





'''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 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Optional[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   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		Any:
		return AutoTokenizer.from_pretrained(self.checkpoint      ,       **UpperCamelCase_		)





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





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.get_tokenizer()

		snake_case__	       =   BarkProcessor(tokenizer=UpperCamelCase_		)

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

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





	@slow
	def   _snake_case   (		self		)      ->		Optional[Any]:
		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   _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(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
		snake_case__	       =   processor(text=self.input_string      ,       voice_preset=UpperCamelCase_		)

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





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.get_tokenizer()

		snake_case__	       =   BarkProcessor(tokenizer=UpperCamelCase_		)

		snake_case__	       =   processor(text=self.input_string		)

		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'''




import cmath
import math




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->complex:
	snake_case__	       =   math.radians(UpperCAmelCase_							)
	snake_case__	       =   math.radians(UpperCAmelCase_							)

	# Convert voltage and current to rectangular form
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   cmath.rect(UpperCAmelCase_					,				UpperCAmelCase_							)

	# Calculate apparent power
	return voltage_rect * current_rect


if __name__ == "__main__":
			import doctest

			doctest.testmod()



