
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class        _UpperCamelCase				(					__SCREAMING_SNAKE_CASE  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = ['image_processor', 'tokenizer']
			__UpperCamelCase      :						Optional[int]					      = 'ChineseCLIPImageProcessor'
			__UpperCamelCase      :						Union[str, Any]					      = ('BertTokenizer', 'BertTokenizerFast')

			def __init__(       self :       Tuple						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       List[Any]=None						,	**snake_case_ :       Dict      ):
							UpperCamelCase_:							Any	=						None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
											    """ instead."""						,	_a						,	)
											UpperCamelCase_:							int	=						kwargs.pop("""feature_extractor"""      )

							UpperCamelCase_:							Dict	=						image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError("""You need to specify an `image_processor`."""      )
							if tokenizer is None:
											raise ValueError("""You need to specify a `tokenizer`."""      )

							super().__init__(_a						,	_a      )
							UpperCamelCase_:							List[Any]	=						self.image_processor

			def __call__(       self :       int						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Optional[Any]=None						,	**snake_case_ :       Optional[int]      ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none."""      )

							if text is not None:
											UpperCamelCase_:							List[str]	=						self.tokenizer(_a						,	return_tensors=_a						,	**_a      )

							if images is not None:
											UpperCamelCase_:							List[Any]	=						self.image_processor(_a						,	return_tensors=_a						,	**_a      )

							if text is not None and images is not None:
											UpperCamelCase_:							List[str]	=						image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**_a      )						,	tensor_type=_a      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	*snake_case_ :       List[str]						,	**snake_case_ :       List[Any]      ):
							return self.tokenizer.batch_decode(*_a						,	**_a      )

			def       lowerCAmelCase__	(       self :       Any						,	*snake_case_ :       Tuple						,	**snake_case_ :       str      ):
							return self.tokenizer.decode(*_a						,	**_a      )

			@property
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							Tuple	=						self.tokenizer.model_input_names
							UpperCamelCase_:							Dict	=						self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




			@property
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,	_a						,	)
							return self.image_processor_class








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"""						,	)






