import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class      lowerCAmelCase						(					__snake_case			,		__snake_case			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     List[Any]		=IFImgaImgSuperResolutionPipeline
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}			)
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		=PipelineTesterMixin.required_optional_params - {'latents'}



     def 			a__  (					self       ):
            return self._get_superresolution_dummy_components()



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0       ):




            if str(A_       ).startswith('mps'       ):
                   _A=	torch.manual_seed(A_       )
            else:
                   _A=	torch.Generator(device=A_       ).manual_seed(A_       )

            _A=	floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
            _A=	floats_tensor((1, 3, 16, 16)				,      rng=random.Random(A_       )       ).to(A_       )

            _A=	{
                "prompt": "A painting of a squirrel eating a burger",
                "image": image,
                "original_image": original_image,
                "generator": generator,
                "num_inference_steps": 2,
                "output_type": "numpy",
            }

            return inputs



     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
     def 			a__  (					self       ):
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3       )



     def 			a__  (					self       ):
            self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
     def 			a__  (					self       ):
            super().test_save_load_floataa(expected_max_diff=1E-1       )



     def 			a__  (					self       ):
            self._test_attention_slicing_forward_pass(expected_max_diff=1E-2       )



     def 			a__  (					self       ):
            self._test_save_load_local()





     def 			a__  (					self       ):
            self._test_inference_batch_single_identical(
                expected_max_diff=1E-2				,      )






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_   ))       ,					)