





"""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        lowercase__ :

			"""simple docstring"""







			def __init__(			self	,      _A	,      _A=sys.maxsize						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = """bilinear"""
									UpperCamelCase							:		Dict							      = max_size
									UpperCamelCase							:		List[str]							      = short_edge_length







			def __call__(			self	,      _A						):






									'''simple docstring'''







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

															if img.dtype == np.uinta:
																					UpperCamelCase							:		int							      = Image.fromarray(_lowerCAmelCase						)
																					UpperCamelCase							:		Union[str, Any]							      = pil_image.resize((neww, newh)	,      PILImageResampling.BILINEAR						)
																					UpperCamelCase							:		Tuple							      = np.asarray(_lowerCAmelCase						)
															else:
																					UpperCamelCase							:		List[Any]							      = img.permute(2	,      0	,      1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																					UpperCamelCase							:		Union[str, Any]							      = nn.functional.interpolate(
																					    _lowerCAmelCase	,      (newh, neww)	,      mode=self.interp_method	,      align_corners=_lowerCAmelCase						).squeeze(0						)
															img_augs.append(_lowerCAmelCase						)

									return img_augs






class        lowercase__ :

			"""simple docstring"""







			def __init__(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		Optional[Any]							      = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]	,      cfg.INPUT.MAX_SIZE_TEST						)
									UpperCamelCase							:		Optional[Any]							      = cfg.INPUT.FORMAT
									UpperCamelCase							:		Optional[Any]							      = cfg.SIZE_DIVISIBILITY
									UpperCamelCase							:		Any							      = cfg.PAD_VALUE
									UpperCamelCase							:		Union[str, Any]							      = cfg.INPUT.MAX_SIZE_TEST
									UpperCamelCase							:		List[str]							      = cfg.MODEL.DEVICE
									UpperCamelCase							:		Tuple							      = torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)	,      1	,      1						)
									UpperCamelCase							:		Any							      = torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)	,      1	,      1						)
									UpperCamelCase							:		List[str]							      = lambda _A						: (x - self.pixel_mean) / self.pixel_std







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = tuple(max(_lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
									UpperCamelCase							:		List[Any]							      = [im.shape[-2:] for im in images]
									UpperCamelCase							:		Dict							      = [
									    nn.functional.pad(
									        _lowerCAmelCase	,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,      value=self.pad_value	,      )
									    for size, im in zip(_lowerCAmelCase	,      _lowerCAmelCase						)
									]

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







			def __call__(			self	,      _A	,      _A=False						):






									'''simple docstring'''







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

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


def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
						boxes[:, 0::2] *= scale_yx[:, 1]
						boxes[:, 1::2] *= scale_yx[:, 0]
						return boxes

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










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		Tuple							      = 0
						for plain_chr in plain_text:
												UpperCamelCase							:		List[str]							      = (
												    ord(SCREAMING_SNAKE_CASE    ) + (hash_value << 6) + (hash_value << 16) - hash_value
												)
						return hash_value



