






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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__   :						List[str]													=   logging.get_logger(__name__)


if is_vision_available():
 import PIL


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = ["""pixel_values"""]




  def __init__(  self		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = PILImageResampling.BICUBIC		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		_UpperCAmelCase = 1 / 255		,		_UpperCAmelCase = True		,		_UpperCAmelCase = None		,		_UpperCAmelCase = None		,		_UpperCAmelCase = True		,		**_UpperCAmelCase		,		)      ->			None:
     super().__init__(**_UpperCAmelCase			)
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  crop_size if crop_size is not None else {'height': 224, 'width': 224}
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase		,		default_to_square=_UpperCAmelCase		,		param_name='crop_size'			)

     UpperCamelCase_						=  do_resize
     UpperCamelCase_						=  size
     UpperCamelCase_						=  resample
     UpperCamelCase_						=  do_center_crop
     UpperCamelCase_						=  crop_size
     UpperCamelCase_						=  do_rescale
     UpperCamelCase_						=  rescale_factor
     UpperCamelCase_						=  do_normalize
     UpperCamelCase_						=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     UpperCamelCase_						=  image_std if image_std is not None else OPENAI_CLIP_STD
     UpperCamelCase_						=  do_convert_rgb




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = PILImageResampling.BICUBIC		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  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()}"""			)
     UpperCamelCase_						=  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		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     UpperCamelCase_						=  get_size_dict(_UpperCAmelCase			)
     if "height" not in size or "width" not in size:
        raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""			)
     return center_crop(_UpperCAmelCase		,		size=(size['height'], size['width'])		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			str:
     return rescale(_UpperCAmelCase		,		scale=_UpperCAmelCase		,		data_format=_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		**_UpperCAmelCase		,		)      ->			np.ndarray:
     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 = None		,		_UpperCAmelCase = ChannelDimension.FIRST		,		**_UpperCAmelCase		,		)      ->			PIL.Image.Image:
     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		,		param_name='size'		,		default_to_square=_UpperCAmelCase			)
     UpperCamelCase_						=  resample if resample is not None else self.resample
     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'		,		default_to_square=_UpperCAmelCase			)
     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_						=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     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_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.'			)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
        UpperCamelCase_						=  [convert_to_rgb(_UpperCAmelCase			) for image in images]

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

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

     if do_center_crop:
        UpperCamelCase_						=  [self.center_crop(image=_UpperCAmelCase		,		size=_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'''



from queue import PriorityQueue
from typing import Any

import numpy as np


def 				lowercase_					(     __A			:							dict       ,   __A			:							str       ,   __A			:							set       ,   __A			:							set       ,   __A			:							dict       ,   __A			:							dict       ,   __A			:							PriorityQueue       ,   __A			:							dict       ,   __A			:							float | int       ,   )							->   float | int:



 """simple docstring"""




 for nxt, d in graph[v]:
  if nxt in visited_forward:
   continue
  lowercase       : str						=cst_fwd.get(__A       ,   np.inf      )
  lowercase       : str						=cst_fwd[v] + d
  if new_cost_f < old_cost_f:
   queue.put((new_cost_f, nxt)      )
   lowercase       : int						=new_cost_f
   lowercase       : Optional[int]						=v
  if nxt in visited_backward:
   if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
    lowercase       : List[Any]						=cst_fwd[v] + d + cst_bwd[nxt]
 return shortest_distance


def 				lowercase_					(     __A			:							str       ,   __A			:							str       ,   __A			:							dict       ,   __A			:							dict      )							->   int:



 """simple docstring"""




 lowercase       : Optional[Any]						=-1

 lowercase       : Tuple						=set()
 lowercase       : str						=set()
 lowercase       : List[str]						={source: 0}
 lowercase       : List[str]						={destination: 0}
 lowercase       : Tuple						={source: None}
 lowercase       : List[Any]						={destination: None}
 lowercase       : PriorityQueue[Any]						=PriorityQueue()
 lowercase       : PriorityQueue[Any]						=PriorityQueue()

 lowercase       : Any						=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():
  lowercase	,		lowercase       : int						=queue_forward.get()
  visited_forward.add(__A      )

  lowercase	,		lowercase       : int						=queue_backward.get()
  visited_backward.add(__A      )

  lowercase       : Dict						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

  lowercase       : List[Any]						=pass_and_relaxation(
      __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )

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

 if shortest_distance != np.inf:
  lowercase       : Union[str, Any]						=shortest_distance
 return shortest_path_distance


SCREAMING_SNAKE_CASE							=						{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE							=						{
    '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()



