


'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class     lowercase__  (						a__					):





							'''simple docstring'''


							A_							:      Dict      	=  ["""image_processor""", """tokenizer"""]
							A_							:      Optional[int]      	=  """BlipImageProcessor"""
							A_							:      List[Any]      	=  """AutoTokenizer"""






							def __init__(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											super().__init__(lowerCAmelCase__    ,					lowerCAmelCase__  )

											# add QFormer tokenizer
											_SCREAMING_SNAKE_CASE :       Any     		=      qformer_tokenizer






							def __call__(    self    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = True    ,					__snake_case = False    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = 0    ,					__snake_case = None    ,					__snake_case = None    ,					__snake_case = False    ,					__snake_case = False    ,					__snake_case = False    ,					__snake_case = False    ,					__snake_case = False    ,					__snake_case = True    ,					__snake_case = None    ,					**__snake_case    ,					):
											if images is None and text is None:
															raise ValueError("""You have to specify at least images or text."""  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      BatchFeature()

											if text is not None:
															_SCREAMING_SNAKE_CASE :       Dict     		=      self.tokenizer(
															    text=lowerCAmelCase__    ,					add_special_tokens=lowerCAmelCase__    ,					padding=lowerCAmelCase__    ,					truncation=lowerCAmelCase__    ,					max_length=lowerCAmelCase__    ,					stride=lowerCAmelCase__    ,					pad_to_multiple_of=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					return_overflowing_tokens=lowerCAmelCase__    ,					return_special_tokens_mask=lowerCAmelCase__    ,					return_offsets_mapping=lowerCAmelCase__    ,					return_token_type_ids=lowerCAmelCase__    ,					return_length=lowerCAmelCase__    ,					verbose=lowerCAmelCase__    ,					return_tensors=lowerCAmelCase__    ,					**lowerCAmelCase__    ,					)
															encoding.update(lowerCAmelCase__  )
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.qformer_tokenizer(
															    text=lowerCAmelCase__    ,					add_special_tokens=lowerCAmelCase__    ,					padding=lowerCAmelCase__    ,					truncation=lowerCAmelCase__    ,					max_length=lowerCAmelCase__    ,					stride=lowerCAmelCase__    ,					pad_to_multiple_of=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					return_overflowing_tokens=lowerCAmelCase__    ,					return_special_tokens_mask=lowerCAmelCase__    ,					return_offsets_mapping=lowerCAmelCase__    ,					return_token_type_ids=lowerCAmelCase__    ,					return_length=lowerCAmelCase__    ,					verbose=lowerCAmelCase__    ,					return_tensors=lowerCAmelCase__    ,					**lowerCAmelCase__    ,					)
															_SCREAMING_SNAKE_CASE :       str     		=      qformer_text_encoding.pop("""input_ids"""  )
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      qformer_text_encoding.pop("""attention_mask"""  )

											if images is not None:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processor(lowerCAmelCase__    ,					return_tensors=lowerCAmelCase__  )
															encoding.update(lowerCAmelCase__  )

											return encoding






							def UpperCAmelCase_	(    self    ,					*__snake_case    ,					**__snake_case  ):
											return self.tokenizer.batch_decode(*lowerCAmelCase__    ,					**lowerCAmelCase__  )






							def UpperCAmelCase_	(    self    ,					*__snake_case    ,					**__snake_case  ):
											return self.tokenizer.decode(*lowerCAmelCase__    ,					**lowerCAmelCase__  )






							@property
							# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					**__snake_case  ):
											if os.path.isfile(lowerCAmelCase__  ):
															raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
											os.makedirs(lowerCAmelCase__    ,					exist_ok=lowerCAmelCase__  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      os.path.join(lowerCAmelCase__    ,					"""qformer_tokenizer"""  )
											self.qformer_tokenizer.save_pretrained(lowerCAmelCase__  )
											return super().save_pretrained(lowerCAmelCase__    ,					**lowerCAmelCase__  )






							@classmethod
							def UpperCAmelCase_	(    cls    ,					__snake_case    ,					**__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      AutoTokenizer.from_pretrained(lowerCAmelCase__    ,					subfolder="""qformer_tokenizer"""  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      cls._get_arguments_from_pretrained(lowerCAmelCase__    ,					**lowerCAmelCase__  )
											args.append(lowerCAmelCase__  )
											return cls(*lowerCAmelCase__  )











"""simple docstring"""




import heapq as hq
import math
from collections.abc import Iterator


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__      )	-> Optional[int]:
								a      :		List[str]	=				str(id_      )
								a      :		Optional[Any]	=				None
								a      :		Tuple	=				None
								a      :		str	=				[]
								a      :		Any	=				{}  # {vertex:distance}






							def __lt__(   self      ,					lowerCAmelCase__      )	-> Any:
								return self.key < other.key






							def __repr__(   self      )	-> Optional[Any]:
								return self.id






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Any:
								self.neighbors.append(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Tuple:
								a      :		Optional[Any]	=				weight






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    int    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	graph[a - 1].add_neighbor(graph[b - 1]					)
	graph[b - 1].add_neighbor(graph[a - 1]					)
	# add the edges:
	graph[a - 1].add_edge(graph[b - 1]    ,       _lowercase					)
	graph[b - 1].add_edge(graph[a - 1]    ,       _lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->list:






	'''simple docstring'''






	a      :		int	=				[]
	for u in graph:
		a      :		List[str]	=				math.inf
		a      :		int	=				None
	a      :		str	=				0
	a      :		Union[str, Any]	=				graph[:]
	while q:
		a      :		List[Any]	=				min(_lowercase					)
		q.remove(_lowercase					)
		for v in u.neighbors:
			if (v in q) and (u.edges[v.id] < v.key):
				a      :		List[Any]	=				u
				a      :		Optional[int]	=				u.edges[v.id]
	for i in range(1    ,       len(_lowercase					)					):
		a.append((int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)					)
	return a






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list    ,       _lowercase						:    Vertex					) ->Iterator[tuple]:






	'''simple docstring'''






	for u in graph:
		a      :		str	=				math.inf
		a      :		Dict	=				None
	a      :		Dict	=				0

	a      :		List[Any]	=				list(_lowercase					)
	hq.heapify(_lowercase					)

	while h:
		a      :		Dict	=				hq.heappop(_lowercase					)
		for v in u.neighbors:
			if (v in h) and (u.edges[v.id] < v.key):
				a      :		Dict	=				u
				a      :		Optional[Any]	=				u.edges[v.id]
				hq.heapify(_lowercase					)

	for i in range(1    ,       len(_lowercase					)					):
		yield (int(graph[i].id					) + 1, int(graph[i].pi.id					) + 1)






def       _SCREAMING_SNAKE_CASE		(	) ->None:






	'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()


