




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    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					List[str]:
          SCREAMING_SNAKE_CASE           =					"""laion/clap-htsat-unfused"""
          SCREAMING_SNAKE_CASE           =					tempfile.mkdtemp()





       def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     **a       :	Dict			)			->					Optional[Any]:
          return RobertaTokenizer.from_pretrained(self.checkpoint       ,     **a			)





       def    _UpperCAmelCase     (					self       :	List[str]       ,     **a       :	Dict			)			->					int:
          return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,     **a			)





       def    _UpperCAmelCase     (					self       :	Tuple			)			->					List[str]:
          shutil.rmtree(self.tmpdirname			)





       def    _UpperCAmelCase     (					self       :	List[str]			)			->					str:
          SCREAMING_SNAKE_CASE           =					self.get_tokenizer()
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()

          SCREAMING_SNAKE_CASE           =					ClapProcessor(tokenizer=a       ,     feature_extractor=a			)

          processor.save_pretrained(self.tmpdirname			)
          SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
          SCREAMING_SNAKE_CASE           =					ClapProcessor(tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

          SCREAMING_SNAKE_CASE           =					self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""			)
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor(do_normalize=a       ,     padding_value=1.0			)

          SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Dict:
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
          SCREAMING_SNAKE_CASE           =					self.get_tokenizer()

          SCREAMING_SNAKE_CASE           =					ClapProcessor(tokenizer=a       ,     feature_extractor=a			)

          SCREAMING_SNAKE_CASE           =					floats_list((3, 1_000)			)

          SCREAMING_SNAKE_CASE           =					feature_extractor(a       ,     return_tensors="""np"""			)
          SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
          SCREAMING_SNAKE_CASE           =					self.get_tokenizer()

          SCREAMING_SNAKE_CASE           =					ClapProcessor(tokenizer=a       ,     feature_extractor=a			)

          SCREAMING_SNAKE_CASE           =					"""This is a test string"""

          SCREAMING_SNAKE_CASE           =					processor(text=a			)

          SCREAMING_SNAKE_CASE           =					tokenizer(a			)

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





       def    _UpperCAmelCase     (					self       :	Tuple			)			->					Tuple:
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
          SCREAMING_SNAKE_CASE           =					self.get_tokenizer()

          SCREAMING_SNAKE_CASE           =					ClapProcessor(tokenizer=a       ,     feature_extractor=a			)

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

          SCREAMING_SNAKE_CASE           =					processor.batch_decode(a			)
          SCREAMING_SNAKE_CASE           =					tokenizer.batch_decode(a			)

          self.assertListEqual(a       ,     a			)







       def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
          SCREAMING_SNAKE_CASE           =					self.get_feature_extractor()
          SCREAMING_SNAKE_CASE           =					self.get_tokenizer()

          SCREAMING_SNAKE_CASE           =					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"""       ,     )












import warnings

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




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					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       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					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			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[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







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






