




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

class __lowercase(      _snake_case				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= ['image_processor', 'tokenizer']
					__a						:						List[Any]     		= 'AutoImageProcessor'
					__a						:						Optional[Any]     		= 'AutoTokenizer'
					def __init__(    self						,	__a						,	__a     ):
								super().__init__(lowerCAmelCase__						,	lowerCAmelCase__     )
								__lowerCamelCase					:	Union[str, Any]	       =				self.image_processor
					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:
											__lowerCamelCase					:	List[Any]	       =				self.tokenizer(lowerCAmelCase__						,	return_tensors=lowerCAmelCase__						,	**lowerCAmelCase__     )

								if images is not None:
											__lowerCamelCase					:	Optional[Any]	       =				self.image_processor(lowerCAmelCase__						,	return_tensors=lowerCAmelCase__						,	**lowerCAmelCase__     )

								if text is not None and images is not None:
											__lowerCamelCase					:	Dict	       =				image_features.pixel_values
											return encoding
								elif text is not None:
											return encoding
								else:
											return BatchEncoding(data=dict(**lowerCAmelCase__     )						,	tensor_type=lowerCAmelCase__     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.batch_decode(*lowerCAmelCase__						,	**lowerCAmelCase__     )
					def     snake_case_				(    self						,	*__a						,	**__a     ):
								return self.tokenizer.decode(*lowerCAmelCase__						,	**lowerCAmelCase__     )





					@property
					def     snake_case_				(    self     ):
								return ["input_ids", "attention_mask", "pixel_values"]









"""simple docstring"""
from typing import TYPE_CHECKING

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


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

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



