

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   List[str]       				=  "Wav2Vec2FeatureExtractor"
	_lowercase  :   Any       				=  "AutoTokenizer"







	def __init__(   self: Dict  , __UpperCamelCase: List[str]  , __UpperCamelCase: Tuple      ):

						'''simple docstring'''





						super().__init__(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    self.feature_extractor
						__magic_name__							=    False







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: Union[str, Any]  , __UpperCamelCase: Dict  , **__UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





						try:
											return super().from_pretrained(__UpperCamelCase  , **__UpperCamelCase      )
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '  , __UpperCamelCase  , )

											__magic_name__							=    WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase  , **__UpperCamelCase      )
											__magic_name__							=    WavaVecaCTCTokenizer.from_pretrained(__UpperCamelCase  , **__UpperCamelCase      )

											return cls(feature_extractor=__UpperCamelCase  , tokenizer=__UpperCamelCase      )







	def __call__(   self: str  , *__UpperCamelCase: Any  , **__UpperCamelCase: List[str]      ):

						'''simple docstring'''





						if self._in_target_context_manager:
											return self.current_processor(*__UpperCamelCase  , **__UpperCamelCase      )

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'      )
											__magic_name__							=    kwargs.pop('raw_speech'      )
						else:
											__magic_name__							=    kwargs.pop('audio'  , __UpperCamelCase      )
						__magic_name__							=    kwargs.pop('sampling_rate'  , __UpperCamelCase      )
						__magic_name__							=    kwargs.pop('text'  , __UpperCamelCase      )
						if len(__UpperCamelCase      ) > 0:
											__magic_name__							=    args[0]
											__magic_name__							=    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:
											__magic_name__							=    self.feature_extractor(__UpperCamelCase  , *__UpperCamelCase  , sampling_rate=__UpperCamelCase  , **__UpperCamelCase      )
						if text is not None:
											__magic_name__							=    self.tokenizer(__UpperCamelCase  , **__UpperCamelCase      )

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											__magic_name__							=    encodings['input_ids']
											return inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , *__UpperCamelCase: Optional[Any]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						if self._in_target_context_manager:
											return self.current_processor.pad(*__UpperCamelCase  , **__UpperCamelCase      )

						__magic_name__							=    kwargs.pop('input_features'  , __UpperCamelCase      )
						__magic_name__							=    kwargs.pop('labels'  , __UpperCamelCase      )
						if len(__UpperCamelCase      ) > 0:
											__magic_name__							=    args[0]
											__magic_name__							=    args[1:]

						if input_features is not None:
											__magic_name__							=    self.feature_extractor.pad(__UpperCamelCase  , *__UpperCamelCase  , **__UpperCamelCase      )
						if labels is not None:
											__magic_name__							=    self.tokenizer.pad(__UpperCamelCase  , **__UpperCamelCase      )

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											__magic_name__							=    labels['input_ids']
											return input_features







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , *__UpperCamelCase: Optional[int]  , **__UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*__UpperCamelCase  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , *__UpperCamelCase: Tuple  , **__UpperCamelCase: int      ):

						'''simple docstring'''





						return self.tokenizer.decode(*__UpperCamelCase  , **__UpperCamelCase      )







	@contextmanager
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						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.'      )
						__magic_name__							=    True
						__magic_name__							=    self.tokenizer
						yield
						__magic_name__							=    self.feature_extractor
						__magic_name__							=    False







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			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    'laion/clap-htsat-unfused'
						__magic_name__							=    tempfile.mkdtemp()







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						return RobertaTokenizer.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , **__UpperCamelCase: Dict      ):

						'''simple docstring'''





						return ClapFeatureExtractor.from_pretrained(self.checkpoint  , **__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_tokenizer()
						__magic_name__							=    self.get_feature_extractor()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						processor.save_pretrained(self.tmpdirname      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    ClapProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()      )
						processor.save_pretrained(self.tmpdirname      )

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

						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[str]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    floats_list((3, 10_00)      )

						__magic_name__							=    feature_extractor(__UpperCamelCase  , return_tensors='np'      )
						__magic_name__							=    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 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

						__magic_name__							=    'This is a test string'

						__magic_name__							=    processor(text=__UpperCamelCase      )

						__magic_name__							=    tokenizer(__UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    ClapProcessor(tokenizer=__UpperCamelCase  , feature_extractor=__UpperCamelCase      )

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

						__magic_name__							=    processor.batch_decode(__UpperCamelCase      )
						__magic_name__							=    tokenizer.batch_decode(__UpperCamelCase      )

						self.assertListEqual(__UpperCamelCase  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    self.get_feature_extractor()
						__magic_name__							=    self.get_tokenizer()

						__magic_name__							=    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'  , )




