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       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [tensor.shape for tensor in tensor_list]
		return all(shape == shapes[0] for shape in shapes[1:]	)






class 		a__						(					A__    ,		A__    ,		A__    ,		unittest.TestCase					):
					A	       =							StableDiffusionLatentUpscalePipeline
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    'height',
					    'width',
					    'cross_attention_kwargs',
					    'negative_prompt_embeds',
					    'prompt_embeds',
					}
					A	       =							PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					A	       =							frozenset(
					    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A	       =							frozenset([]					)

					A	       =							True

					@property
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






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

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

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		str				      =    UNetaDConditionModel(
							    act_fn="gelu",attention_head_dim=8,norm_num_groups=_A,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=_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"),)
							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_  :		int				      =    EulerDiscreteScheduler(prediction_type="sample"    )
							SCREAMING_SNAKE_CASE_  :		List[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=1000,hidden_act="quick_gelu",projection_dim=512,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    CLIPTextModel(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       int,_A      :       Tuple=0    ):







							"""simple docstring"""






							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.Generator(device=_A    ).manual_seed(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "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      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "cpu"

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

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    pipe(**_A    ).images
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape,(1, 256, 256, 3)    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.array(
							    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(_A,1E-3    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		Any				      =    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    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    2

							SCREAMING_SNAKE_CASE_  :		Optional[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_  :		Tuple				      =    getattr(_A,scheduler_enum.name    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    scheduler_cls.from_config(pipe.scheduler.config    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(**_A    )[0]
									outputs.append(_A    )

							assert check_same_shape(_A    )







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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    "a photo of an astronaut high resolution, unreal engine, ultra realistic"

							SCREAMING_SNAKE_CASE_  :		str				      =    pipe(_A,generator=_A,output_type="latent"    ).images

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    upscaler(
							    prompt=_A,image=_A,num_inference_steps=20,guidance_scale=0,generator=_A,output_type="np",).images[0]

							SCREAMING_SNAKE_CASE_  :		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 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    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_  :		Any				      =    "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

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

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

							SCREAMING_SNAKE_CASE_  :		Any				      =    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






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

