



"""simple docstring"""
A:   List[Any]								=		{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

A:   Optional[int]								=		{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}

def       _snake_case						(						UpperCamelCase						:		dict[int, list[int]]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		list[bool]						):
 UpperCAmelCase	:					Optional[Any]	=	True
 UpperCAmelCase	:					List[Any]	=	[]

 for neighbour in graph[vert]:
  if not visited[neighbour]:
   order += topology_sort(__lowercase			,		__lowercase			,		__lowercase						)

 order.append(__lowercase						)

 return order

def       _snake_case						(						UpperCamelCase						:		dict[int, list[int]]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		list[bool]						):
 UpperCAmelCase	:					int	=	True
 UpperCAmelCase	:					Any	=	[vert]

 for neighbour in reversed_graph[vert]:
  if not visited[neighbour]:
   component += find_components(__lowercase			,		__lowercase			,		__lowercase						)

 return component

def       _snake_case						(						UpperCamelCase						:		dict[int, list[int]]						):
 UpperCAmelCase	:					List[str]	=	len(__lowercase						) * [False]
 UpperCAmelCase	:					Optional[Any]	=	{vert: [] for vert in range(len(__lowercase						)						)}

 for vert, neighbours in graph.items():
  for neighbour in neighbours:
   reversed_graph[neighbour].append(__lowercase						)

 UpperCAmelCase	:					Any	=	[]
 for i, was_visited in enumerate(__lowercase						):
  if not was_visited:
   order += topology_sort(__lowercase			,		__lowercase			,		__lowercase						)

 UpperCAmelCase	:					Dict	=	[]
 UpperCAmelCase	:					Optional[int]	=	len(__lowercase						) * [False]

 for i in range(len(__lowercase						)						):
  UpperCAmelCase	:					List[Any]	=	order[len(__lowercase						) - i - 1]
  if not visited[vert]:
   UpperCAmelCase	:					Optional[Any]	=	find_components(__lowercase			,		__lowercase			,		__lowercase						)
   components_list.append(__lowercase						)

 return components_list







'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=("BertTokenizer", "BertTokenizerFast")






 def __init__(       self       :							Dict							,	__A       :							Optional[int]							,	__A       :							List[Any]	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






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

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

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

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

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							Dict							,	**__A       :							Optional[int]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							List[Any]							,	*__A       :							List[str]							,	**__A       :							Dict	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
