



"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler
class    a		(					unittest.TestCase    ):
  # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
  UpperCamelCase		:			Tuple         =   (ScoreSdeVeScheduler,)
  UpperCamelCase		:			Optional[int]         =   ()



  @property
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =4
     SCREAMING_SNAKE_CASE_:	List[str]            =3
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =8
     SCREAMING_SNAKE_CASE_:	Any            =8

     SCREAMING_SNAKE_CASE_:	str            =torch.rand((batch_size, num_channels, height, width)						)

     return sample



  @property
  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[Any]            =4
     SCREAMING_SNAKE_CASE_:	Dict            =3
     SCREAMING_SNAKE_CASE_:	Any            =8
     SCREAMING_SNAKE_CASE_:	Any            =8

     SCREAMING_SNAKE_CASE_:	Optional[int]            =batch_size * num_channels * height * width
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =torch.arange(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            =sample.reshape(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =sample / num_elems
     SCREAMING_SNAKE_CASE_:	List[str]            =sample.permute(3    ,						0    ,						1    ,						2						)

     return sample



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     def model(lowerCAmelCase	:   Dict    ,						lowerCAmelCase	:   Union[str, Any]    ,						*lowerCAmelCase	:   str						):
        return sample * t / (t + 1)

     return model



  def 		lowerCamelCase__    (  self	:   Union[str, Any]    ,						**lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            ={
         "num_train_timesteps": 2000,
         "snr": 0.1_5,
         "sigma_min": 0.0_1,
         "sigma_max": 1348,
         "sampling_eps": 1E-5,
     }

     config.update(**lowerCAmelCase						)
     return config



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   Optional[Any]=0    ,						**lowerCAmelCase	:   Tuple						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =dict(self.forward_default_kwargs						)

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_:	List[Any]            =self.dummy_sample
        SCREAMING_SNAKE_CASE_:	Any            =0.1 * sample

        SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.get_scheduler_config(**lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =scheduler_class(**lowerCAmelCase						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	int            =scheduler_class.from_pretrained(lowerCAmelCase						)

        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =scheduler.step_pred(
            lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:	Any            =new_scheduler.step_pred(
            lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample

        assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

        SCREAMING_SNAKE_CASE_:	Dict            =scheduler.step_correct(lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:	List[Any]            =new_scheduler.step_correct(
            lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample

        assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler correction are not identical"



  def 		lowerCamelCase__    (  self	:   List[str]    ,						lowerCAmelCase	:   Optional[Any]=0    ,						**lowerCAmelCase	:   Dict						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =dict(self.forward_default_kwargs						)
     kwargs.update(lowerCAmelCase						)

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =self.dummy_sample
        SCREAMING_SNAKE_CASE_:	Dict            =0.1 * sample

        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_:	Optional[int]            =scheduler_class(**lowerCAmelCase						)

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	Any            =scheduler_class.from_pretrained(lowerCAmelCase						)

        SCREAMING_SNAKE_CASE_:	Tuple            =scheduler.step_pred(
            lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =new_scheduler.step_pred(
            lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample

        assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler outputs are not identical"

        SCREAMING_SNAKE_CASE_:	Any            =scheduler.step_correct(lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:	int            =new_scheduler.step_correct(
            lowerCAmelCase    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample

        assert torch.sum(torch.abs(output - new_output						)						) < 1E-5, "Scheduler correction are not identical"



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   Tuple:


     '''simple docstring'''




     for timesteps in [10, 100, 1000]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   int:


     '''simple docstring'''




     for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]    ,						[1, 100, 1000]						):
        self.check_over_configs(sigma_min=lowerCAmelCase    ,						sigma_max=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Optional[int]						)       ->   List[Any]:


     '''simple docstring'''




     for t in [0.1, 0.5, 0.7_5]:
        self.check_over_forward(time_step=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =dict(self.forward_default_kwargs						)

     SCREAMING_SNAKE_CASE_:	str            =self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE_:	Any            =self.get_scheduler_config()
     SCREAMING_SNAKE_CASE_:	Optional[int]            =scheduler_class(**lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =3

     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.dummy_model()
     SCREAMING_SNAKE_CASE_:	Tuple            =self.dummy_sample_deter

     scheduler.set_sigmas(lowerCAmelCase						)
     scheduler.set_timesteps(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	List[str]            =torch.manual_seed(0						)

     for i, t in enumerate(scheduler.timesteps						):
        SCREAMING_SNAKE_CASE_:	Dict            =scheduler.sigmas[i]

        for _ in range(scheduler.config.correct_steps						):
           with torch.no_grad():
              SCREAMING_SNAKE_CASE_:	Optional[int]            =model(lowerCAmelCase    ,						lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	List[Any]            =scheduler.step_correct(lowerCAmelCase    ,						lowerCAmelCase    ,						generator=lowerCAmelCase    ,						**lowerCAmelCase						).prev_sample

        with torch.no_grad():
           SCREAMING_SNAKE_CASE_:	Any            =model(lowerCAmelCase    ,						lowerCAmelCase						)

        SCREAMING_SNAKE_CASE_:	List[Any]            =scheduler.step_pred(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						generator=lowerCAmelCase    ,						**lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	Tuple            =output.prev_sample, output.prev_sample_mean

     SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.sum(torch.abs(lowerCAmelCase						)						)
     SCREAMING_SNAKE_CASE_:	List[str]            =torch.mean(torch.abs(lowerCAmelCase						)						)

     assert np.isclose(result_sum.item()    ,						143_7275_8528.0						)
     assert np.isclose(result_mean.item()    ,						1871_4530.0						)





  def 		lowerCamelCase__    (  self	:   Any						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =dict(self.forward_default_kwargs						)

     SCREAMING_SNAKE_CASE_:	str            =kwargs.pop("""num_inference_steps"""    ,						lowerCAmelCase						)

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_:	Dict            =self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_:	List[Any]            =scheduler_class(**lowerCAmelCase						)

        SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.dummy_sample
        SCREAMING_SNAKE_CASE_:	str            =0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCAmelCase    ,						"""set_timesteps"""						):
           scheduler.set_timesteps(lowerCAmelCase						)
        elif num_inference_steps is not None and not hasattr(lowerCAmelCase    ,						"""set_timesteps"""						):
           SCREAMING_SNAKE_CASE_:	List[str]            =num_inference_steps

        SCREAMING_SNAKE_CASE_:	str            =scheduler.step_pred(lowerCAmelCase    ,						0    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample
        SCREAMING_SNAKE_CASE_:	str            =scheduler.step_pred(lowerCAmelCase    ,						1    ,						lowerCAmelCase    ,						generator=torch.manual_seed(0						)    ,						**lowerCAmelCase						).prev_sample

        self.assertEqual(output_a.shape    ,						sample.shape						)
        self.assertEqual(output_a.shape    ,						output_a.shape						)








"""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	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




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

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

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =tuple(max(lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[
         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	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]=False						)       ->   Optional[Any]:


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[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
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =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
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

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







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)



