






import warnings
from typing import List, Optional, Union

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





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					Dict											=   ['''image_processor''', '''tokenizer''']
	lowercase__						:					Optional[int]											=   '''ViltImageProcessor'''
	lowercase__						:					Optional[int]											=   ('''BertTokenizer''', '''BertTokenizerFast''')
	def __init__(    self      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       **lowerCAmelCase__			)							->				int:
							__magic_name__      :       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."""      ,       lowerCAmelCase__      ,       )
													__magic_name__      :       str       						= kwargs.pop("""feature_extractor"""			)

							__magic_name__      :       Optional[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__(lowerCAmelCase__      ,       lowerCAmelCase__			)
							__magic_name__      :       int       						= self.image_processor
	def __call__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = 0      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = False      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = None      ,       **lowerCAmelCase__      ,       )							->				BatchEncoding:
							__magic_name__      :       List[str]       						= self.tokenizer(
							    text=lowerCAmelCase__      ,       add_special_tokens=lowerCAmelCase__      ,       padding=lowerCAmelCase__      ,       truncation=lowerCAmelCase__      ,       max_length=lowerCAmelCase__      ,       stride=lowerCAmelCase__      ,       pad_to_multiple_of=lowerCAmelCase__      ,       return_token_type_ids=lowerCAmelCase__      ,       return_attention_mask=lowerCAmelCase__      ,       return_overflowing_tokens=lowerCAmelCase__      ,       return_special_tokens_mask=lowerCAmelCase__      ,       return_offsets_mapping=lowerCAmelCase__      ,       return_length=lowerCAmelCase__      ,       verbose=lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )
							# add pixel_values + pixel_mask
							__magic_name__      :       int       						= self.image_processor(lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__			)
							encoding.update(lowerCAmelCase__			)

							return encoding
	def       __magic_name__    (    self      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							return self.tokenizer.batch_decode(*lowerCAmelCase__      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Union[str, Any]:
							return self.tokenizer.decode(*lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@property
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Dict       						= self.tokenizer.model_input_names
							__magic_name__      :       Any       						= self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)
	@property
	def       __magic_name__    (    self			)							->				int:
							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       __magic_name__    (    self			)							->				List[Any]:
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,       lowerCAmelCase__      ,       )
							return self.image_processor








def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			) or number < 0:
												raise ValueError("""Input must be a non-negative integer"""			)

						__magic_name__      :       str       						= 0
						while number:
												# This way we arrive at next set bit (next 1) instead of looping
												# through each bit and checking for 1s hence the
												# loop won't run 32 times it will only run the number of `1` times
												number &= number - 1
												count += 1
						return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()
