




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 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''image_processor''', '''tokenizer''']
    UpperCAmelCase__							  =      '''LayoutLMv3ImageProcessor'''
    UpperCAmelCase__							  =      ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')


    def __init__(			self      :	Optional[int]					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	List[str]=None					, **UpperCAmelCase__      :	str)						->Tuple:





           '''simple docstring'''

           A__       =    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__					, )
                  A__       =    kwargs.pop('''feature_extractor''')

           A__       =    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__)


    def __call__(			self      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					, UpperCAmelCase__      :	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					, UpperCAmelCase__      :	Union[List[List[int]], List[List[List[int]]]] = None					, UpperCAmelCase__      :	Optional[Union[List[int], List[List[int]]]] = None					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Union[bool, str, PaddingStrategy] = False					, UpperCAmelCase__      :	Union[bool, str, TruncationStrategy] = None					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	int = 0					, UpperCAmelCase__      :	Optional[int] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	Optional[Union[str, TensorType]] = None					, **UpperCAmelCase__      :	List[str]					, )						->BatchEncoding:





           '''simple docstring'''

           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.''')

           # first, apply the image processor
           A__       =    self.image_processor(images=UpperCAmelCase__					, return_tensors=UpperCAmelCase__)

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

           A__       =    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=UpperCAmelCase__					, add_special_tokens=UpperCAmelCase__					, padding=UpperCAmelCase__					, truncation=UpperCAmelCase__					, max_length=UpperCAmelCase__					, stride=UpperCAmelCase__					, pad_to_multiple_of=UpperCAmelCase__					, return_token_type_ids=UpperCAmelCase__					, return_attention_mask=UpperCAmelCase__					, return_overflowing_tokens=UpperCAmelCase__					, return_special_tokens_mask=UpperCAmelCase__					, return_offsets_mapping=UpperCAmelCase__					, return_length=UpperCAmelCase__					, verbose=UpperCAmelCase__					, return_tensors=UpperCAmelCase__					, **UpperCAmelCase__					, )

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

           return encoded_inputs


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[str])						->List[Any]:





           '''simple docstring'''

           A__       =    []
           for sample_idx in overflow_to_sample_mapping:
                  images_with_overflow.append(images[sample_idx])

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

           return images_with_overflow


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Dict)						->Dict:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict					, *UpperCAmelCase__      :	Optional[Any]					, **UpperCAmelCase__      :	Optional[Any])						->Union[str, Any]:





           '''simple docstring'''

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


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[int]:





           '''simple docstring'''

           return ["input_ids", "bbox", "attention_mask", "pixel_values"]


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Union[str, Any]:





           '''simple docstring'''

           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        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Union[str, Any]:





           '''simple docstring'''

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








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


