


"""simple docstring"""




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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 					lowerCAmelCase	(					lowerCamelCase_	,    lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[Any]								=     CycleDiffusionPipeline
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    """negative_prompt""",
		    """height""",
		    """width""",
		    """negative_prompt_embeds""",
		}
		SCREAMING_SNAKE_CASE_						:     Tuple								=     PipelineTesterMixin.required_optional_params - {"""latents"""}
		SCREAMING_SNAKE_CASE_						:     Tuple								=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}    )
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     IMAGE_TO_IMAGE_IMAGE_PARAMS
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     IMAGE_TO_IMAGE_IMAGE_PARAMS







		def 							__A						(	self				)     ->							List[str]:
								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')						,       cross_attention_dim=32						,       )
								SCREAMING_SNAKE_CASE													=					DDIMScheduler(
								    beta_start=0.0_00_85						,       beta_end=0.0_12						,       beta_schedule='scaled_linear'						,       num_train_timesteps=1_000						,       clip_sample=lowerCAmelCase__						,       set_alpha_to_one=lowerCAmelCase__						,       )
								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						,       )
								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=1_000						,       )
								SCREAMING_SNAKE_CASE													=					CLIPTextModel(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

								SCREAMING_SNAKE_CASE													=					{
								    'unet': unet,
								    'scheduler': scheduler,
								    'vae': vae,
								    'text_encoder': text_encoder,
								    'tokenizer': tokenizer,
								    'safety_checker': None,
								    'feature_extractor': None,
								}
								return components







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=0				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					floats_tensor((1, 3, 32, 32)						,       rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					image / 2 + 0.5
								if str(lowerCAmelCase__				).startswith('mps'				):
														SCREAMING_SNAKE_CASE													=					torch.manual_seed(lowerCAmelCase__				)
								else:
														SCREAMING_SNAKE_CASE													=					torch.Generator(device=lowerCAmelCase__				).manual_seed(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					{
								    'prompt': 'An astronaut riding an elephant',
								    'source_prompt': 'An astronaut riding a horse',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'eta': 0.1,
								    'strength': 0.8,
								    'guidance_scale': 3,
								    'source_guidance_scale': 1,
								    'output_type': 'numpy',
								}
								return inputs







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					'cpu'  # ensure determinism for the device-dependent torch.Generator

								SCREAMING_SNAKE_CASE													=					self.get_dummy_components()
								SCREAMING_SNAKE_CASE													=					CycleDiffusionPipeline(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					self.get_dummy_inputs(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					output.images

								SCREAMING_SNAKE_CASE													=					images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								SCREAMING_SNAKE_CASE													=					np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]				)

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







		@unittest.skipIf(torch_device != 'cuda'						,       'This test requires a GPU'				)
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.get_dummy_components()
								for name, module in components.items():
														if hasattr(lowerCAmelCase__						,       'half'				):
																				SCREAMING_SNAKE_CASE													=					module.half()
								SCREAMING_SNAKE_CASE													=					CycleDiffusionPipeline(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					self.get_dummy_inputs(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					output.images

								SCREAMING_SNAKE_CASE													=					images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								SCREAMING_SNAKE_CASE													=					np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]				)

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







		@skip_mps
		def 							__A						(	self				)     ->							List[str]:
								return super().test_save_load_local()







		@unittest.skip('non-deterministic pipeline'				)
		def 							__A						(	self				)     ->							List[Any]:
								return super().test_inference_batch_single_identical()







		@skip_mps
		def 							__A						(	self				)     ->							Tuple:
								return super().test_dict_tuple_outputs_equivalent()







		@skip_mps
		def 							__A						(	self				)     ->							List[str]:
								return super().test_save_load_optional_components()







		@skip_mps
		def 							__A						(	self				)     ->							Optional[int]:
								return super().test_attention_slicing_forward_pass()





