
"""simple docstring"""







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_      (       lowercase   )  ->				Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_    :     List[str]            =	[tensor.shape for tensor in tensor_list]
     return all(shape == shapes[0] for shape in shapes[1:]   )



class 		UpperCAmelCase_						(lowerCamelCase_	,			lowerCamelCase_	,			lowerCamelCase_	,			unittest.TestCase    ):
 """simple docstring"""




 UpperCamelCase_    :  Optional[int]					 =     StableDiffusionLatentUpscalePipeline
 UpperCamelCase_    :  List[str]					 =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     """height""",
     """width""",
     """cross_attention_kwargs""",
     """negative_prompt_embeds""",
     """prompt_embeds""",
 }
 UpperCamelCase_    :  str					 =     PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
 UpperCamelCase_    :  Union[str, Any]					 =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 UpperCamelCase_    :  List[Any]					 =     frozenset(
     []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 UpperCamelCase_    :  int					 =     frozenset([]    )

 UpperCamelCase_    :  List[str]					 =     True




 @property
 def 	a   (			self							:			List[str]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	1
      UpperCAmelCase_    :     int            =	4
      UpperCAmelCase_    :     Any            =	(16, 16)

      UpperCAmelCase_    :     str            =	floats_tensor((batch_size, num_channels) + sizes  , rng=random.Random(0			)			).to(a_			)
      return image




 def 	a   (			self							:			Optional[Any]			)->       Tuple:



      """simple docstring"""

      torch.manual_seed(0			)
      UpperCAmelCase_    :     Tuple            =	UNetaDConditionModel(
          act_fn="""gelu"""  , attention_head_dim=8  , norm_num_groups=a_  , block_out_channels=[32, 32, 64, 64]  , time_cond_proj_dim=1_60  , 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=a_  , only_cross_attention=a_  , out_channels=5  , resnet_time_scale_shift="""scale_shift"""  , time_embedding_type="""fourier"""  , timestep_post_act="""gelu"""  , up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")  , )
      UpperCAmelCase_    :     str            =	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  , )
      UpperCAmelCase_    :     Optional[Any]            =	EulerDiscreteScheduler(prediction_type="""sample"""			)
      UpperCAmelCase_    :     Any            =	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=10_00  , hidden_act="""quick_gelu"""  , projection_dim=5_12  , )
      UpperCAmelCase_    :     Dict            =	CLIPTextModel(a_			)
      UpperCAmelCase_    :     Optional[int]            =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      UpperCAmelCase_    :     List[str]            =	{
          """unet""": model.eval(),
          """vae""": vae.eval(),
          """scheduler""": scheduler,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
      }

      return components




 def 	a   (			self							:			str  , a_							:			Union[str, Any]  , a_							:			Any=0			)->       List[Any]:



      """simple docstring"""

      if str(a_			).startswith("""mps"""			):
           UpperCAmelCase_    :     str            =	torch.manual_seed(a_			)
      else:
           UpperCAmelCase_    :     Optional[int]            =	torch.Generator(device=a_			).manual_seed(a_			)
      UpperCAmelCase_    :     int            =	{
          """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 	a   (			self							:			Union[str, Any]			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	"""cpu"""

      UpperCAmelCase_    :     Optional[int]            =	self.get_dummy_components()
      UpperCAmelCase_    :     Optional[int]            =	self.pipeline_class(**a_			)
      pipe.to(a_			)
      pipe.set_progress_bar_config(disable=a_			)

      UpperCAmelCase_    :     Dict            =	self.get_dummy_inputs(a_			)
      UpperCAmelCase_    :     List[str]            =	pipe(**a_			).images
      UpperCAmelCase_    :     str            =	image[0, -3:, -3:, -1]

      self.assertEqual(image.shape  , (1, 2_56, 2_56, 3)			)
      UpperCAmelCase_    :     List[Any]            =	np.array(
          [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]			)
      UpperCAmelCase_    :     str            =	np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(a_  , 1E-3			)




 def 	a   (			self							:			Dict			)->       Union[str, Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Dict			)->       Union[str, Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Tuple			)->       Tuple:



