



"""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  _UpperCAmelCase :

       def __init__(  self	: Any						,  lowercase_	: Optional[int]						,  lowercase_	: Union[str, Any]=sys.maxsize       ):
           snake_case_				:  List[str]       =			'''bilinear'''
           snake_case_				:  Optional[Any]       =			max_size
           snake_case_				:  Optional[Any]       =			short_edge_length






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

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

           return img_augs





class  _UpperCAmelCase :

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

       def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: List[Any]       ):
           snake_case_				:  List[Any]       =			tuple(max(lowercase_       ) for s in zip(*[img.shape for img in images]       )       )
           snake_case_				:  Union[str, Any]       =			[im.shape[-2:] for im in images]
           snake_case_				:  List[Any]       =			[
               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	: Any						,  lowercase_	: Optional[int]						,  lowercase_	: Tuple=False       ):
           with torch.no_grad():
               if not isinstance(lowercase_						,  lowercase_       ):
                   snake_case_				:  Any       =			[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
               snake_case_				:  str       =			torch.tensor([im.shape[:2] for im in images]       )
               snake_case_				:  List[Any]       =			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
               snake_case_				:  Any       =			[self.normalizer(lowercase_       ) for x in images]
               # now pad them to do the following operations
               snake_case_				:  Dict       =			self.pad(lowercase_       )
               # Normalize

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





def __lowercase    (							_a	,    _a					):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes






def __lowercase    (							_a	,    _a					):
    assert torch.isfinite(_a					).all(), "Box tensor contains infinite or NaN!"
    snake_case_				:  str       =			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					)










"""simple docstring"""

from statistics import mean, stdev




def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Optional[int]       =			min(_a					)
    snake_case_				:  str       =			max(_a					)
    # normalize data
    return [round((x - x_min) / (x_max - x_min)	,    _a					) for x in data]






def __lowercase    (							_a	,    _a = 3					):
    snake_case_				:  Any       =			mean(_a					)
    snake_case_				:  str       =			stdev(_a					)
    # standardize data
    return [round((x - mu) / (sigma)	,    _a					) for x in data]





