






'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__    :Optional[Any] 							=      logging.get_logger(__name__)

UpperCAmelCase__    :Optional[int] 							=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}







class 						SCREAMING_SNAKE_CASE						(   lowerCamelCase__	):
       snake_case__     :  Optional[Any] 		=      'megatron-bert'




       def __init__(       self   :			List[str]						,     A__   :			Union[str, Any]=29056						,     A__   :			Any=1024						,     A__   :			Optional[Any]=24						,     A__   :			Dict=16						,     A__   :			str=4096						,     A__   :			Any="gelu"						,     A__   :			Union[str, Any]=0.1						,     A__   :			Union[str, Any]=0.1						,     A__   :			Optional[Any]=512						,     A__   :			Any=2						,     A__   :			Tuple=0.02						,     A__   :			Union[str, Any]=1e-1_2						,     A__   :			List[Any]=0						,     A__   :			Tuple="absolute"						,     A__   :			List[Any]=True						,     **A__   :			List[Any]						,     ):


            """simple docstring"""







            super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__						,     **SCREAMING_SNAKE_CASE__       )

            __lowerCamelCase    :							Union[str, Any]					 =					vocab_size
            __lowerCamelCase    :							Optional[int]					 =					hidden_size
            __lowerCamelCase    :							List[Any]					 =					num_hidden_layers
            __lowerCamelCase    :							Optional[Any]					 =					num_attention_heads
            __lowerCamelCase    :							List[str]					 =					hidden_act
            __lowerCamelCase    :							List[str]					 =					intermediate_size
            __lowerCamelCase    :							int					 =					hidden_dropout_prob
            __lowerCamelCase    :							List[str]					 =					attention_probs_dropout_prob
            __lowerCamelCase    :							Any					 =					max_position_embeddings
            __lowerCamelCase    :							Dict					 =					type_vocab_size
            __lowerCamelCase    :							Any					 =					initializer_range
            __lowerCamelCase    :							Tuple					 =					layer_norm_eps
            __lowerCamelCase    :							Tuple					 =					position_embedding_type
            __lowerCamelCase    :							Any					 =					use_cache







"""simple docstring"""






import warnings

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






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	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__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""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					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''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					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



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





		@property
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



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

