





'''simple docstring'''

import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class __a			( unittest.TestCase      ):





   def __init__(       self					:      int					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Dict[str, int] = None					,lowerCamelCase					:      int = 32					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Union[int, float] = 1 / 255					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]					,lowerCamelCase					:      Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      int=7					,lowerCamelCase					:      Optional[int]=30					,lowerCamelCase					:      Any=400					,lowerCamelCase					:      Union[str, Any]=3					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							parent
      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""shortest_edge""": 288}
      __SCREAMING_SNAKE_CASE   				=							size_divisor
      __SCREAMING_SNAKE_CASE   				=							do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							do_center_crop
      __SCREAMING_SNAKE_CASE   				=							image_mean
      __SCREAMING_SNAKE_CASE   				=							image_std
      __SCREAMING_SNAKE_CASE   				=							do_pad
      __SCREAMING_SNAKE_CASE   				=							batch_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							min_resolution
      __SCREAMING_SNAKE_CASE   				=							max_resolution





   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''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   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[int]					,lowerCamelCase					:      Union[str, Any]=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((1333 / 800) * 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 __a			( _snake_case, unittest.TestCase      ):
   __UpperCamelCase :			Optional[int]						   =						BridgeTowerImageProcessor if is_vision_available() else None





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							BridgeTowerImageProcessingTester(self     )





   @property
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()





   def   UpperCAmelCase__  (       self					:      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   UpperCAmelCase__  (       self					:      List[str]     ):

      '''simple docstring'''

      pass





   def   UpperCAmelCase__  (       self					:      Optional[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   UpperCAmelCase__  (       self					:      List[str]     ):

      '''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   UpperCAmelCase__  (       self					:      str     ):

      '''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,
          )					,)








from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

