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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_    		=				logging.get_logger(__name__)

UpperCAmelCase_    		=				{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}





class   lowerCAmelCase    (       _SCREAMING_SNAKE_CASE      ):
     _SCREAMING_SNAKE_CASE  :  Any		="""layoutlm"""



     def __init__(       self					,   lowerCAmelCase__=30522					,   lowerCAmelCase__=768					,   lowerCAmelCase__=12					,   lowerCAmelCase__=12					,   lowerCAmelCase__=3072					,   lowerCAmelCase__="gelu"					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__=0.1					,   lowerCAmelCase__=512					,   lowerCAmelCase__=2					,   lowerCAmelCase__=0.02					,   lowerCAmelCase__=1E-12					,   lowerCAmelCase__=0					,   lowerCAmelCase__="absolute"					,   lowerCAmelCase__=True					,   lowerCAmelCase__=1024					,   **lowerCAmelCase__					,   ):
            super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE							)
            _A=						vocab_size
            _A=						hidden_size
            _A=						num_hidden_layers
            _A=						num_attention_heads
            _A=						hidden_act
            _A=						intermediate_size
            _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=						position_embedding_type
            _A=						use_cache
            _A=						max_ad_position_embeddings





class   lowerCAmelCase    (       _SCREAMING_SNAKE_CASE      ):



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = "default"					,   lowerCAmelCase__ = None					,   ):
            super().__init__(_SCREAMING_SNAKE_CASE					,   task=_SCREAMING_SNAKE_CASE					,   patching_specs=_SCREAMING_SNAKE_CASE							)
            _A=						config.max_ad_position_embeddings - 1



     @property
     def       a__	(       self							):
            return OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'sequence'}),
                    ('bbox', {0: 'batch', 1: 'sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'sequence'}),
                    ('token_type_ids', {0: 'batch', 1: 'sequence'}),
                ]							)





     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = -1					,   lowerCAmelCase__ = False					,   lowerCAmelCase__ = None					,   ):
            _A=						super().generate_dummy_inputs(
                _SCREAMING_SNAKE_CASE					,   batch_size=_SCREAMING_SNAKE_CASE					,   seq_length=_SCREAMING_SNAKE_CASE					,   is_pair=_SCREAMING_SNAKE_CASE					,   framework=_SCREAMING_SNAKE_CASE							)

            # Generate a dummy bbox
            _A=						[48, 84, 73, 128]

            if not framework == TensorType.PYTORCH:
                   raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'							)

            if not is_torch_available():
                   raise ValueError('Cannot generate dummy inputs without PyTorch installed.'							)
            import torch

            _A,      _A=						input_dict['input_ids'].shape
            _A=						torch.tensor([*[box] * seq_length]							).tile(_SCREAMING_SNAKE_CASE					,   1					,   1							)
            return input_dict



from collections import deque

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict	)     ->	Tuple:



	"""simple docstring"""






	return input(UpperCamelCase	).strip().split(""" """	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[int]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> """	))
		graph[x].append(UpperCamelCase	)
	return graph

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[int]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> """	))
		graph[x].append(UpperCamelCase	)
		graph[y].append(UpperCamelCase	)
	return graph

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      int	)     ->	dict[int, list[tuple[int, int]]]:



	"""simple docstring"""






	a_     			=   {}
	for i in range(UpperCamelCase	):
		a_     			=   []

	for e in range(UpperCamelCase	):
		a_			, a_			, a_     			=   (int(UpperCamelCase	) for i in _input(F"""Edge {e + 1}: <node1> <node2> <weight> """	))
		graph[x].append((y, w)	)
		graph[y].append((x, w)	)
	return graph


if __name__ == "__main__":
			_A     ,				_A							     =       (int(i) for i in _input('Number of nodes and edges: '))

			_A							     =       int(
			    _input(
			        'Press 1 or 2 or 3 \n'
			        '1. Unweighted directed \n'
			        '2. Unweighted undirected \n'
			        '3. Weighted undirected \n'
			    )[0]
			)

			_A							     =       {
			    1: initialize_unweighted_directed_graph,
			    2: initialize_unweighted_undirected_graph,
			    3: initialize_weighted_undirected_graph,
			}[graph_choice](n, m)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      Tuple	)     ->	List[Any]:



	"""simple docstring"""






	a_			, a_     			=   {s}, [s]
	print(UpperCamelCase	)
	while _s:
		a_     			=   0
		for i in g[_s[-1]]:
			if i not in vis:
				_s.append(UpperCamelCase	)
				vis.add(UpperCamelCase	)
				a_     			=   1
				print(UpperCamelCase	)
				break
		if not flag:
			_s.pop()

