
'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		['image_processor', 'tokenizer']
    lowerCAmelCase_					     =		'AutoImageProcessor'
    lowerCAmelCase_					     =		'AutoTokenizer'



    def __init__(						self   :							str,__A   :							List[str]=None,__A   :							Optional[Any]=None,**__A   :							str  ):
          _lowerCamelCase					: Optional[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,)
                _lowerCamelCase					: Optional[int]		    = kwargs.pop("feature_extractor"  )

          _lowerCamelCase					: List[Any]		    = 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  )
          _lowerCamelCase					: Any		    = self.image_processor
          _lowerCamelCase					: str		    = False



    def __call__(						self   :							List[Any],*__A   :							Tuple,**__A   :							Optional[int]  ):
          # For backward compatibility
          if self._in_target_context_manager:
                return self.current_processor(*__A,**__A  )

          _lowerCamelCase					: List[str]		    = kwargs.pop("images",__A  )
          _lowerCamelCase					: Any		    = kwargs.pop("text",__A  )
          if len(__A  ) > 0:
                _lowerCamelCase					: Dict		    = args[0]
                _lowerCamelCase					: Tuple		    = args[1:]

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

          if images is not None:
                _lowerCamelCase					: str		    = self.image_processor(__A,*__A,**__A  )
          if text is not None:
                _lowerCamelCase					: Optional[int]		    = self.tokenizer(__A,**__A  )

          if text is None:
                return inputs
          elif images is None:
                return encodings
          else:
                _lowerCamelCase					: Union[str, Any]		    = encodings["input_ids"]
                return inputs



    def 	lowerCamelCase_	(						self   :							int,*__A   :							List[str],**__A   :							Any  ):
          return self.tokenizer.batch_decode(*__A,**__A  )



    def 	lowerCamelCase_	(						self   :							Dict,*__A   :							List[Any],**__A   :							List[str]  ):
          return self.tokenizer.decode(*__A,**__A  )



    @contextmanager
    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          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 images inputs, or in a separate call."  )
          _lowerCamelCase					: Tuple		    = True
          _lowerCamelCase					: Optional[int]		    = self.tokenizer
          yield
          _lowerCamelCase					: Any		    = self.image_processor
          _lowerCamelCase					: List[str]		    = False



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							str,__A   :							int=False,__A   :							int=None  ):
          if added_vocab is None:
                _lowerCamelCase					: List[Any]		    = self.tokenizer.get_added_vocab()

          _lowerCamelCase					: str		    = {}

          while tokens:
                _lowerCamelCase					: Tuple		    = re.search(r"<s_(.*?)>",__A,re.IGNORECASE  )
                if start_token is None:
                      break
                _lowerCamelCase					: Any		    = start_token.group(1  )
                _lowerCamelCase					: Optional[Any]		    = re.search(rf'</s_{key}>',__A,re.IGNORECASE  )
                _lowerCamelCase					: int		    = start_token.group()
                if end_token is None:
                      _lowerCamelCase					: Any		    = tokens.replace(__A,""  )
                else:
                      _lowerCamelCase					: Dict		    = end_token.group()
                      _lowerCamelCase					: Union[str, Any]		    = re.escape(__A  )
                      _lowerCamelCase					: Union[str, Any]		    = re.escape(__A  )
                      _lowerCamelCase					: Union[str, Any]		    = re.search(f'{start_token_escaped}(.*?){end_token_escaped}',__A,re.IGNORECASE  )
                      if content is not None:
                            _lowerCamelCase					: str		    = content.group(1  ).strip()
                            if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                  _lowerCamelCase					: Union[str, Any]		    = self.tokenajson(__A,is_inner_value=__A,added_vocab=__A  )
                                  if value:
                                        if len(__A  ) == 1:
                                              _lowerCamelCase					: Optional[Any]		    = value[0]
                                        _lowerCamelCase					: Dict		    = value
                            else:  # leaf nodes
                                  _lowerCamelCase					: Tuple		    = []
                                  for leaf in content.split(r"<sep/>"  ):
                                        _lowerCamelCase					: List[Any]		    = leaf.strip()
                                        if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                              _lowerCamelCase					: int		    = leaf[1:-2]  # for categorical special tokens
                                        output[key].append(__A  )
                                  if len(output[key]  ) == 1:
                                        _lowerCamelCase					: Any		    = output[key][0]

                      _lowerCamelCase					: List[Any]		    = tokens[tokens.find(__A  ) + len(__A  ) :].strip()
                      if tokens[:6] == r"<sep/>":  # non-leaf nodes
                            return [output] + self.tokenajson(tokens[6:],is_inner_value=__A,added_vocab=__A  )

          if len(__A  ):
                return [output] if is_inner_value else output
          else:
                return [] if is_inner_value else {"text_sequence": tokens}



    @property
    def 	lowerCamelCase_	(						self   :							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 	lowerCamelCase_	(						self   :							Dict  ):
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",__A,)
          return self.image_processor







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 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

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

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

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

           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

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

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       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 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

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

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

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


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

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

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

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

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       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"       ,    )






