



"""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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							CycleDiffusionPipeline
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    'negative_prompt',
					    'height',
					    'width',
					    'negative_prompt_embeds',
					}
					_a         =							PipelineTesterMixin.required_optional_params - {'latents'}
					_a         =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}     )
					_a         =							IMAGE_TO_IMAGE_IMAGE_PARAMS
					_a         =							IMAGE_TO_IMAGE_IMAGE_PARAMS



					def 	snake_case ( self							:						Dict  )->      Union[str, Any]:
								torch.manual_seed(0  )
								lowerCamelCase__	:	Any				=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,						)
								lowerCamelCase__	:	Tuple				=DDIMScheduler(
								    beta_start=0.00_085,						beta_end=0.012,						beta_schedule='''scaled_linear''',						num_train_timesteps=1000,						clip_sample=lowerCamelCase,						set_alpha_to_one=lowerCamelCase,						)
								torch.manual_seed(0  )
								lowerCamelCase__	:	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  )
								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=1000,						)
								lowerCamelCase__	:	Optional[Any]				=CLIPTextModel(lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

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



					def 	snake_case ( self							:						str,						lowerCamelCase							:						Dict,						lowerCamelCase							:						Optional[Any]=0  )->      str:
								lowerCamelCase__	:	Dict				=floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=image / 2 + 0.5
								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	int				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	Dict				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	int				={
								    '''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 	snake_case ( self							:						Any  )->      Optional[Any]:
								lowerCamelCase__	:	Union[str, Any]				='''cpu'''  # ensure determinism for the device-dependent torch.Generator

								lowerCamelCase__	:	Any				=self.get_dummy_components()
								lowerCamelCase__	:	Any				=CycleDiffusionPipeline(**lowerCamelCase  )
								lowerCamelCase__	:	int				=pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Optional[Any]				=self.get_dummy_inputs(lowerCamelCase  )
								lowerCamelCase__	:	int				=pipe(**lowerCamelCase  )
								lowerCamelCase__	:	Union[str, Any]				=output.images

								lowerCamelCase__	:	Dict				=images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								lowerCamelCase__	:	List[str]				=np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]  )

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



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

								lowerCamelCase__	:	Union[str, Any]				=self.get_dummy_inputs(lowerCamelCase  )
								lowerCamelCase__	:	Any				=pipe(**lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=output.images

								lowerCamelCase__	:	Union[str, Any]				=images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								lowerCamelCase__	:	List[Any]				=np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]  )

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



					@skip_mps
					def 	snake_case ( self							:						Optional[Any]  )->      Union[str, Any]:
								return super().test_save_load_local()



					@unittest.skip('''non-deterministic pipeline'''  )
					def 	snake_case ( self							:						Optional[int]  )->      Tuple:
								return super().test_inference_batch_single_identical()



					@skip_mps
					def 	snake_case ( self							:						Union[str, Any]  )->      List[Any]:
								return super().test_dict_tuple_outputs_equivalent()



					@skip_mps
					def 	snake_case ( self							:						List[Any]  )->      Optional[int]:
								return super().test_save_load_optional_components()




					@skip_mps
					def 	snake_case ( self							:						Any  )->      Union[str, Any]:
								return super().test_attention_slicing_forward_pass()





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


					'''simple docstring'''





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



					def 	snake_case ( self							:						Tuple  )->      Union[str, Any]:
								lowerCamelCase__	:	List[Any]				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''  )
								lowerCamelCase__	:	List[str]				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''  )
								lowerCamelCase__	:	Dict				=init_image.resize((512, 512)  )

								lowerCamelCase__	:	Dict				='''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase__	:	Union[str, Any]				=DDIMScheduler.from_pretrained(lowerCamelCase,						subfolder='''scheduler'''  )
								lowerCamelCase__	:	str				=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()

								lowerCamelCase__	:	Tuple				='''A black colored car'''
								lowerCamelCase__	:	List[str]				='''A blue colored car'''

								lowerCamelCase__	:	List[Any]				=torch.manual_seed(0  )
								lowerCamelCase__	:	Union[str, Any]				=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''',						)
								lowerCamelCase__	:	List[str]				=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 	snake_case ( self							:						Optional[int]  )->      Tuple:
								lowerCamelCase__	:	List[Any]				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/cycle-diffusion/black_colored_car.png'''  )
								lowerCamelCase__	:	Tuple				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''  )
								lowerCamelCase__	:	Tuple				=init_image.resize((512, 512)  )

								lowerCamelCase__	:	int				='''CompVis/stable-diffusion-v1-4'''
								lowerCamelCase__	:	Union[str, Any]				=DDIMScheduler.from_pretrained(lowerCamelCase,						subfolder='''scheduler'''  )
								lowerCamelCase__	:	str				=CycleDiffusionPipeline.from_pretrained(lowerCamelCase,						scheduler=lowerCamelCase,						safety_checker=lowerCamelCase  )

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

								lowerCamelCase__	:	Any				='''A black colored car'''
								lowerCamelCase__	:	int				='''A blue colored car'''

								lowerCamelCase__	:	int				=torch.manual_seed(0  )
								lowerCamelCase__	:	Any				=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''',						)
								lowerCamelCase__	:	List[str]				=output.images

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









"""simple docstring"""






_lowercase				:				Optional[Any]   		=		{
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "rhoknp": "rhoknp>=1.1.0,<1.3.1",
    "rjieba": "rjieba",
    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
    "ruff": "ruff>=0.0.241,<=0.0.259",
    "sacrebleu": "sacrebleu>=1.4.12,<2.0.0",
    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
    "tensorflow-text": "tensorflow-text<2.14",
    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
    "unidic": "unidic>=1.0.2",
    "unidic_lite": "unidic_lite>=1.0.7",
    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}