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Tuple     ,					UpperCamelCase		:      int	)     ->	int:



	"""simple docstring"""






	a_			, a_     			=   {s}, deque([s]	)
	print(UpperCamelCase	)
	while q:
		a_     			=   q.popleft()
		for v in g[u]:
			if v not in vis:
				vis.add(UpperCamelCase	)
				q.append(UpperCamelCase	)
				print(UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      Any	)     ->	Union[str, Any]:



	"""simple docstring"""






	a_			, a_			, a_     			=   {s: 0}, set(), {s: 0}
	while True:
		if len(UpperCamelCase	) == len(UpperCamelCase	) - 1:
			break
		a_     			=   10_0000
		for i in dist:
			if i not in known and dist[i] < mini:
				a_     			=   dist[i]
				a_     			=   i
		known.add(UpperCamelCase	)
		for v in g[u]:
			if v[0] not in known and dist[u] + v[1] < dist.get(v[0]     ,					10_0000	):
				a_     			=   dist[u] + v[1]
				a_     			=   u
	for i in dist:
		if i != s:
			print(dist[i]	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      int=None     ,					UpperCamelCase		:      int=None	)     ->	Union[str, Any]:



	"""simple docstring"""






	if q is None:
		a_     			=   [1]
	if ind is None:
		a_     			=   [0] * (len(UpperCamelCase	) + 1)  # SInce oth Index is ignored
		for u in g:
			for v in g[u]:
				ind[v] += 1
		a_     			=   deque()
		for i in g:
			if ind[i] == 0:
				q.append(UpperCamelCase	)
	if len(UpperCamelCase	) == 0:
		return
	a_     			=   q.popleft()
	print(UpperCamelCase	)
	for w in g[v]:
		ind[w] -= 1
		if ind[w] == 0:
			q.append(UpperCamelCase	)
	topo(UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		)     ->	List[Any]:



	"""simple docstring"""






	a_     			=   input().strip()
	a_     			=   []
	for _ in range(UpperCamelCase	):
		a.append(map(UpperCamelCase     ,					input().strip().split()	)	)
	return a, n

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[Any]	)     ->	List[str]:



	"""simple docstring"""






	((a_)			, (a_))     			=   a_and_n
	a_     			=   list(UpperCamelCase	)
	a_     			=   [[0] * n for i in range(UpperCamelCase	)]
	for k in range(UpperCamelCase	):
		for i in range(UpperCamelCase	):
			for j in range(UpperCamelCase	):
				if dist[i][j] > dist[i][k] + dist[k][j]:
					a_     			=   dist[i][k] + dist[k][j]
					a_     			=   k
	print(UpperCamelCase	)

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Dict     ,					UpperCamelCase		:      List[Any]	)     ->	str:



	"""simple docstring"""






	a_			, a_			, a_     			=   {s: 0}, set(), {s: 0}
	while True:
		if len(UpperCamelCase	) == len(UpperCamelCase	) - 1:
			break
		a_     			=   10_0000
		for i in dist:
			if i not in known and dist[i] < mini:
				a_     			=   dist[i]
				a_     			=   i
		known.add(UpperCamelCase	)
		for v in g[u]:
			if v[0] not in known and v[1] < dist.get(v[0]     ,					10_0000	):
				a_     			=   v[1]
				a_     			=   u
	return dist

def 			__SCREAMING_SNAKE_CASE		(		)     ->	int:



	"""simple docstring"""






	a_			, a_     			=   map(UpperCamelCase     ,					input().split(""" """	)	)
	a_     			=   []
	for _ in range(UpperCamelCase	):
		edges.append(map(UpperCamelCase     ,					input().split(""" """	)	)	)
	return edges, n

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      List[str]	)     ->	List[str]:



	"""simple docstring"""






	((a_)			, (a_))     			=   e_and_n
	e.sort(reverse=UpperCamelCase     ,					key=lambda UpperCamelCase	: x[2]	)
	a_     			=   [{i} for i in range(1     ,					n + 1	)]
	while True:
		if len(UpperCamelCase	) == 1:
			break
		print(UpperCamelCase	)
		a_     			=   e.pop()
		for i in range(len(UpperCamelCase	)	):
			if x[0] in s[i]:
				break
		for j in range(len(UpperCamelCase	)	):
			if x[1] in s[j]:
				if i == j:
					break
				s[j].update(s[i]	)
				s.pop(UpperCamelCase	)
				break

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	Optional[Any]:



	"""simple docstring"""






	a_     			=   []
	for node in graph:
		if not graph[node]:
			isolated.append(UpperCamelCase	)
	return isolated