



"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


a_			   =					{
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_			   =					[
			    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ASTForAudioClassification''',
			    '''ASTModel''',
			    '''ASTPreTrainedModel''',
			]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_			   =					['''ASTFeatureExtractor''']

if TYPE_CHECKING:
			from .configuration_audio_spectrogram_transformer import (
			    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ASTConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_audio_spectrogram_transformer import (
						    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ASTForAudioClassification,
						    ASTModel,
						    ASTPreTrainedModel,
						)

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
			import sys

			a_			   =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









"""simple docstring"""

import warnings

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







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""ChineseCLIPImageProcessor"""
						_A		:							Tuple							       =		("""BertTokenizer""", """BertTokenizerFast""")







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):
											snake_case_					:		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."""   ,		lowercase__   ,		)
																snake_case_					:		Optional[Any] 					=					kwargs.pop("""feature_extractor"""       )

											snake_case_					:		str 					=					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__(lowercase__   ,		lowercase__       )
											snake_case_					:		Union[str, Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		**lowercase__       ):

											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:
																snake_case_					:		Any 					=					self.tokenizer(lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )

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

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







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					self.tokenizer.model_input_names
											snake_case_					:		Union[str, Any] 					=					self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







						@property
						def 						__UpperCamelCase							(self       ):
											warnings.warn(
											    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,		lowercase__   ,		)
											return self.image_processor_class




