


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							(			UpperCamelCase__			):
							_lowercase	:			Dict        = ['''image_processor''', '''tokenizer''']
							_lowercase	:			Union[str, Any]        = '''BlipImageProcessor'''
							_lowercase	:			Tuple        = '''AutoTokenizer'''

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	List[str]							)			->   Any:





									"""simple docstring"""







									lowercase__            =      False
									super().__init__(UpperCamelCase_    ,						UpperCamelCase_							)
									lowercase__            =      self.image_processor

							def __call__(							self:	Tuple    ,						UpperCamelCase_:	ImageInput = None    ,						UpperCamelCase_:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Union[bool, str, PaddingStrategy] = False    ,						UpperCamelCase_:	Union[bool, str, TruncationStrategy] = None    ,						UpperCamelCase_:	Optional[int] = None    ,						UpperCamelCase_:	int = 0    ,						UpperCamelCase_:	Optional[int] = None    ,						UpperCamelCase_:	Optional[bool] = None    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = False    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	Optional[Union[str, TensorType]] = None    ,						**UpperCamelCase_:	List[Any]    ,						)			->   BatchEncoding:





									"""simple docstring"""







									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:
											lowercase__            =      self.tokenizer
											lowercase__            =      self.tokenizer(
											    text=UpperCamelCase_    ,						add_special_tokens=UpperCamelCase_    ,						padding=UpperCamelCase_    ,						truncation=UpperCamelCase_    ,						max_length=UpperCamelCase_    ,						stride=UpperCamelCase_    ,						pad_to_multiple_of=UpperCamelCase_    ,						return_attention_mask=UpperCamelCase_    ,						return_overflowing_tokens=UpperCamelCase_    ,						return_special_tokens_mask=UpperCamelCase_    ,						return_offsets_mapping=UpperCamelCase_    ,						return_token_type_ids=UpperCamelCase_    ,						return_length=UpperCamelCase_    ,						verbose=UpperCamelCase_    ,						return_tensors=UpperCamelCase_    ,						**UpperCamelCase_    ,						)
											return text_encoding

									# add pixel_values
									lowercase__            =      self.image_processor(UpperCamelCase_    ,						return_tensors=UpperCamelCase_							)

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

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

									return encoding_image_processor

							def       lowerCamelCase_ (							self:	List[str]    ,						*UpperCamelCase_:	Optional[Any]    ,						**UpperCamelCase_:	Optional[int]							)			->   Any:





									"""simple docstring"""







									return self.tokenizer.batch_decode(*UpperCamelCase_    ,						**UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	List[str]    ,						*UpperCamelCase_:	Optional[Any]    ,						**UpperCamelCase_:	Union[str, Any]							)			->   Any:





									"""simple docstring"""







									return self.tokenizer.decode(*UpperCamelCase_    ,						**UpperCamelCase_							)


							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def       lowerCamelCase_ (							self:	Dict							)			->   int:





									"""simple docstring"""







									lowercase__            =      self.tokenizer.model_input_names
									lowercase__            =      self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)






from __future__ import annotations

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


