




import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[Any]										=["""image_processor""", """tokenizer"""]
				__a							: Dict										="""AutoImageProcessor"""
				__a							: Tuple										="""AutoTokenizer"""



				def __init__( self       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_						):
											lowerCAmelCase   =		None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																		    ''' instead.'''       , UpperCAmelCase_       , )
																		lowerCAmelCase   =		kwargs.pop('''feature_extractor'''						)

											lowerCAmelCase   =		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__(UpperCAmelCase_       , UpperCAmelCase_						)
											lowerCAmelCase   =		self.image_processor
											lowerCAmelCase   =		False



				def __call__( self       , *UpperCAmelCase_       , **UpperCAmelCase_						):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*UpperCAmelCase_       , **UpperCAmelCase_						)

											lowerCAmelCase   =		kwargs.pop('''images'''       , UpperCAmelCase_						)
											lowerCAmelCase   =		kwargs.pop('''text'''       , UpperCAmelCase_						)
											if len(UpperCAmelCase_						) > 0:
																		lowerCAmelCase   =		args[0]
																		lowerCAmelCase   =		args[1:]

											if images is None and text is None:
																		raise ValueError('''You need to specify either an `images` or `text` input to process.'''						)

											if images is not None:
																		lowerCAmelCase   =		self.image_processor(UpperCAmelCase_       , *UpperCAmelCase_       , **UpperCAmelCase_						)
											if text is not None:
																		lowerCAmelCase   =		self.tokenizer(UpperCAmelCase_       , **UpperCAmelCase_						)

											if text is None:
																		return inputs
											elif images is None:
																		return encodings
											else:
																		lowerCAmelCase   =		encodings['''input_ids''']
																		return inputs



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



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



				@contextmanager
				def       __snake_case		( self						):
											warnings.warn(
											    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
											    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
											    '''your images inputs, or in a separate call.'''						)
											lowerCAmelCase   =		True
											lowerCAmelCase   =		self.tokenizer
											yield
											lowerCAmelCase   =		self.image_processor
											lowerCAmelCase   =		False



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=False       , UpperCAmelCase_=None						):
											if added_vocab is None:
																		lowerCAmelCase   =		self.tokenizer.get_added_vocab()

											lowerCAmelCase   =		{}

											while tokens:
																		lowerCAmelCase   =		re.search(r'''<s_(.*?)>'''       , UpperCAmelCase_       , re.IGNORECASE						)
																		if start_token is None:
																									break
																		lowerCAmelCase   =		start_token.group(1						)
																		lowerCAmelCase   =		re.search(rF"""</s_{key}>"""       , UpperCAmelCase_       , re.IGNORECASE						)
																		lowerCAmelCase   =		start_token.group()
																		if end_token is None:
																									lowerCAmelCase   =		tokens.replace(UpperCAmelCase_       , ''''''						)
																		else:
																									lowerCAmelCase   =		end_token.group()
																									lowerCAmelCase   =		re.escape(UpperCAmelCase_						)
																									lowerCAmelCase   =		re.escape(UpperCAmelCase_						)
																									lowerCAmelCase   =		re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""       , UpperCAmelCase_       , re.IGNORECASE						)
																									if content is not None:
																																lowerCAmelCase   =		content.group(1						).strip()
																																if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																							lowerCAmelCase   =		self.tokenajson(UpperCAmelCase_       , is_inner_value=UpperCAmelCase_       , added_vocab=UpperCAmelCase_						)
																																							if value:
																																														if len(UpperCAmelCase_						) == 1:
																																																					lowerCAmelCase   =		value[0]
																																														lowerCAmelCase   =		value
																																else:  # leaf nodes
																																							lowerCAmelCase   =		[]
																																							for leaf in content.split(r'''<sep/>'''						):
																																														lowerCAmelCase   =		leaf.strip()
																																														if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																					lowerCAmelCase   =		leaf[1:-2]  # for categorical special tokens
																																														output[key].append(UpperCAmelCase_						)
																																							if len(output[key]						) == 1:
																																														lowerCAmelCase   =		output[key][0]

																									lowerCAmelCase   =		tokens[tokens.find(UpperCAmelCase_						) + len(UpperCAmelCase_						) :].strip()
																									if tokens[:6] == r"<sep/>":  # non-leaf nodes
																																return [output] + self.tokenajson(tokens[6:]       , is_inner_value=UpperCAmelCase_       , added_vocab=UpperCAmelCase_						)

											if len(UpperCAmelCase_						):
																		return [output] if is_inner_value else output
											else:
																		return [] if is_inner_value else {"text_sequence": tokens}



				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor_class






				@property
				def       __snake_case		( self						):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''       , UpperCAmelCase_       , )
											return self.image_processor









from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase_						=TypeVar("""T""")


class 							__UpperCamelCase			( Generic[T]  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		data
											lowerCAmelCase   =		self
											lowerCAmelCase   =		0




class 							__UpperCamelCase			( Generic[T]  ):





				'''simple docstring'''



				def __init__( self						):
											# map from node name to the node object
											lowerCAmelCase   =		{}



				def       __snake_case		( self       , UpperCAmelCase_						):
											# create a new set with x as its member
											lowerCAmelCase   =		DisjointSetTreeNode(UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											# find the set x belongs to (with path-compression)
											lowerCAmelCase   =		self.map[data]
											if elem_ref != elem_ref.parent:
																		lowerCAmelCase   =		self.find_set(elem_ref.parent.data						)
											return elem_ref.parent



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											# helper function for union operation
											if nodea.rank > nodea.rank:
																		lowerCAmelCase   =		nodea
											else:
																		lowerCAmelCase   =		nodea
																		if nodea.rank == nodea.rank:
																									nodea.rank += 1






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_						):
											# merge 2 disjoint sets
											self.link(self.find_set(UpperCAmelCase_						)       , self.find_set(UpperCAmelCase_						)						)




class 							__UpperCamelCase			( Generic[T]  ):





				'''simple docstring'''



				def __init__( self						):
											# connections: map from the node to the neighbouring nodes (with weights)
											lowerCAmelCase   =		{}



				def       __snake_case		( self       , UpperCAmelCase_						):
											# add a node ONLY if its not present in the graph
											if node not in self.connections:
																		lowerCAmelCase   =		{}



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											# add an edge with the given weight
											self.add_node(UpperCAmelCase_						)
											self.add_node(UpperCAmelCase_						)
											lowerCAmelCase   =		weight
											lowerCAmelCase   =		weight






				def       __snake_case		( self						):
											lowerCAmelCase   =		[]
											lowerCAmelCase   =		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
											lowerCAmelCase   =		DisjointSetTree[T]()
											for node in self.connections:
																		disjoint_set.make_set(UpperCAmelCase_						)

											# MST generation
											lowerCAmelCase   =		0
											lowerCAmelCase   =		0
											lowerCAmelCase   =		GraphUndirectedWeighted[T]()
											while num_edges < len(self.connections						) - 1:
																		lowerCAmelCase   ,     lowerCAmelCase   ,     lowerCAmelCase   =		edges[index]
																		index += 1
																		lowerCAmelCase   =		disjoint_set.find_set(UpperCAmelCase_						)
																		lowerCAmelCase   =		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



