






from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_pt_objects import *  # noqa F403
else:
       from .scheduling_consistency_models import CMStochasticIterativeScheduler
       from .scheduling_ddim import DDIMScheduler
       from .scheduling_ddim_inverse import DDIMInverseScheduler
       from .scheduling_ddim_parallel import DDIMParallelScheduler
       from .scheduling_ddpm import DDPMScheduler
       from .scheduling_ddpm_parallel import DDPMParallelScheduler
       from .scheduling_deis_multistep import DEISMultistepScheduler
       from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
       from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
       from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
       from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
       from .scheduling_euler_discrete import EulerDiscreteScheduler
       from .scheduling_heun_discrete import HeunDiscreteScheduler
       from .scheduling_ipndm import IPNDMScheduler
       from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
       from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
       from .scheduling_karras_ve import KarrasVeScheduler
       from .scheduling_pndm import PNDMScheduler
       from .scheduling_repaint import RePaintScheduler
       from .scheduling_sde_ve import ScoreSdeVeScheduler
       from .scheduling_sde_vp import ScoreSdeVpScheduler
       from .scheduling_unclip import UnCLIPScheduler
       from .scheduling_unipc_multistep import UniPCMultistepScheduler
       from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
       from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       from ..utils.dummy_flax_objects import *  # noqa F403
else:
       from .scheduling_ddim_flax import FlaxDDIMScheduler
       from .scheduling_ddpm_flax import FlaxDDPMScheduler
       from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
       from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
       from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
       from .scheduling_pndm_flax import FlaxPNDMScheduler
       from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
       from .scheduling_utils_flax import (
           FlaxKarrasDiffusionSchedulers,
           FlaxSchedulerMixin,
           FlaxSchedulerOutput,
           broadcast_to_shape_from_left,
       )


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 .scheduling_lms_discrete 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 .scheduling_dpmsolver_sde import DPMSolverSDEScheduler









import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






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






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

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




