






"""simple docstring"""





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class    __lowerCamelCase    (  _a  ):
   a      :				Optional[Any] 						=["""image_processor""", """tokenizer"""]
   a      :				Tuple 						="""LayoutLMv2ImageProcessor"""
   a      :				Union[str, Any] 						=("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")

   def __init__(					self ,				snake_case_=None ,				snake_case_=None ,				**snake_case_      )		->       Union[str, Any]:
       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__         =    kwargs.pop('feature_extractor'      )

       UpperCamelCase__         =    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_ ,				snake_case_ = None ,				snake_case_ = None ,				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_ = None ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = False ,				snake_case_ = True ,				snake_case_ = None ,				**snake_case_ ,				)		->       BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               'You cannot provide bounding boxes '
               'if you initialized the image processor with apply_ocr set to True.'      )

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'      )

       if return_overflowing_tokens is True and return_offsets_mapping is False:
           raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'      )

       # first, apply the image processor
       UpperCamelCase__         =    self.image_processor(images=snake_case_ ,				return_tensors=snake_case_      )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    [text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCamelCase__         =    features['words']

       UpperCamelCase__         =    self.tokenizer(
           text=text if text is not None else features['words'] ,				text_pair=text_pair if text_pair is not None else None ,				boxes=boxes if boxes is not None else features['boxes'] ,				word_labels=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_token_type_ids=snake_case_ ,				return_attention_mask=snake_case_ ,				return_overflowing_tokens=snake_case_ ,				return_special_tokens_mask=snake_case_ ,				return_offsets_mapping=snake_case_ ,				return_length=snake_case_ ,				verbose=snake_case_ ,				return_tensors=snake_case_ ,				**snake_case_ ,				)

       # add pixel values
       UpperCamelCase__         =    features.pop('pixel_values'      )
       if return_overflowing_tokens is True:
           UpperCamelCase__         =    self.get_overflowing_images(snake_case_ ,				encoded_inputs['overflow_to_sample_mapping']      )
       UpperCamelCase__         =    images

       return encoded_inputs

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       int:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCamelCase__         =    []
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]      )

       if len(snake_case_      ) != len(snake_case_      ):
           raise ValueError(
               'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
               F' {len(snake_case_      )} and {len(snake_case_      )}'      )

       return images_with_overflow

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       int:
       return self.tokenizer.batch_decode(*snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				*snake_case_ ,				**snake_case_      )		->       Optional[Any]:
       return self.tokenizer.decode(*snake_case_ ,				**snake_case_      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       return ["input_ids", "bbox", "attention_mask", "image"]

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' ,				snake_case_ ,				)
       return self.image_processor












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

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

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







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







class    __lowerCamelCase    (  Generic[T]  ):

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

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    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 snake_case_      : x[2]      )

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

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




