






'''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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     int	  =	[tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:]				)

class _lowercase     (    _lowercase    ,				_lowercase    ,				_lowercase    ,				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 		lowerCamelCase_    (							self:    Optional[int]					):
														lowerCamelCase__ :     Any	  =	1
														lowerCamelCase__ :     int	  =	4
														lowerCamelCase__ :     Union[str, Any]	  =	(16, 16)

														lowerCamelCase__ :     Union[str, Any]	  =	floats_tensor((batch_size, num_channels) + sizes      ,							rng=random.Random(0					)					).to(UpperCamelCase__					)
														return image




							def 		lowerCamelCase_    (							self:    List[str]					):
														torch.manual_seed(0					)
														lowerCamelCase__ :     str	  =	UNetaDConditionModel(
														    act_fn="""gelu"""      ,							attention_head_dim=8      ,							norm_num_groups=UpperCamelCase__      ,							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=UpperCamelCase__      ,							only_cross_attention=UpperCamelCase__      ,							out_channels=5      ,							resnet_time_scale_shift="""scale_shift"""      ,							time_embedding_type="""fourier"""      ,							timestep_post_act="""gelu"""      ,							up_block_types=("""KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KCrossAttnUpBlock2D""", """KUpBlock2D""")      ,							)
														lowerCamelCase__ :     Optional[int]	  =	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      ,							)
														lowerCamelCase__ :     str	  =	EulerDiscreteScheduler(prediction_type="""sample"""					)
														lowerCamelCase__ :     List[str]	  =	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=1_000      ,							hidden_act="""quick_gelu"""      ,							projection_dim=512      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	CLIPTextModel(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[int]	  =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

														lowerCamelCase__ :     Optional[Any]	  =	{
														    """unet""": model.eval(),
														    """vae""": vae.eval(),
														    """scheduler""": scheduler,
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														}

														return components




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Optional[int]=0					):
														if str(UpperCamelCase__					).startswith("""mps"""					):
																					lowerCamelCase__ :     Dict	  =	torch.manual_seed(UpperCamelCase__					)
														else:
																					lowerCamelCase__ :     Optional[Any]	  =	torch.Generator(device=UpperCamelCase__					).manual_seed(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[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 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Dict	  =	"""cpu"""

														lowerCamelCase__ :     Optional[Any]	  =	self.get_dummy_components()
														lowerCamelCase__ :     Tuple	  =	self.pipeline_class(**UpperCamelCase__					)
														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)

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

														self.assertEqual(image.shape      ,							(1, 256, 256, 3)					)
														lowerCamelCase__ :     int	  =	np.array(
														    [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]					)
														lowerCamelCase__ :     List[str]	  =	np.abs(image_slice.flatten() - expected_slice					).max()
														self.assertLessEqual(UpperCamelCase__      ,							1e-3					)




							def 		lowerCamelCase_    (							self:    Dict					):
														super().test_attention_slicing_forward_pass(expected_max_diff=7e-3					)




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														super().test_cpu_offload_forward_pass(expected_max_diff=3e-3					)




							def 		lowerCamelCase_    (							self:    str					):
														super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)




							def 		lowerCamelCase_    (							self:    Any					):
														super().test_inference_batch_single_identical(expected_max_diff=7e-3					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3					)




							def 		lowerCamelCase_    (							self:    str					):
														super().test_save_load_local(expected_max_difference=3e-3					)




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														super().test_save_load_optional_components(expected_max_difference=3e-3					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[Any]	  =	[
														    """DDIMScheduler""",
														    """DDPMScheduler""",
														    """PNDMScheduler""",
														    """HeunDiscreteScheduler""",
														    """EulerAncestralDiscreteScheduler""",
														    """KDPM2DiscreteScheduler""",
														    """KDPM2AncestralDiscreteScheduler""",
														    """DPMSolverSDEScheduler""",
														]
														lowerCamelCase__ :     Optional[int]	  =	self.get_dummy_components()
														lowerCamelCase__ :     Optional[int]	  =	self.pipeline_class(**UpperCamelCase__					)

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

														pipe.to(UpperCamelCase__					)
														pipe.set_progress_bar_config(disable=UpperCamelCase__					)
														lowerCamelCase__ :     List[str]	  =	self.get_dummy_inputs(UpperCamelCase__					)
														lowerCamelCase__ :     Optional[Any]	  =	2

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

																					lowerCamelCase__ :     Any	  =	getattr(UpperCamelCase__      ,							scheduler_enum.name					)
																					lowerCamelCase__ :     Dict	  =	scheduler_cls.from_config(pipe.scheduler.config					)
																					lowerCamelCase__ :     List[str]	  =	pipe(**UpperCamelCase__					)[0]
																					outputs.append(UpperCamelCase__					)

														assert check_same_shape(UpperCamelCase__					)



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




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Optional[Any]	  =	torch.manual_seed(33					)

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

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

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

														lowerCamelCase__ :     Optional[Any]	  =	pipe(UpperCamelCase__      ,							generator=UpperCamelCase__      ,							output_type="""latent"""					).images

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

														lowerCamelCase__ :     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 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     List[Any]	  =	torch.manual_seed(33					)

														lowerCamelCase__ :     int	  =	StableDiffusionLatentUpscalePipeline.from_pretrained(
														    """stabilityai/sd-x2-latent-upscaler"""      ,							torch_dtype=torch.floataa					)
														upscaler.to("""cuda"""					)

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

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

														lowerCamelCase__ :     Tuple	  =	upscaler(
														    prompt=UpperCamelCase__      ,							image=UpperCamelCase__      ,							num_inference_steps=20      ,							guidance_scale=0      ,							generator=UpperCamelCase__      ,							output_type="""np"""      ,							).images[0]

														lowerCamelCase__ :     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'''







from torch import nn





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if act_fn in ["swish", "silu"]:
														return nn.SiLU()
							elif act_fn == "mish":
														return nn.Mish()
							elif act_fn == "gelu":
														return nn.GELU()
							else:
														raise ValueError(f'''Unsupported activation function: {act_fn}'''				)

