
import warnings

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





class 				UpperCamelCase__		(	__lowerCamelCase	):






       """simple docstring"""


       UpperCAmelCase_				       =["image_processor", "tokenizer"]
       UpperCAmelCase_				       ="AutoImageProcessor"
       UpperCAmelCase_				       ="AutoTokenizer"



       def __init__(							self ,     _A=None ,     _A=None ,     **_A	)				->				Optional[Any]:
          SCREAMING_SNAKE_CASE_	=      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_ ,     )
             SCREAMING_SNAKE_CASE_	=      kwargs.pop('''feature_extractor'''	)

          SCREAMING_SNAKE_CASE_	=      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__(lowerCamelCase_ ,     lowerCamelCase_	)
          SCREAMING_SNAKE_CASE_	=      self.image_processor



       def __call__(							self ,     _A=None ,     _A=None ,     _A=None ,     **_A	)				->				Tuple:






          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:
             SCREAMING_SNAKE_CASE_	=      self.tokenizer(lowerCamelCase_ ,     return_tensors=lowerCamelCase_ ,     **lowerCamelCase_	)

          if images is not None:
             SCREAMING_SNAKE_CASE_	=      self.image_processor(lowerCamelCase_ ,     return_tensors=lowerCamelCase_ ,     **lowerCamelCase_	)

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



       def     _UpperCamelCase	(							self ,     *_A ,     **_A	)				->				Dict:
          return self.tokenizer.batch_decode(*lowerCamelCase_ ,     **lowerCamelCase_	)



       def     _UpperCamelCase	(							self ,     *_A ,     **_A	)				->				str:
          return self.tokenizer.decode(*lowerCamelCase_ ,     **lowerCamelCase_	)



       @property
       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      self.tokenizer.model_input_names
          SCREAMING_SNAKE_CASE_	=      self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



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



"""simple docstring"""







from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

SCREAMING_SNAKE_CASE						=    TypeVar('T', bound=bool)



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     T      					=				value
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None
					_lowercase			:     RandomizedHeapNode[T] | None      					=				None







				@property
				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					return self._value







				@staticmethod
				def 				__UpperCAmelCase       (			lowerCamelCase_     :							RandomizedHeapNode[T] | None      ,       lowerCamelCase_     :							RandomizedHeapNode[T] | None    ):

					"""simple docstring"""






					if not roota:
						return roota

					if not roota:
						return roota

					if roota.value > roota.value:
						_lowercase					,   _lowercase			:     List[Any]      					=				roota, roota

					if random.choice([True, False]    ):
						_lowercase					,   _lowercase			:     Optional[int]      					=				roota.right, roota.left

					_lowercase			:     Tuple      					=				RandomizedHeapNode.merge(roota.left      ,       lowerCamelCase_    )

					return roota



class 			_lowerCamelCase      (Generic[T]  ):







				def __init__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							Iterable[T] | None = ()    ):

					"""simple docstring"""






					_lowercase			:     RandomizedHeapNode[T] | None      					=				None

					if data:
						for item in data:
							self.insert(lowerCamelCase_    )







				def 				__UpperCAmelCase       (			self     :							Tuple      ,       lowerCamelCase_     :							T    ):

					"""simple docstring"""






					_lowercase			:     str      					=				RandomizedHeapNode.merge(self._root      ,       RandomizedHeapNode(lowerCamelCase_    )    )







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     Any      					=				self.top()

					if self._root is None:
						return None

					_lowercase			:     List[Any]      					=				RandomizedHeapNode.merge(self._root.left      ,       self._root.right    )

					return result







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






					if not self._root:
						raise IndexError('Can\'t get top element for the empty heap.'    )
					return self._root.value







				def 				__UpperCAmelCase       (			self     :							Any    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				None







				def 				__UpperCAmelCase       (			self     :							Union[str, Any]    ):

					"""simple docstring"""






					_lowercase			:     List[Any]      					=				[]
					while self:
						result.append(self.pop()    )

					return result







				def __bool__(			self     :							List[str]    ):

					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()



