



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class   snake_case_ (_UpperCAmelCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""dpt"""



       def __init__(     self     ,lowercase=768     ,lowercase=12     ,lowercase=12     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.0     ,lowercase=0.0     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=384     ,lowercase=16     ,lowercase=3     ,lowercase=False     ,lowercase=True     ,lowercase=[2, 5, 8, 11]     ,lowercase="project"     ,lowercase=[4, 2, 1, 0.5]     ,lowercase=[96, 192, 384, 768]     ,lowercase=256     ,lowercase=-1     ,lowercase=False     ,lowercase=True     ,lowercase=0.4     ,lowercase=255     ,lowercase=0.1     ,lowercase=[1, 1024, 24, 24]     ,lowercase=[0, 1]     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase__)

             UpperCAmelCase_  :	Any				      =		hidden_size
             UpperCAmelCase_  :	Union[str, Any]				      =		is_hybrid

             if self.is_hybrid:
                   if backbone_config is None:
                         logger.info("Initializing the config with a `BiT` backbone.")
                         UpperCAmelCase_  :	Tuple				      =		{
                             "global_padding": "same",
                             "layer_type": "bottleneck",
                             "depths": [3, 4, 9],
                             "out_features": ["stage1", "stage2", "stage3"],
                             "embedding_dynamic_padding": True,
                         }
                         UpperCAmelCase_  :	Union[str, Any]				      =		BitConfig(**lowercase__)
                   elif isinstance(lowercase__     ,lowercase__):
                         logger.info("Initializing the config with a `BiT` backbone.")
                         UpperCAmelCase_  :	Dict				      =		BitConfig(**lowercase__)
                   elif isinstance(lowercase__     ,lowercase__):
                         UpperCAmelCase_  :	Optional[int]				      =		backbone_config
                   else:
                         raise ValueError(
                             F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""")

                   UpperCAmelCase_  :	List[Any]				      =		backbone_featmap_shape
                   UpperCAmelCase_  :	Union[str, Any]				      =		neck_ignore_stages

                   if readout_type != "project":
                         raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode.")
             else:
                   UpperCAmelCase_  :	Optional[Any]				      =		None
                   UpperCAmelCase_  :	int				      =		None
                   UpperCAmelCase_  :	List[Any]				      =		[]

             UpperCAmelCase_  :	int				      =		num_hidden_layers
             UpperCAmelCase_  :	Union[str, Any]				      =		num_attention_heads
             UpperCAmelCase_  :	Tuple				      =		intermediate_size
             UpperCAmelCase_  :	Optional[int]				      =		hidden_act
             UpperCAmelCase_  :	Dict				      =		hidden_dropout_prob
             UpperCAmelCase_  :	str				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	str				      =		initializer_range
             UpperCAmelCase_  :	List[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		image_size
             UpperCAmelCase_  :	Union[str, Any]				      =		patch_size
             UpperCAmelCase_  :	Union[str, Any]				      =		num_channels
             UpperCAmelCase_  :	Union[str, Any]				      =		qkv_bias
             UpperCAmelCase_  :	Optional[Any]				      =		backbone_out_indices
             if readout_type not in ["ignore", "add", "project"]:
                   raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']")
             UpperCAmelCase_  :	Any				      =		readout_type
             UpperCAmelCase_  :	Optional[Any]				      =		reassemble_factors
             UpperCAmelCase_  :	str				      =		neck_hidden_sizes
             UpperCAmelCase_  :	Union[str, Any]				      =		fusion_hidden_size
             UpperCAmelCase_  :	Any				      =		head_in_index
             UpperCAmelCase_  :	str				      =		use_batch_norm_in_fusion_residual
             # auxiliary head attributes (semantic segmentation)
             UpperCAmelCase_  :	List[Any]				      =		use_auxiliary_head
             UpperCAmelCase_  :	int				      =		auxiliary_loss_weight
             UpperCAmelCase_  :	Union[str, Any]				      =		semantic_loss_ignore_index
             UpperCAmelCase_  :	Any				      =		semantic_classifier_dropout



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		copy.deepcopy(self.__dict__)

             if output["backbone_config"] is not None:
                   UpperCAmelCase_  :	List[str]				      =		self.backbone_config.to_dict()

             UpperCAmelCase_  :	Optional[int]				      =		self.__class__.model_type
             return output





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


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""ClapFeatureExtractor"""
       _lowerCamelCase			    =				("""RobertaTokenizer""", """RobertaTokenizerFast""")



       def __init__(     self     ,lowercase     ,lowercase):

             """simple docstring"""





             super().__init__(lowercase     ,lowercase)



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

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		kwargs.pop("sampling_rate"     ,lowercase)

             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:
                   UpperCAmelCase_  :	List[str]				      =		self.tokenizer(lowercase     ,return_tensors=lowercase     ,**lowercase)

             if audios is not None:
                   UpperCAmelCase_  :	str				      =		self.feature_extractor(
                       lowercase     ,sampling_rate=lowercase     ,return_tensors=lowercase     ,**lowercase)

             if text is not None and audios is not None:
                   UpperCAmelCase_  :	Optional[int]				      =		audio_features.input_features
                   return encoding
             elif text is not None:
                   return encoding
             else:
                   return BatchEncoding(data=dict(**lowercase)     ,tensor_type=lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.batch_decode(*lowercase     ,**lowercase)



       def  A_   (     self     ,*lowercase     ,**lowercase):

             """simple docstring"""





             return self.tokenizer.decode(*lowercase     ,**lowercase)



       @property
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		self.tokenizer.model_input_names
             UpperCAmelCase_  :	str				      =		self.feature_extractor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))
