






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

class 		_snake_case	(	A__       ):
			_lowercase		:     List[str]        =				'''ClapFeatureExtractor'''
			_lowercase		:     str        =				('''RobertaTokenizer''', '''RobertaTokenizerFast''')






			def __init__(							self		,       a		,       a)   ->							Optional[int]:
						super().__init__(a		,       a)






			def __call__(							self		,       a=None		,       a=None		,       a=None		,       **a)   ->							int:
						SCREAMING_SNAKE_CASE							=			kwargs.pop('sampling_rate'		,       a)

						if text is None and audios is None:
									raise ValueError('You have to specify either text or audios. Both cannot be none.')

						if text is not None:
									SCREAMING_SNAKE_CASE							=			self.tokenizer(a		,       return_tensors=a		,       **a)

						if audios is not None:
									SCREAMING_SNAKE_CASE							=			self.feature_extractor(
									    a		,       sampling_rate=a		,       return_tensors=a		,       **a)

						if text is not None and audios is not None:
									SCREAMING_SNAKE_CASE							=			audio_features.input_features
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**a)		,       tensor_type=a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							List[str]:
						return self.tokenizer.batch_decode(*a		,       **a)






			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							Union[str, Any]:
						return self.tokenizer.decode(*a		,       **a)







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			self.tokenizer.model_input_names
						SCREAMING_SNAKE_CASE							=			self.feature_extractor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))








'''simple docstring'''





from typing import TYPE_CHECKING

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


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



