




"""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 __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				'laion/clap-htsat-unfused'
								__lowerCamelCase					:	Tuple	       =				tempfile.mkdtemp()
					def     snake_case_				(    self						,	**__a     ):
								return RobertaTokenizer.from_pretrained(self.checkpoint						,	**__a     )
					def     snake_case_				(    self						,	**__a     ):
								return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**__a     )
					def     snake_case_				(    self     ):
								shutil.rmtree(self.tmpdirname     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.get_tokenizer()
								__lowerCamelCase					:	Optional[Any]	       =				self.get_feature_extractor()

								__lowerCamelCase					:	Union[str, Any]	       =				ClapProcessor(tokenizer=__a						,	feature_extractor=__a     )

								processor.save_pretrained(self.tmpdirname     )
								__lowerCamelCase					:	Any	       =				ClapProcessor.from_pretrained(self.tmpdirname     )

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

								self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()     )
								self.assertIsInstance(processor.feature_extractor						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()     )
								processor.save_pretrained(self.tmpdirname     )

								__lowerCamelCase					:	Optional[Any]	       =				self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)'     )
								__lowerCamelCase					:	Tuple	       =				self.get_feature_extractor(do_normalize=__a						,	padding_value=1.0     )

								__lowerCamelCase					:	int	       =				ClapProcessor.from_pretrained(
								    self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=__a						,	padding_value=1.0     )

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

								self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()     )
								self.assertIsInstance(processor.feature_extractor						,	__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.get_feature_extractor()
								__lowerCamelCase					:	str	       =				self.get_tokenizer()

								__lowerCamelCase					:	str	       =				ClapProcessor(tokenizer=__a						,	feature_extractor=__a     )

								__lowerCamelCase					:	Optional[int]	       =				floats_list((3, 1000)     )

								__lowerCamelCase					:	Optional[Any]	       =				feature_extractor(__a						,	return_tensors='np'     )
								__lowerCamelCase					:	Union[str, Any]	       =				processor(audios=__a						,	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     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.get_feature_extractor()
								__lowerCamelCase					:	Tuple	       =				self.get_tokenizer()

								__lowerCamelCase					:	Tuple	       =				ClapProcessor(tokenizer=__a						,	feature_extractor=__a     )

								__lowerCamelCase					:	List[str]	       =				'This is a test string'

								__lowerCamelCase					:	Union[str, Any]	       =				processor(text=__a     )

								__lowerCamelCase					:	Tuple	       =				tokenizer(__a     )

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.get_feature_extractor()
								__lowerCamelCase					:	Union[str, Any]	       =				self.get_tokenizer()

								__lowerCamelCase					:	Tuple	       =				ClapProcessor(tokenizer=__a						,	feature_extractor=__a     )

								__lowerCamelCase					:	Optional[Any]	       =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__lowerCamelCase					:	List[Any]	       =				processor.batch_decode(__a     )
								__lowerCamelCase					:	List[Any]	       =				tokenizer.batch_decode(__a     )

								self.assertListEqual(__a						,	__a     )





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				self.get_feature_extractor()
								__lowerCamelCase					:	Dict	       =				self.get_tokenizer()

								__lowerCamelCase					:	Tuple	       =				ClapProcessor(tokenizer=__a						,	feature_extractor=__a     )

								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 warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= 'Speech2TextFeatureExtractor'
					__a						:						Optional[int]     		= 'Speech2TextTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(__a						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False
					def __call__(    self						,	*__a						,	**__a     ):
								# For backward compatibility
								if self._in_target_context_manager:
											return self.current_processor(*__a						,	**__a     )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'     )
											__lowerCamelCase					:	str	       =				kwargs.pop('raw_speech'     )
								else:
											__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('audio'						,	__a     )
								__lowerCamelCase					:	Optional[Any]	       =				kwargs.pop('sampling_rate'						,	__a     )
								__lowerCamelCase					:	Optional[int]	       =				kwargs.pop('text'						,	__a     )
								if len(__a     ) > 0:
											__lowerCamelCase					:	Optional[int]	       =				args[0]
											__lowerCamelCase					:	str	       =				args[1:]

								if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'     )

								if audio is not None:
											__lowerCamelCase					:	Dict	       =				self.feature_extractor(__a						,	*__a						,	sampling_rate=__a						,	**__a     )
								if text is not None:
											__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(__a						,	**__a     )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											__lowerCamelCase					:	Any	       =				encodings['input_ids']
											return inputs
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*__a						,	**__a     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*__a						,	**__a     )





					@contextmanager
					def     snake_case_				(    self     ):
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'     )
								__lowerCamelCase					:	int	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				self.tokenizer
								yield
								__lowerCamelCase					:	Tuple	       =				self.feature_extractor
								__lowerCamelCase					:	List[str]	       =				False



