
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class     lowerCamelCase      :







	def __init__(     self      :List[str]   ,     lowercase      :Optional[int]   ,     lowercase      :int=sys.maxsize			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''bilinear'''
				SCREAMING_SNAKE_CASE     						= max_size
				SCREAMING_SNAKE_CASE     						= short_edge_length







	def __call__(     self      :Optional[Any]   ,     lowercase      :Tuple			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= []
				for img in imgs:
							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= img.shape[:2]
							# later: provide list and randomly choose index for resize
							SCREAMING_SNAKE_CASE     						= np.random.randint(self.short_edge_length[0]   ,     self.short_edge_length[1] + 1			)
							if size == 0:
										return img
							SCREAMING_SNAKE_CASE     						= size * 1.0 / min(lowercase   ,     lowercase			)
							if h < w:
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= size, scale * w
							else:
										SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= scale * h, size
							if max(lowercase   ,     lowercase			) > self.max_size:
										SCREAMING_SNAKE_CASE     						= self.max_size * 1.0 / max(lowercase   ,     lowercase			)
										SCREAMING_SNAKE_CASE     						= newh * scale
										SCREAMING_SNAKE_CASE     						= neww * scale
							SCREAMING_SNAKE_CASE     						= int(neww + 0.5			)
							SCREAMING_SNAKE_CASE     						= int(newh + 0.5			)

							if img.dtype == np.uinta:
										SCREAMING_SNAKE_CASE     						= Image.fromarray(lowercase			)
										SCREAMING_SNAKE_CASE     						= pil_image.resize((neww, newh)   ,     PILImageResampling.BILINEAR			)
										SCREAMING_SNAKE_CASE     						= np.asarray(lowercase			)
							else:
										SCREAMING_SNAKE_CASE     						= img.permute(2   ,     0   ,     1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
										SCREAMING_SNAKE_CASE     						= nn.functional.interpolate(
										    lowercase   ,     (newh, neww)   ,     mode=self.interp_method   ,     align_corners=lowercase			).squeeze(0			)
							img_augs.append(lowercase			)

				return img_augs





class     lowerCamelCase      :







	def __init__(     self      :Any   ,     lowercase      :Optional[Any]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,     cfg.INPUT.MAX_SIZE_TEST			)
				SCREAMING_SNAKE_CASE     						= cfg.INPUT.FORMAT
				SCREAMING_SNAKE_CASE     						= cfg.SIZE_DIVISIBILITY
				SCREAMING_SNAKE_CASE     						= cfg.PAD_VALUE
				SCREAMING_SNAKE_CASE     						= cfg.INPUT.MAX_SIZE_TEST
				SCREAMING_SNAKE_CASE     						= cfg.MODEL.DEVICE
				SCREAMING_SNAKE_CASE     						= torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)   ,     1   ,     1			)
				SCREAMING_SNAKE_CASE     						= torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)   ,     1   ,     1			)
				SCREAMING_SNAKE_CASE     						= lambda lowercase			: (x - self.pixel_mean) / self.pixel_std







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= tuple(max(lowercase			) for s in zip(*[img.shape for img in images]			)			)
				SCREAMING_SNAKE_CASE     						= [im.shape[-2:] for im in images]
				SCREAMING_SNAKE_CASE     						= [
				    nn.functional.pad(
				        lowercase   ,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]   ,     value=self.pad_value   ,     )
				    for size, im in zip(lowercase   ,     lowercase			)
				]

				return torch.stack(lowercase			), torch.tensor(lowercase			)







	def __call__(     self      :Dict   ,     lowercase      :str   ,     lowercase      :List[Any]=False			)		->      int:







				"""simple docstring"""






				with torch.no_grad():
							if not isinstance(lowercase   ,     lowercase			):
										SCREAMING_SNAKE_CASE     						= [images]
							if single_image:
										assert len(lowercase			) == 1
							for i in range(len(lowercase			)			):
										if isinstance(images[i]   ,     torch.Tensor			):
													images.insert(lowercase   ,     images.pop(lowercase			).to(self.device			).float()			)
										elif not isinstance(images[i]   ,     torch.Tensor			):
													images.insert(
													    lowercase   ,     torch.as_tensor(img_tensorize(images.pop(lowercase			)   ,     input_format=self.input_format			)			)
													    .to(self.device			)
													    .float()   ,     )
            # resize smallest edge
							SCREAMING_SNAKE_CASE     						= torch.tensor([im.shape[:2] for im in images]			)
							SCREAMING_SNAKE_CASE     						= self.aug(lowercase			)
							# transpose images and convert to torch tensors
							# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
							# now normalize before pad to avoid useless arithmetic
							SCREAMING_SNAKE_CASE     						= [self.normalizer(lowercase			) for x in images]
							# now pad them to do the following operations
							SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= self.pad(lowercase			)
							# Normalize

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							SCREAMING_SNAKE_CASE     						= torch.true_divide(lowercase   ,     lowercase			)
							if single_image:
										return images[0], sizes[0], scales_yx[0]
							else:
										return images, sizes, scales_yx



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




			'''simple docstring'''


			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes





def    a      ( a		,						a	)     ->Any:




