





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case__					:							Tuple													=     logging.get_logger(__name__)





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""pixel_values"""]




   def __init__(self	:Any    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BICUBIC    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Dict[str, int] = None    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Union[int, float] = 1 / 255    ,   _UpperCamelCase	:bool = True    ,   _UpperCamelCase	:Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN    ,   _UpperCamelCase	:Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD    ,   **_UpperCamelCase	:Optional[int]    ,   )->					None:
         super().__init__(**_UpperCamelCase )
         __A          =				size if size is not None else {'''shortest_edge''': 224}
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         __A          =				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''crop_size''' )

         __A          =				do_resize
         __A          =				size
         __A          =				resample
         __A          =				do_center_crop
         __A          =				crop_size
         __A          =				do_rescale
         __A          =				rescale_factor
         __A          =				do_normalize
         __A          =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         __A          =				image_std if image_std is not None else IMAGENET_DEFAULT_STD




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:PILImageResampling = PILImageResampling.BICUBIC    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:int    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
         if "shortest_edge" in size:
               __A          =				int((256 / 224) * size['''shortest_edge'''] )
               __A          =				get_resize_output_image_size(_UpperCamelCase    ,   size=_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
               __A          =				{'''height''': output_size[0], '''width''': output_size[1]}
         if "height" not in size_dict or "width" not in size_dict:
               raise ValueError(
                   f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
         return resize(
             _UpperCamelCase    ,   size=(size_dict['''height'''], size_dict['''width'''])    ,   resample=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Dict[str, int]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Dict    ,   )->					np.ndarray:
         __A          =				get_size_dict(_UpperCamelCase )
         if "height" not in size or "width" not in size:
               raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
         return center_crop(_UpperCamelCase    ,   size=(size['''height'''], size['''width'''])    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[int, float]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:Optional[int]    ,   )->					np.ndarray:
         return rescale(_UpperCamelCase    ,   scale=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:np.ndarray    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Union[float, List[float]]    ,   _UpperCamelCase	:Optional[Union[str, ChannelDimension]] = None    ,   **_UpperCamelCase	:List[str]    ,   )->					np.ndarray:
         return normalize(_UpperCamelCase    ,   mean=_UpperCamelCase    ,   std=_UpperCamelCase    ,   data_format=_UpperCamelCase    ,   **_UpperCamelCase )






   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:ImageInput    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[Dict[str, int]] = None    ,   _UpperCamelCase	:PILImageResampling = None    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[Dict[str, int]] = None    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[float] = None    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[Union[float, Iterable[float]]] = None    ,   _UpperCamelCase	:Optional[Union[float, Iterable[float]]] = None    ,   _UpperCamelCase	:Optional[TensorType] = None    ,   _UpperCamelCase	:ChannelDimension = ChannelDimension.FIRST    ,   **_UpperCamelCase	:str    ,   )->					BatchFeature:
         __A          =				do_resize if do_resize is not None else self.do_resize
         __A          =				resample if resample is not None else self.resample
         __A          =				do_center_crop if do_center_crop is not None else self.do_center_crop
         __A          =				do_rescale if do_rescale is not None else self.do_rescale
         __A          =				rescale_factor if rescale_factor is not None else self.rescale_factor
         __A          =				do_normalize if do_normalize is not None else self.do_normalize
         __A          =				image_mean if image_mean is not None else self.image_mean
         __A          =				image_std if image_std is not None else self.image_std

         __A          =				size if size is not None else self.size
         __A          =				get_size_dict(_UpperCamelCase    ,   default_to_square=_UpperCamelCase )
         __A          =				crop_size if crop_size is not None else self.crop_size
         __A          =				get_size_dict(_UpperCamelCase    ,   param_name='''crop_size''' )

         __A          =				make_list_of_images(_UpperCamelCase )

         if not valid_images(_UpperCamelCase ):
               raise ValueError(
                   '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                   '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

         if do_resize and size is None:
               raise ValueError('''Size must be specified if do_resize is True.''' )

         if do_center_crop and crop_size is None:
               raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

         if do_rescale and rescale_factor is None:
               raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

         if do_normalize and (image_mean is None or image_std is None):
               raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

         # All transformations expect numpy arrays.
         __A          =				[to_numpy_array(_UpperCamelCase ) for image in images]

         if do_resize:
               __A          =				[self.resize(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         if do_center_crop:
               __A          =				[self.center_crop(_UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         if do_rescale:
               __A          =				[self.rescale(_UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         if do_normalize:
               __A          =				[self.normalize(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         __A          =				[to_channel_dimension_format(_UpperCamelCase    ,   _UpperCamelCase ) for image in images]

         __A          =				{'''pixel_values''': images}
         return BatchFeature(data=_UpperCamelCase    ,   tensor_type=_UpperCamelCase )












from __future__ import annotations



def _a				(	lowerCamelCase:   list[float]					,				lowerCamelCase:   Tuple )	->						List[str]:







      '''simple docstring'''






      print(F"""Vertex\tShortest Distance from vertex {src}""" )
      for i, d in enumerate(lowerCamelCase ):
            print(F"""{i}\t\t{d}""" )



def _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   list[float]					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      for j in range(lowerCamelCase ):
            __A   ,						__A   ,						__A          =				(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 _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						list[float]:







      '''simple docstring'''






      __A          =				[float('''inf''' )] * vertex_count
      __A          =				0.0

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

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

      __A          =				check_negative_cycle(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      if negative_cycle_exists:
            raise Exception('''Negative cycle found''' )

      return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       snake_case__					:							Dict													=     int(input('Enter number of vertices: ').strip())
       snake_case__					:							Optional[int]													=     int(input('Enter number of edges: ').strip())

       snake_case__					:							list[dict[str, int]]													=     [{} for _ in range(E)]

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

       snake_case__					:							Union[str, Any]													=     int(input('\nEnter shortest path source:').strip())
       snake_case__					:							List[Any]													=     bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)





