


import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


UpperCamelCase								=	HfApi()

UpperCamelCase								=	{}
# fmt: off
UpperCamelCase								=	torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
UpperCamelCase								=	torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
UpperCamelCase								=	torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
UpperCamelCase								=	torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
UpperCamelCase								=	torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
UpperCamelCase								=	torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
UpperCamelCase								=	torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
UpperCamelCase								=	torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
UpperCamelCase								=	torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
UpperCamelCase								=	torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
UpperCamelCase								=	torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
UpperCamelCase								=	torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
UpperCamelCase								=	torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
UpperCamelCase								=	torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
UpperCamelCase								=	torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

UpperCamelCase								=	api.list_models(filter="""diffusers""")
for mod in models:
     if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
          UpperCamelCase								=	'''/home/patrick/google_checkpoints/''' + mod.modelId.split("""/""")[-1]

          print(F'''Started running {mod.modelId}!!!''')

          if mod.modelId.startswith("""CompVis"""):
               UpperCamelCase								=	UNetaDModel.from_pretrained(local_checkpoint, subfolder="""unet""")
          else:
               UpperCamelCase								=	UNetaDModel.from_pretrained(local_checkpoint)

          torch.manual_seed(0)
          random.seed(0)

          UpperCamelCase								=	torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
          UpperCamelCase								=	torch.tensor([10] * noise.shape[0])
          with torch.no_grad():
               UpperCamelCase								=	model(noise, time_step).sample

          assert torch.allclose(
              logits[0, 0, 0, :30], results["""_""".join("""_""".join(mod.modelId.split("""/""")).split("""-"""))], atol=1e-3
          )
          print(F'''{mod.modelId} has passed successfully!!!''')





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
