
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = DDIMPipeline
	lowerCAmelCase__         = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	lowerCAmelCase__         = PipelineTesterMixin.required_optional_params - {
	    'num_images_per_prompt',
	    'latents',
	    'callback',
	    'callback_steps',
	}
	lowerCAmelCase__         = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					torch.manual_seed(0 )
					lowerCamelCase_						=			UNetaDModel(
					    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=3						,  out_channels=3						,  down_block_types=("DownBlock2D", "AttnDownBlock2D")						,  up_block_types=("AttnUpBlock2D", "UpBlock2D")						,  )
					lowerCamelCase_						=			DDIMScheduler()
					lowerCamelCase_						=			{"unet": unet, "scheduler": scheduler}
					return components



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=0 )				->       Any:
					if str(lowercase ).startswith("mps" ):
									lowerCamelCase_						=			torch.manual_seed(lowercase )
					else:
									lowerCamelCase_						=			torch.Generator(device=lowercase ).manual_seed(lowercase )
					lowerCamelCase_						=			{
					    "batch_size": 1,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "numpy",
					}
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			"cpu"

					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			self.pipeline_class(**lowercase )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			self.get_dummy_inputs(lowercase )
					lowerCamelCase_						=			pipe(**lowercase ).images
					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					self.assertEqual(image.shape						,  (1, 32, 32, 3) )
					lowerCamelCase_						=			np.array(
					    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
					lowerCamelCase_						=			np.abs(image_slice.flatten() - expected_slice ).max()
					self.assertLessEqual(lowercase						,  1e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					super().test_save_load_local(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					super().test_save_load_optional_components(expected_max_difference=3e-3 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					super().test_inference_batch_single_identical(expected_max_diff=3e-3 )




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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			"google/ddpm-cifar10-32"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			DDIMScheduler()

					lowerCamelCase_						=			DDIMPipeline(unet=lowercase						,  scheduler=lowercase )
					ddim.to(lowercase )
					ddim.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			ddim(generator=lowercase						,  eta=0.0						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			"google/ddpm-ema-bedroom-256"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			DDIMScheduler.from_pretrained(lowercase )

					lowerCamelCase_						=			DDIMPipeline(unet=lowercase						,  scheduler=lowercase )
					ddpm.to(lowercase )
					ddpm.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			ddpm(generator=lowercase						,  output_type="numpy" ).images

					lowerCamelCase_						=			image[0, -3:, -3:, -1]

					assert image.shape == (1, 256, 256, 3)
					lowerCamelCase_						=			np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )

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









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

