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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						{'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}



class   snake_case_       (		__A       ):
   __A	:		str         =				"ctrl"
   __A	:		Tuple         =				["past_key_values"]
   __A	:		Optional[int]         =				{
       "max_position_embeddings": "n_positions",
       "hidden_size": "n_embd",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(      self				:     Dict	,						lowercase_				:     Tuple=24_65_34	,						lowercase_				:     List[str]=2_56	,						lowercase_				:     Tuple=12_80	,						lowercase_				:     List[Any]=81_92	,						lowercase_				:     Union[str, Any]=48	,						lowercase_				:     Any=16	,						lowercase_				:     List[str]=0.1	,						lowercase_				:     Any=0.1	,						lowercase_				:     Optional[Any]=1E-6	,						lowercase_				:     Optional[Any]=0.02	,						lowercase_				:     Optional[int]=True	,						**lowercase_				:     Optional[Any]	,						)					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				vocab_size
       lowercase__ :   Optional[Any]    					=				n_positions
       lowercase__ :   Optional[Any]    					=				n_embd
       lowercase__ :   Tuple    					=				n_layer
       lowercase__ :   List[str]    					=				n_head
       lowercase__ :   Union[str, Any]    					=				dff
       lowercase__ :   Dict    					=				resid_pdrop
       lowercase__ :   Any    					=				embd_pdrop
       lowercase__ :   List[str]    					=				layer_norm_epsilon
       lowercase__ :   int    					=				initializer_range

       lowercase__ :   Union[str, Any]    					=				use_cache

       super().__init__(**lowercase_  )






'''simple docstring'''






import warnings

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






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = 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__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     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:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






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






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

