





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


a__: List[Any]							   =						logging.get_logger(__name__)


if is_vision_available():
       import PIL




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      ['''pixel_values''']



 def __init__(				self,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = 1 / 255,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase					)
        A__		   =		size if size is not None else {'''shortest_edge''': 224}
        A__		   =		get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase					)
        A__		   =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        A__		   =		get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase,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 OPENAI_CLIP_MEAN
        A__		   =		image_std if image_std is not None else OPENAI_CLIP_STD
        A__		   =		do_convert_rgb



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = PILImageResampling.BICUBIC,__lowerCamelCase = None,**__lowerCamelCase,):
        A__		   =		get_size_dict(__lowerCamelCase,default_to_square=__lowerCamelCase					)
        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(__lowerCamelCase,size=size['''shortest_edge'''],default_to_square=__lowerCamelCase					)
        return resize(__lowerCamelCase,size=__lowerCamelCase,resample=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        A__		   =		get_size_dict(__lowerCamelCase					)
        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(__lowerCamelCase,size=(size['''height'''], size['''width''']),data_format=__lowerCamelCase,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return rescale(__lowerCamelCase,scale=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = None,**__lowerCamelCase,):
        return normalize(__lowerCamelCase,mean=__lowerCamelCase,std=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase					)




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        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(__lowerCamelCase,param_name='''size''',default_to_square=__lowerCamelCase					)
        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(__lowerCamelCase,param_name='''crop_size''',default_to_square=__lowerCamelCase					)
        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__		   =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        A__		   =		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:
               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:
               A__		   =		[convert_to_rgb(__lowerCamelCase					) for image in images]

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

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

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

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

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

        A__		   =		[to_channel_dimension_format(__lowerCamelCase,__lowerCamelCase					) for image in images]

        A__		   =		{'''pixel_values''': images}
        return BatchFeature(data=__lowerCamelCase,tensor_type=__lowerCamelCase					)












from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a__: Tuple							   =						TypeVar('T')


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (position - 1) // 2


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 1


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->int:
       return (2 * position) + 2




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		[]
        A__		   =		{}
        A__		   =		0



 def __len__(				self					):
        return self.elements



 def __repr__(				self					):
        return str(self.heap					)



 def 							UpperCamelCase     (				self					):
        # Check if the priority queue is empty
        return self.elements == 0



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Add an element with given priority to the queue
        self.heap.append((elem, weight)					)
        A__		   =		self.elements
        self.elements += 1
        self._bubble_up(__lowerCamelCase					)



 def 							UpperCamelCase     (				self					):
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
               self._swap_nodes(0,self.elements - 1					)
        A__   ,      A__		   =		self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
               A__   ,      A__		   =		self.heap[0]
               self._bubble_down(__lowerCamelCase					)
        return elem



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Update the weight of the given key
        A__		   =		self.position_map[elem]
        A__		   =		(elem, weight)
        if position > 0:
               A__		   =		get_parent_position(__lowerCamelCase					)
               A__   ,      A__		   =		self.heap[parent_position]
               if parent_weight > weight:
                      self._bubble_up(__lowerCamelCase					)
               else:
                      self._bubble_down(__lowerCamelCase					)
        else:
               self._bubble_down(__lowerCamelCase					)



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        A__		   =		self.position_map[elem]
        if curr_pos == 0:
               return None
        A__		   =		get_parent_position(__lowerCamelCase					)
        A__   ,      A__		   =		self.heap[curr_pos]
        A__   ,      A__		   =		self.heap[parent_position]
        if parent_weight > weight:
               self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
               return self._bubble_up(__lowerCamelCase					)
        return None



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        A__		   =		self.position_map[elem]
        A__   ,      A__		   =		self.heap[curr_pos]
        A__		   =		get_child_left_position(__lowerCamelCase					)
        A__		   =		get_child_right_position(__lowerCamelCase					)
        if child_left_position < self.elements and child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < child_left_weight and child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        if child_left_position < self.elements:
               A__   ,      A__		   =		self.heap[child_left_position]
               if child_left_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        else:
               return None
        if child_right_position < self.elements:
               A__   ,      A__		   =		self.heap[child_right_position]
               if child_right_weight < weight:
                      self._swap_nodes(__lowerCamelCase,__lowerCamelCase					)
                      return self._bubble_down(__lowerCamelCase					)
        return None




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        # Swap the nodes at the given positions
        A__		   =		self.heap[nodea_pos][0]
        A__		   =		self.heap[nodea_pos][0]
        A__   ,      A__		   =		(
            self.heap[nodea_pos],
            self.heap[nodea_pos],
        )
        A__		   =		nodea_pos
        A__		   =		nodea_pos




class        SCREAMING_SNAKE_CASE__					(		Generic[T] ):



 def __init__(				self					):
        A__		   =		{}
        A__		   =		0



 def __repr__(				self					):
        return str(self.connections					)



 def __len__(				self					):
        return self.nodes



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
               A__		   =		{}
               self.nodes += 1




 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        # Add an edge between 2 nodes in the graph
        self.add_node(__lowerCamelCase					)
        self.add_node(__lowerCamelCase					)
        A__		   =		weight
        A__		   =		weight


def       UpperCamelCase__(			UpperCamelCase__     :			GraphUndirectedWeighted[T]					,	)->tuple[dict[T, int], dict[T, T | None]]:
       A__		   =		{node: maxsize for node in graph.connections}
       A__		   =		{node: None for node in graph.connections}

       A__		   =		MinPriorityQueue()
       for node, weight in dist.items():
              priority_queue.push(UpperCamelCase__					,	UpperCamelCase__    )

       if priority_queue.is_empty():
              return dist, parent

       # initialization
       A__		   =		priority_queue.extract_min()
       A__		   =		0
       for neighbour in graph.connections[node]:
              if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                     A__		   =		dist[node] + graph.connections[node][neighbour]
                     priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                     A__		   =		node

    # running prim's algorithm
       while not priority_queue.is_empty():
              A__		   =		priority_queue.extract_min()
              for neighbour in graph.connections[node]:
                     if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                            A__		   =		dist[node] + graph.connections[node][neighbour]
                            priority_queue.update_key(UpperCamelCase__					,	dist[neighbour]    )
                            A__		   =		node
       return dist, parent





