


'''simple docstring'''
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 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      Any	):
													A_			    =		"laion/clap-htsat-unfused"
													A_			    =		tempfile.mkdtemp()






						def 				_UpperCAmelCase       (  self							:      str ,  **lowerCAmelCase							:      List[str]	):
													return RobertaTokenizer.from_pretrained(self.checkpoint ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  **lowerCAmelCase							:      Optional[Any]	):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													shutil.rmtree(self.tmpdirname	)






						def 				_UpperCAmelCase       (  self							:      int	):
													A_			    =		self.get_tokenizer()
													A_			    =		self.get_feature_extractor()

													A_			    =		ClapProcessor(tokenizer=lowerCAmelCase ,  feature_extractor=lowerCAmelCase	)

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

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

													self.assertEqual(processor.feature_extractor.to_json_string() ,  feature_extractor.to_json_string()	)
													self.assertIsInstance(processor.feature_extractor ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		ClapProcessor(tokenizer=self.get_tokenizer() ,  feature_extractor=self.get_feature_extractor()	)
													processor.save_pretrained(self.tmpdirname	)

													A_			    =		self.get_tokenizer(bos_token="(BOS)" ,  eos_token="(EOS)"	)
													A_			    =		self.get_feature_extractor(do_normalize=lowerCAmelCase ,  padding_value=1.0	)

													A_			    =		ClapProcessor.from_pretrained(
													    self.tmpdirname ,  bos_token="(BOS)" ,  eos_token="(EOS)" ,  do_normalize=lowerCAmelCase ,  padding_value=1.0	)

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

													self.assertEqual(processor.feature_extractor.to_json_string() ,  feature_extractor_add_kwargs.to_json_string()	)
													self.assertIsInstance(processor.feature_extractor ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													A_			    =		self.get_feature_extractor()
													A_			    =		self.get_tokenizer()

													A_			    =		ClapProcessor(tokenizer=lowerCAmelCase ,  feature_extractor=lowerCAmelCase	)

													A_			    =		floats_list((3, 10_00)	)

													A_			    =		feature_extractor(lowerCAmelCase ,  return_tensors="np"	)
													A_			    =		processor(audios=lowerCAmelCase ,  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 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		self.get_feature_extractor()
													A_			    =		self.get_tokenizer()

													A_			    =		ClapProcessor(tokenizer=lowerCAmelCase ,  feature_extractor=lowerCAmelCase	)

													A_			    =		"This is a test string"

													A_			    =		processor(text=lowerCAmelCase	)

													A_			    =		tokenizer(lowerCAmelCase	)

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






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													A_			    =		self.get_feature_extractor()
													A_			    =		self.get_tokenizer()

													A_			    =		ClapProcessor(tokenizer=lowerCAmelCase ,  feature_extractor=lowerCAmelCase	)

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

													A_			    =		processor.batch_decode(lowerCAmelCase	)
													A_			    =		tokenizer.batch_decode(lowerCAmelCase	)

													self.assertListEqual(lowerCAmelCase ,  lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      str	):
													A_			    =		self.get_feature_extractor()
													A_			    =		self.get_tokenizer()

													A_			    =		ClapProcessor(tokenizer=lowerCAmelCase ,  feature_extractor=lowerCAmelCase	)

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









'''simple docstring'''
import string
from math import logaa







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		document.translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) ).replace("\n"						,  "" )
							A_			    =		document_without_punctuation.split(" " )  # word tokenization
							return len([word for word in tokenize_document if word.lower() == term.lower()] )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		corpus.lower().translate(
							    str.maketrans(""						,  ""						,  string.punctuation ) )  # strip all punctuation and replace it with ''
							A_			    =		corpus_without_punctuation.split("\n" )
							A_			    =		term.lower()
							return (len([doc for doc in docs if term in doc] ), len(UpperCamelCase_ ))







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=False ):
							if smoothing:
														if n == 0:
																					raise ValueError("log10(0) is undefined." )
														return round(1 + logaa(n / (1 + df) )						,  3 )

							if df == 0:
														raise ZeroDivisionError("df must be > 0" )
							elif n == 0:
														raise ValueError("log10(0) is undefined." )
							return round(logaa(n / df )						,  3 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							return round(tf * idf						,  3 )





