



'''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 _A     (			__A			):
		_SCREAMING_SNAKE_CASE    :			Any  					=	['image_processor', 'tokenizer']
		_SCREAMING_SNAKE_CASE    :			List[str]  					=	'BlipImageProcessor'
		_SCREAMING_SNAKE_CASE    :			Dict  					=	'AutoTokenizer'





		def __init__(   self       ,				__UpperCAmelCase       ,				__UpperCAmelCase       ,				__UpperCAmelCase )				->   Tuple:

					'''simple docstring'''
					super().__init__(__UpperCAmelCase       ,				__UpperCAmelCase )

					# add QFormer tokenizer
					__UpperCAmelCase      :					Optional[Any]   =   qformer_tokenizer





		def __call__(   self       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = True       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = 0       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = None       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = False       ,				__UpperCAmelCase = True       ,				__UpperCAmelCase = None       ,				**__UpperCAmelCase       ,				)				->   BatchFeature:

					'''simple docstring'''
					if images is None and text is None:
								raise ValueError("""You have to specify at least images or text.""" )

					__UpperCAmelCase      :					List[Any]   =   BatchFeature()

					if text is not None:
								__UpperCAmelCase      :					Optional[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       ,				)
								encoding.update(__UpperCAmelCase )
								__UpperCAmelCase      :					str   =   self.qformer_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       ,				)
								__UpperCAmelCase      :					List[Any]   =   qformer_text_encoding.pop("""input_ids""" )
								__UpperCAmelCase      :					Any   =   qformer_text_encoding.pop("""attention_mask""" )

					if images is not None:
								__UpperCAmelCase      :					Tuple   =   self.image_processor(__UpperCAmelCase       ,				return_tensors=__UpperCAmelCase )
								encoding.update(__UpperCAmelCase )

					return encoding





		def 							__A				(   self       ,				*__UpperCAmelCase       ,				**__UpperCAmelCase )				->   List[str]:

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





		def 							__A				(   self       ,				*__UpperCAmelCase       ,				**__UpperCAmelCase )				->   Tuple:

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





		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					Any   =   self.tokenizer.model_input_names
					__UpperCAmelCase      :					str   =   self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )





		def 							__A				(   self       ,				__UpperCAmelCase       ,				**__UpperCAmelCase )				->   Dict:

					'''simple docstring'''
					if os.path.isfile(__UpperCAmelCase ):
								raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file' )
					os.makedirs(__UpperCAmelCase       ,				exist_ok=__UpperCAmelCase )
					__UpperCAmelCase      :					List[Any]   =   os.path.join(__UpperCAmelCase       ,				"""qformer_tokenizer""" )
					self.qformer_tokenizer.save_pretrained(__UpperCAmelCase )
					return super().save_pretrained(__UpperCAmelCase       ,				**__UpperCAmelCase )





		@classmethod
		def 							__A				(   cls       ,				__UpperCAmelCase       ,				**__UpperCAmelCase )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[Any]   =   AutoTokenizer.from_pretrained(__UpperCAmelCase       ,				subfolder="""qformer_tokenizer""" )
					__UpperCAmelCase      :					Dict   =   cls._get_arguments_from_pretrained(__UpperCAmelCase       ,				**__UpperCAmelCase )
					args.append(__UpperCAmelCase )
					return cls(*__UpperCAmelCase )





"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

__lowerCamelCase							 =     TypeVar("T")
class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self						,__UpperCAmelCase   )    -> None:
													A__		   =							data
													A__		   =							self
													A__		   =							0






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# map from node name to the node object
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# create a new set with x as its member
													A__		   =							DisjointSetTreeNode(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> DisjointSetTreeNode[T]:
													# find the set x belongs to (with path-compression)
													A__		   =							self.map[data]
													if elem_ref != elem_ref.parent:
																				A__		   =							self.find_set(elem_ref.parent.data   )
													return elem_ref.parent





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# helper function for union operation
													if nodea.rank > nodea.rank:
																				A__		   =							nodea
													else:
																				A__		   =							nodea
																				if nodea.rank == nodea.rank:
																											nodea.rank += 1





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# merge 2 disjoint sets
													self.link(self.find_set(__UpperCAmelCase   )						,self.find_set(__UpperCAmelCase   )   )






class 			UpperCamelCase__(	Generic[T]       ):





						def __init__(				self   )    -> None:
													# connections: map from the node to the neighbouring nodes (with weights)
													A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> None:
													# add a node ONLY if its not present in the graph
													if node not in self.connections:
																				A__		   =							{}





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> None:
													# add an edge with the given weight
													self.add_node(__UpperCAmelCase   )
													self.add_node(__UpperCAmelCase   )
													A__		   =							weight
													A__		   =							weight





						def  snake_case__		(				self   )    -> GraphUndirectedWeighted[T]:
													A__		   =							[]
													A__		   =							set()
													for start in self.connections:
																				for end in self.connections[start]:
																											if (start, end) not in seen:
																																		seen.add((end, start)   )
																																		edges.append((start, end, self.connections[start][end])   )
													edges.sort(key=lambda __UpperCAmelCase   : x[2]   )

													# creating the disjoint set
													A__		   =							DisjointSetTree[T]()
													for node in self.connections:
																				disjoint_set.make_set(__UpperCAmelCase   )

													# MST generation
													A__		   =							0
													A__		   =							0
													A__		   =							GraphUndirectedWeighted[T]()
													while num_edges < len(self.connections   ) - 1:
																				A__   ,     A__   ,     A__		   =							edges[index]
																				index += 1
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				A__		   =							disjoint_set.find_set(__UpperCAmelCase   )
																				if parent_u != parent_v:
																											num_edges += 1
																											graph.add_edge(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
																											disjoint_set.union(__UpperCAmelCase						,__UpperCAmelCase   )
													return graph