			'''simple docstring'''


			assert torch.isfinite(a	).all(), "Box tensor contains infinite or NaN!"
			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= box_size
			tensor[:, 0].clamp_(min=0		,						max=a	)
			tensor[:, 1].clamp_(min=0		,						max=a	)
			tensor[:, 2].clamp_(min=0		,						max=a	)
			tensor[:, 3].clamp_(min=0		,						max=a	)





class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE = ""      ,     SCREAMING_SNAKE_CASE = False      )  ->					None:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{}

				# A node will be a leaf if the tree contains its word
				SCREAMING_SNAKE_CASE_								=						is_leaf

				SCREAMING_SNAKE_CASE_								=						prefix





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					tuple[str, str, str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for q, w in zip(self.prefix      ,     SCREAMING_SNAKE_CASE      ):
							if q != w:
										break

							x += 1

				return self.prefix[:x], self.prefix[x:], word[x:]





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				for word in words:
							self.insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if self.prefix == word:
							SCREAMING_SNAKE_CASE_								=						True

				# Case 2: The node has no edges that have a prefix to the word
				# Solution: We create an edge from the current node to a new one
				# containing the word
				elif word[0] not in self.nodes:
							SCREAMING_SNAKE_CASE_								=						RadixNode(prefix=SCREAMING_SNAKE_CASE      ,     is_leaf=SCREAMING_SNAKE_CASE      )

				else:
							SCREAMING_SNAKE_CASE_								=						self.nodes[word[0]]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )

							# Case 3: The node prefix is equal to the matching
							# Solution: We insert remaining word on the next node
							if remaining_prefix == "":
										self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )

							# Case 4: The word is greater equal to the matching
							# Solution: Create a node in between both nodes, change
							# prefixes and add the new node for the remaining word
							else:
										SCREAMING_SNAKE_CASE_								=						remaining_prefix

										SCREAMING_SNAKE_CASE_								=						self.nodes[matching_string[0]]
										SCREAMING_SNAKE_CASE_								=						RadixNode(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )
										SCREAMING_SNAKE_CASE_								=						aux_node

										if remaining_word == "":
													SCREAMING_SNAKE_CASE_								=						True
										else:
													self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# This applies when the word and the prefix are equal
							elif remaining_word == "":
										return incoming_node.is_leaf
							# We have word remaining so we check the next node
							else:
										return incoming_node.find(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.nodes.get(word[0]      ,     SCREAMING_SNAKE_CASE      )
				if not incoming_node:
							return False
				else:
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						incoming_node.match(
							    SCREAMING_SNAKE_CASE      )
							# If there is remaining prefix, the word can't be on the tree
							if remaining_prefix != "":
										return False
							# We have word remaining so we check the next node
							elif remaining_word != "":
										return incoming_node.delete(SCREAMING_SNAKE_CASE      )
							else:
										# If it is not a leaf, we don't have to delete
										if not incoming_node.is_leaf:
													return False
										else:
													# We delete the nodes if no edges go from it
													if len(incoming_node.nodes      ) == 0:
																del self.nodes[word[0]]
																# We merge the current node with its only child
																if len(self.nodes      ) == 1 and not self.is_leaf:
																			SCREAMING_SNAKE_CASE_								=						list(self.nodes.values()      )[0]
																			SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																			self.prefix += merging_node.prefix
																			SCREAMING_SNAKE_CASE_								=						merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
													elif len(incoming_node.nodes      ) > 1:
																SCREAMING_SNAKE_CASE_								=						False
													# If there is 1 edge, we merge it with its child
													else:
																SCREAMING_SNAKE_CASE_								=						list(incoming_node.nodes.values()      )[0]
																SCREAMING_SNAKE_CASE_								=						merging_node.is_leaf
																incoming_node.prefix += merging_node.prefix
																SCREAMING_SNAKE_CASE_								=						merging_node.nodes

													return True





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					None:




				"""simple docstring"""



				if self.prefix != "":
							print('-' * height      ,     self.prefix      ,     '  (leaf)' if self.is_leaf else ''      )

				for value in self.nodes.values():
							value.print_tree(height + 1      )




def lowercase    (  )			->							bool:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						'banana bananas bandana band apple all beast'.split()
			SCREAMING_SNAKE_CASE_								=						RadixNode()
			root.insert_many(SCREAMING_SNAKE_CASE )

			assert all(root.find(SCREAMING_SNAKE_CASE ) for word in words )
			assert not root.find('bandanas' )
			assert not root.find('apps' )
			root.delete('all' )
			assert not root.find('all' )
			root.delete('banana' )
			assert not root.find('banana' )
			assert root.find('bananas' )

			return True

def lowercase    (  )			->							None:


			'''simple docstring'''







			assert test_trie()

def lowercase    (  )			->							None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						RadixNode()
			SCREAMING_SNAKE_CASE_								=						'banana bananas bandanas bandana band apple all beast'.split()
			root.insert_many(SCREAMING_SNAKE_CASE )

			print('Words:'       ,    SCREAMING_SNAKE_CASE )
			print('Tree:' )
			root.print_tree()


if __name__ == "__main__":
				main()
