




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 		__lowerCAmelCase	(	__magic_name__ ):
							UpperCamelCase__					     =  ['''image_processor''', '''tokenizer''']
							UpperCamelCase__					     =  '''BlipImageProcessor'''
							UpperCamelCase__					     =  '''AutoTokenizer'''
							def __init__(						self						:Optional[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							False
								super().__init__(__magic_name__		,      __magic_name__				)
								a										=							self.image_processor
							def __call__(						self						:List[Any]		,      __magic_name__						:ImageInput = None		,      __magic_name__						:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,      __magic_name__						:bool = True		,      __magic_name__						:Union[bool, str, PaddingStrategy] = False		,      __magic_name__						:Union[bool, str, TruncationStrategy] = None		,      __magic_name__						:Optional[int] = None		,      __magic_name__						:int = 0		,      __magic_name__						:Optional[int] = None		,      __magic_name__						:Optional[bool] = None		,      __magic_name__						:bool = False		,      __magic_name__						:bool = False		,      __magic_name__						:bool = False		,      __magic_name__						:bool = False		,      __magic_name__						:bool = False		,      __magic_name__						:bool = True		,      __magic_name__						:Optional[Union[str, TensorType]] = None		,      **__magic_name__						:int		,      ):







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

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

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

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

								return encoding_image_processor
							def       lowerCamelCase__							(						self						:Dict		,      *__magic_name__						:Tuple		,      **__magic_name__						:Any				):







								'''simple docstring'''







								return self.tokenizer.batch_decode(*__magic_name__		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Dict		,      *__magic_name__						:str		,      **__magic_name__						:str				):







								'''simple docstring'''







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





							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								a										=							self.tokenizer.model_input_names
								a										=							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 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							value
								a										=							None
								a										=							None







class 		__lowerCAmelCase	:
							def __init__(						self						:str		,      __magic_name__						:Node				):







								'''simple docstring'''







								a										=							tree
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Node | None				):







								'''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						:Tuple				):







								'''simple docstring'''







								yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

