from __future__ import annotations

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






@dataclass
class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:int
  lowerCamelCase_		:int







class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       list[list[Edge]]       =				[[] for _ in range(snake_case_						)]
       UpperCAmelCase_							:       Any       =				size


  def __getitem__(			self				,		snake_case_						):




       '''simple docstring'''

       return iter(self._graph[vertex]						)


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return self._size


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''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(snake_case_				,		snake_case_						)						)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				deque([start_vertex]						)
       UpperCAmelCase_							:       list[int | None]       =				[None] * self.size
       UpperCAmelCase_							:       str       =				0

       while queue:
            UpperCAmelCase_							:       Optional[int]       =				queue.popleft()
            UpperCAmelCase_							:       List[str]       =				distances[current_vertex]
            if current_distance is None:
                 continue

            for edge in self[current_vertex]:
                 UpperCAmelCase_							:       Union[str, Any]       =				current_distance + edge.weight
                 UpperCAmelCase_							:       Union[str, Any]       =				distances[edge.destination_vertex]
                 if (
                     isinstance(snake_case_				,		snake_case_						)
                     and new_distance >= dest_vertex_distance
                 ):
                      continue
                 UpperCAmelCase_							:       Optional[Any]       =				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()







'''simple docstring'''


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		['''image_processor''', '''tokenizer''']
  lowerCamelCase_		:Optional[Any]						   =		'''ViTImageProcessor'''
  lowerCamelCase_		:int						   =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				None
       if "feature_extractor" in kwargs:
            warnings.warn(
                'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                ' instead.'				,		snake_case_				,		)
            UpperCAmelCase_							:       int       =				kwargs.pop('feature_extractor'						)

       UpperCAmelCase_							:       str       =				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('You need to specify an `image_processor`.'						)
       if tokenizer is None:
            raise ValueError('You need to specify a `tokenizer`.'						)

       super().__init__(snake_case_				,		snake_case_						)


  def __call__(			self				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if text is None and visual_prompt is None and images is None:
            raise ValueError('You have to specify either text, visual prompt or images.'						)

       if text is not None and visual_prompt is not None:
            raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'						)

       if text is not None:
            UpperCAmelCase_							:       Optional[int]       =				self.tokenizer(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None:
            UpperCAmelCase_							:       Optional[Any]       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if images is not None:
            UpperCAmelCase_							:       int       =				self.image_processor(snake_case_				,		return_tensors=snake_case_				,		**snake_case_						)

       if visual_prompt is not None and images is not None:
            UpperCAmelCase_							:       Tuple       =				{
                'pixel_values': image_features.pixel_values,
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       elif text is not None and images is not None:
            UpperCAmelCase_							:       Optional[int]       =				image_features.pixel_values
            return encoding
       elif text is not None:
            return encoding
       elif visual_prompt is not None:
            UpperCAmelCase_							:       Dict       =				{
                'conditional_pixel_values': prompt_features.pixel_values,
            }
            return encoding
       else:
            return BatchEncoding(data=dict(**snake_case_						)				,		tensor_type=snake_case_						)


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

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


  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,		snake_case_				,		)
       return self.image_processor_class






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,		snake_case_				,		)
       return self.image_processor






