




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 ViltImageProcessor







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   def __init__(					self      :			Optional[Any]						,   __lowerCamelCase      :			Tuple						,   __lowerCamelCase      :			str=7						,   __lowerCamelCase      :			Optional[Any]=3						,   __lowerCamelCase      :			List[str]=1_8						,   __lowerCamelCase      :			Optional[Any]=3_0						,   __lowerCamelCase      :			List[Any]=4_0_0						,   __lowerCamelCase      :			Optional[Any]=True						,   __lowerCamelCase      :			Union[str, Any]=None						,   __lowerCamelCase      :			Optional[Any]=2						,   __lowerCamelCase      :			Optional[int]=True						,   __lowerCamelCase      :			Tuple=[0.5, 0.5, 0.5]						,   __lowerCamelCase      :			Dict=[0.5, 0.5, 0.5]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   size if size is not None else {"shortest_edge": 3_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 =   size_divisor
      _SCREAMING_SNAKE_CASE =   do_normalize
      _SCREAMING_SNAKE_CASE =   image_mean
      _SCREAMING_SNAKE_CASE =   image_std



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """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,
          "size_divisor": self.size_divisor,
      }



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			int=False					):

      """simple docstring"""
      if not batched:
         _SCREAMING_SNAKE_CASE =   self.size["shortest_edge"]
         _SCREAMING_SNAKE_CASE =   image_inputs[0]
         if isinstance(__lowerCamelCase						,   Image.Image					):
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image.size
         else:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   image.shape[1], image.shape[2]
         _SCREAMING_SNAKE_CASE =   size / min(__lowerCamelCase						,   __lowerCamelCase					)
         if h < w:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   size, scale * w
         else:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   scale * h, size

         _SCREAMING_SNAKE_CASE =   int((1_3_3_3 / 8_0_0) * size					)
         if max(__lowerCamelCase						,   __lowerCamelCase					) > max_size:
            _SCREAMING_SNAKE_CASE =   max_size / max(__lowerCamelCase						,   __lowerCamelCase					)
            _SCREAMING_SNAKE_CASE =   newh * scale
            _SCREAMING_SNAKE_CASE =   neww * scale

         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   int(newh + 0.5					), int(neww + 0.5					)
         _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   (
             newh // self.size_divisor * self.size_divisor,
             neww // self.size_divisor * self.size_divisor,
         )

      else:
         _SCREAMING_SNAKE_CASE =   []
         for image in image_inputs:
            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.get_expected_values([image]					)
            expected_values.append((expected_height, expected_width)					)
         _SCREAMING_SNAKE_CASE =   max(__lowerCamelCase						,   key=lambda __lowerCamelCase					: item[0]					)[0]
         _SCREAMING_SNAKE_CASE =   max(__lowerCamelCase						,   key=lambda __lowerCamelCase					: item[1]					)[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		ViltImageProcessor if is_vision_available() else None



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   ViltImageProcessingTester(self					)



   @property
   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

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



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "image_mean"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "image_std"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "do_normalize"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "do_resize"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "size"					)					)
      self.assertTrue(hasattr(__lowerCamelCase						,   "size_divisor"					)					)



   def 							lowerCAmelCase_    (					self      :			Any					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.image_processing_class.from_dict(self.image_processor_dict					)
      self.assertEqual(image_processor.size						,   {"shortest_edge": 3_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 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      _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=__lowerCamelCase					)
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase						,   Image.Image					)

      # Test not batched input
      _SCREAMING_SNAKE_CASE =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _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=__lowerCamelCase						,   numpify=__lowerCamelCase					)
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase						,   np.ndarray					)

      # Test not batched input
      _SCREAMING_SNAKE_CASE =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,   )



   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      _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=__lowerCamelCase						,   torchify=__lowerCamelCase					)
      for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase						,   torch.Tensor					)

      # Test not batched input
      _SCREAMING_SNAKE_CASE =   image_processing(image_inputs[0]						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

      # Test batched
      _SCREAMING_SNAKE_CASE =   image_processing(__lowerCamelCase						,   return_tensors="pt"					).pixel_values

      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   self.image_processor_tester.get_expected_values(__lowerCamelCase						,   batched=__lowerCamelCase					)
      self.assertEqual(
          encoded_images.shape						,   (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )						,   )







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

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

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

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

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

   page_rank(__A						)


if __name__ == "__main__":
 main()