      """simple docstring"""

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




 def 	a   (			self							:			List[Any]			)->       Dict:



      """simple docstring"""

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




 def 	a   (			self							:			Dict			)->       Optional[int]:



      """simple docstring"""

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




 def 	a   (			self							:			str			)->       Optional[Any]:



      """simple docstring"""

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




 def 	a   (			self							:			Union[str, Any]			)->       str:



      """simple docstring"""

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






 def 	a   (			self							:			Union[str, Any]			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	[
          """DDIMScheduler""",
          """DDPMScheduler""",
          """PNDMScheduler""",
          """HeunDiscreteScheduler""",
          """EulerAncestralDiscreteScheduler""",
          """KDPM2DiscreteScheduler""",
          """KDPM2AncestralDiscreteScheduler""",
          """DPMSolverSDEScheduler""",
      ]
      UpperCAmelCase_    :     Any            =	self.get_dummy_components()
      UpperCAmelCase_    :     str            =	self.pipeline_class(**a_			)

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

      pipe.to(a_			)
      pipe.set_progress_bar_config(disable=a_			)
      UpperCAmelCase_    :     Any            =	self.get_dummy_inputs(a_			)
      UpperCAmelCase_    :     str            =	2

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

           UpperCAmelCase_    :     str            =	getattr(a_  , scheduler_enum.name			)
           UpperCAmelCase_    :     Optional[int]            =	scheduler_cls.from_config(pipe.scheduler.config			)
           UpperCAmelCase_    :     int            =	pipe(**a_			)[0]
           outputs.append(a_			)

      assert check_same_shape(a_			)



@require_torch_gpu
@slow
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 def 	a   (			self							:			Any			)->       str:



      """simple docstring"""

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




 def 	a   (			self							:			Optional[int]			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     str            =	torch.manual_seed(33			)

      UpperCAmelCase_    :     int            =	StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""  , torch_dtype=torch.floataa			)
      pipe.to("""cuda"""			)

      UpperCAmelCase_    :     List[Any]            =	StableDiffusionLatentUpscalePipeline.from_pretrained(
          """stabilityai/sd-x2-latent-upscaler"""  , torch_dtype=torch.floataa			)
      upscaler.to("""cuda"""			)

      UpperCAmelCase_    :     str            =	"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

      UpperCAmelCase_    :     Any            =	pipe(a_  , generator=a_  , output_type="""latent"""			).images

      UpperCAmelCase_    :     int            =	upscaler(
          prompt=a_  , image=a_  , num_inference_steps=20  , guidance_scale=0  , generator=a_  , output_type="""np"""  , ).images[0]

      UpperCAmelCase_    :     List[str]            =	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 	a   (			self							:			List[str]			)->       Any:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	torch.manual_seed(33			)

      UpperCAmelCase_    :     List[str]            =	StableDiffusionLatentUpscalePipeline.from_pretrained(
          """stabilityai/sd-x2-latent-upscaler"""  , torch_dtype=torch.floataa			)
      upscaler.to("""cuda"""			)

      UpperCAmelCase_    :     Optional[Any]            =	"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

      UpperCAmelCase_    :     int            =	load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""			)

      UpperCAmelCase_    :     Optional[int]            =	upscaler(
          prompt=a_  , image=a_  , num_inference_steps=20  , guidance_scale=0  , generator=a_  , output_type="""np"""  , ).images[0]

      UpperCAmelCase_    :     List[str]            =	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



"""simple docstring"""







def       A_      (       lowercase = 1000   )  ->				int:


     """simple docstring"""







     return sum(e for e in range(3    ,			lowercase   ) if e % 3 == 0 or e % 5 == 0   )


if __name__ == "__main__":
       print(f"""{solution() = }""")

