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      _A (     lowercase_						,			lowercase_						,			unittest.TestCase				):
  SCREAMING_SNAKE_CASE_						:						str    =IFImgaImgSuperResolutionPipeline
  SCREAMING_SNAKE_CASE_						:						Any    =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
  SCREAMING_SNAKE_CASE_						:						Any    =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}				)
  SCREAMING_SNAKE_CASE_						:						List[Any]    =PipelineTesterMixin.required_optional_params - {"latents"}






  def 		_a       (self						)		->   str:





       '''simple docstring'''





       return self._get_superresolution_dummy_components()






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_=0						)		->   List[Any]:





       '''simple docstring'''





       if str(_lowercase						).startswith('''mps'''						):
            UpperCamelCase__					    =   torch.manual_seed(_lowercase						)
       else:
            UpperCamelCase__					    =   torch.Generator(device=_lowercase						).manual_seed(_lowercase						)

       UpperCamelCase__					    =   floats_tensor((1, 3, 32, 32) ,					rng=random.Random(_lowercase						)						).to(_lowercase						)
       UpperCamelCase__					    =   floats_tensor((1, 3, 16, 16) ,					rng=random.Random(_lowercase						)						).to(_lowercase						)

       UpperCamelCase__					    =   {
           '''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						)		->   List[Any]:





       '''simple docstring'''





       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)






  def 		_a       (self						)		->   List[str]:





       '''simple docstring'''





       self._test_save_load_optional_components()






  @unittest.skipIf(torch_device != '''cuda''' ,					reason='''float16 requires CUDA'''						)
  def 		_a       (self						)		->   Union[str, Any]:





       '''simple docstring'''





       super().test_save_load_floataa(expected_max_diff=1E-1						)






  def 		_a       (self						)		->   str:





       '''simple docstring'''





       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)






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





       '''simple docstring'''





       self._test_save_load_local()






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





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






'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





