
'''simple docstring'''

def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       str  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       int  ,		)   ->    tuple[list[list[int]], list[list[str]]]:







			'''simple docstring'''



			lowerCAmelCase__									=							list(UpperCamelCase_ )
			lowerCAmelCase__									=							list(UpperCamelCase_ )
			lowerCAmelCase__									=							len(UpperCamelCase_ )
			lowerCAmelCase__									=							len(UpperCamelCase_ )

			lowerCAmelCase__									=							[
			    [0 for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
			]
			lowerCAmelCase__									=							[
			    ["0" for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
			]

			for i in range(1  ,		len_source_seq + 1 ):
						lowerCAmelCase__									=							i * delete_cost
						lowerCAmelCase__									=							F"""D{source_seq[i - 1]:c}"""

			for i in range(1  ,		len_destination_seq + 1 ):
						lowerCAmelCase__									=							i * insert_cost
						lowerCAmelCase__									=							F"""I{destination_seq[i - 1]:c}"""

			for i in range(1  ,		len_source_seq + 1 ):
						for j in range(1  ,		len_destination_seq + 1 ):
									if source_seq[i - 1] == destination_seq[j - 1]:
												lowerCAmelCase__									=							costs[i - 1][j - 1] + copy_cost
												lowerCAmelCase__									=							F"""C{source_seq[i - 1]:c}"""
									else:
												lowerCAmelCase__									=							costs[i - 1][j - 1] + replace_cost
												lowerCAmelCase__									=							F"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1] )

									if costs[i - 1][j] + delete_cost < costs[i][j]:
												lowerCAmelCase__									=							costs[i - 1][j] + delete_cost
												lowerCAmelCase__									=							F"""D{source_seq[i - 1]:c}"""

									if costs[i][j - 1] + insert_cost < costs[i][j]:
												lowerCAmelCase__									=							costs[i][j - 1] + insert_cost
												lowerCAmelCase__									=							F"""I{destination_seq[j - 1]:c}"""

			return costs, ops







def 					A       ( UpperCamelCase_   :       list[list[str]]  ,		UpperCamelCase_   :       int  ,		UpperCamelCase_   :       int )   ->    list[str]:







			'''simple docstring'''



			if i == 0 and j == 0:
						return []
			else:
						if ops[i][j][0] == "C" or ops[i][j][0] == "R":
									lowerCAmelCase__									=							assemble_transformation(UpperCamelCase_  ,		i - 1  ,		j - 1 )
									seq.append(ops[i][j] )
									return seq
						elif ops[i][j][0] == "D":
									lowerCAmelCase__									=							assemble_transformation(UpperCamelCase_  ,		i - 1  ,		UpperCamelCase_ )
									seq.append(ops[i][j] )
									return seq
						else:
									lowerCAmelCase__									=							assemble_transformation(UpperCamelCase_  ,		UpperCamelCase_  ,		j - 1 )
									seq.append(ops[i][j] )
									return seq


if __name__ == "__main__":
		UpperCAmelCase__		,						UpperCAmelCase__				:	str					=						compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

		UpperCAmelCase__				:	str					=						len(operations)
		UpperCAmelCase__				:	Optional[int]					=						len(operations[0])
		UpperCAmelCase__				:	int					=						assemble_transformation(operations, m - 1, n - 1)

		UpperCAmelCase__				:	Union[str, Any]					=						list("Python")
		UpperCAmelCase__				:	List[str]					=						0
		UpperCAmelCase__				:	Optional[int]					=						0

		with open("min_cost.txt", "w") as file:
				for op in sequence:
						print("".join(string))

						if op[0] == "C":
								file.write("%-16s" % "Copy %c" % op[1])
								file.write("\t\t\t" + "".join(string))
								file.write("\r\n")

								cost -= 1
						elif op[0] == "R":
								UpperCAmelCase__				:	Any					=						op[2]

								file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
								file.write("\t\t" + "".join(string))
								file.write("\r\n")

								cost += 1
						elif op[0] == "D":
								string.pop(i)

								file.write("%-16s" % "Delete %c" % op[1])
								file.write("\t\t\t" + "".join(string))
								file.write("\r\n")

								cost += 2
						else:
								string.insert(i, op[1])

								file.write("%-16s" % "Insert %c" % op[1])
								file.write("\t\t\t" + "".join(string))
								file.write("\r\n")

								cost += 2

						i += 1

				print("".join(string))
				print("Cost: ", cost)

				file.write("\r\nMinimum cost: " + str(cost))







'''simple docstring'''

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 				A       :





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    Union[str, Any] ,							__magic_name__     :    Optional[Any]=sys.maxsize							):


				"""simple docstring"""

				lowerCAmelCase__									=							"bilinear"
				lowerCAmelCase__									=							max_size
				lowerCAmelCase__									=							short_edge_length







	def __call__(   self     :    List[str] ,							__magic_name__     :    int							):


				"""simple docstring"""

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

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

				return img_augs






class 				A       :





	def __init__(   self     :    str ,							__magic_name__     :    Union[str, Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[Any] ,							__magic_name__     :    Optional[int]							):


				"""simple docstring"""

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

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







	def __call__(   self     :    List[str] ,							__magic_name__     :    int ,							__magic_name__     :    Optional[int]=False							):


				"""simple docstring"""

				with torch.no_grad():
							if not isinstance(__magic_name__ ,							__magic_name__							):
										lowerCAmelCase__									=							[images]
							if single_image:
										assert len(__magic_name__							) == 1
							for i in range(len(__magic_name__							)							):
										if isinstance(images[i] ,							torch.Tensor							):
													images.insert(__magic_name__ ,							images.pop(__magic_name__							).to(self.device							).float()							)
										elif not isinstance(images[i] ,							torch.Tensor							):
													images.insert(
													    __magic_name__ ,							torch.as_tensor(img_tensorize(images.pop(__magic_name__							) ,							input_format=self.input_format							)							)
													    .to(self.device							)
													    .float() ,							)
            # resize smallest edge
							lowerCAmelCase__									=							torch.tensor([im.shape[:2] for im in images]							)
							lowerCAmelCase__									=							self.aug(__magic_name__							)
							# 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
							lowerCAmelCase__									=							[self.normalizer(__magic_name__							) for x in images]
							# now pad them to do the following operations
							lowerCAmelCase__		,lowerCAmelCase__									=							self.pad(__magic_name__							)
							# Normalize

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







def 					A       ( UpperCamelCase_   :       int  ,		UpperCamelCase_   :       Optional[Any] )   ->    List[str]:







			'''simple docstring'''



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







def 					A       ( UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Tuple[int, int] )   ->    Tuple:







			'''simple docstring'''



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





