




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class       a_       (     SCREAMING_SNAKE_CASE__  ,    SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= StableDiffusionXLImgaImgPipeline
	A    							= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
	A    							= PipelineTesterMixin.required_optional_params - {'''latents'''}
	A    							= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	A    							= IMAGE_TO_IMAGE_IMAGE_PARAMS
	A    							= IMAGE_TO_IMAGE_IMAGE_PARAMS





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




				"""simple docstring"""



				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						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')      ,     attention_head_dim=(2, 4)      ,     use_linear_projection=SCREAMING_SNAKE_CASE      ,     addition_embed_type='text_time'      ,     addition_time_embed_dim=8      ,     transformer_layers_per_block=(1, 2)      ,     projection_class_embeddings_input_dim=80      ,     cross_attention_dim=64      ,     )
				SCREAMING_SNAKE_CASE_								=						EulerDiscreteScheduler(
				    beta_start=0.0_0_0_8_5      ,     beta_end=0.0_1_2      ,     steps_offset=1      ,     beta_schedule='scaled_linear'      ,     timestep_spacing='leading'      ,     )
				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						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      ,     sample_size=128      ,     )
				torch.manual_seed(0      )
				SCREAMING_SNAKE_CASE_								=						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='gelu'      ,     projection_dim=32      ,     )
				SCREAMING_SNAKE_CASE_								=						CLIPTextModel(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,     local_files_only=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      ,     local_files_only=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						{
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'text_encoder_2': text_encoder_a,
				    'tokenizer_2': tokenizer_a,
				    # "safety_checker": None,
				    # "feature_extractor": None,
				}
				return components





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE=0      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						image / 2 + 0.5
				if str(SCREAMING_SNAKE_CASE      ).startswith('mps'      ):
							SCREAMING_SNAKE_CASE_								=						torch.manual_seed(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 5.0,
				    'output_type': 'numpy',
				    'strength': 0.7_5,
				}
				return inputs





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'cpu'  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						StableDiffusionXLImgaImgPipeline(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						sd_pipe.to(SCREAMING_SNAKE_CASE      )
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						sd_pipe(**SCREAMING_SNAKE_CASE      ).images
				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)

				SCREAMING_SNAKE_CASE_								=						np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]      )

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





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




				"""simple docstring"""



				super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )





	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						self.get_dummy_components()
				SCREAMING_SNAKE_CASE_								=						StableDiffusionXLImgaImgPipeline(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						sd_pipe.to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						sd_pipe.to(SCREAMING_SNAKE_CASE      )
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				# forward without prompt embeds
				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						3 * ['this is a negative prompt']
				SCREAMING_SNAKE_CASE_								=						negative_prompt
				SCREAMING_SNAKE_CASE_								=						3 * [inputs['prompt']]

				SCREAMING_SNAKE_CASE_								=						sd_pipe(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				SCREAMING_SNAKE_CASE_								=						self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						3 * ['this is a negative prompt']
				SCREAMING_SNAKE_CASE_								=						3 * [inputs.pop('prompt'      )]

				(
				    (
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		(
				    SCREAMING_SNAKE_CASE_
				)						,		
				)								=						sd_pipe.encode_prompt(SCREAMING_SNAKE_CASE      ,     negative_prompt=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						sd_pipe(
				    **SCREAMING_SNAKE_CASE      ,     prompt_embeds=SCREAMING_SNAKE_CASE      ,     negative_prompt_embeds=SCREAMING_SNAKE_CASE      ,     pooled_prompt_embeds=SCREAMING_SNAKE_CASE      ,     negative_pooled_prompt_embeds=SCREAMING_SNAKE_CASE      ,     )
				SCREAMING_SNAKE_CASE_								=						output.images[0, -3:, -3:, -1]

				# make sure that it's equal
				assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1e-4




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





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE="cpu"      ,     SCREAMING_SNAKE_CASE=torch.floataa      ,     SCREAMING_SNAKE_CASE=0      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						np.random.RandomState(SCREAMING_SNAKE_CASE      ).standard_normal((1, 4, 64, 64)      )
				SCREAMING_SNAKE_CASE_								=						torch.from_numpy(SCREAMING_SNAKE_CASE      ).to(device=SCREAMING_SNAKE_CASE      ,     dtype=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						{
				    'prompt': 'a photograph of an astronaut riding a horse',
				    'latents': latents,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 7.5,
				    'output_type': 'numpy',
				}
				return inputs





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
				pipe.to(SCREAMING_SNAKE_CASE      )
				pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						self.get_inputs(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						pipe(**SCREAMING_SNAKE_CASE      ).images
				SCREAMING_SNAKE_CASE_								=						image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE_								=						np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]      )
				assert np.abs(image_slice - expected_slice      ).max() < 7e-3






from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
				import accelerate

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if not is_accelerate_available():
						return method
			SCREAMING_SNAKE_CASE_								=						version.parse(accelerate.__version__ ).base_version
			if version.parse(SCREAMING_SNAKE_CASE ) < version.parse('0.17.0' ):
						return method

			def wrapper(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE ):
						if hasattr(self       ,    '_hf_hook' ) and hasattr(self._hf_hook       ,    'pre_forward' ):
									self._hf_hook.pre_forward(self )
						return method(self       ,    *SCREAMING_SNAKE_CASE       ,    **SCREAMING_SNAKE_CASE )

			return wrapper
