


from typing import TYPE_CHECKING

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


lowerCAmelCase__						:Union[str, Any]         =							{
    '''configuration_bark''': [
        '''BARK_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BarkCoarseConfig''',
        '''BarkConfig''',
        '''BarkFineConfig''',
        '''BarkSemanticConfig''',
    ],
    '''processing_bark''': ['''BarkProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__						:List[str]         =							[
		    '''BARK_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BarkFineModel''',
		    '''BarkSemanticModel''',
		    '''BarkCoarseModel''',
		    '''BarkModel''',
		    '''BarkPreTrainedModel''',
		    '''BarkCausalModel''',
		]

if TYPE_CHECKING:
		from .configuration_bark import (
		    BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BarkCoarseConfig,
		    BarkConfig,
		    BarkFineConfig,
		    BarkSemanticConfig,
		)
		from .processing_bark import BarkProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bark import (
				    BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BarkCausalModel,
				    BarkCoarseModel,
				    BarkFineModel,
				    BarkModel,
				    BarkPreTrainedModel,
				    BarkSemanticModel,
				)

else:
		import sys

		lowerCAmelCase__						:Union[str, Any]         =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





import warnings

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

class 	__a   (       UpperCAmelCase    ):
		_a			:						int    			=			['image_processor', 'tokenizer']
		_a			:						Union[str, Any]    			=			'AutoImageProcessor'
		_a			:						Optional[int]    			=			'AutoTokenizer'
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
																_UpperCAmelCase					=    kwargs.pop('feature_extractor'		)

									_UpperCAmelCase					=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You have to specify an image_processor.'		)
									if tokenizer is None:
																raise ValueError('You have to specify a tokenizer.'		)

									super().__init__(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    self.image_processor
		def __call__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					**_SCREAMING_SNAKE_CASE		)				-> Dict:




									"""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:
																_UpperCAmelCase					=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if images is not None:
																_UpperCAmelCase					=    self.image_processor(_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)

									if text is not None and images is not None:
																_UpperCAmelCase					=    image_features.pixel_values
																return encoding
									elif text is not None:
																return encoding
									else:
																return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)    ,					tensor_type=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self    ,					*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)				-> Tuple:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    self.tokenizer.model_input_names
									_UpperCAmelCase					=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor_class






		@property
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					_SCREAMING_SNAKE_CASE    ,					)
									return self.image_processor
