

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	):




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Optional[Any]		    =			"""ylacombe/bark-small"""
		__A :			Any		    =			tempfile.mkdtemp()
		__A :			Optional[int]		    =			"""en_speaker_1"""
		__A :			List[Any]		    =			"""This is a test string"""
		__A :			int		    =			"""speaker_embeddings_path.json"""
		__A :			List[str]		    =			"""speaker_embeddings"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     Any							):
		return AutoTokenizer.from_pretrained(self.checkpoint					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		shutil.rmtree(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			str		    =			self.get_tokenizer()

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

		processor.save_pretrained(self.tmpdirname							)
		__A :			List[str]		    =			BarkProcessor.from_pretrained(self.tmpdirname							)

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




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Optional[int]		    =			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					,      )

		__A :			str		    =			self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""							)

		__A :			Optional[int]		    =			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						:     Optional[Any]							):
		__A :			List[str]		    =			BarkProcessor.from_pretrained(
		    pretrained_processor_name_or_path=self.checkpoint					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      )

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

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

		# test providing already loaded voice_preset
		__A :			Union[str, Any]		    =			processor(text=self.input_string					,      voice_preset=__A							)

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

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

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

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




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			str		    =			self.get_tokenizer()

		__A :			str		    =			BarkProcessor(tokenizer=__A							)

		__A :			int		    =			processor(text=self.input_string							)

		__A :			int		    =			tokenizer(
		    self.input_string					,      padding="""max_length"""					,      max_length=256					,      add_special_tokens=__A					,      return_attention_mask=__A					,      return_token_type_ids=__A					,      )

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








from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





