
"""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   lowerCamelCase__  (					self    :       Any						)   ->			Dict:







								"""simple docstring"""
								A_								=    "laion/clap-htsat-unfused"
								A_								=    tempfile.mkdtemp()




			def   lowerCamelCase__  (					self    :       str							, **_snake_case    :       List[str]						)   ->			List[str]:







								"""simple docstring"""
								return RobertaTokenizer.from_pretrained(self.checkpoint							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, **_snake_case    :       Optional[Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								return ClapFeatureExtractor.from_pretrained(self.checkpoint							, **_snake_case						)




			def   lowerCamelCase__  (					self    :       List[Any]						)   ->			int:







								"""simple docstring"""
								shutil.rmtree(self.tmpdirname						)




			def   lowerCamelCase__  (					self    :       int						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.get_tokenizer()
								A_								=    self.get_feature_extractor()

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

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

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

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




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			List[str]:







								"""simple docstring"""
								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=_snake_case							, padding_value=1.0						)

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

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

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




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    self.get_feature_extractor()
								A_								=    self.get_tokenizer()

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

								A_								=    floats_list((3, 1_000)						)

								A_								=    feature_extractor(_snake_case							, return_tensors="np"						)
								A_								=    processor(audios=_snake_case							, 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    :       List[Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    self.get_feature_extractor()
								A_								=    self.get_tokenizer()

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

								A_								=    "This is a test string"

								A_								=    processor(text=_snake_case						)

								A_								=    tokenizer(_snake_case						)

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




			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Any:







								"""simple docstring"""
								A_								=    self.get_feature_extractor()
								A_								=    self.get_tokenizer()

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

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

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

								self.assertListEqual(_snake_case							, _snake_case						)






			def   lowerCamelCase__  (					self    :       str						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.get_feature_extractor()
								A_								=    self.get_tokenizer()

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

								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'''
from __future__ import annotations

from typing import Any







def       a_				(	UpperCamelCase_ ):
							create_state_space_tree(UpperCamelCase_						,  []						,  0 )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):

							if index == len(UpperCamelCase_ ):
														print(UpperCamelCase_ )
														return

							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.append(sequence[index] )
							create_state_space_tree(UpperCamelCase_						,  UpperCamelCase_						,  index + 1 )
							current_subsequence.pop()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE					:				list[Any]									=		[3, 1, 2, 4]
							generate_all_subsequences(seq)

							seq.clear()
							seq.extend(['''A''', '''B''', '''C'''])
							generate_all_subsequences(seq)





