






'''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,
    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, is_vision_available, logging


if is_vision_available():
	import PIL


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		__UpperCAmelCase			):
				__lowerCamelCase:   Dict		       =    ['pixel_values']




				def __init__(			self  :       int  ,     a  :       bool = True  ,     a  :       Dict[str, int] = None  ,     a  :       float = None  ,     a  :       PILImageResampling = PILImageResampling.BILINEAR  ,     a  :       bool = True  ,     a  :       Union[int, float] = 1 / 2_5_5  ,     a  :       bool = True  ,     a  :       Optional[Union[float, List[float]]] = None  ,     a  :       Optional[Union[float, List[float]]] = None  ,     **a  :       List[Any]  ,     ):






							'''simple docstring'''






							super().__init__(**lowerCAmelCase_			)
							lowercase_	:    int           =     size if size is not None else {"shortest_edge": 3_8_4}
							lowercase_	:    Union[str, Any]           =     get_size_dict(lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_			)

							lowercase_	:    List[Any]           =     do_resize
							lowercase_	:    int           =     size
							# Default value set here for backwards compatibility where the value in config is None
							lowercase_	:    Optional[int]           =     crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
							lowercase_	:    List[Any]           =     resample
							lowercase_	:    int           =     do_rescale
							lowercase_	:    int           =     rescale_factor
							lowercase_	:    Optional[Any]           =     do_normalize
							lowercase_	:    int           =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase_	:    List[str]           =     image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       np.ndarray  ,     a  :       Dict[str, int]  ,     a  :       float  ,     a  :       PILImageResampling = PILImageResampling.BICUBIC  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Optional[int]  ,     ):






							'''simple docstring'''






							lowercase_	:    Optional[int]           =     get_size_dict(lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_			)
							if "shortest_edge" not in size:
										raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""			)
							lowercase_	:    List[str]           =     size["shortest_edge"]

							if shortest_edge < 3_8_4:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										lowercase_	:    Optional[Any]           =     int(shortest_edge / crop_pct			)
										lowercase_	:    Tuple           =     get_resize_output_image_size(lowerCAmelCase_  ,     size=lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_			)
										lowercase_	:    Any           =     resize(image=lowerCAmelCase_  ,     size=lowerCAmelCase_  ,     resample=lowerCAmelCase_  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=lowerCAmelCase_  ,     size=(shortest_edge, shortest_edge)  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)
							else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    lowerCAmelCase_  ,     size=(shortest_edge, shortest_edge)  ,     resample=lowerCAmelCase_  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)




				def 				lowerCAmelCase__				(			self  :       List[Any]  ,     a  :       np.ndarray  ,     a  :       Union[int, float]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       Dict  ,     ):






							'''simple docstring'''






							return rescale(lowerCAmelCase_  ,     scale=lowerCAmelCase_  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)




				def 				lowerCAmelCase__				(			self  :       List[str]  ,     a  :       np.ndarray  ,     a  :       Union[float, List[float]]  ,     a  :       Union[float, List[float]]  ,     a  :       Optional[Union[str, ChannelDimension]] = None  ,     **a  :       List[str]  ,     ):






							'''simple docstring'''






							return normalize(lowerCAmelCase_  ,     mean=lowerCAmelCase_  ,     std=lowerCAmelCase_  ,     data_format=lowerCAmelCase_  ,     **lowerCAmelCase_			)





				def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       ImageInput  ,     a  :       bool = None  ,     a  :       Dict[str, int] = None  ,     a  :       float = None  ,     a  :       PILImageResampling = None  ,     a  :       bool = None  ,     a  :       float = None  ,     a  :       bool = None  ,     a  :       Optional[Union[float, List[float]]] = None  ,     a  :       Optional[Union[float, List[float]]] = None  ,     a  :       Optional[Union[str, TensorType]] = None  ,     a  :       ChannelDimension = ChannelDimension.FIRST  ,     **a  :       Optional[int]  ,     ):






