






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			["""image_processor""", """tokenizer"""]
				lowercase__											=			"""AutoImageProcessor"""
				lowercase__											=			"""AutoTokenizer"""




				def __init__(	self		:       Union[str, Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     self.image_processor




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

								'''simple docstring'''

								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:
												lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )

								if images is not None:
												lowercase__  					=     self.image_processor(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )

								if text is not None and images is not None:
												lowercase__  					=     image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**lowerCamelCase    ),    tensor_type=lowerCamelCase    )




				def 							lowercase__  (	self		:       Dict,    *lowerCamelCase		:       Any,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,    **lowerCamelCase    )




				def 							lowercase__  (	self		:       str,    *lowerCamelCase		:       Any,    **lowerCamelCase		:       int    ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,    **lowerCamelCase    )




				@property
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								return ["input_ids", "attention_mask", "pixel_values"]












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__      :     Optional[int]       =					{
    'configuration_clap': [
        'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ClapAudioConfig',
        'ClapConfig',
        'ClapTextConfig',
    ],
    'processing_clap': ['ClapProcessor'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__      :     str       =					[
							    'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ClapModel',
							    'ClapPreTrainedModel',
							    'ClapTextModel',
							    'ClapTextModelWithProjection',
							    'ClapAudioModel',
							    'ClapAudioModelWithProjection',
							]
							A__      :     List[Any]       =					['ClapFeatureExtractor']

if TYPE_CHECKING:
							from .configuration_clap import (
							    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
							    ClapAudioConfig,
							    ClapConfig,
							    ClapTextConfig,
							)
							from .processing_clap import ClapProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clap import ClapFeatureExtractor
														from .modeling_clap import (
														    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ClapAudioModel,
														    ClapAudioModelWithProjection,
														    ClapModel,
														    ClapPreTrainedModel,
														    ClapTextModel,
														    ClapTextModelWithProjection,
														)


else:
							import sys

							A__      :     int       =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




