



'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
      import PIL


lowercase__     :  str    					=			logging.get_logger(__name__)

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     Union[str, Any]				=	["pixel_values"]


   def __init__(  self     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						__UpperCamelCase = PILImageResampling.BICUBIC     ,						__UpperCamelCase = True     ,						__UpperCamelCase = 1 / 2_55     ,						__UpperCamelCase = True     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       super().__init__(**__UpperCamelCase )
       UpperCamelCase__      =			crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase     ,						param_name="crop_size" )
       UpperCamelCase__      =			size if size is not None else {"height": 2_24, "width": 2_24}
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase )

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			self.size if size is None else size
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase )
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase     ,						param_name="crop_size" )

       UpperCamelCase__    ,  UpperCamelCase__      =			get_image_size(__UpperCamelCase )
       UpperCamelCase__      =			min(__UpperCamelCase     ,						__UpperCamelCase )
       UpperCamelCase__      =			(size["height"] / crop_size["height"]) * min_dim
       UpperCamelCase__      =			(size["width"] / crop_size["width"]) * min_dim
       return center_crop(__UpperCamelCase     ,						size=(cropped_height, cropped_width)     ,						data_format=__UpperCamelCase     ,						**__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = PIL.Image.BICUBIC     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase )
       if "height" not in size or "width" not in size:
           raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}' )
       return resize(
           __UpperCamelCase     ,						size=(size["height"], size["width"])     ,						resample=__UpperCamelCase     ,						data_format=__UpperCamelCase     ,						**__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       return rescale(__UpperCamelCase     ,						scale=__UpperCamelCase     ,						data_format=__UpperCamelCase     ,						**__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						**__UpperCamelCase     ,						):
       return normalize(__UpperCamelCase     ,						mean=__UpperCamelCase     ,						std=__UpperCamelCase     ,						data_format=__UpperCamelCase     ,						**__UpperCamelCase )







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = None     ,						__UpperCamelCase = ChannelDimension.FIRST     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase__      =			crop_size if crop_size is not None else self.crop_size
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase     ,						param_name="crop_size" )
       UpperCamelCase__      =			do_resize if do_resize is not None else self.do_resize
       UpperCamelCase__      =			size if size is not None else self.size
       UpperCamelCase__      =			get_size_dict(__UpperCamelCase )
       UpperCamelCase__      =			resample if resample is not None else self.resample
       UpperCamelCase__      =			do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase__      =			rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase__      =			do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase__      =			image_mean if image_mean is not None else self.image_mean
       UpperCamelCase__      =			image_std if image_std is not None else self.image_std

       UpperCamelCase__      =			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_center_crop and crop_size is None:
           raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided." )

       if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True." )

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

       if do_center_crop:
           UpperCamelCase__      =			[self.center_crop(__UpperCamelCase     ,						__UpperCamelCase     ,						size=__UpperCamelCase ) for image in images]

       if do_resize:
           UpperCamelCase__      =			[self.resize(image=__UpperCamelCase     ,						size=__UpperCamelCase     ,						resample=__UpperCamelCase ) for image in images]

       if do_rescale:
           UpperCamelCase__      =			[self.rescale(image=__UpperCamelCase     ,						scale=__UpperCamelCase ) for image in images]

       if do_normalize:
           UpperCamelCase__      =			[self.normalize(image=__UpperCamelCase     ,						mean=__UpperCamelCase     ,						std=__UpperCamelCase ) for image in images]

       UpperCamelCase__      =			[to_channel_dimension_format(__UpperCamelCase     ,						__UpperCamelCase ) for image in images]

       UpperCamelCase__      =			{"pixel_values": images}
       return BatchFeature(data=__UpperCamelCase     ,						tensor_type=__UpperCamelCase )









'''simple docstring'''







import heapq





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			[(0, start)]  # cost from start node,end node
    UpperCamelCase__      =			set()
    while heap:
        ((UpperCamelCase__)    ,  (UpperCamelCase__))      =			heapq.heappop(lowerCAmelCase_	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase_	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCamelCase__      =			cost + c
            heapq.heappush(lowerCAmelCase_					,  (next_item, v)	)
    return -1


lowercase__     :  Any    					=			{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
lowercase__     :  Optional[int]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
lowercase__     :  Union[str, Any]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

lowercase__     :  Union[str, Any]    					=			dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowercase__     :  Optional[int]    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

lowercase__     :  Any    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()




