


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


_lowerCAmelCase:						Union[str, Any]   				=       logging.get_logger(__name__)

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




class       lowercase_					(lowercase__   ):
				snake_case	    ='layoutlmv3'





				def __init__(				self       ,  lowercase_=50265       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-5       ,  lowercase_=1       ,  lowercase_=0       ,  lowercase_=2       ,  lowercase_=1024       ,  lowercase_=128       ,  lowercase_=128       ,  lowercase_=True       ,  lowercase_=32       ,  lowercase_=128       ,  lowercase_=64       ,  lowercase_=256       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=224       ,  lowercase_=3       ,  lowercase_=16       ,  lowercase_=None       ,  **lowercase_       ,  )  -> int:
											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_       ,  )
											a__      =max_ad_position_embeddings
											a__      =coordinate_size
											a__      =shape_size
											a__      =has_relative_attention_bias
											a__      =rel_pos_bins
											a__      =max_rel_pos
											a__      =has_spatial_attention_bias
											a__      =rel_ad_pos_bins
											a__      =max_rel_ad_pos
											a__      =text_embed
											a__      =visual_embed
											a__      =input_size
											a__      =num_channels
											a__      =patch_size
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):
				snake_case	    =version.parse('1.12'   )





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, str]]:
											# 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 						__UpperCamelCase    (				self)  -> float:
											return 1e-5





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return 12





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = -1       ,  lowercase_ = -1       ,  lowercase_ = False       ,  lowercase_ = None       ,  lowercase_ = 3       ,  lowercase_ = 40       ,  lowercase_ = 40       ,  )  -> Mapping[str, Any]:
											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
											a__      =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
											a__      =processor.tokenizer.num_special_tokens_to_add(lowercase_)
											a__      =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
											a__      =[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

											# Generate dummy bounding boxes
											a__      =[[[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)
											a__      =self._generate_dummy_images(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

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

											return inputs





from __future__ import annotations

from typing import Any




class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> None:
											a__      =num_of_nodes
											a__      =[]
											a__      ={}





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:
											self.m_edges.append([u_node, v_node, weight])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:

											if self.m_component[u_node] == u_node:
																		return u_node
											return self.find_component(self.m_component[u_node])





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> None:

											if self.m_component[u_node] != u_node:
																		for k in self.m_component:
																									a__      =self.find_component(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_)  -> None:

											if component_size[u_node] <= component_size[v_node]:
																		a__      =v_node
																		component_size[v_node] += component_size[u_node]
																		self.set_component(lowercase_)

											elif component_size[u_node] >= component_size[v_node]:
																		a__      =self.find_component(lowercase_)
																		component_size[u_node] += component_size[v_node]
																		self.set_component(lowercase_)





				def 						__UpperCamelCase    (				self)  -> None:
											a__      =[]
											a__      =0

											a__      =[-1] * self.m_num_of_nodes

											# A list of components (initialized to all of the nodes)
											for node in range(self.m_num_of_nodes):
																		self.m_component.update({node: node})
																		component_size.append(1)

											a__      =self.m_num_of_nodes

											while num_of_components > 1:
																		for edge in self.m_edges:
																									a__    ,     a__    ,     a__      =edge

																									a__      =self.m_component[u]
																									a__      =self.m_component[v]

																									if u_component != v_component:

																																for component in (u_component, v_component):
																																							if (
																																							    minimum_weight_edge[component] == -1
																																							    or minimum_weight_edge[component][2] > w
																																							):
																																														a__      =[u, v, w]

																		for edge in minimum_weight_edge:
																									if isinstance(lowercase_       ,  lowercase_):
																																a__    ,     a__    ,     a__      =edge

																																a__      =self.m_component[u]
																																a__      =self.m_component[v]

																																if u_component != v_component:
																																							mst_weight += w
																																							self.union(lowercase_       ,  lowercase_       ,  lowercase_)
																																							print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n""")
																																							num_of_components -= 1

																		a__      =[-1] * self.m_num_of_nodes
											print(F"""The total weight of the minimal spanning tree is: {mst_weight}""")







def   _lowercase(					):
							pass


if __name__ == "__main__":
	import doctest

	doctest.testmod()

