
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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor







def 				UpperCAmelCase_(    a__ ,						a__=10 ,						a__=10 ,						a__=False ,						a__=False   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				List[str]				      = []
    for i in range(image_processor_tester.num_frames   ):
        video.append(np.random.randint(255 ,						size=(image_processor_tester.num_channels, width, height) ,						dtype=np.uinta   )   )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        SCREAMING_SNAKE_CASE				:				str				      = [Image.fromarray(np.moveaxis(a__ ,						0 ,						-1   )   ) for frame in video]

    if torchify:
        SCREAMING_SNAKE_CASE				:				int				      = [torch.from_numpy(a__   ) for frame in video]

    return video







def 				UpperCAmelCase_(    a__ ,						a__=False ,						a__=False ,						a__=False   ):



    """simple docstring"""



    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    SCREAMING_SNAKE_CASE				:				str				      = []
    for i in range(image_processor_tester.batch_size   ):
        if equal_resolution:
            SCREAMING_SNAKE_CASE				:				Tuple				      = image_processor_tester.max_resolution
        else:
            SCREAMING_SNAKE_CASE				:				Any				      = np.random.choice(
                np.arange(image_processor_tester.min_resolution ,						image_processor_tester.max_resolution   ) ,						2   )
            SCREAMING_SNAKE_CASE				:				Optional[Any]				      = prepare_video(
                image_processor_tester=a__ ,						width=a__ ,						height=a__ ,						numpify=a__ ,						torchify=a__ ,						)
        video_inputs.append(a__   )

    return video_inputs






class     a_       (			unittest.TestCase							):



       """simple docstring"""






       def __init__(		self				,       _lowerCamelCase				,       _lowerCamelCase=7				,       _lowerCamelCase=3				,       _lowerCamelCase=4				,       _lowerCamelCase=18				,       _lowerCamelCase=30				,       _lowerCamelCase=400				,       _lowerCamelCase=True				,       _lowerCamelCase=None				,       _lowerCamelCase=True				,       _lowerCamelCase=[0.5, 0.5, 0.5]				,       _lowerCamelCase=[0.5, 0.5, 0.5]				,       _lowerCamelCase=True				,       _lowerCamelCase=None				,       )					->Union[str, Any]:
           SCREAMING_SNAKE_CASE				:				int				      = size if size is not None else {'''shortest_edge''': 18}
           SCREAMING_SNAKE_CASE				:				int				      = crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

           SCREAMING_SNAKE_CASE				:				List[str]				      = parent
           SCREAMING_SNAKE_CASE				:				str				      = batch_size
           SCREAMING_SNAKE_CASE				:				str				      = num_channels
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = num_frames
           SCREAMING_SNAKE_CASE				:				Any				      = image_size
           SCREAMING_SNAKE_CASE				:				Dict				      = min_resolution
           SCREAMING_SNAKE_CASE				:				List[str]				      = max_resolution
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = do_resize
           SCREAMING_SNAKE_CASE				:				Tuple				      = size
           SCREAMING_SNAKE_CASE				:				Tuple				      = do_normalize
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = image_mean
           SCREAMING_SNAKE_CASE				:				List[str]				      = image_std
           SCREAMING_SNAKE_CASE				:				str				      = do_center_crop
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = crop_size






       def 	__lowerCAmelCase     (		self   )					->str:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






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



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[Any]						=  TvltImageProcessor if is_vision_available() else None






       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = TvltImageProcessorTester(self   )






       @property
       def 	__lowerCAmelCase     (		self   )					->Optional[Any]:
           return self.image_processor_tester.prepare_image_processor_dict()






       def 	__lowerCAmelCase     (		self   )					->Any:
           SCREAMING_SNAKE_CASE				:				List[Any]				      = self.image_processing_class(**self.image_processor_dict   )
           self.assertTrue(hasattr(a__				,       '''image_mean'''   )   )
           self.assertTrue(hasattr(a__				,       '''image_std'''   )   )
           self.assertTrue(hasattr(a__				,       '''do_normalize'''   )   )
           self.assertTrue(hasattr(a__				,       '''do_resize'''   )   )
           self.assertTrue(hasattr(a__				,       '''do_center_crop'''   )   )
           self.assertTrue(hasattr(a__				,       '''size'''   )   )






       def 	__lowerCAmelCase     (		self   )					->Dict:
           # Initialize image_processor
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = self.image_processing_class(**self.image_processor_dict   )
           # create random PIL videos
           SCREAMING_SNAKE_CASE				:				str				      = prepare_video_inputs(self.image_processor_tester				,       equal_resolution=a__   )
           for video in video_inputs:
               self.assertIsInstance(a__				,       a__   )
               self.assertIsInstance(video[0]				,       Image.Image   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = image_processor(video_inputs[0]				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				List[str]				      = image_processor(a__				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )






       def 	__lowerCAmelCase     (		self   )					->Any:
           # Initialize image_processor
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = self.image_processing_class(**self.image_processor_dict   )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE				:				int				      = prepare_video_inputs(self.image_processor_tester				,       equal_resolution=a__				,       numpify=a__   )
           for video in video_inputs:
               self.assertIsInstance(a__				,       a__   )
               self.assertIsInstance(video[0]				,       np.ndarray   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				Any				      = image_processor(video_inputs[0]				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_processor(a__				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )






       def 	__lowerCAmelCase     (		self   )					->List[Any]:
           # Initialize image_processor
           SCREAMING_SNAKE_CASE				:				int				      = self.image_processing_class(**self.image_processor_dict   )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE				:				List[Any]				      = prepare_video_inputs(self.image_processor_tester				,       equal_resolution=a__				,       torchify=a__   )
           for video in video_inputs:
               self.assertIsInstance(a__				,       a__   )
               self.assertIsInstance(video[0]				,       torch.Tensor   )

           # Test not batched input
           SCREAMING_SNAKE_CASE				:				int				      = image_processor(video_inputs[0]				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )

           # Test batched
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = image_processor(a__				,       return_tensors='''pt'''   ).pixel_values
           self.assertEqual(
               encoded_videos.shape				,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size['''height'''],
                   self.image_processor_tester.crop_size['''width'''],
               )				,       )






'''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()

