





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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   ["""pixel_values"""]





     def __init__(     self				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = PILImageResampling.BICUBIC				,							_UpperCamelCase = True				,							_UpperCamelCase = True				,							_UpperCamelCase = 1 / 255				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        super().__init__(**_UpperCamelCase					)
        _UpperCAmelCase           =   size if size is not None else {'''height''': 224, '''width''': 224}
        _UpperCAmelCase           =   get_size_dict(_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           =   do_rescale
        _UpperCAmelCase           =   do_normalize
        _UpperCAmelCase           =   do_center_crop
        _UpperCAmelCase           =   crop_size
        _UpperCAmelCase           =   size
        _UpperCAmelCase           =   resample
        _UpperCAmelCase           =   rescale_factor
        _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 = PILImageResampling.BILINEAR				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if "shortest_edge" in size:
           _UpperCAmelCase           =   get_resize_output_image_size(_UpperCamelCase				,							size=size['''shortest_edge''']				,							default_to_square=_UpperCamelCase					)
           # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
           _UpperCAmelCase           =   (size['''height'''], size['''width'''])
        else:
           raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''					)
        return resize(_UpperCamelCase				,							size=_UpperCamelCase				,							resample=_UpperCamelCase				,							data_format=_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _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					):
        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_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase           =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase           =   do_normalize if do_normalize is not None else self.do_normalize
        _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           =   resample if resample is not None else self.resample
        _UpperCAmelCase           =   rescale_factor if rescale_factor is not None else self.rescale_factor
        _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           =   size if size is not None else self.size
        _UpperCAmelCase           =   get_size_dict(_UpperCamelCase					)

        if not is_batched(_UpperCamelCase					):
           _UpperCAmelCase           =   [images]

        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.'''					)

        # 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					)






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   TypeVar('''T''')



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



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



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

class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Any   )       -> None:

			'''simple docstring'''




			__lowercase											=     []
			__lowercase											=     {}
			__lowercase											=     0




	def __len__(						self							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			return self.elements




	def __repr__(						self							:     Optional[Any]   )       -> str:

			'''simple docstring'''




			return str(self.heap   )




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> bool:

			'''simple docstring'''




			return self.elements == 0




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.heap.append((elem, weight)   )
			__lowercase											=     self.elements
			self.elements += 1
			self._bubble_up(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Dict   )       -> T:

			'''simple docstring'''




			if self.elements > 1:
					self._swap_nodes(0					,      self.elements - 1   )
			__lowercase						,      __lowercase											=     self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
					__lowercase						,      __lowercase											=     self.heap[0]
					self._bubble_down(__lowerCamelCase   )
			return elem




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase											=     (elem, weight)
			if position > 0:
					__lowercase											=     get_parent_position(__lowerCamelCase   )
					__lowercase						,      __lowercase											=     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							:     Tuple					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			if curr_pos == 0:
					return None
			__lowercase											=     get_parent_position(__lowerCamelCase   )
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase						,      __lowercase											=     self.heap[parent_position]
			if parent_weight > weight:
					self._swap_nodes(__lowerCamelCase					,      __lowerCamelCase   )
					return self._bubble_up(__lowerCamelCase   )
			return None




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.position_map[elem]
			__lowercase						,      __lowercase											=     self.heap[curr_pos]
			__lowercase											=     get_child_left_position(__lowerCamelCase   )
			__lowercase											=     get_child_right_position(__lowerCamelCase   )
			if child_left_position < self.elements and child_right_position < self.elements:
					__lowercase						,      __lowercase											=     self.heap[child_left_position]
					__lowercase						,      __lowercase											=     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:
					__lowercase						,      __lowercase											=     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:
					__lowercase						,      __lowercase											=     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							:     Optional[int]					,      __lowerCamelCase							:     int					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase											=     self.heap[nodea_pos][0]
			__lowercase						,      __lowercase											=     (
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__lowercase											=     nodea_pos
			__lowercase											=     nodea_pos





class 		snake_case_   (							Generic[T]						):







	'''simple docstring'''




	def __init__(						self							:     Optional[Any]   )       -> None:

			'''simple docstring'''




			__lowercase											=     {}
			__lowercase											=     0




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

			'''simple docstring'''




			return str(self.connections   )




	def __len__(						self							:     int   )       -> int:

			'''simple docstring'''




			return self.nodes




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     T   )       -> None:

			'''simple docstring'''




			if node not in self.connections:
					__lowercase											=     {}
					self.nodes += 1






	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     T					,      __lowerCamelCase							:     int   )       -> None:

			'''simple docstring'''




			self.add_node(__lowerCamelCase   )
			self.add_node(__lowerCamelCase   )
			__lowercase											=     weight
			__lowercase											=     weight





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

		__lowercase											=     MinPriorityQueue()
		for node, weight in dist.items():
				priority_queue.push(snake_case						,   snake_case							)

		if priority_queue.is_empty():
				return dist, parent

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

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




