






'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase			(     snake_case_							,    snake_case_							,    unittest.TestCase						):




   """simple docstring"""

   snake_case    				=						CycleDiffusionPipeline
   snake_case    				=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       '''negative_prompt''',
       '''height''',
       '''width''',
       '''negative_prompt_embeds''',
   }
   snake_case    				=						PipelineTesterMixin.required_optional_params - {'''latents'''}
   snake_case    				=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}						)
   snake_case    				=						IMAGE_TO_IMAGE_IMAGE_PARAMS
   snake_case    				=						IMAGE_TO_IMAGE_IMAGE_PARAMS

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         torch.manual_seed(0		)
         _A			     =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,      layers_per_block=2      ,      sample_size=32      ,      in_channels=4      ,      out_channels=4      ,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,      cross_attention_dim=32      ,      )
         _A			     =	DDIMScheduler(
             beta_start=0.00085      ,      beta_end=0.012      ,      beta_schedule="scaled_linear"      ,      num_train_timesteps=1000      ,      clip_sample=__UpperCAmelCase      ,      set_alpha_to_one=__UpperCAmelCase      ,      )
         torch.manual_seed(0		)
         _A			     =	AutoencoderKL(
             block_out_channels=[32, 64]      ,      in_channels=3      ,      out_channels=3      ,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,      latent_channels=4      ,      )
         torch.manual_seed(0		)
         _A			     =	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      ,      )
         _A			     =	CLIPTextModel(__UpperCAmelCase		)
         _A			     =	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

         _A			     =	{
             "unet": unet,
             "scheduler": scheduler,
             "vae": vae,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "safety_checker": None,
             "feature_extractor": None,
         }
         return components

   def 						lowerCAmelCase (		self :	List[Any]      ,      __UpperCAmelCase :	Tuple      ,      __UpperCAmelCase :	Optional[Any]=0		):





         '''simple docstring'''





         _A			     =	floats_tensor((1, 3, 32, 32)      ,      rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
         _A			     =	image / 2 + 0.5
         if str(__UpperCAmelCase		).startswith("mps"		):
               _A			     =	torch.manual_seed(__UpperCAmelCase		)
         else:
               _A			     =	torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
         _A			     =	{
             "prompt": "An astronaut riding an elephant",
             "source_prompt": "An astronaut riding a horse",
             "image": image,
             "generator": generator,
             "num_inference_steps": 2,
             "eta": 0.1,
             "strength": 0.8,
             "guidance_scale": 3,
             "source_guidance_scale": 1,
             "output_type": "numpy",
         }
         return inputs

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





         _A			     =	"cpu"  # ensure determinism for the device-dependent torch.Generator

         _A			     =	self.get_dummy_components()
         _A			     =	CycleDiffusionPipeline(**__UpperCAmelCase		)
         _A			     =	pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         _A			     =	self.get_dummy_inputs(__UpperCAmelCase		)
         _A			     =	pipe(**__UpperCAmelCase		)
         _A			     =	output.images

         _A			     =	images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         _A			     =	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

   @unittest.skipIf(torch_device != "cuda"      ,      "This test requires a GPU"		)
   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	self.get_dummy_components()
         for name, module in components.items():
               if hasattr(__UpperCAmelCase      ,      "half"		):
                     _A			     =	module.half()
         _A			     =	CycleDiffusionPipeline(**__UpperCAmelCase		)
         _A			     =	pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         _A			     =	self.get_dummy_inputs(__UpperCAmelCase		)
         _A			     =	pipe(**__UpperCAmelCase		)
         _A			     =	output.images

         _A			     =	images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         _A			     =	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

   @skip_mps
   def 						lowerCAmelCase (		self :	Any		):





         '''simple docstring'''





         return super().test_save_load_local()

   @unittest.skip("non-deterministic pipeline"		)
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return super().test_inference_batch_single_identical()

   @skip_mps
   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         return super().test_dict_tuple_outputs_equivalent()

   @skip_mps
   def 						lowerCAmelCase (		self :	List[Any]		):





         '''simple docstring'''





         return super().test_save_load_optional_components()

   @skip_mps
   def 						lowerCAmelCase (		self :	str		):





         '''simple docstring'''





         return super().test_attention_slicing_forward_pass()





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




   """simple docstring"""

   def 						lowerCAmelCase (		self :	Optional[int]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Union[str, Any]		):





         '''simple docstring'''





         _A			     =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/cycle-diffusion/black_colored_car.png"		)
         _A			     =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"		)
         _A			     =	init_image.resize((512, 512)		)

         _A			     =	"CompVis/stable-diffusion-v1-4"
         _A			     =	DDIMScheduler.from_pretrained(__UpperCAmelCase      ,      subfolder="scheduler"		)
         _A			     =	CycleDiffusionPipeline.from_pretrained(
             __UpperCAmelCase      ,      scheduler=__UpperCAmelCase      ,      safety_checker=__UpperCAmelCase      ,      torch_dtype=torch.floataa      ,      revision="fp16"		)

         pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)
         pipe.enable_attention_slicing()

