




'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
							from ...processing_utils import ProcessorMixin
							from ...utils import TensorType


a  :				Optional[Any]									=  logging.get_logger(__name__)

a  :				Tuple									=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		"layoutlmv3"


				def __init__(		self    :					Optional[Any]       ,				lowercase_    :					Optional[Any]=5_0265       ,				lowercase_    :					Optional[Any]=768       ,				lowercase_    :					int=12       ,				lowercase_    :					Optional[Any]=12       ,				lowercase_    :					Dict=3072       ,				lowercase_    :					str="gelu"       ,				lowercase_    :					Tuple=0.1       ,				lowercase_    :					Tuple=0.1       ,				lowercase_    :					List[Any]=512       ,				lowercase_    :					Union[str, Any]=2       ,				lowercase_    :					Dict=0.02       ,				lowercase_    :					Optional[int]=1e-5       ,				lowercase_    :					List[str]=1       ,				lowercase_    :					Tuple=0       ,				lowercase_    :					List[str]=2       ,				lowercase_    :					Union[str, Any]=1024       ,				lowercase_    :					Union[str, Any]=128       ,				lowercase_    :					Optional[int]=128       ,				lowercase_    :					Optional[Any]=True       ,				lowercase_    :					Optional[int]=32       ,				lowercase_    :					Tuple=128       ,				lowercase_    :					Dict=64       ,				lowercase_    :					Tuple=256       ,				lowercase_    :					Optional[int]=True       ,				lowercase_    :					List[str]=True       ,				lowercase_    :					str=True       ,				lowercase_    :					Any=224       ,				lowercase_    :					List[Any]=3       ,				lowercase_    :					Optional[Any]=16       ,				lowercase_    :					int=None       ,				**lowercase_    :					Optional[Any]       ,				):
									super().__init__(
									    vocab_size=lowercase_       ,				hidden_size=lowercase_       ,				num_hidden_layers=lowercase_       ,				num_attention_heads=lowercase_       ,				intermediate_size=lowercase_       ,				hidden_act=lowercase_       ,				hidden_dropout_prob=lowercase_       ,				attention_probs_dropout_prob=lowercase_       ,				max_position_embeddings=lowercase_       ,				type_vocab_size=lowercase_       ,				initializer_range=lowercase_       ,				layer_norm_eps=lowercase_       ,				pad_token_id=lowercase_       ,				bos_token_id=lowercase_       ,				eos_token_id=lowercase_       ,				**lowercase_       ,				)
									snake_case_									=				max_ad_position_embeddings
									snake_case_									=				coordinate_size
									snake_case_									=				shape_size
									snake_case_									=				has_relative_attention_bias
									snake_case_									=				rel_pos_bins
									snake_case_									=				max_rel_pos
									snake_case_									=				has_spatial_attention_bias
									snake_case_									=				rel_ad_pos_bins
									snake_case_									=				max_rel_ad_pos
									snake_case_									=				text_embed
									snake_case_									=				visual_embed
									snake_case_									=				input_size
									snake_case_									=				num_channels
									snake_case_									=				patch_size
									snake_case_									=				classifier_dropout







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		version.parse("1.12"     )


				@property
				def   A_ (		self    :					List[Any]					):
									# The order of inputs is different for question answering and sequence classification
									if self.task in ["question-answering", "sequence-classification"]:
														return OrderedDict(
														    [
														        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
														        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
														        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
														        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
														    ]					)
									else:
														return OrderedDict(
														    [
														        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
														        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
														        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
														        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
														    ]					)


				@property
				def   A_ (		self    :					Dict					):
									return 1e-5


				@property
				def   A_ (		self    :					int					):
									return 12


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					"ProcessorMixin"       ,				lowercase_    :					int = -1       ,				lowercase_    :					int = -1       ,				lowercase_    :					bool = False       ,				lowercase_    :					Optional["TensorType"] = None       ,				lowercase_    :					int = 3       ,				lowercase_    :					int = 40       ,				lowercase_    :					int = 40       ,				):
									setattr(processor.image_processor       ,				'''apply_ocr'''       ,				lowercase_					)

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									snake_case_									=				compute_effective_axis_dimension(
									    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_batch       ,				num_token_to_add=0					)
									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									snake_case_									=				processor.tokenizer.num_special_tokens_to_add(lowercase_					)
									snake_case_									=				compute_effective_axis_dimension(
									    lowercase_       ,				fixed_dimension=OnnxConfig.default_fixed_sequence       ,				num_token_to_add=lowercase_					)
									# Generate dummy inputs according to compute batch and sequence
									snake_case_									=				[[''' '''.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

									# Generate dummy bounding boxes
									snake_case_									=				[[[48, 84, 73, 128]]] * batch_size

									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
									snake_case_									=				self._generate_dummy_images(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_					)

									snake_case_									=				dict(
									    processor(
									        lowercase_       ,				text=lowercase_       ,				boxes=lowercase_       ,				return_tensors=lowercase_       ,				)					)

									return inputs






from __future__ import annotations

__UpperCAmelCase          =       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Union[str, Any]							=	graph
          # mapping node to its parent in resulting breadth first tree
          UpperCamelCase    :  dict[str, str | None]							=	{}
          UpperCamelCase    :  Union[str, Any]							=	source_vertex
       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  str							=	{self.source_vertex}
          UpperCamelCase    :  str							=	None
          UpperCamelCase    :  int							=	[self.source_vertex]  # first in first out queue

          while queue:
             UpperCamelCase    :  Dict							=	queue.pop(0       )
             for adjacent_vertex in self.graph[vertex]:
                if adjacent_vertex not in visited:
                   visited.add(SCREAMING_SNAKE_CASE_       )
                   UpperCamelCase    :  Tuple							=	vertex
                   queue.append(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          if target_vertex == self.source_vertex:
             return self.source_vertex

          UpperCamelCase    :  Optional[Any]							=	self.parent.get(SCREAMING_SNAKE_CASE_       )
          if target_vertex_parent is None:
             UpperCamelCase    :  Union[str, Any]							=	(
                 F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
             )
             raise ValueError(SCREAMING_SNAKE_CASE_       )

          return self.shortest_path(SCREAMING_SNAKE_CASE_       ) + F"""->{target_vertex}"""


if __name__ == "__main__":
    __UpperCAmelCase          =       Graph(graph, '''G''')
    g.breath_first_search()
    print(g.shortest_path('''D'''))
    print(g.shortest_path('''G'''))
    print(g.shortest_path('''Foo'''))



