




"""simple docstring"""


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


class _lowerCamelCase				(     a_						):
   _lowerCamelCase				:int	      =	"ClapFeatureExtractor"
   _lowerCamelCase				:Dict	      =	("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self   :		List[Any]    ,	UpperCamelCase   :		Tuple    ,	UpperCamelCase   :		List[Any]    ) ->						str:

         """simple docstring"""
         super().__init__(snake_case__    ,	snake_case__    )






   def __call__(		self   :		Tuple    ,	UpperCamelCase   :		List[str]=None    ,	UpperCamelCase   :		List[str]=None    ,	UpperCamelCase   :		Tuple=None    ,	**UpperCamelCase   :		Any    ) ->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__			: Union[str, Any]  					=       kwargs.pop("""sampling_rate"""    ,	snake_case__    )

         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:
               lowerCAmelCase__			: int  					=       self.tokenizer(snake_case__    ,	return_tensors=snake_case__    ,	**snake_case__    )

         if audios is not None:
               lowerCAmelCase__			: Tuple  					=       self.feature_extractor(
                   snake_case__    ,	sampling_rate=snake_case__    ,	return_tensors=snake_case__    ,	**snake_case__    )

         if text is not None and audios is not None:
               lowerCAmelCase__			: Union[str, Any]  					=       audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**snake_case__    )    ,	tensor_type=snake_case__    )






   def        _lowerCAmelCase    (		self   :		Union[str, Any]    ,	*UpperCamelCase   :		int    ,	**UpperCamelCase   :		int    ) ->						Union[str, Any]:

         """simple docstring"""
         return self.tokenizer.batch_decode(*snake_case__    ,	**snake_case__    )






   def        _lowerCAmelCase    (		self   :		List[Any]    ,	*UpperCamelCase   :		str    ,	**UpperCamelCase   :		Union[str, Any]    ) ->						int:

         """simple docstring"""
         return self.tokenizer.decode(*snake_case__    ,	**snake_case__    )






   @property
   def        _lowerCAmelCase    (		self   :		Dict    ) ->						str:

         """simple docstring"""
         lowerCAmelCase__			: List[Any]  					=       self.tokenizer.model_input_names
         lowerCAmelCase__			: Optional[int]  					=       self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names    )    )









"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="data2vec-vision"

       def __init__(						self		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=224		,     snake_case__=16		,     snake_case__=3		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=True		,     snake_case__=[3, 5, 7, 11]		,     snake_case__=[1, 2, 3, 6]		,     snake_case__=True		,     snake_case__=0.4		,     snake_case__=256		,     snake_case__=1		,     snake_case__=False		,     snake_case__=255		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Tuple								=							hidden_size
         lowerCAmelCase     :     List[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Tuple								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Dict								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Dict								=							layer_norm_eps

         lowerCAmelCase     :     Optional[int]								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     Union[str, Any]								=							use_mask_token
         lowerCAmelCase     :     str								=							use_absolute_position_embeddings
         lowerCAmelCase     :     Any								=							use_relative_position_bias
         lowerCAmelCase     :     List[str]								=							use_shared_relative_position_bias
         lowerCAmelCase     :     str								=							layer_scale_init_value
         lowerCAmelCase     :     Union[str, Any]								=							drop_path_rate
         lowerCAmelCase     :     Any								=							use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase     :     Optional[int]								=							out_indices
         lowerCAmelCase     :     Union[str, Any]								=							pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase     :     str								=							use_auxiliary_head
         lowerCAmelCase     :     int								=							auxiliary_loss_weight
         lowerCAmelCase     :     Tuple								=							auxiliary_channels
         lowerCAmelCase     :     List[str]								=							auxiliary_num_convs
         lowerCAmelCase     :     Tuple								=							auxiliary_concat_input
         lowerCAmelCase     :     List[str]								=							semantic_loss_ignore_index


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4