         _A			     =	"A black colored car"
         _A			     =	"A blue colored car"

         _A			     =	torch.manual_seed(0		)
         _A			     =	pipe(
             prompt=__UpperCAmelCase      ,      source_prompt=__UpperCAmelCase      ,      image=__UpperCAmelCase      ,      num_inference_steps=100      ,      eta=0.1      ,      strength=0.85      ,      guidance_scale=3      ,      source_guidance_scale=1      ,      generator=__UpperCAmelCase      ,      output_type="np"      ,      )
         _A			     =	output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image		).max() < 5E-1

   def 						lowerCAmelCase (		self :	List[str]		):





         '''simple docstring'''





         _A			     =	load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/cycle-diffusion/black_colored_car.png"		)
         _A			     =	load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"		)
         _A			     =	init_image.resize((512, 512)		)

         _A			     =	"CompVis/stable-diffusion-v1-4"
         _A			     =	DDIMScheduler.from_pretrained(__UpperCAmelCase      ,      subfolder="scheduler"		)
         _A			     =	CycleDiffusionPipeline.from_pretrained(__UpperCAmelCase      ,      scheduler=__UpperCAmelCase      ,      safety_checker=__UpperCAmelCase		)

         pipe.to(__UpperCAmelCase		)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)
         pipe.enable_attention_slicing()

         _A			     =	"A black colored car"
         _A			     =	"A blue colored car"

         _A			     =	torch.manual_seed(0		)
         _A			     =	pipe(
             prompt=__UpperCAmelCase      ,      source_prompt=__UpperCAmelCase      ,      image=__UpperCAmelCase      ,      num_inference_steps=100      ,      eta=0.1      ,      strength=0.85      ,      guidance_scale=3      ,      source_guidance_scale=1      ,      generator=__UpperCAmelCase      ,      output_type="np"      ,      )
         _A			     =	output.images

         assert np.abs(image - expected_image		).max() < 2E-2












import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Tuple							=    'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'


      def    a (self       :      int				,			a__       :      List[Any]=0      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor((1, 3, 128, 128)				,			rng=random.Random(a__      )      )
         __snake_case							       =   np.random.RandomState(a__      )
         __snake_case							       =   {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''strength''': 0.7_5,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]      )
         assert np.abs(image_slice - expected_slice      ).max() < 1E-1


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   PNDMScheduler.from_config(pipe.scheduler.config				,			skip_prk_steps=a__      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         # warmup pass to apply optimizations
         __snake_case							       =   pipe(**self.get_dummy_inputs()      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint				,			provider='''CPUExecutionProvider'''      )
         __snake_case							       =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   self.get_dummy_inputs()
         __snake_case							       =   pipe(**a__      ).images
         __snake_case							       =   image[0, -3:, -3:, -1]

         assert image.shape == (1, 128, 128, 3)
         __snake_case							       =   np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]      )

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-1






@nightly
@require_onnxruntime
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @property
      def    a (self       :      List[str]      ):





         """simple docstring"""



         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )


      @property
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   ort.SessionOptions()
         __snake_case							       =   False
         return options


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''      )
         __snake_case							       =   init_image.resize((768, 512)      )
         # using the PNDM scheduler by default
         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''				,			revision='''onnx'''				,			safety_checker=a__				,			feature_extractor=a__				,			provider=self.gpu_provider				,			sess_options=self.gpu_options				,			)
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A fantasy landscape, trending on artstation'''

         __snake_case							       =   np.random.RandomState(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			strength=0.7_5				,			guidance_scale=7.5				,			num_inference_steps=10				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         __snake_case							       =   images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case							       =   np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]      )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2E-2


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/img2img/sketch-mountains-input.jpg'''      )
         __snake_case							       =   init_image.resize((768, 512)      )
         __snake_case							       =   LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''				,			subfolder='''scheduler'''				,			revision='''onnx'''      )
         __snake_case							       =   OnnxStableDiffusionImgaImgPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''				,			revision='''onnx'''				,			scheduler=a__				,			safety_checker=a__				,			feature_extractor=a__				,			provider=self.gpu_provider				,			sess_options=self.gpu_options				,			)
         pipe.set_progress_bar_config(disable=a__      )

         __snake_case							       =   '''A fantasy landscape, trending on artstation'''

         __snake_case							       =   np.random.RandomState(0      )
         __snake_case							       =   pipe(
             prompt=a__				,			image=a__				,			strength=0.7_5				,			guidance_scale=7.5				,			num_inference_steps=20				,			generator=a__				,			output_type='''np'''				,			)
         __snake_case							       =   output.images
         __snake_case							       =   images[0, 255:258, 383:386, -1]

         assert images.shape == (1, 512, 768, 3)
         __snake_case							       =   np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]      )
         # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

         assert np.abs(image_slice.flatten() - expected_slice      ).max() < 2E-2

