




"""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	:	Any   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	str=sys.maxsize		):
       _A        =       'bilinear'
       _A        =       max_size
       _A        =       short_edge_length



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

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

       return img_augs





class     lowercase_	:



 '''simple docstring'''

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

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

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






def 						_snake_case					( _snake_case							: int    ,  _snake_case							: List[str]							)   -> Any:






      '''simple docstring'''


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







def 						_snake_case					( _snake_case							: Optional[Any]    ,  _snake_case							: Tuple[int, int]							)   -> Union[str, Any]:






      '''simple docstring'''


      assert torch.isfinite(_snake_case							).all(), "Box tensor contains infinite or NaN!"
      _A       ,   _A        =       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							)







"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (IPNDMScheduler,)
 UpperCAmelCase   :					Optional[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	List[Any]		):
       _A        =       {'num_train_timesteps': 1_000}
       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]=0   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)
             # copy over dummy past residuals
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   new_scheduler.set_timesteps(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Any		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

             _A        =       scheduler.timesteps[5]
             _A        =       scheduler.timesteps[6]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_mean.item() - 2_540_529		) < 10

