


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 			UpperCamelCase (     __magic_name__					:							Optional[Any]					)       ->  Tuple:
		"""simple docstring"""




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



class 							A   (	UpperCamelCase_			,  UpperCamelCase_			,  UpperCamelCase_			,  unittest.TestCase							):
					'''simple docstring'''






					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   :						Tuple		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		1
							lowercase__					      =		4
							lowercase__					      =		(16, 16)

							lowercase__					      =		floats_tensor((batch_size, num_channels) + sizes				, rng=random.Random(0		)		).to(_a		)
							return image

					def   lowerCamelCase__					(self   :						List[Any]		)						->       str:




							"""simple docstring"""


							torch.manual_seed(0		)
							lowercase__					      =		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""")				, )
							lowercase__					      =		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				, )
							lowercase__					      =		EulerDiscreteScheduler(prediction_type="""sample"""		)
							lowercase__					      =		CLIPTextConfig(
							    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-0_5				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1000				, hidden_act="""quick_gelu"""				, projection_dim=512				, )
							lowercase__					      =		CLIPTextModel(_a		)
							lowercase__					      =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

							lowercase__					      =		{
							    """unet""": model.eval(),
							    """vae""": vae.eval(),
							    """scheduler""": scheduler,
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							}

							return components

					def   lowerCamelCase__					(self   :						List[str]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Any=0		)						->       List[Any]:




							"""simple docstring"""


							if str(_a		).startswith("""mps"""		):
									lowercase__					      =		torch.manual_seed(_a		)
							else:
									lowercase__					      =		torch.Generator(device=_a		).manual_seed(_a		)
							lowercase__					      =		{
							    """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   :						List[Any]		)						->       List[str]:




							"""simple docstring"""


							lowercase__					      =		"""cpu"""

							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		self.pipeline_class(**_a		)
							pipe.to(_a		)
							pipe.set_progress_bar_config(disable=_a		)

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

							self.assertEqual(image.shape				, (1, 256, 256, 3)		)
							lowercase__					      =		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]		)
							lowercase__					      =		np.abs(image_slice.flatten() - expected_slice		).max()
							self.assertLessEqual(_a				, 1E-3		)

					def   lowerCamelCase__					(self   :						Any		)						->       Tuple:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						List[Any]		)						->       Optional[Any]:




							"""simple docstring"""


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

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




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						List[str]		)						->       Any:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						List[Any]		)						->       Optional[Any]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Optional[Any]		)						->       List[Any]:




							"""simple docstring"""


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

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




							"""simple docstring"""


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


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




							"""simple docstring"""


							lowercase__					      =		[
							    """DDIMScheduler""",
							    """DDPMScheduler""",
							    """PNDMScheduler""",
							    """HeunDiscreteScheduler""",
							    """EulerAncestralDiscreteScheduler""",
							    """KDPM2DiscreteScheduler""",
							    """KDPM2AncestralDiscreteScheduler""",
							    """DPMSolverSDEScheduler""",
							]
							lowercase__					      =		self.get_dummy_components()
							lowercase__					      =		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		)
							lowercase__					      =		self.get_dummy_inputs(_a		)
							lowercase__					      =		2

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

									lowercase__					      =		getattr(_a				, scheduler_enum.name		)
									lowercase__					      =		scheduler_cls.from_config(pipe.scheduler.config		)
									lowercase__					      =		pipe(**_a		)[0]
									outputs.append(_a		)

							assert check_same_shape(_a		)






@require_torch_gpu
@slow
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Any		)						->       List[Any]:




							"""simple docstring"""


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

					def   lowerCamelCase__					(self   :						Dict		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		torch.manual_seed(33		)

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

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

							lowercase__					      =		"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

							lowercase__					      =		pipe(_a				, generator=_a				, output_type="""latent"""		).images

							lowercase__					      =		upscaler(
							    prompt=_a				, image=_a				, num_inference_steps=20				, guidance_scale=0				, generator=_a				, output_type="""np"""				, ).images[0]

							lowercase__					      =		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   :						Optional[Any]		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		torch.manual_seed(33		)

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

							lowercase__					      =		"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

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

							lowercase__					      =		upscaler(
							    prompt=_a				, image=_a				, num_inference_steps=20				, guidance_scale=0				, generator=_a				, output_type="""np"""				, ).images[0]

							lowercase__					      =		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









class 							A   :
					'''simple docstring'''






					def __init__(self   :						int				, _UpperCAmelCase   :						list		)						->       None:




							"""simple docstring"""


							lowercase__					      =		set_counts
							lowercase__					      =		max(_UpperCAmelCase		)
							lowercase__					      =		len(_UpperCAmelCase		)
							lowercase__					      =		[1] * num_sets
							lowercase__					      =		list(range(_UpperCAmelCase		)		)

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						int		)						->       bool:




							"""simple docstring"""


							lowercase__					      =		self.get_parent(_UpperCAmelCase		)
							lowercase__					      =		self.get_parent(_UpperCAmelCase		)

							if src_parent == dst_parent:
									return False

							if self.ranks[dst_parent] >= self.ranks[src_parent]:
									self.set_counts[dst_parent] += self.set_counts[src_parent]
									lowercase__					      =		0
									lowercase__					      =		dst_parent
									if self.ranks[dst_parent] == self.ranks[src_parent]:
											self.ranks[dst_parent] += 1
									lowercase__					      =		self.set_counts[dst_parent]
							else:
									self.set_counts[src_parent] += self.set_counts[dst_parent]
									lowercase__					      =		0
									lowercase__					      =		src_parent
									lowercase__					      =		self.set_counts[src_parent]

							lowercase__					      =		max(self.max_set				, _UpperCAmelCase		)
							return True


					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int		)						->       int:




							"""simple docstring"""


							if self.parents[disj_set] == disj_set:
									return disj_set
							lowercase__					      =		self.get_parent(self.parents[disj_set]		)
							return self.parents[disj_set]

