






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 ConvNextImageProcessor
class 	a_(		unittest.TestCase	):



     """simple docstring"""



     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	int=7  ,  lowerCAmelCase__					:	Tuple=3  ,  lowerCAmelCase__					:	List[str]=1_8  ,  lowerCAmelCase__					:	Any=3_0  ,  lowerCAmelCase__					:	Any=4_0_0  ,  lowerCAmelCase__					:	Tuple=True  ,  lowerCAmelCase__					:	Dict=None  ,  lowerCAmelCase__					:	Optional[int]=0.8_75  ,  lowerCAmelCase__					:	List[str]=True  ,  lowerCAmelCase__					:	List[Any]=[0.5, 0.5, 0.5]  ,  lowerCAmelCase__					:	Dict=[0.5, 0.5, 0.5]  ,  )							->	List[str]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		size if size is not None else {'''shortest_edge''': 2_0}
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		num_channels
      SCREAMING_SNAKE_CASE				    =		image_size
      SCREAMING_SNAKE_CASE				    =		min_resolution
      SCREAMING_SNAKE_CASE				    =		max_resolution
      SCREAMING_SNAKE_CASE				    =		do_resize
      SCREAMING_SNAKE_CASE				    =		size
      SCREAMING_SNAKE_CASE				    =		crop_pct
      SCREAMING_SNAKE_CASE				    =		do_normalize
      SCREAMING_SNAKE_CASE				    =		image_mean
      SCREAMING_SNAKE_CASE				    =		image_std





     def     __UpperCamelCase						(		self					:	List[Any])							->	str:

      """simple docstring"""
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "crop_pct": self.crop_pct,
      }

@require_torch
@require_vision
class 	a_(		UpperCAmelCase_			,							unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				str  					=ConvNextImageProcessor if is_vision_available() else None





     def     __UpperCamelCase						(		self					:	int)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ConvNextImageProcessingTester(self)





     @property
     def     __UpperCamelCase						(		self					:	str)							->	Union[str, Any]:

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()





     def     __UpperCamelCase						(		self					:	List[Any])							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(a__  ,  'do_resize'))
      self.assertTrue(hasattr(a__  ,  'size'))
      self.assertTrue(hasattr(a__  ,  'crop_pct'))
      self.assertTrue(hasattr(a__  ,  'do_normalize'))
      self.assertTrue(hasattr(a__  ,  'image_mean'))
      self.assertTrue(hasattr(a__  ,  'image_std'))





     def     __UpperCamelCase						(		self					:	List[str])							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size  ,  {'shortest_edge': 2_0})

      SCREAMING_SNAKE_CASE				    =		self.image_processing_class.from_dict(self.image_processor_dict  ,  size=4_2)
      self.assertEqual(image_processor.size  ,  {'shortest_edge': 4_2})





     def     __UpperCamelCase						(		self					:	List[str])							->	Union[str, Any]:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Dict)							->	Union[str, Any]:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a__)
      for image in image_inputs:
       self.assertIsInstance(a__  ,  Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(a__  ,  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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	Tuple)							->	Tuple:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a__  ,  numpify=a__)
      for image in image_inputs:
       self.assertIsInstance(a__  ,  np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(a__  ,  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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )





     def     __UpperCamelCase						(		self					:	Any)							->	str:

      """simple docstring"""
      # Initialize image_processing
      SCREAMING_SNAKE_CASE				    =		self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE				    =		prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=a__  ,  torchify=a__)
      for image in image_inputs:
       self.assertIsInstance(a__  ,  torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE				    =		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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )

      # Test batched
      SCREAMING_SNAKE_CASE				    =		image_processing(a__  ,  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['shortest_edge'],
              self.image_processor_tester.size['shortest_edge'],
          )  ,  )










'''simple docstring'''





lowercase__								=						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]






class     lowercase  :






      def __init__(self			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			name
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         SCREAMING_SNAKE_CASE__      :					Any							    =			[]






      def  A__(self			,	a__       )							-> Dict:
         self.inbound.append(a__       )






      def  A__(self			,	a__       )							-> Any:
         self.outbound.append(a__       )






      def __repr__(self       )							-> List[str]:
         return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 					UpperCamelCase						(	snake_case						,  snake_case=3						,  snake_case=0.85 ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			1

   SCREAMING_SNAKE_CASE__      :					int							    =			{}
   for node in nodes:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(node.outbound )

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


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Any							    =			list(input('''Enter Names of the Nodes: ''' ).split() )

   SCREAMING_SNAKE_CASE__      :					str							    =			[Node(snake_case ) for name in names]

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

   print('''======= Nodes =======''' )
   for node in nodes:
      print(snake_case )

   page_rank(snake_case )


if __name__ == "__main__":
       main()

