





'''simple docstring'''


import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput



@dataclass
class 							__UpperCAmelCase  (       A__						):







		'''simple docstring'''







		__lowerCAmelCase 				=    42






class 							__UpperCAmelCase  (       unittest.TestCase						):







		'''simple docstring'''







		def        A     (self						:      List[str]      ):
			A							  =      CustomOutput(images=np.random.rand(1      ,      3      ,      4      ,      4      )      )

			# check every way of getting the attribute
			assert isinstance(outputs.images      ,      np.ndarray      )
			assert outputs.images.shape == (1, 3, 4, 4)
			assert isinstance(outputs["""images"""]      ,      np.ndarray      )
			assert outputs["images"].shape == (1, 3, 4, 4)
			assert isinstance(outputs[0]      ,      np.ndarray      )
			assert outputs[0].shape == (1, 3, 4, 4)

			# test with a non-tensor attribute
			A							  =      CustomOutput(images=[PIL.Image.new("""RGB"""      ,      (4, 4)      )]      )

			# check every way of getting the attribute
			assert isinstance(outputs.images      ,      _lowerCAmelCase      )
			assert isinstance(outputs.images[0]      ,      PIL.Image.Image      )
			assert isinstance(outputs["""images"""]      ,      _lowerCAmelCase      )
			assert isinstance(outputs["""images"""][0]      ,      PIL.Image.Image      )
			assert isinstance(outputs[0]      ,      _lowerCAmelCase      )
			assert isinstance(outputs[0][0]      ,      PIL.Image.Image      )







		def        A     (self						:      List[str]      ):
			# test output reinitialization with a `dict` for compatibility with `accelerate`
			A							  =      CustomOutput({"""images""": np.random.rand(1      ,      3      ,      4      ,      4      )}      )

			# check every way of getting the attribute
			assert isinstance(outputs.images      ,      np.ndarray      )
			assert outputs.images.shape == (1, 3, 4, 4)
			assert isinstance(outputs["""images"""]      ,      np.ndarray      )
			assert outputs["images"].shape == (1, 3, 4, 4)
			assert isinstance(outputs[0]      ,      np.ndarray      )
			assert outputs[0].shape == (1, 3, 4, 4)

			# test with a non-tensor attribute
			A							  =      CustomOutput({"""images""": [PIL.Image.new("""RGB"""      ,      (4, 4)      )]}      )

			# check every way of getting the attribute
			assert isinstance(outputs.images      ,      _lowerCAmelCase      )
			assert isinstance(outputs.images[0]      ,      PIL.Image.Image      )
			assert isinstance(outputs["""images"""]      ,      _lowerCAmelCase      )
			assert isinstance(outputs["""images"""][0]      ,      PIL.Image.Image      )
			assert isinstance(outputs[0]      ,      _lowerCAmelCase      )
			assert isinstance(outputs[0][0]      ,      PIL.Image.Image      )








'''simple docstring'''


_lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class 							__UpperCAmelCase  :







		'''simple docstring'''







		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
			A							  =      name
			A							  =      []
			A							  =      []







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
			self.inbound.append(_lowerCAmelCase      )







		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
			self.outbound.append(_lowerCAmelCase      )







		def __repr__(self						:      Tuple      ):
			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:



	"""simple docstring"""







	A							  =      {}
	for node in nodes:
		A							  =      1

	A							  =      {}
	for node in nodes:
		A							  =      len(node.outbound    )

	for i in range(UpperCAmelCase    ):
		print(f"""======= Iteration {i + 1} ======="""    )
		for _, node in enumerate(UpperCAmelCase    ):
			A							  =      (1 - d) + d * sum(
			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
		print(UpperCAmelCase    )




def    __a (     )      ->Any:



	"""simple docstring"""







	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )

	A							  =      [Node(UpperCAmelCase    ) for name in names]

	for ri, row in enumerate(UpperCAmelCase    ):
		for ci, col in enumerate(UpperCAmelCase    ):
			if col == 1:
				nodes[ci].add_inbound(names[ri]    )
				nodes[ri].add_outbound(names[ci]    )

	print("""======= Nodes ======="""    )
	for node in nodes:
		print(UpperCAmelCase    )

	page_rank(UpperCAmelCase    )


if __name__ == "__main__":
				main()

