)

          lowercase_      :    List[str]       		= tokenizer(
              self.input_string       ,    padding="""max_length"""       ,    max_length=256       ,    add_special_tokens=lowercase_       ,    return_attention_mask=lowercase_       ,    return_token_type_ids=lowercase_       ,    )

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






'''simple docstring'''




from __future__ import annotations




def 							lowerCamelCase			(   UpperCAmelCase__ :      list   ,   UpperCAmelCase__ :      int | None = None   ,   UpperCAmelCase__ :      int | None = None				)    ->   None:
							if start is None:
														lowercase_      :    Any       		= 0

							if end is None:
														lowercase_      :    List[Any]       		= len(UpperCAmelCase__				) - 1

							if start >= end:
														return

							lowercase_      :    Optional[int]       		= (start + end) // 2

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)
							slowsort(UpperCAmelCase__   ,   mid + 1   ,   UpperCAmelCase__				)

							if sequence[end] < sequence[mid]:
														lowercase_		,		lowercase_      :    Dict       		= sequence[mid], sequence[end]

							slowsort(UpperCAmelCase__   ,   UpperCAmelCase__   ,   end - 1				)


if __name__ == "__main__":
						from doctest import testmod

						testmod()





