


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		,     UpperCAmelCase		,     UpperCAmelCase=sys.maxsize       )     ->   str:
      _lowercase							      ='''bilinear'''
      _lowercase							      =max_size
      _lowercase							      =short_edge_length





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

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

      return img_augs







class       lowerCamelCase__   :





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





     def      __A					(self		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =tuple(max(UpperCAmelCase       ) for s in zip(*[img.shape for img in images]       )       )
      _lowercase							      =[im.shape[-2:] for im in images]
      _lowercase							      =[
          nn.functional.pad(
              UpperCAmelCase		,     [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,     value=self.pad_value		,     )
          for size, im in zip(UpperCAmelCase		,     UpperCAmelCase       )
      ]

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





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

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



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


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



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


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





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			1





     @register_to_config
     def __init__(self		,     UpperCAmelCase=2_0_0_0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=2_0		,     UpperCAmelCase=1e-3       )     ->   List[str]:
      _lowercase							      =None
      _lowercase							      =None
      _lowercase							      =None





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase = None       )     ->   str:
      _lowercase							      =torch.linspace(1		,     self.config.sampling_eps		,     UpperCAmelCase		,     device=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None       )     ->   Optional[int]:
      if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

      # TODO(Patrick) better comments + non-PyTorch
      # postprocess model score
      _lowercase							      =(
          -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
      )
      _lowercase							      =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
      _lowercase							      =std.flatten()
      while len(std.shape       ) < len(score.shape       ):
       _lowercase							      =std.unsqueeze(-1       )
      _lowercase							      =-score / std

      # compute
      _lowercase							      =-1.0 / len(self.timesteps       )

      _lowercase							      =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
      _lowercase							      =beta_t.flatten()
      while len(beta_t.shape       ) < len(x.shape       ):
       _lowercase							      =beta_t.unsqueeze(-1       )
      _lowercase							      =-0.5 * beta_t * x

      _lowercase							      =torch.sqrt(UpperCAmelCase       )
      _lowercase							      =drift - diffusion**2 * score
      _lowercase							      =x + drift * dt

      # add noise
      _lowercase							      =randn_tensor(x.shape		,     layout=x.layout		,     generator=UpperCAmelCase		,     device=x.device		,     dtype=x.dtype       )
      _lowercase							      =x_mean + diffusion * math.sqrt(-dt       ) * noise

      return x, x_mean





     def __len__(self       )     ->   str:
      return self.config.num_train_timesteps

