





import warnings

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

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							['''image_processor''', '''tokenizer''']
      a						       =							'''CLIPImageProcessor'''
      a						       =							('''CLIPTokenizer''', '''CLIPTokenizerFast''')
      def __init__(		self      ,       a__=None      ,       a__=None      ,       **a__       ):
         A_     :				List[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__      ,       )
            A_     :				Any			      =	kwargs.pop("""feature_extractor"""       )

         A_     :				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__       )
      def __call__(		self      ,       a__=None      ,       a__=None      ,       a__=None      ,       **a__       ):

         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:
            A_     :				str			      =	self.tokenizer(a__      ,       return_tensors=a__      ,       **a__       )

         if images is not None:
            A_     :				str			      =	self.image_processor(a__      ,       return_tensors=a__      ,       **a__       )

         if text is not None and images is not None:
            A_     :				Tuple			      =	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      ,       *a__      ,       **a__       ):
         return self.tokenizer.batch_decode(*a__      ,       **a__       )
      def 		_lowerCamelCase      (		self      ,       *a__      ,       **a__       ):
         return self.tokenizer.decode(*a__      ,       **a__       )
      @property
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	self.tokenizer.model_input_names
         A_     :				Dict			      =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )
      @property
      def 		_lowerCamelCase      (		self       ):
         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       ):
         warnings.warn(
             """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,       a__      ,       )
         return self.image_processor













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






