

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   _SCREAMING_SNAKE_CASE   (   __a     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					(DDPMParallelScheduler,)



 def 			snake_case__  (   self		:   List[Any]			,				**a__		:   List[Any]      ):
      __magic_name__						       =    {
          "num_train_timesteps": 1000,
          "beta_start": 0.0_001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
          "variance_type": "fixed_small",
          "clip_sample": True,
      }

      config.update(**_a      )
      return config



 def 			snake_case__  (   self		:   List[Any]      ):
      for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=_a      )



 def 			snake_case__  (   self		:   int      ):
      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,				[0.002, 0.02, 0.2, 2]      ):
           self.check_over_configs(beta_start=_a			,				beta_end=_a      )



 def 			snake_case__  (   self		:   Tuple      ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=_a      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_a      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_a      )



 def 			snake_case__  (   self		:   Dict      ):
      self.check_over_configs(thresholding=_a      )
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                self.check_over_configs(
                    thresholding=_a			,				prediction_type=_a			,				sample_max_value=_a			,				)



 def 			snake_case__  (   self		:   Tuple      ):
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
           self.check_over_configs(prediction_type=_a      )



 def 			snake_case__  (   self		:   Optional[Any]      ):
      for t in [0, 500, 999]:
           self.check_over_forward(time_step=_a      )



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.00_979      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1E-5



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    len(_a      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter
      __magic_name__						       =    self.dummy_sample_deter + 0.1
      __magic_name__						       =    self.dummy_sample_deter - 0.1

      __magic_name__						       =    samplea.shape[0]
      __magic_name__						       =    torch.stack([samplea, samplea, samplea]			,				dim=0      )
      __magic_name__						       =    torch.arange(_a      )[0:3, None].repeat(1			,				_a      )

      __magic_name__						       =    model(samples.flatten(0			,				1      )			,				timesteps.flatten(0			,				1      )      )
      __magic_name__						       =    scheduler.batch_step_no_noise(_a			,				timesteps.flatten(0			,				1      )			,				samples.flatten(0			,				1      )      )

      __magic_name__						       =    torch.sum(torch.abs(_a      )      )
      __magic_name__						       =    torch.mean(torch.abs(_a      )      )

      assert abs(result_sum.item() - 1153.1833      ) < 1E-2
      assert abs(result_mean.item() - 0.5_005      ) < 1E-3



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    len(_a      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter
      __magic_name__						       =    torch.manual_seed(0      )

      for t in reversed(range(_a      )      ):
           # 1. predict noise residual
           __magic_name__						       =    model(_a			,				_a      )

           # 2. predict previous mean of sample x_t-1
           __magic_name__						       =    scheduler.step(_a			,				_a			,				_a			,				generator=_a      ).prev_sample

           __magic_name__						       =    pred_prev_sample

      __magic_name__						       =    torch.sum(torch.abs(_a      )      )
      __magic_name__						       =    torch.mean(torch.abs(_a      )      )

      assert abs(result_sum.item() - 258.9606      ) < 1E-2
      assert abs(result_mean.item() - 0.3_372      ) < 1E-3



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config(prediction_type='''v_prediction'''      )
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    len(_a      )

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter
      __magic_name__						       =    torch.manual_seed(0      )

      for t in reversed(range(_a      )      ):
           # 1. predict noise residual
           __magic_name__						       =    model(_a			,				_a      )

           # 2. predict previous mean of sample x_t-1
           __magic_name__						       =    scheduler.step(_a			,				_a			,				_a			,				generator=_a      ).prev_sample

           __magic_name__						       =    pred_prev_sample

      __magic_name__						       =    torch.sum(torch.abs(_a      )      )
      __magic_name__						       =    torch.mean(torch.abs(_a      )      )

      assert abs(result_sum.item() - 202.0296      ) < 1E-2
      assert abs(result_mean.item() - 0.2_631      ) < 1E-3



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    [100, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_a      )

      __magic_name__						       =    scheduler.timesteps

      for i, timestep in enumerate(_a      ):
           if i == len(_a      ) - 1:
                __magic_name__						       =    -1
           else:
                __magic_name__						       =    timesteps[i + 1]

           __magic_name__						       =    scheduler.previous_timestep(_a      )
           __magic_name__						       =    prev_t.item()

           self.assertEqual(_a			,				_a      )



 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    [100, 87, 50, 51, 0]

      with self.assertRaises(_a			,				msg='''`custom_timesteps` must be in descending order.'''      ):
           scheduler.set_timesteps(timesteps=_a      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    [100, 87, 50, 1, 0]
      __magic_name__						       =    len(_a      )

      with self.assertRaises(_a			,				msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''      ):
           scheduler.set_timesteps(num_inference_steps=_a			,				timesteps=_a      )



 def 			snake_case__  (   self		:   Optional[int]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**_a      )

      __magic_name__						       =    [scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _a			,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''			,				):
           scheduler.set_timesteps(timesteps=_a      )









'''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   _SCREAMING_SNAKE_CASE   :



 def __init__(   self		:   Optional[Any]			,				a__		:   Any			,				a__		:   Tuple=sys.maxsize      ):
      __magic_name__						       =    '''bilinear'''
      __magic_name__						       =    max_size
      __magic_name__						       =    short_edge_length



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

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

      return img_augs






class   _SCREAMING_SNAKE_CASE   :



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



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

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



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

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




def 						UpperCamelCase	(			a		,						a			)				->     List[Any]:






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




def 						UpperCamelCase	(			a		,						a			)				->     Any:






     '''simple docstring'''
     assert torch.isfinite(a			).all(), "Box tensor contains infinite or NaN!"
     __magic_name__       ,  __magic_name__						       =    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			)