							'''simple docstring'''






							lowercase_	:    Any           =     do_resize if do_resize is not None else self.do_resize
							lowercase_	:    Tuple           =     crop_pct if crop_pct is not None else self.crop_pct
							lowercase_	:    Any           =     resample if resample is not None else self.resample
							lowercase_	:    Any           =     do_rescale if do_rescale is not None else self.do_rescale
							lowercase_	:    Optional[int]           =     rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_	:    int           =     do_normalize if do_normalize is not None else self.do_normalize
							lowercase_	:    Optional[int]           =     image_mean if image_mean is not None else self.image_mean
							lowercase_	:    Union[str, Any]           =     image_std if image_std is not None else self.image_std

							lowercase_	:    str           =     size if size is not None else self.size
							lowercase_	:    Any           =     get_size_dict(lowerCAmelCase_  ,     default_to_square=lowerCAmelCase_			)

							lowercase_	:    Any           =     make_list_of_images(lowerCAmelCase_			)

							if not valid_images(lowerCAmelCase_			):
										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 or resample is None:
										raise ValueError("Size and resample must be specified if do_resize is True."			)

							if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
										raise ValueError("crop_pct must be specified if size < 384."			)

							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.
							lowercase_	:    str           =     [to_numpy_array(lowerCAmelCase_			) for image in images]

							if do_resize:
										lowercase_	:    Union[str, Any]           =     [self.resize(image=lowerCAmelCase_  ,     size=lowerCAmelCase_  ,     crop_pct=lowerCAmelCase_  ,     resample=lowerCAmelCase_			) for image in images]

							if do_rescale:
										lowercase_	:    List[Any]           =     [self.rescale(image=lowerCAmelCase_  ,     scale=lowerCAmelCase_			) for image in images]

							if do_normalize:
										lowercase_	:    Optional[int]           =     [self.normalize(image=lowerCAmelCase_  ,     mean=lowerCAmelCase_  ,     std=lowerCAmelCase_			) for image in images]

							lowercase_	:    Any           =     [to_channel_dimension_format(lowerCAmelCase_  ,     lowerCAmelCase_			) for image in images]

							lowercase_	:    Any           =     {"pixel_values": images}
							return BatchFeature(data=lowerCAmelCase_  ,     tensor_type=lowerCAmelCase_			)












'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator


class        _UpperCAmelCase						:




    def __init__(			self  :       Optional[Any]  ,     a  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     str(id_			)
       lowercase_	:    List[str]           =     None
       lowercase_	:    Tuple           =     None
       lowercase_	:    Dict           =     []
       lowercase_	:    Union[str, Any]           =     {}  # {vertex:distance}




    def __lt__(			self  :       Optional[Any]  ,     a  :       int			):






       '''simple docstring'''






       return self.key < other.key




    def __repr__(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return self.id




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Optional[int]			):






       '''simple docstring'''






       self.neighbors.append(a			)





    def 				lowerCAmelCase__				(			self  :       Dict  ,     a  :       int  ,     a  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    int           =     weight




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   graph[a - 1].add_neighbor(graph[b - 1]							)
   graph[b - 1].add_neighbor(graph[a - 1]							)
   # add the edges:
   graph[a - 1].add_edge(graph[b - 1]						,		_UpperCamelCase							)
   graph[b - 1].add_edge(graph[a - 1]						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   for u in graph:
      lowercase_	:    List[Any]           =     math.inf
      lowercase_	:    str           =     None
   lowercase_	:    Tuple           =     0
   lowercase_	:    Tuple           =     graph[:]
   while q:
      lowercase_	:    List[Any]           =     min(_UpperCamelCase							)
      q.remove(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in q) and (u.edges[v.id] < v.key):
            lowercase_	:    Optional[int]           =     u
            lowercase_	:    Union[str, Any]           =     u.edges[v.id]
   for i in range(1						,		len(_UpperCamelCase							)							):
      a.append((int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)							)
   return a




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   for u in graph:
      lowercase_	:    str           =     math.inf
      lowercase_	:    int           =     None
   lowercase_	:    List[Any]           =     0

   lowercase_	:    str           =     list(_UpperCamelCase							)
   hq.heapify(_UpperCamelCase							)

   while h:
      lowercase_	:    List[Any]           =     hq.heappop(_UpperCamelCase							)
      for v in u.neighbors:
         if (v in h) and (u.edges[v.id] < v.key):
            lowercase_	:    str           =     u
            lowercase_	:    Optional[int]           =     u.edges[v.id]
            hq.heapify(_UpperCamelCase							)

   for i in range(1						,		len(_UpperCamelCase							)							):
      yield (int(graph[i].id							) + 1, int(graph[i].pi.id							) + 1)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""






if __name__ == "__main__":
 import doctest

 doctest.testmod()




