


"""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[int]            =     logging.get_logger(__name__)

A	:  List[str]            =     {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Tuple							 ="""layoutlmv3"""

							def __init__(	self       ,  __a=5_02_65       ,  __a=7_68       ,  __a=12       ,  __a=12       ,  __a=30_72       ,  __a="gelu"       ,  __a=0.1       ,  __a=0.1       ,  __a=5_12       ,  __a=2       ,  __a=0.0_2       ,  __a=1e-5       ,  __a=1       ,  __a=0       ,  __a=2       ,  __a=10_24       ,  __a=1_28       ,  __a=1_28       ,  __a=True       ,  __a=32       ,  __a=1_28       ,  __a=64       ,  __a=2_56       ,  __a=True       ,  __a=True       ,  __a=True       ,  __a=2_24       ,  __a=3       ,  __a=16       ,  __a=None       ,  **__a       ,  ):
										super().__init__(
										    vocab_size=__a       ,  hidden_size=__a       ,  num_hidden_layers=__a       ,  num_attention_heads=__a       ,  intermediate_size=__a       ,  hidden_act=__a       ,  hidden_dropout_prob=__a       ,  attention_probs_dropout_prob=__a       ,  max_position_embeddings=__a       ,  type_vocab_size=__a       ,  initializer_range=__a       ,  layer_norm_eps=__a       ,  pad_token_id=__a       ,  bos_token_id=__a       ,  eos_token_id=__a       ,  **__a       ,  )
										__lowerCAmelCase							       =     max_ad_position_embeddings
										__lowerCAmelCase							       =     coordinate_size
										__lowerCAmelCase							       =     shape_size
										__lowerCAmelCase							       =     has_relative_attention_bias
										__lowerCAmelCase							       =     rel_pos_bins
										__lowerCAmelCase							       =     max_rel_pos
										__lowerCAmelCase							       =     has_spatial_attention_bias
										__lowerCAmelCase							       =     rel_ad_pos_bins
										__lowerCAmelCase							       =     max_rel_ad_pos
										__lowerCAmelCase							       =     text_embed
										__lowerCAmelCase							       =     visual_embed
										__lowerCAmelCase							       =     input_size
										__lowerCAmelCase							       =     num_channels
										__lowerCAmelCase							       =     patch_size
										__lowerCAmelCase							       =     classifier_dropout
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 =version.parse("""1.12"""						)

							@property
							def      snake_case							(	self      ):
										# 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      snake_case							(	self      ):
										return 1e-5

							@property
							def      snake_case							(	self      ):
										return 12



							def      snake_case							(	self       ,  __a       ,  __a = -1       ,  __a = -1       ,  __a = False       ,  __a = None       ,  __a = 3       ,  __a = 40       ,  __a = 40       ,  ):
										setattr(processor.image_processor       ,  "apply_ocr"       ,  __a      )

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

										# Generate dummy bounding boxes
										__lowerCAmelCase							       =     [[[48, 84, 73, 1_28]]] * 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)
										__lowerCAmelCase							       =     self._generate_dummy_images(__a       ,  __a       ,  __a       ,  __a      )

										__lowerCAmelCase							       =     dict(
										    processor(
										        __a       ,  text=__a       ,  boxes=__a       ,  return_tensors=__a       ,  )      )

										return inputs








"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator
class     _UpperCamelCase    :
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										__lowerCAmelCase							       =     str(id_      )
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     []
										__lowerCAmelCase							       =     {}  # {vertex:distance}

							def __lt__(	self       ,  __a      ):
										return self.key < other.key

							def __repr__(	self      ):
										return self.id

							def      snake_case							(	self       ,  __a      ):
										self.neighbors.append(__a      )



							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     weight


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			graph[a - 1].add_neighbor(graph[b - 1] )
			graph[b - 1].add_neighbor(graph[a - 1] )
			# add the edges:
			graph[a - 1].add_edge(graph[b - 1]       ,					_UpperCamelCase )
			graph[b - 1].add_edge(graph[a - 1]       ,					_UpperCamelCase )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     []
			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0
			__lowerCAmelCase							       =     graph[:]
			while q:
						__lowerCAmelCase							       =     min(_UpperCamelCase )
						q.remove(_UpperCamelCase )
						for v in u.neighbors:
									if (v in q) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
			for i in range(1       ,					len(_UpperCamelCase ) ):
						a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
			return a


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for u in graph:
						__lowerCAmelCase							       =     math.inf
						__lowerCAmelCase							       =     None
			__lowerCAmelCase							       =     0

			__lowerCAmelCase							       =     list(_UpperCamelCase )
			hq.heapify(_UpperCamelCase )

			while h:
						__lowerCAmelCase							       =     hq.heappop(_UpperCamelCase )
						for v in u.neighbors:
									if (v in h) and (u.edges[v.id] < v.key):
												__lowerCAmelCase							       =     u
												__lowerCAmelCase							       =     u.edges[v.id]
												hq.heapify(_UpperCamelCase )

			for i in range(1       ,					len(_UpperCamelCase ) ):
						yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def _lowerCamelCase      (						):

			'''simple docstring'''






if __name__ == "__main__":
	import doctest

	doctest.testmod()




