
"""simple docstring"""







from typing import List

from .keymap import KEYMAP, get_character
def 					__lowerCAmelCase       (_UpperCamelCase	):

	def decorator(_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   getattr(_UpperCamelCase ,						'handle_key' ,						[]	)
		handle += [key]
		setattr(_UpperCamelCase ,						'handle_key' ,						_UpperCamelCase	)
		return func

	return decorator
def 					__lowerCAmelCase       (*_UpperCamelCase	):

	def decorator(_UpperCamelCase	):
		__lowerCAmelCase    :		List[str]		    =   getattr(_UpperCamelCase ,						'handle_key' ,						[]	)
		handle += keys
		setattr(_UpperCamelCase ,						'handle_key' ,						_UpperCamelCase	)
		return func

	return decorator




class 				A__ (			_lowerCamelCase):

		def __new__(   cls ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   super().__new__(cls ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			if not hasattr(_SCREAMING_SNAKE_CASE ,	'key_handler'				):
				setattr(_SCREAMING_SNAKE_CASE ,	'key_handler' ,	{}				)
			setattr(_SCREAMING_SNAKE_CASE ,	'handle_input' ,	KeyHandler.handle_input				)

			for value in attrs.values():
				__lowerCAmelCase    :		int		    =   getattr(_SCREAMING_SNAKE_CASE ,	'handle_key' ,	[]				)
				for key in handled_keys:
					__lowerCAmelCase    :		List[str]		    =   value
			return new_cls







		@staticmethod
		def 							__lowerCamelCase							(   cls				):
			__lowerCAmelCase    :		Optional[int]		    =   get_character()
			if char != KEYMAP["undefined"]:
				__lowerCAmelCase    :		Dict		    =   ord(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   cls.key_handler.get(_SCREAMING_SNAKE_CASE				)
			if handler:
				__lowerCAmelCase    :		Dict		    =   char
				return handler(cls				)
			else:
				return None



def 					__lowerCAmelCase       (cls	):
	return KeyHandler(cls.__name__ ,						cls.__bases__ ,						cls.__dict__.copy()	)

"""simple docstring"""







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			['image_processor', 'tokenizer']
		A_		: Optional[Any]            =			'BlipImageProcessor'
		A_		: int            =			'AutoTokenizer'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   False
			super().__init__(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor

		def __call__(   self ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 0 ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = False ,	_SCREAMING_SNAKE_CASE = True ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE ,	):
			if images is None and text is None:
				raise ValueError('You have to specify either images or text.'				)

			# Get only text
			if images is None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer
				__lowerCAmelCase    :		List[str]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
				return text_encoding

			# add pixel_values
			__lowerCAmelCase    :		List[Any]		    =   self.image_processor(_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE				)

			if text is not None:
				__lowerCAmelCase    :		Optional[int]		    =   self.tokenizer(
				    text=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	padding=_SCREAMING_SNAKE_CASE ,	truncation=_SCREAMING_SNAKE_CASE ,	max_length=_SCREAMING_SNAKE_CASE ,	stride=_SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,	return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,	return_offsets_mapping=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	return_length=_SCREAMING_SNAKE_CASE ,	verbose=_SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)
			else:
				__lowerCAmelCase    :		int		    =   None

			if text_encoding is not None:
				encoding_image_processor.update(_SCREAMING_SNAKE_CASE				)

			return encoding_image_processor

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				):
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.tokenizer.model_input_names
			__lowerCAmelCase    :		Union[str, Any]		    =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)