


"""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   ):





				"""simple docstring"""







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



				def   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
								return AutoTokenizer.from_pretrained(self.checkpoint       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								shutil.rmtree(self.tmpdirname     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

								SCREAMING_SNAKE_CASE__     							=				BarkProcessor(tokenizer=UpperCAmelCase__     )

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

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



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_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       ,		)

								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer(bos_token="(BOS)"       ,		eos_token="(EOS)"     )

								SCREAMING_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   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

								SCREAMING_SNAKE_CASE__     							=				35
								SCREAMING_SNAKE_CASE__     							=				2
								SCREAMING_SNAKE_CASE__     							=				8

								SCREAMING_SNAKE_CASE__     							=				{
								    "semantic_prompt": np.ones(UpperCAmelCase__     ),
								    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)     ),
								    "fine_prompt": np.ones((nb_codebooks_total, seq_len)     ),
								}

								# test providing already loaded voice_preset
								SCREAMING_SNAKE_CASE__     							=				processor(text=self.input_string       ,		voice_preset=UpperCAmelCase__     )

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

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

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

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






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

								SCREAMING_SNAKE_CASE__     							=				BarkProcessor(tokenizer=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				processor(text=self.input_string     )

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

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




"""simple docstring"""






from math import pi


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
	print(arc_length(90, 10))
