

'''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 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   torch.manual_seed(0			)
   __lowercase							  = UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)					,layers_per_block=2					,sample_size=3_2					,in_channels=4					,out_channels=4					,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,cross_attention_dim=3_2					,)
   __lowercase							  = DDIMScheduler(
       beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='''scaled_linear'''					,num_train_timesteps=1_0_0_0					,clip_sample=lowercase__					,set_alpha_to_one=lowercase__					,)
   torch.manual_seed(0			)
   __lowercase							  = AutoencoderKL(
       block_out_channels=[3_2, 6_4]					,in_channels=3					,out_channels=3					,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,latent_channels=4					,)
   torch.manual_seed(0			)
   __lowercase							  = CLIPTextConfig(
       bos_token_id=0					,eos_token_id=2					,hidden_size=3_2					,intermediate_size=3_7					,layer_norm_eps=1e-0_5					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_0_0_0					,)
   __lowercase							  = CLIPTextModel(lowercase__			)
   __lowercase							  = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Union[str, Any]					,lowercase__   :     List[str]=0			):
   __lowercase							  = floats_tensor((1, 3, 3_2, 3_2)					,rng=random.Random(lowercase__			)			).to(lowercase__			)
   __lowercase							  = image / 2 + 0.5
   if str(lowercase__			).startswith('''mps'''			):
    __lowercase							  = torch.manual_seed(lowercase__			)
   else:
    __lowercase							  = torch.Generator(device=lowercase__			).manual_seed(lowercase__			)
   __lowercase							  = {
       '''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 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = '''cpu'''  # ensure determinism for the device-dependent torch.Generator

   __lowercase							  = self.get_dummy_components()
   __lowercase							  = CycleDiffusionPipeline(**lowercase__			)
   __lowercase							  = pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe(**lowercase__			)
   __lowercase							  = output.images

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

   assert images.shape == (1, 3_2, 3_2, 3)
   __lowercase							  = np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]			)

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






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

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = pipe(**lowercase__			)
   __lowercase							  = output.images

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

   assert images.shape == (1, 3_2, 3_2, 3)
   __lowercase							  = np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]			)

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






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return super().test_save_load_local()






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






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   return super().test_dict_tuple_outputs_equivalent()






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return super().test_save_load_optional_components()






  @skip_mps
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return super().test_attention_slicing_forward_pass()






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




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''			)
   __lowercase							  = init_image.resize((5_1_2, 5_1_2)			)

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

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

   __lowercase							  = '''A black colored car'''
   __lowercase							  = '''A blue colored car'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,source_prompt=lowercase__					,image=lowercase__					,num_inference_steps=1_0_0					,eta=0.1					,strength=0.8_5					,guidance_scale=3					,source_guidance_scale=1					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/cycle-diffusion/black_colored_car.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''			)
   __lowercase							  = init_image.resize((5_1_2, 5_1_2)			)

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

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

   __lowercase							  = '''A black colored car'''
   __lowercase							  = '''A blue colored car'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,source_prompt=lowercase__					,image=lowercase__					,num_inference_steps=1_0_0					,eta=0.1					,strength=0.8_5					,guidance_scale=3					,source_guidance_scale=1					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images

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



"""simple docstring"""





__magic_name__       =			"0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
							if not is_onnx_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_onnx_objects import *  # noqa F403
else:
							from .pipelines import OnnxRuntimeModel

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

try:
							if not (is_torch_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    VQDiffusionPipeline,
							)

try:
							if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
							from .pipelines import StableDiffusionKDiffusionPipeline

try:
							if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
							from .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

try:
							if not (is_torch_available() and is_librosa_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
							from .pipelines import AudioDiffusionPipeline, Mel

try:
							if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
							from .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


try:
							if not (is_flax_available() and is_transformers_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
							from .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor




