


'''simple docstring'''



from ... import PretrainedConfig


_lowercase    :    Union[str, Any]			     =  {
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class  UpperCamelCase__(     _UpperCAmelCase ):
				__magic_name__       :   Optional[int]							= NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
				__magic_name__       :   Any							= "nezha"




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				Tuple=21128   ,					lowerCAmelCase    :				Dict=768   ,					lowerCAmelCase    :				Dict=12   ,					lowerCAmelCase    :				Optional[int]=12   ,					lowerCAmelCase    :				str=3072   ,					lowerCAmelCase    :				Dict="gelu"   ,					lowerCAmelCase    :				List[str]=0.1   ,					lowerCAmelCase    :				List[str]=0.1   ,					lowerCAmelCase    :				Dict=512   ,					lowerCAmelCase    :				str=64   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=0.02   ,					lowerCAmelCase    :				Optional[int]=1E-12   ,					lowerCAmelCase    :				Tuple=0.1   ,					lowerCAmelCase    :				List[str]=0   ,					lowerCAmelCase    :				Optional[Any]=2   ,					lowerCAmelCase    :				str=3   ,					lowerCAmelCase    :				Optional[int]=True   ,					**lowerCAmelCase    :				Dict   ,					)->  Tuple:

										"""simple docstring"""




										super().__init__(pad_token_id=lowercase_   ,					bos_token_id=lowercase_   ,					eos_token_id=lowercase_   ,					**lowercase_   )

										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			max_position_embeddings
										UpperCAmelCase         =			max_relative_position
										UpperCAmelCase         =			type_vocab_size
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			layer_norm_eps
										UpperCAmelCase         =			classifier_dropout
										UpperCAmelCase         =			use_cache









'''simple docstring'''



import warnings

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






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""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.'''   ,					lowerCAmelCase   ,					)
																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 need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




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




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




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




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




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





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




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





