


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import EfficientNetImageProcessor

class 			lowerCamelCase_					(    unittest.TestCase    ):





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE=13			,       _SCREAMING_SNAKE_CASE=3			,       _SCREAMING_SNAKE_CASE=18			,       _SCREAMING_SNAKE_CASE=30			,       _SCREAMING_SNAKE_CASE=400			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]			,       ):
								a_     			=   size if size is not None else {"""height""": 18, """width""": 18}
								a_     			=   parent
								a_     			=   batch_size
								a_     			=   num_channels
								a_     			=   image_size
								a_     			=   min_resolution
								a_     			=   max_resolution
								a_     			=   do_resize
								a_     			=   size
								a_     			=   do_normalize
								a_     			=   image_mean
								a_     			=   image_std





							def   __magic_name__      (							self    ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}







@require_torch
@require_vision
class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE   ,		unittest.TestCase    ):
							_lowerCamelCase				: Any       =					EfficientNetImageProcessor if is_vision_available() else None





							def   __magic_name__      (							self    ):
								a_     			=   EfficientNetImageProcessorTester(self    )





							@property
							def   __magic_name__      (							self    ):
								return self.image_processor_tester.prepare_image_processor_dict()





							def   __magic_name__      (							self    ):
								a_     			=   self.image_processing_class(**self.image_processor_dict    )
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,       """image_mean"""    )    )
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,       """image_std"""    )    )
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,       """do_normalize"""    )    )
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,       """do_resize"""    )    )
								self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,       """size"""    )    )





							def   __magic_name__      (							self    ):
								a_     			=   self.image_processing_class.from_dict(self.image_processor_dict    )
								self.assertEqual(image_processor.size			,       {"""height""": 18, """width""": 18}    )

								a_     			=   self.image_processing_class.from_dict(self.image_processor_dict			,       size=42    )
								self.assertEqual(image_processor.size			,       {"""height""": 42, """width""": 42}    )





							def   __magic_name__      (							self    ):
								# Initialize image_processing
								a_     			=   self.image_processing_class(**self.image_processor_dict    )
								# create random PIL images
								a_     			=   prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       Image.Image    )

								# Test not batched input
								a_     			=   image_processing(image_inputs[0]			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )

								# Test batched
								a_     			=   image_processing(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )





							def   __magic_name__      (							self    ):
								# Initialize image_processing
								a_     			=   self.image_processing_class(**self.image_processor_dict    )
								# create random numpy tensors
								a_     			=   prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_SCREAMING_SNAKE_CASE			,       numpify=_SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       np.ndarray    )

								# Test not batched input
								a_     			=   image_processing(image_inputs[0]			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )

								# Test batched
								a_     			=   image_processing(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )





							def   __magic_name__      (							self    ):
								# Initialize image_processing
								a_     			=   self.image_processing_class(**self.image_processor_dict    )
								# create random PyTorch tensors
								a_     			=   prepare_image_inputs(self.image_processor_tester			,       equal_resolution=_SCREAMING_SNAKE_CASE			,       torchify=_SCREAMING_SNAKE_CASE    )
								for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,       torch.Tensor    )

								# Test not batched input
								a_     			=   image_processing(image_inputs[0]			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )

								# Test batched
								a_     			=   image_processing(_SCREAMING_SNAKE_CASE			,       return_tensors="""pt"""    ).pixel_values
								self.assertEqual(
								    encoded_images.shape			,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )			,       )



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