






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list


@require_torchaudio
@require_sentencepiece
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Any						)		->		Tuple:
											snake_case__									=     'laion/clap-htsat-unfused'
											snake_case__									=     tempfile.mkdtemp()
							def lowerCAmelCase_ (     self:      Any       ,	**UpperCamelCase:      int						)		->		Any:
											return RobertaTokenizer.from_pretrained(self.checkpoint       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	**UpperCamelCase:      Optional[Any]						)		->		Dict:
											return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Any:
											shutil.rmtree(self.tmpdirname						)
							def lowerCAmelCase_ (     self:      Any						)		->		Any:
											snake_case__									=     self.get_tokenizer()
											snake_case__									=     self.get_feature_extractor()

											snake_case__									=     ClapProcessor(tokenizer=UpperCamelCase       ,	feature_extractor=UpperCamelCase						)

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

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

											self.assertEqual(processor.feature_extractor.to_json_string()       ,	feature_extractor.to_json_string()						)
											self.assertIsInstance(processor.feature_extractor       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											snake_case__									=     ClapProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()						)
											processor.save_pretrained(self.tmpdirname						)

											snake_case__									=     self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'						)
											snake_case__									=     self.get_feature_extractor(do_normalize=UpperCamelCase       ,	padding_value=1.0						)

											snake_case__									=     ClapProcessor.from_pretrained(
											    self.tmpdirname       ,	bos_token='(BOS)'       ,	eos_token='(EOS)'       ,	do_normalize=UpperCamelCase       ,	padding_value=1.0						)

											self.assertEqual(processor.tokenizer.get_vocab()       ,	tokenizer_add_kwargs.get_vocab()						)
											self.assertIsInstance(processor.tokenizer       ,	UpperCamelCase						)

											self.assertEqual(processor.feature_extractor.to_json_string()       ,	feature_extractor_add_kwargs.to_json_string()						)
											self.assertIsInstance(processor.feature_extractor       ,	UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		Union[str, Any]:
											snake_case__									=     self.get_feature_extractor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     ClapProcessor(tokenizer=UpperCamelCase       ,	feature_extractor=UpperCamelCase						)

											snake_case__									=     floats_list((3, 10_00)						)

											snake_case__									=     feature_extractor(UpperCamelCase       ,	return_tensors='np'						)
											snake_case__									=     processor(audios=UpperCamelCase       ,	return_tensors='np'						)

											for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()       ,	input_processor[key].sum()       ,	delta=1e-2						)
							def lowerCAmelCase_ (     self:      str						)		->		int:
											snake_case__									=     self.get_feature_extractor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     ClapProcessor(tokenizer=UpperCamelCase       ,	feature_extractor=UpperCamelCase						)

											snake_case__									=     'This is a test string'

											snake_case__									=     processor(text=UpperCamelCase						)

											snake_case__									=     tokenizer(UpperCamelCase						)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key]						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											snake_case__									=     self.get_feature_extractor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     ClapProcessor(tokenizer=UpperCamelCase       ,	feature_extractor=UpperCamelCase						)

											snake_case__									=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											snake_case__									=     processor.batch_decode(UpperCamelCase						)
											snake_case__									=     tokenizer.batch_decode(UpperCamelCase						)

											self.assertListEqual(UpperCamelCase       ,	UpperCamelCase						)


							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     self.get_feature_extractor()
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     ClapProcessor(tokenizer=UpperCamelCase       ,	feature_extractor=UpperCamelCase						)

											self.assertListEqual(
											    processor.model_input_names[2:]       ,	feature_extractor.model_input_names       ,	msg='`processor` and `feature_extractor` model input names do not match'       ,	)












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




