






"""simple docstring"""

from __future__ import annotations

from random import random

class 						A_							:







				def __init__(							self							:						str						,    __lowerCamelCase							:						int | None = None	)				->					Dict:
										__magic_name__            =     value
										__magic_name__            =     random()
										__magic_name__            =     None
										__magic_name__            =     None







				def __repr__(							self							:						List[str]	)				->					str:
										from pprint import pformat

										if self.left is None and self.right is None:
																return f'''\'{self.value}: {self.prior:.5}\''''
										else:
																return pformat(
																    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}						,    indent=1	)







				def __str__(							self							:						str	)				->					str:
										__magic_name__            =     str(self.value	) + " "
										__magic_name__            =     str(self.left or ""	)
										__magic_name__            =     str(self.right or ""	)
										return value + left + right






def _lowerCAmelCase	(  __lowerCamelCase:Node | None ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						if root is None:  # None tree is split into 2 Nones
												return None, None
						elif root.value is None:
												return None, None
						else:
												if value < root.value:
																		__magic_name__      ,    __magic_name__            =     split(root.left ,						__lowerCamelCase   )
																		return left, root
												else:
																		__magic_name__      ,    __magic_name__            =     split(root.right ,						__lowerCamelCase   )
																		return root, right






def _lowerCAmelCase	(  __lowerCamelCase:Node | None ,						__lowerCamelCase:Node | None   ):

						'''simple docstring'''






						if (not left) or (not right):  # If one node is None, return the other
												return left or right
						elif left.prior < right.prior:
												__magic_name__            =     merge(left.right ,						__lowerCamelCase   )
												return left
						else:
												__magic_name__            =     merge(__lowerCamelCase ,						right.left   )
												return right






def _lowerCAmelCase	(  __lowerCamelCase:Node | None ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     Node(__lowerCamelCase   )
						__magic_name__      ,    __magic_name__            =     split(__lowerCamelCase ,						__lowerCamelCase   )
						return merge(merge(__lowerCamelCase ,						__lowerCamelCase   ) ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Node | None ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__      ,    __magic_name__            =     split(__lowerCamelCase ,						value - 1   )
						__magic_name__      ,    __magic_name__            =     split(__lowerCamelCase ,						__lowerCamelCase   )
						return merge(__lowerCamelCase ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Node | None   ):

						'''simple docstring'''






						if not root:  # None
												return
						else:
												inorder(root.left   )
												print(root.value ,						end=","   )
												inorder(root.right   )






def _lowerCAmelCase	(  __lowerCamelCase:Node | None ,						__lowerCamelCase:str   ):

						'''simple docstring'''






						for arg in args.split():
												if arg[0] == "+":
																		__magic_name__            =     insert(__lowerCamelCase ,						int(arg[1:]   )   )

												elif arg[0] == "-":
																		__magic_name__            =     erase(__lowerCamelCase ,						int(arg[1:]   )   )

												else:
																		print("Unknown command"   )

						return root






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     None
						print(
						    "enter numbers to create a tree, + value to add value into treap, "
						    "- value to erase all nodes with value. 'q' to quit. "   )

						__magic_name__            =     input()
						while args != "q":
												__magic_name__            =     interact_treap(__lowerCamelCase ,						__lowerCamelCase   )
												print(__lowerCamelCase   )
												__magic_name__            =     input()

						print("good by!"   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()











"""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_							(      snake_case_  ):
				UpperCAmelCase__        =				['''image_processor''', '''tokenizer''']
				UpperCAmelCase__        =				'''BlipImageProcessor'''
				UpperCAmelCase__        =				'''AutoTokenizer'''







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int	)				->					str:
										__magic_name__            =     False
										super().__init__(__lowerCamelCase						,    __lowerCamelCase	)
										__magic_name__            =     self.image_processor







				def __call__(							self							:						List[Any]						,    __lowerCamelCase							:						ImageInput = None						,    __lowerCamelCase							:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Union[bool, str, PaddingStrategy] = False						,    __lowerCamelCase							:						Union[bool, str, TruncationStrategy] = None						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						int = 0						,    __lowerCamelCase							:						Optional[int] = None						,    __lowerCamelCase							:						Optional[bool] = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						Optional[Union[str, TensorType]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					BatchEncoding:
										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:
																__magic_name__            =     self.tokenizer
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
																return text_encoding

										# add pixel_values
										__magic_name__            =     self.image_processor(__lowerCamelCase						,    return_tensors=__lowerCamelCase	)

										if text is not None:
																__magic_name__            =     self.tokenizer(
																    text=__lowerCamelCase						,    add_special_tokens=__lowerCamelCase						,    padding=__lowerCamelCase						,    truncation=__lowerCamelCase						,    max_length=__lowerCamelCase						,    stride=__lowerCamelCase						,    pad_to_multiple_of=__lowerCamelCase						,    return_attention_mask=__lowerCamelCase						,    return_overflowing_tokens=__lowerCamelCase						,    return_special_tokens_mask=__lowerCamelCase						,    return_offsets_mapping=__lowerCamelCase						,    return_token_type_ids=__lowerCamelCase						,    return_length=__lowerCamelCase						,    verbose=__lowerCamelCase						,    return_tensors=__lowerCamelCase						,    **__lowerCamelCase						,    )
										else:
																__magic_name__            =     None

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

										return encoding_image_processor







				def     _snake_case					(							self							:						Union[str, Any]						,    *__lowerCamelCase							:						Union[str, Any]						,    **__lowerCamelCase							:						Dict	)				->					Dict:
										return self.tokenizer.batch_decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				def     _snake_case					(							self							:						str						,    *__lowerCamelCase							:						Optional[Any]						,    **__lowerCamelCase							:						Tuple	)				->					Optional[Any]:
										return self.tokenizer.decode(*__lowerCamelCase						,    **__lowerCamelCase	)







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     _snake_case					(							self							:						List[str]	)				->					Optional[Any]:
										__magic_name__            =     self.tokenizer.model_input_names
										__magic_name__            =     self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



