

'''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 lowerCamelCase (  lowerCAmelCase__						):



			'''simple docstring'''






			__snake_case    	=       ["""image_processor""", """tokenizer"""]
			__snake_case    	=       """BlipImageProcessor"""
			__snake_case    	=       """AutoTokenizer"""


			def __init__(      self       :						List[str]		,			lowerCAmelCase_       :						int		,			lowerCAmelCase_       :						Union[str, Any] )			->		List[str]:





									'''simple docstring'''
									A__     :   Dict      =False
									super().__init__(UpperCamelCase__		,			UpperCamelCase__ )
									A__     :   List[Any]      =self.image_processor


			def __call__(      self       :						int		,			lowerCAmelCase_       :						Tuple = None		,			lowerCAmelCase_       :						List[Any] = None		,			lowerCAmelCase_       :						Dict = True		,			lowerCAmelCase_       :						int = False		,			lowerCAmelCase_       :						int = None		,			lowerCAmelCase_       :						List[str] = None		,			lowerCAmelCase_       :						int = 0		,			lowerCAmelCase_       :						List[str] = None		,			lowerCAmelCase_       :						List[str] = None		,			lowerCAmelCase_       :						Tuple = False		,			lowerCAmelCase_       :						int = False		,			lowerCAmelCase_       :						List[Any] = False		,			lowerCAmelCase_       :						Dict = False		,			lowerCAmelCase_       :						List[Any] = False		,			lowerCAmelCase_       :						List[str] = True		,			lowerCAmelCase_       :						Dict = None		,			**lowerCAmelCase_       :						Tuple		,			)			->		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:
															A__     :   int      =self.tokenizer
															A__     :   str      =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
									A__     :   Dict      =self.image_processor(UpperCamelCase__		,			return_tensors=UpperCamelCase__ )

									if text is not None:
															A__     :   Union[str, Any]      =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:
															A__     :   List[Any]      =None

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

									return encoding_image_processor


			def lowercase__						(      self       :						Union[str, Any]		,			*lowerCAmelCase_       :						Tuple		,			**lowerCAmelCase_       :						Optional[Any] )			->		Optional[Any]:





									'''simple docstring'''
									return self.tokenizer.batch_decode(*UpperCamelCase__		,			**UpperCamelCase__ )


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





									'''simple docstring'''
									return self.tokenizer.decode(*UpperCamelCase__		,			**UpperCamelCase__ )



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





									'''simple docstring'''
									A__     :   str      =self.tokenizer.model_input_names
									A__     :   Optional[int]      =self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







import random

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> tuple:
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Any			 =					[], [], []
       for element in data:
              if element < pivot:
                     less.append(_SCREAMING_SNAKE_CASE    )
              elif element > pivot:
                     greater.append(_SCREAMING_SNAKE_CASE    )
              else:
                     equal.append(_SCREAMING_SNAKE_CASE    )
       return less, equal, greater


def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(_SCREAMING_SNAKE_CASE    ) or index < 0:
              return None

       lowerCamelCase   :      List[Any]			 =					items[random.randint(0 ,len(_SCREAMING_SNAKE_CASE    ) - 1    )]
       lowerCamelCase   :      Dict			 =					0
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					_partition(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					len(_SCREAMING_SNAKE_CASE    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # must be in larger
       else:
              return quick_select(_SCREAMING_SNAKE_CASE ,index - (m + count)    )






