



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 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Union[str, Any]													=					"ylacombe/bark-small"
									lowercase__					:   int													=					tempfile.mkdtemp()
									lowercase__					:   Optional[Any]													=					"en_speaker_1"
									lowercase__					:   Union[str, Any]													=					"This is a test string"
									lowercase__					:   int													=					"speaker_embeddings_path.json"
									lowercase__					:   str													=					"speaker_embeddings"







					def 			snake_case					(							self :     int				,       **SCREAMING_SNAKE_CASE :     Optional[int]					):
									return AutoTokenizer.from_pretrained(self.checkpoint				,       **SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]					):
									shutil.rmtree(self.tmpdirname					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   List[Any]													=					self.get_tokenizer()

									lowercase__					:   Any													=					BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE					)

									processor.save_pretrained(self.tmpdirname					)
									lowercase__					:   Optional[Any]													=					BarkProcessor.from_pretrained(self.tmpdirname					)

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







					@slow
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   List[str]													=					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__					:   Optional[int]													=					self.get_tokenizer(bos_token="(BOS)"				,       eos_token="(EOS)"					)

									lowercase__					:   Union[str, 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 			snake_case					(							self :     List[Any]					):
									lowercase__					:   List[str]													=					BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint				,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,       )

									lowercase__					:   Dict													=					35
									lowercase__					:   Optional[int]													=					2
									lowercase__					:   Optional[int]													=					8

									lowercase__					:   List[Any]													=					{
									    "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
									lowercase__					:   Tuple													=					processor(text=self.input_string				,       voice_preset=SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[Any]													=					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
									lowercase__					:   List[str]													=					os.path.join(self.tmpdirname				,       "file.npz"					)
									np.savez(SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)
									lowercase__					:   List[Any]													=					processor(text=self.input_string				,       voice_preset=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Any													=					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
									lowercase__					:   List[Any]													=					processor(text=self.input_string				,       voice_preset=self.voice_preset					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   Optional[int]													=					self.get_tokenizer()

									lowercase__					:   Optional[int]													=					BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE					)

									lowercase__					:   List[str]													=					processor(text=self.input_string					)

									lowercase__					:   Optional[int]													=					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 math import ceil, sqrt


def  __lowerCamelCase (       lowerCamelCase__ = 1_000_000						):

				"""simple docstring"""


				lowercase__					:   int													=					0

				for outer_width in range(3					,			(limit // 4) + 2						):
								if outer_width**2 > limit:
												lowercase__					:   List[str]													=					max(ceil(sqrt(outer_width**2 - limit						)						)					,			1						)
								else:
												lowercase__					:   List[str]													=					1
								if (outer_width - hole_width_lower_bound) % 2:
												hole_width_lower_bound += 1

								answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

				return answer


if __name__ == "__main__":
				print(f'''{solution() = }''')




