import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any		=(UnCLIPScheduler,)



     def 			a__  (					self				,      **lowerCAmelCase__       ):
            _A=	{
                'num_train_timesteps': 1000,
                'variance_type': 'fixed_small_log',
                'clip_sample': True,
                'clip_sample_range': 1.0,
                'prediction_type': 'epsilon',
            }

            config.update(**lowerCAmelCase__       )
            return config



     def 			a__  (					self       ):
            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for variance in ["fixed_small_log", "learned_range"]:
                   self.check_over_configs(variance_type=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for clip_sample_range in [1, 5, 10, 20]:
                   self.check_over_configs(clip_sample_range=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for prediction_type in ["epsilon", "sample"]:
                   self.check_over_configs(prediction_type=lowerCAmelCase__       )



     def 			a__  (					self       ):
            for time_step in [0, 500, 999]:
                   for prev_timestep in [None, 5, 100, 250, 500, 750]:
                          if prev_timestep is not None and prev_timestep >= time_step:
                                 continue

                          self.check_over_forward(time_step=lowerCAmelCase__				,      prev_timestep=lowerCAmelCase__       )



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(variance_type='fixed_small_log'       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000E-10       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0549625       )       ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9994987       )       ) < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config(variance_type='learned_range'       )
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	0.5

            assert scheduler._get_variance(1				,      predicted_variance=lowerCAmelCase__       ) - -10.1712790 < 1E-5
            assert scheduler._get_variance(487				,      predicted_variance=lowerCAmelCase__       ) - -5.7998052 < 1E-5
            assert scheduler._get_variance(999				,      predicted_variance=lowerCAmelCase__       ) - -0.0010011 < 1E-5



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            _A=	scheduler.timesteps

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

            for i, t in enumerate(lowerCAmelCase__       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

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

                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 252.2682495       ) < 1E-2
            assert abs(result_mean.item() - 0.3284743       ) < 1E-3



     def 			a__  (					self       ):
            _A=	self.scheduler_classes[0]
            _A=	self.get_scheduler_config()
            _A=	scheduler_class(**lowerCAmelCase__       )

            scheduler.set_timesteps(25       )

            _A=	scheduler.timesteps

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

            for i, t in enumerate(lowerCAmelCase__       ):
                   # 1. predict noise residual
                   _A=	model(lowerCAmelCase__				,      lowerCAmelCase__       )

                   if i + 1 == timesteps.shape[0]:
                          _A=	None
                   else:
                          _A=	timesteps[i + 1]

                   # 2. predict previous mean of sample x_t-1
                   _A=	scheduler.step(
                       lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      prev_timestep=lowerCAmelCase__				,      generator=lowerCAmelCase__       ).prev_sample

                   _A=	pred_prev_sample

            _A=	torch.sum(torch.abs(lowerCAmelCase__       )       )
            _A=	torch.mean(torch.abs(lowerCAmelCase__       )       )

            assert abs(result_sum.item() - 258.2044983       ) < 1E-2
            assert abs(result_mean.item() - 0.3362038       ) < 1E-3



     def 			a__  (					self       ):
            pass





     def 			a__  (					self       ):
            pass
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch





@dataclass
class      lowerCAmelCase						:
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     torch.Tensor  # [batch_size x 3]
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     int
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     float
     _SCREAMING_SNAKE_CASE     :     Tuple[int]



     def 			a__  (					self       ):
            assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
            assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
            assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.width, self.height]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            return torch.from_numpy(np.array([self.x_fov, self.y_fov]				,      dtype=np.floataa       )       )



     def 			a__  (					self       ):
            _A=	torch.arange(self.height * self.width       )
            _A=	torch.stack(
                [
                    pixel_indices % self.width,
                    torch.div(lowerCAmelCase__				,      self.width				,      rounding_mode='trunc'       ),
                ]				,      axis=1				,      )
            return coords



     @property
     def 			a__  (					self       ):
            _A,			*_A=	self.shape
            _A=	int(np.prod(lowerCAmelCase__       )       )

            _A=	self.get_image_coords()
            _A=	torch.broadcast_to(coords.unsqueeze(0       )				,      [batch_size * inner_batch_size, *coords.shape]       )
            _A=	self.get_camera_rays(lowerCAmelCase__       )

            _A=	rays.view(lowerCAmelCase__				,      inner_batch_size * self.height * self.width				,      2				,      3       )

            return rays



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A,			*_A,			_A=	coords.shape
            assert n_coords == 2
            assert batch_size == self.origin.shape[0]

            _A=	coords.view(lowerCAmelCase__				,      -1				,      2       )

            _A=	self.resolution()
            _A=	self.fov()

            _A=	(flat.float() / (res - 1)) * 2 - 1
            _A=	fracs * torch.tan(fov / 2       )

            _A=	fracs.view(lowerCAmelCase__				,      -1				,      2       )
            _A=	(
                self.z.view(lowerCAmelCase__				,      1				,      3       )
                + self.x.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, :1]
                + self.y.view(lowerCAmelCase__				,      1				,      3       ) * fracs[:, :, 1:]
            )
            _A=	directions / directions.norm(dim=-1				,      keepdim=lowerCAmelCase__       )
            _A=	torch.stack(
                [
                    torch.broadcast_to(self.origin.view(lowerCAmelCase__				,      1				,      3       )				,      [batch_size, directions.shape[1], 3]       ),
                    directions,
                ]				,      dim=2				,      )
            return rays.view(lowerCAmelCase__				,      *lowerCAmelCase__				,      2				,      3       )





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
            assert width * self.height == height * self.width, "The aspect ratio should not change."
            return DifferentiableProjectiveCamera(
                origin=self.origin				,      x=self.x				,      y=self.y				,      z=self.z				,      width=lowerCAmelCase__				,      height=lowerCAmelCase__				,      x_fov=self.x_fov				,      y_fov=self.y_fov				,      )


def 							UpperCamelCase				(    lowerCAmelCase_   )		->						DifferentiableProjectiveCamera:




       '''simple docstring'''






       _A=	[]
       _A=	[]
       _A=	[]
       _A=	[]
       for theta in np.linspace(0       ,					2 * np.pi       ,					num=20   ):
              _A=	np.array([np.sin(lowerCAmelCase_   ), np.cos(lowerCAmelCase_   ), -0.5]   )
              z /= np.sqrt(np.sum(z**2   )   )
              _A=	-z * 4
              _A=	np.array([np.cos(lowerCAmelCase_   ), -np.sin(lowerCAmelCase_   ), 0.0]   )
              _A=	np.cross(lowerCAmelCase_       ,					lowerCAmelCase_   )
              origins.append(lowerCAmelCase_   )
              xs.append(lowerCAmelCase_   )
              ys.append(lowerCAmelCase_   )
              zs.append(lowerCAmelCase_   )
       return DifferentiableProjectiveCamera(
           origin=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					x=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					y=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					z=torch.from_numpy(np.stack(lowerCAmelCase_       ,					axis=0   )   ).float()       ,					width=lowerCAmelCase_       ,					height=lowerCAmelCase_       ,					x_fov=0.7       ,					y_fov=0.7       ,					shape=(1, len(lowerCAmelCase_   ))       ,					)