

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[Any]           =     [tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]  )
class  _A		(  __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      int       =  StableDiffusionLatentUpscalePipeline
  SCREAMING_SNAKE_CASE    :      Any       =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''height''',
      '''width''',
      '''cross_attention_kwargs''',
      '''negative_prompt_embeds''',
      '''prompt_embeds''',
  }
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
  SCREAMING_SNAKE_CASE    :      Tuple       =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  SCREAMING_SNAKE_CASE    :      str       =  frozenset(
      [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  frozenset([])

  SCREAMING_SNAKE_CASE    :      Optional[Any]       =  True

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     1
        SCREAMING_SNAKE_CASE_				:  Dict           =     4
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     (16, 16)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     floats_tensor((batch_size, num_channels) + sizes ,					rng=random.Random(0					)					).to(_SCREAMING_SNAKE_CASE					)
        return image

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     UNetaDConditionModel(
            act_fn='gelu' ,					attention_head_dim=8 ,					norm_num_groups=_SCREAMING_SNAKE_CASE ,					block_out_channels=[32, 32, 64, 64] ,					time_cond_proj_dim=160 ,					conv_in_kernel=1 ,					conv_out_kernel=1 ,					cross_attention_dim=32 ,					down_block_types=(
                'KDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
                'KCrossAttnDownBlock2D',
            ) ,					in_channels=8 ,					mid_block_type=_SCREAMING_SNAKE_CASE ,					only_cross_attention=_SCREAMING_SNAKE_CASE ,					out_channels=5 ,					resnet_time_scale_shift='scale_shift' ,					time_embedding_type='fourier' ,					timestep_post_act='gelu' ,					up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') ,					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     AutoencoderKL(
            block_out_channels=[32, 32, 64, 64] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=[
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
                'DownEncoderBlock2D',
            ] ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'] ,					latent_channels=4 ,					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     EulerDiscreteScheduler(prediction_type='sample'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     CLIPTextConfig(
            bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=32 ,					intermediate_size=37 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1000 ,					hidden_act='quick_gelu' ,					projection_dim=512 ,					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     CLIPTextModel(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     {
            'unet': model.eval(),
            'vae': vae.eval(),
            'scheduler': scheduler,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
        }

        return components

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        if str(_SCREAMING_SNAKE_CASE					).startswith('mps'					):
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        else:
              SCREAMING_SNAKE_CASE_				:  Dict           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     {
            'prompt': 'A painting of a squirrel eating a burger',
            'image': self.dummy_image.cpu(),
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  str           =     self.pipeline_class(**_SCREAMING_SNAKE_CASE					)
        pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  List[str]           =     image[0, -3:, -3:, -1]

        self.assertEqual(image.shape ,					(1, 256, 256, 3)					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     np.array(
            [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.abs(image_slice.flatten() - expected_slice					).max()
        self.assertLessEqual(_SCREAMING_SNAKE_CASE ,					1e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_attention_slicing_forward_pass(expected_max_diff=7e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_cpu_offload_forward_pass(expected_max_diff=3e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_inference_batch_single_identical(expected_max_diff=7e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_save_load_local(expected_max_difference=3e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_save_load_optional_components(expected_max_difference=3e-3					)



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     [
            'DDIMScheduler',
            'DDPMScheduler',
            'PNDMScheduler',
            'HeunDiscreteScheduler',
            'EulerAncestralDiscreteScheduler',
            'KDPM2DiscreteScheduler',
            'KDPM2AncestralDiscreteScheduler',
            'DPMSolverSDEScheduler',
        ]
        SCREAMING_SNAKE_CASE_				:  int           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  Any           =     self.pipeline_class(**_SCREAMING_SNAKE_CASE					)

        # make sure that PNDM does not need warm-up
        pipe.scheduler.register_to_config(skip_prk_steps=_SCREAMING_SNAKE_CASE					)

        pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     2

        SCREAMING_SNAKE_CASE_				:  int           =     []
        for scheduler_enum in KarrasDiffusionSchedulers:
              if scheduler_enum.name in skip_schedulers:
                    # no sigma schedulers are not supported
                    # no schedulers
                    continue

              SCREAMING_SNAKE_CASE_				:  int           =     getattr(_SCREAMING_SNAKE_CASE ,					scheduler_enum.name					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_cls.from_config(pipe.scheduler.config					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     pipe(**_SCREAMING_SNAKE_CASE					)[0]
              outputs.append(_SCREAMING_SNAKE_CASE					)

        assert check_same_shape(_SCREAMING_SNAKE_CASE					)

@require_torch_gpu
@slow
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.manual_seed(33					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' ,					torch_dtype=torch.floataa					)
        pipe.to('cuda'					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler' ,					torch_dtype=torch.floataa					)
        upscaler.to('cuda'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     'a photo of an astronaut high resolution, unreal engine, ultra realistic'

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     pipe(_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					output_type='latent'					).images

        SCREAMING_SNAKE_CASE_				:  str           =     upscaler(
            prompt=_SCREAMING_SNAKE_CASE ,					image=_SCREAMING_SNAKE_CASE ,					num_inference_steps=20 ,					guidance_scale=0 ,					generator=_SCREAMING_SNAKE_CASE ,					output_type='np' ,					).images[0]

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'					)
        assert np.abs((expected_image - image).mean()					) < 5e-2



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.manual_seed(33					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     StableDiffusionLatentUpscalePipeline.from_pretrained(
            'stabilityai/sd-x2-latent-upscaler' ,					torch_dtype=torch.floataa					)
        upscaler.to('cuda'					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     upscaler(
            prompt=_SCREAMING_SNAKE_CASE ,					image=_SCREAMING_SNAKE_CASE ,					num_inference_steps=20 ,					guidance_scale=0 ,					generator=_SCREAMING_SNAKE_CASE ,					output_type='np' ,					).images[0]

        SCREAMING_SNAKE_CASE_				:  int           =     load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'					)
        assert np.abs((expected_image - image).max()					) < 5e-2









def    A_  (							a					,   a  ):

      """simple docstring"""



      return numa ^ numa < 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()






