



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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case     :int			       =logging.get_logger(__name__)



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     str								=     ['pixel_values']


				def __init__(							self      :  Tuple						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Dict[str, int]] = None						, __UpperCamelCase      :  PILImageResampling = PILImageResampling.BILINEAR						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Union[int, float] = 1 / 255						, __UpperCamelCase      :  bool = True						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, **__UpperCamelCase      :  List[str]						, )		->     None:
											super().__init__(**__UpperCamelCase       )
											A      						=    size if size is not None else {'shortest_edge': 256}
											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       )
											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_STANDARD_MEAN
											A      						=    image_std if image_std is not None else IMAGENET_STANDARD_STD


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Dict[str, int]						, __UpperCamelCase      :  PILImageResampling = PILImageResampling.BICUBIC						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  Any						, )		->     np.ndarray:
											A      						=    get_size_dict(__UpperCamelCase						, default_to_square=__UpperCamelCase       )
											if "shortest_edge" not in size:
																		raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
											A      						=    get_resize_output_image_size(__UpperCamelCase						, size=size['shortest_edge']						, default_to_square=__UpperCamelCase       )
											return resize(__UpperCamelCase						, size=__UpperCamelCase						, resample=__UpperCamelCase						, data_format=__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]						, __UpperCamelCase      :  np.ndarray						, __UpperCamelCase      :  Dict[str, int]						, __UpperCamelCase      :  Optional[Union[str, ChannelDimension]] = None						, **__UpperCamelCase      :  Optional[Any]						, )		->     np.ndarray:
											A      						=    get_size_dict(__UpperCamelCase       )
											return center_crop(__UpperCamelCase						, size=(size['height'], size['width'])						, data_format=__UpperCamelCase						, **__UpperCamelCase       )


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


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


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  ImageInput						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  PILImageResampling = None						, __UpperCamelCase      :  bool = None						, __UpperCamelCase      :  Dict[str, int] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[float] = None						, __UpperCamelCase      :  Optional[bool] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[float, List[float]]] = None						, __UpperCamelCase      :  Optional[Union[str, TensorType]] = None						, __UpperCamelCase      :  Union[str, ChannelDimension] = ChannelDimension.FIRST						, **__UpperCamelCase      :  str						, )		->     Any:
											A      						=    do_resize if do_resize is not None else self.do_resize
											A      						=    size if size is not None else self.size
											A      						=    get_size_dict(__UpperCamelCase						, default_to_square=__UpperCamelCase       )
											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      						=    crop_size if crop_size is not None else self.crop_size
											A      						=    get_size_dict(__UpperCamelCase       )
											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      						=    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(image=__UpperCamelCase						, size=__UpperCamelCase						, resample=__UpperCamelCase       ) for image in images]

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

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

											if do_normalize:
																		A      						=    [self.normalize(image=__UpperCamelCase						, mean=__UpperCamelCase						, std=__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       )







'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						set,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						PriorityQueue,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						float | int,       )	->      float | int:

   """simple docstring"""
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      __a					  =     cst_fwd.get(SCREAMING_SNAKE_CASE__,       np.inf     )
      __a					  =     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)     )
         __a					  =     new_cost_f
         __a					  =     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            __a					  =     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						dict,       SCREAMING_SNAKE_CASE__:						dict     )	->      int:

   """simple docstring"""
   __a					  =     -1

   __a					  =     set()
   __a					  =     set()
   __a					  =     {source: 0}
   __a					  =     {destination: 0}
   __a					  =     {source: None}
   __a					  =     {destination: None}
   __a					  =     PriorityQueue()
   __a					  =     PriorityQueue()

   __a					  =     np.inf

   queue_forward.put((0, source)     )
   queue_backward.put((0, destination)     )

   if source == destination:
      return 0

   while not queue_forward.empty() and not queue_backward.empty():
      __a  ,      __a					  =     queue_forward.get()
      visited_forward.add(SCREAMING_SNAKE_CASE__     )

      __a  ,      __a					  =     queue_backward.get()
      visited_backward.add(SCREAMING_SNAKE_CASE__     )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      __a					  =     pass_and_relaxation(
          SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       )

      if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
         break

   if shortest_distance != np.inf:
      __a					  =     shortest_distance
   return shortest_path_distance


__UpperCamelCase   :							Dict         =   {
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__UpperCamelCase   :							int         =   {
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
 import doctest

 doctest.testmod()