

from ...configuration_utils import PretrainedConfig


a__				=		{
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    "tapas"


							def __init__(			self       ,							_a=3_0_5_2_2       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_0_2_4       ,							_a=[3, 2_5_6, 2_5_6, 2, 2_5_6, 2_5_6, 1_0]       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=10.0       ,							_a=0       ,							_a=1.0       ,							_a=None       ,							_a=1.0       ,							_a=False       ,							_a=None       ,							_a=1.0       ,							_a=1.0       ,							_a=False       ,							_a=False       ,							_a="ratio"       ,							_a=None       ,							_a=None       ,							_a=6_4       ,							_a=3_2       ,							_a=False       ,							_a=True       ,							_a=False       ,							_a=False       ,							_a=True       ,							_a=False       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    int:
												super().__init__(pad_token_id=_a       ,							**_a       )

												# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
												_a	:    str         =		vocab_size
												_a	:    Optional[int]         =		hidden_size
												_a	:    Optional[int]         =		num_hidden_layers
												_a	:    Union[str, Any]         =		num_attention_heads
												_a	:    Any         =		hidden_act
												_a	:    List[Any]         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    str         =		attention_probs_dropout_prob
												_a	:    List[str]         =		max_position_embeddings
												_a	:    List[Any]         =		type_vocab_sizes
												_a	:    str         =		initializer_range
												_a	:    Optional[Any]         =		layer_norm_eps

												# Fine-tuning task hyperparameters
												_a	:    Union[str, Any]         =		positive_label_weight
												_a	:    Any         =		num_aggregation_labels
												_a	:    Tuple         =		aggregation_loss_weight
												_a	:    List[Any]         =		use_answer_as_supervision
												_a	:    str         =		answer_loss_importance
												_a	:    int         =		use_normalized_answer_loss
												_a	:    Tuple         =		huber_loss_delta
												_a	:    Tuple         =		temperature
												_a	:    int         =		aggregation_temperature
												_a	:    Any         =		use_gumbel_for_cells
												_a	:    List[Any]         =		use_gumbel_for_aggregation
												_a	:    int         =		average_approximation_function
												_a	:    str         =		cell_selection_preference
												_a	:    Any         =		answer_loss_cutoff
												_a	:    int         =		max_num_rows
												_a	:    Union[str, Any]         =		max_num_columns
												_a	:    Optional[Any]         =		average_logits_per_cell
												_a	:    Dict         =		select_one_column
												_a	:    Dict         =		allow_empty_column_selection
												_a	:    Union[str, Any]         =		init_cell_selection_weights_to_zero
												_a	:    str         =		reset_position_index_per_cell
												_a	:    Tuple         =		disable_per_token_loss

												# Aggregation hyperparameters
												_a	:    Union[str, Any]         =		aggregation_labels
												_a	:    List[Any]         =		no_aggregation_label_index

												if isinstance(self.aggregation_labels       ,							_a       ):
																	_a	:    Tuple         =		{int(_a       ): v for k, v in aggregation_labels.items()}






import warnings

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

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

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

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

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



