
from typing import List

from .keymap import KEYMAP, get_character







def        A__					(						lowerCamelCase						)  ->	str:






				def decorator(lowerCamelCase						):
								UpperCamelCase_:							Dict	=						getattr(__UpperCamelCase     ,    """handle_key"""     ,    []						)
								handle += [key]
								setattr(__UpperCamelCase     ,    """handle_key"""     ,    __UpperCamelCase						)
								return func

				return decorator







def        A__					(						*lowerCamelCase						)  ->	str:






				def decorator(lowerCamelCase						):
								UpperCamelCase_:							Optional[int]	=						getattr(__UpperCamelCase     ,    """handle_key"""     ,    []						)
								handle += keys
								setattr(__UpperCamelCase     ,    """handle_key"""     ,    __UpperCamelCase						)
								return func

				return decorator

class        _UpperCamelCase				(					SCREAMING_SNAKE_CASE__  ):






			'''simple docstring'''

			def __new__(       cls :       Union[str, Any]						,	snake_case_ :       List[Any]						,	snake_case_ :       Any						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							Any	=						super().__new__(cls						,	_lowercase						,	_lowercase						,	_lowercase      )
							if not hasattr(_lowercase						,	"""key_handler"""      ):
											setattr(_lowercase						,	"""key_handler"""						,	{}      )
							setattr(_lowercase						,	"""handle_input"""						,	KeyHandler.handle_input      )

							for value in attrs.values():
											UpperCamelCase_:							List[str]	=						getattr(_lowercase						,	"""handle_key"""						,	[]      )
											for key in handled_keys:
															UpperCamelCase_:							List[str]	=						value
							return new_cls




			@staticmethod
			def       lowerCAmelCase__	(       cls :       List[Any]      ):
							UpperCamelCase_:							Optional[int]	=						get_character()
							if char != KEYMAP["undefined"]:
											UpperCamelCase_:							int	=						ord(_lowercase      )
							UpperCamelCase_:							Tuple	=						cls.key_handler.get(_lowercase      )
							if handler:
											UpperCamelCase_:							Union[str, Any]	=						char
											return handler(cls      )
							else:
											return None







def        A__					(						cls						)  ->	List[str]:
				return KeyHandler(cls.__name__     ,    cls.__bases__     ,    cls.__dict__.copy()						)








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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						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]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						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 :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						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 :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

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

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						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"""						,	)






