



'''simple docstring'''






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

@skip_mps
class     SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase      ):







	"""simple docstring"""






	lowerCamelCase__      			=	StableDiffusionModelEditingPipeline
	lowerCamelCase__      			=	TEXT_TO_IMAGE_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCamelCase__      			=	TEXT_TO_IMAGE_IMAGE_PARAMS







	def 					A     (						self      :					Union[str, Any]		)      ->		List[str]:
								torch.manual_seed(0		)
								UpperCAmelCase			:	Dict										=		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		,      )
								UpperCAmelCase			:	List[str]										=		DDIMScheduler()
								torch.manual_seed(0		)
								UpperCAmelCase			:	Optional[Any]										=		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		)
								UpperCAmelCase			:	Union[str, 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		,      )
								UpperCAmelCase			:	List[str]										=		CLIPTextModel(__snake_case		)
								UpperCAmelCase			:	List[str]										=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

								UpperCAmelCase			:	List[Any]										=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def 					A     (						self      :					Optional[Any]		,      __snake_case      :					Any		,      __snake_case      :					Tuple=0		)      ->		int:
								UpperCAmelCase			:	int										=		torch.manual_seed(__snake_case		)
								UpperCAmelCase			:	Dict										=		{
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    # Setting height and width to None to prevent OOMs on CPU.
								    '''height''': None,
								    '''width''': None,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}
								return inputs







	def 					A     (						self      :					List[Any]		)      ->		int:
								UpperCAmelCase			:	List[Any]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	int										=		self.get_dummy_components()
								UpperCAmelCase			:	Optional[Any]										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Any										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	int										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	Optional[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	Tuple										=		image[0, -3:, -3:, -1]
								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase			:	Optional[int]										=		np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89]		)

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







	def 					A     (						self      :					List[Any]		)      ->		str:
								UpperCAmelCase			:	Optional[int]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Optional[Any]										=		self.get_dummy_components()
								UpperCAmelCase			:	str										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	List[str]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	int										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		'''french fries'''
								UpperCAmelCase			:	Optional[Any]										=		sd_pipe(**__snake_case		,      negative_prompt=__snake_case		)
								UpperCAmelCase			:	Tuple										=		output.images
								UpperCAmelCase			:	Dict										=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase			:	Optional[Any]										=		np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13]		)

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







	def 					A     (						self      :					List[Any]		)      ->		List[Any]:
								UpperCAmelCase			:	Any										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Any										=		self.get_dummy_components()
								UpperCAmelCase			:	Any										=		EulerAncestralDiscreteScheduler(
								    beta_start=0.0_00_85		,      beta_end=0.0_12		,      beta_schedule='''scaled_linear'''		)
								UpperCAmelCase			:	Any										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	int										=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images
								UpperCAmelCase			:	int										=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)

								UpperCAmelCase			:	List[str]										=		np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27]		)

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







	def 					A     (						self      :					str		)      ->		List[Any]:
								UpperCAmelCase			:	Optional[int]										=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase			:	Tuple										=		self.get_dummy_components()
								UpperCAmelCase			:	int										=		PNDMScheduler()
								UpperCAmelCase			:	Any										=		StableDiffusionModelEditingPipeline(**__snake_case		)
								UpperCAmelCase			:	Union[str, Any]										=		sd_pipe.to(__snake_case		)
								sd_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		self.get_dummy_inputs(__snake_case		)
								# the pipeline does not expect pndm so test if it raises error.
								with self.assertRaises(__snake_case		):
															UpperCAmelCase			:	List[Any]										=		sd_pipe(**__snake_case		).images







	def 					A     (						self      :					int		)      ->		Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=5E-3		)







	def 					A     (						self      :					List[Any]		)      ->		Union[str, Any]:
								super().test_attention_slicing_forward_pass(expected_max_diff=5E-3		)

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







	"""simple docstring"""






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







	def 					A     (						self      :					int		,      __snake_case      :					Optional[int]=0		)      ->		List[Any]:
								UpperCAmelCase			:	Any										=		torch.manual_seed(__snake_case		)
								UpperCAmelCase			:	int										=		{
								    '''prompt''': '''A field of roses''',
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''output_type''': '''numpy''',
								}
								return inputs







	def 					A     (						self      :					List[str]		)      ->		List[str]:
								UpperCAmelCase			:	Optional[Any]										=		'''CompVis/stable-diffusion-v1-4'''
								UpperCAmelCase			:	Dict										=		StableDiffusionModelEditingPipeline.from_pretrained(__snake_case		,      safety_checker=__snake_case		)
								pipe.to(__snake_case		)
								pipe.set_progress_bar_config(disable=__snake_case		)
								pipe.enable_attention_slicing()

								UpperCAmelCase			:	Dict										=		self.get_inputs()
								UpperCAmelCase			:	Optional[Any]										=		pipe(**__snake_case		).images
								UpperCAmelCase			:	str										=		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

								UpperCAmelCase			:	int										=		np.array(
								    [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58]		)

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

								# make sure image changes after editing
								pipe.edit_model('''A pack of roses'''		,      '''A pack of blue roses'''		)

								UpperCAmelCase			:	Dict										=		pipe(**__snake_case		).images
								UpperCAmelCase			:	Union[str, Any]										=		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)

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







	def 					A     (						self      :					str		)      ->		Optional[int]:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase			:	Any										=		'''CompVis/stable-diffusion-v1-4'''
								UpperCAmelCase			:	int										=		DDIMScheduler.from_pretrained(__snake_case		,      subfolder='''scheduler'''		)
								UpperCAmelCase			:	int										=		StableDiffusionModelEditingPipeline.from_pretrained(
								    __snake_case		,      scheduler=__snake_case		,      safety_checker=__snake_case		)
								UpperCAmelCase			:	Tuple										=		pipe.to(__snake_case		)
								pipe.set_progress_bar_config(disable=__snake_case		)
								pipe.enable_attention_slicing(1		)
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase			:	List[str]										=		self.get_inputs()
								UpperCAmelCase			:	int										=		pipe(**__snake_case		)

								UpperCAmelCase			:	Union[str, Any]										=		torch.cuda.max_memory_allocated()
								# make sure that less than 4.4 GB is allocated
								assert mem_bytes < 4.4 * 10**9










'''simple docstring'''






UpperCamelCase__:       str							     =      [0.485, 0.456, 0.406]
UpperCamelCase__:       List[str]							     =      [0.229, 0.224, 0.225]
UpperCamelCase__:       List[str]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       Optional[Any]							     =      [0.5, 0.5, 0.5]
UpperCamelCase__:       List[str]							     =      [0.48145466, 0.4578275, 0.40821073]
UpperCamelCase__:       List[str]							     =      [0.26862954, 0.26130258, 0.27577711]





