






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__  (						SCREAMING_SNAKE_CASE__):
     """simple docstring"""



     _A       =			['image_processor', 'tokenizer']
     _A       =			'BlipImageProcessor'
     _A       =			'AutoTokenizer'







     def __init__(self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		False
           super().__init__(snake_case__				,			snake_case__    )
           lowerCamelCase								=		self.image_processor







     def __call__(self				,			__a = None				,			__a = None				,			__a = True				,			__a = False				,			__a = None				,			__a = None				,			__a = 0				,			__a = None				,			__a = None				,			__a = False				,			__a = False				,			__a = False				,			__a = False				,			__a = False				,			__a = True				,			__a = None				,			**__a				,			):






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

           # add pixel_values
           lowerCamelCase								=		self.image_processor(snake_case__				,			return_tensors=snake_case__    )

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

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

           return encoding_image_processor







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



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







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






           '''simple docstring'''



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




from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			42
     _A       =			42





class    lowerCamelCase__  :
     """simple docstring"""



     def __init__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[[] for _ in range(__a    )]
           lowerCamelCase								=		size







     def __getitem__(self				,			__a    ):






           '''simple docstring'''



           return iter(self._graph[vertex]    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self._size







     def 						_a      (self				,			__a				,			__a				,			__a    ):






           '''simple docstring'''



           if weight not in (0, 1):
                 raise ValueError("Edge weight must be either 0 or 1."    )

           if to_vertex < 0 or to_vertex >= self.size:
                 raise ValueError("Vertex indexes must be in [0; size)."    )

           self._graph[from_vertex].append(Edge(__a				,			__a    )    )







     def 						_a      (self				,			__a				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		deque([start_vertex]    )
           lowerCamelCase								=		[None] * self.size
           lowerCamelCase								=		0

           while queue:
                 lowerCamelCase								=		queue.popleft()
                 lowerCamelCase								=		distances[current_vertex]
                 if current_distance is None:
                       continue

                 for edge in self[current_vertex]:
                       lowerCamelCase								=		current_distance + edge.weight
                       lowerCamelCase								=		distances[edge.destination_vertex]
                       if (
                           isinstance(__a				,			__a    )
                           and new_distance >= dest_vertex_distance
                       ):
                             continue
                       lowerCamelCase								=		new_distance
                       if edge.weight == 0:
                             queue.appendleft(edge.destination_vertex    )
                       else:
                             queue.append(edge.destination_vertex    )

           if distances[finish_vertex] is None:
                 raise ValueError("No path from start_vertex to finish_vertex."    )

           return distances[finish_vertex]


if __name__ == "__main__":
     import doctest

     doctest.testmod()