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




		'''simple docstring'''







		def 							__A						(	self				)     ->							Union[str, Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/cycle-diffusion/black_colored_car.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'				)
								SCREAMING_SNAKE_CASE													=					init_image.resize((512, 512)				)

								SCREAMING_SNAKE_CASE													=					'CompVis/stable-diffusion-v1-4'
								SCREAMING_SNAKE_CASE													=					DDIMScheduler.from_pretrained(lowerCAmelCase__						,       subfolder='scheduler'				)
								SCREAMING_SNAKE_CASE													=					CycleDiffusionPipeline.from_pretrained(
								    lowerCAmelCase__						,       scheduler=lowerCAmelCase__						,       safety_checker=lowerCAmelCase__						,       torch_dtype=torch.floataa						,       revision='fp16'				)

								pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
								pipe.enable_attention_slicing()

								SCREAMING_SNAKE_CASE													=					'A black colored car'
								SCREAMING_SNAKE_CASE													=					'A blue colored car'

								SCREAMING_SNAKE_CASE													=					torch.manual_seed(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       source_prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       num_inference_steps=100						,       eta=0.1						,       strength=0.85						,       guidance_scale=3						,       source_guidance_scale=1						,       generator=lowerCAmelCase__						,       output_type='np'						,       )
								SCREAMING_SNAKE_CASE													=					output.images

								# the values aren't exactly equal, but the images look the same visually
								assert np.abs(image - expected_image				).max() < 5e-1







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/cycle-diffusion/black_colored_car.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'				)
								SCREAMING_SNAKE_CASE													=					init_image.resize((512, 512)				)

								SCREAMING_SNAKE_CASE													=					'CompVis/stable-diffusion-v1-4'
								SCREAMING_SNAKE_CASE													=					DDIMScheduler.from_pretrained(lowerCAmelCase__						,       subfolder='scheduler'				)
								SCREAMING_SNAKE_CASE													=					CycleDiffusionPipeline.from_pretrained(lowerCAmelCase__						,       scheduler=lowerCAmelCase__						,       safety_checker=lowerCAmelCase__				)

								pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)
								pipe.enable_attention_slicing()

								SCREAMING_SNAKE_CASE													=					'A black colored car'
								SCREAMING_SNAKE_CASE													=					'A blue colored car'

								SCREAMING_SNAKE_CASE													=					torch.manual_seed(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       source_prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       num_inference_steps=100						,       eta=0.1						,       strength=0.85						,       guidance_scale=3						,       source_guidance_scale=1						,       generator=lowerCAmelCase__						,       output_type='np'						,       )
								SCREAMING_SNAKE_CASE													=					output.images

								assert np.abs(image - expected_image				).max() < 2e-2






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@property
		def 							__A						(	self				)     ->							List[Any]:
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)







		@property
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					ort.SessionOptions()
								SCREAMING_SNAKE_CASE													=					False
								return options







		def 							__A						(	self				)     ->							Dict:
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
								SCREAMING_SNAKE_CASE													=					load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
								SCREAMING_SNAKE_CASE													=					load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'				)

								# using the PNDM scheduler by default
								SCREAMING_SNAKE_CASE													=					OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'						,       revision='onnx'						,       safety_checker=lowerCAmelCase__						,       feature_extractor=lowerCAmelCase__						,       provider=self.gpu_provider						,       sess_options=self.gpu_options						,       )
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					'A red cat sitting on a park bench'

								SCREAMING_SNAKE_CASE													=					np.random.RandomState(0				)
								SCREAMING_SNAKE_CASE													=					pipe(
								    prompt=lowerCAmelCase__						,       image=lowerCAmelCase__						,       mask_image=lowerCAmelCase__						,       strength=0.75						,       guidance_scale=7.5						,       num_inference_steps=15						,       generator=lowerCAmelCase__						,       output_type='np'						,       )

								SCREAMING_SNAKE_CASE													=					output.images[0]

								assert image.shape == (512, 512, 3)
								assert np.abs(expected_image - image				).max() < 1e-2


