




"""simple docstring"""






import warnings

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






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""ViTImageProcessor"""
			lowerCamelCase__      =			("""CLIPTokenizer""", """CLIPTokenizerFast""")
			def __init__(  self  ,      lowercase=None  ,      lowercase=None  ,      **lowercase  ):
							_lowerCamelCase     :			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.'  ,      lowercase  ,      )
											_lowerCamelCase     :			Any    =    kwargs.pop('feature_extractor'  )

							_lowerCamelCase     :			int    =    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  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      lowercase=None  ,      **lowercase  ):
							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:
											_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer(lowercase  ,      return_tensors=lowercase  ,      **lowercase  )

							if visual_prompt is not None:
											_lowerCamelCase     :			Optional[int]    =    self.image_processor(lowercase  ,      return_tensors=lowercase  ,      **lowercase  )

							if images is not None:
											_lowerCamelCase     :			Tuple    =    self.image_processor(lowercase  ,      return_tensors=lowercase  ,      **lowercase  )

							if visual_prompt is not None and images is not None:
											_lowerCamelCase     :			Optional[Any]    =    {
											    '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:
											_lowerCamelCase     :			Tuple    =    image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							elif visual_prompt is not None:
											_lowerCamelCase     :			List[Any]    =    {
											    'conditional_pixel_values': prompt_features.pixel_values,
											}
											return encoding
							else:
											return BatchEncoding(data=dict(**lowercase  )  ,      tensor_type=lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			def 					A_    (  self  ):
							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 					A_    (  self  ):
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,      lowercase  ,      )
							return self.image_processor





"""simple docstring"""






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


lowercase__     						=						logging.get_logger(__name__)

lowercase__     						=						{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			"""ctrl"""
			lowerCamelCase__      =			["""past_key_values"""]
			lowerCamelCase__      =			{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(  self  ,      lowercase=246534  ,      lowercase=256  ,      lowercase=1280  ,      lowercase=8192  ,      lowercase=48  ,      lowercase=16  ,      lowercase=0.1  ,      lowercase=0.1  ,      lowercase=1E-6  ,      lowercase=0.02  ,      lowercase=True  ,      **lowercase  ,      ):
							_lowerCamelCase     :			Any    =    vocab_size
							_lowerCamelCase     :			Dict    =    n_positions
							_lowerCamelCase     :			Optional[int]    =    n_embd
							_lowerCamelCase     :			str    =    n_layer
							_lowerCamelCase     :			Union[str, Any]    =    n_head
							_lowerCamelCase     :			Any    =    dff
							_lowerCamelCase     :			int    =    resid_pdrop
							_lowerCamelCase     :			Dict    =    embd_pdrop
							_lowerCamelCase     :			Union[str, Any]    =    layer_norm_epsilon
							_lowerCamelCase     :			Tuple    =    initializer_range

							_lowerCamelCase     :			str    =    use_cache

							super().__init__(**lowercase  )