"""simple docstring"""







import warnings

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

class 						UpperCamelCase     (       __lowerCamelCase		):
				UpperCAmelCase							: Optional[Any]       =						["""image_processor""", """tokenizer"""]
				UpperCAmelCase							: Dict       =						"""AutoImageProcessor"""
				UpperCAmelCase							: Dict       =						"""AutoTokenizer"""



				def __init__(self							: List[Any]      ,    _A							: Optional[int]=None      ,    _A							: Dict=None      ,    **_A							: Optional[int])       ->      Any:
					__snake_case     :				Union[str, 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      ,    )
						__snake_case     :				List[str] 						=					kwargs.pop('feature_extractor')

					__snake_case     :				Dict 						=					image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
						raise ValueError('You have to specify an image_processor.')
					if tokenizer is None:
						raise ValueError('You have to specify a tokenizer.')

					super().__init__(_lowercase      ,    _lowercase)
					__snake_case     :				Union[str, Any] 						=					self.image_processor



				def __call__(self							: Tuple      ,    _A							: Union[str, Any]=None      ,    _A							: List[Any]=None      ,    _A							: Optional[int]=None      ,    **_A							: Tuple)       ->      Dict:

					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:
						__snake_case     :				Union[str, Any] 						=					self.tokenizer(_lowercase      ,    return_tensors=_lowercase      ,    **_lowercase)

					if images is not None:
						__snake_case     :				List[str] 						=					self.image_processor(_lowercase      ,    return_tensors=_lowercase      ,    **_lowercase)

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



				def    _lowercase	(self							: Dict      ,    *_A							: Optional[Any]      ,    **_A							: Dict)       ->      Optional[int]:
					return self.tokenizer.batch_decode(*_lowercase      ,    **_lowercase)



				def    _lowercase	(self							: Dict      ,    *_A							: Optional[int]      ,    **_A							: Tuple)       ->      int:
					return self.tokenizer.decode(*_lowercase      ,    **_lowercase)



				@property
				def    _lowercase	(self							: Any)       ->      Optional[int]:
					__snake_case     :				List[str] 						=					self.tokenizer.model_input_names
					__snake_case     :				int 						=					self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



				@property
				def    _lowercase	(self							: Any)       ->      Optional[int]:
					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    _lowercase	(self							: str)       ->      List[str]:
					warnings.warn(
					    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,    _lowercase      ,    )
					return self.image_processor






"""simple docstring"""







from __future__ import annotations


def 	a__   (		UpperCAmelCase__							:      list      )->						int | float:
						UpperCAmelCase__       						=		sorted(UpperCAmelCase__      )
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	a__   (		)->						Dict:
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()

