

'''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  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							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 			UpperCamelCase(					self   ,			**SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							return AutoTokenizer.from_pretrained(self.checkpoint   ,			**SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							shutil.rmtree(self.tmpdirname		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.get_tokenizer()

							lowerCamelCase_  				=      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_		)

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

							self.assertEqual(processor.tokenizer.get_vocab()   ,			tokenizer.get_vocab()		)
		@slow
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							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(SCREAMING_SNAKE_CASE_		),
							    '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=SCREAMING_SNAKE_CASE_		)

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

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

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

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






		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.get_tokenizer()

							lowerCamelCase_  				=      BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      processor(text=self.input_string		)

							lowerCamelCase_  				=      tokenizer(
							    self.input_string   ,			padding='max_length'   ,			max_length=256   ,			add_special_tokens=SCREAMING_SNAKE_CASE_   ,			return_attention_mask=SCREAMING_SNAKE_CASE_   ,			return_token_type_ids=SCREAMING_SNAKE_CASE_   ,			)

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











from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





