

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class  a     (		UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	RePaintPipeline
						_lowercase			  =	IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
						_lowercase			  =	PipelineTesterMixin.required_optional_params - {
						    "latents",
						    "num_images_per_prompt",
						    "callback",
						    "callback_steps",
						}
						_lowercase			  =	IMAGE_INPAINTING_BATCH_PARAMS
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										torch.manual_seed(0		)
										torch.manual_seed(0		)
										_UpperCAmelCase				:						Optional[Any]       						=			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")  ,    )
										_UpperCAmelCase				:						List[Any]       						=			RePaintScheduler()
										_UpperCAmelCase				:						Dict       						=			{"unet": unet, "scheduler": scheduler}
										return components


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_=0		):







										'''simple docstring'''


										if str(A_		).startswith("mps"		):
														_UpperCAmelCase				:						Dict       						=			torch.manual_seed(A_		)
										else:
														_UpperCAmelCase				:						Any       						=			torch.Generator(device=A_		).manual_seed(A_		)
										_UpperCAmelCase				:						int       						=			np.random.RandomState(A_		).standard_normal((1, 3, 32, 32)		)
										_UpperCAmelCase				:						int       						=			torch.from_numpy(A_		).to(device=A_  ,    dtype=torch.floataa		)
										_UpperCAmelCase				:						str       						=			(image > 0).to(device=A_  ,    dtype=torch.floataa		)
										_UpperCAmelCase				:						Dict       						=			{
										    "image": image,
										    "mask_image": mask,
										    "generator": generator,
										    "num_inference_steps": 5,
										    "eta": 0.0,
										    "jump_length": 2,
										    "jump_n_sample": 2,
										    "output_type": "numpy",
										}
										return inputs


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			"cpu"  # ensure determinism for the device-dependent torch.Generator
										_UpperCAmelCase				:						List[str]       						=			self.get_dummy_components()
										_UpperCAmelCase				:						Dict       						=			RePaintPipeline(**A_		)
										_UpperCAmelCase				:						Tuple       						=			sd_pipe.to(A_		)
										sd_pipe.set_progress_bar_config(disable=A_		)

										_UpperCAmelCase				:						Dict       						=			self.get_dummy_inputs(A_		)
										_UpperCAmelCase				:						Any       						=			sd_pipe(**A_		).images
										_UpperCAmelCase				:						Optional[Any]       						=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										_UpperCAmelCase				:						Any       						=			np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

										assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3


						@skip_mps
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return super().test_save_load_local()


						@unittest.skip("non-deterministic pipeline"		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return super().test_inference_batch_single_identical()


						@skip_mps
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return super().test_dict_tuple_outputs_equivalent()


						@skip_mps
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return super().test_save_load_optional_components()





						@skip_mps
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return super().test_attention_slicing_forward_pass()







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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256.png"		)
										_UpperCAmelCase				:						Tuple       						=			load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
										_UpperCAmelCase				:						Tuple       						=			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256_result.npy"		)

										_UpperCAmelCase				:						Optional[int]       						=			"google/ddpm-ema-celebahq-256"
										_UpperCAmelCase				:						Optional[Any]       						=			UNetaDModel.from_pretrained(A_		)
										_UpperCAmelCase				:						List[Any]       						=			RePaintScheduler.from_pretrained(A_		)

										_UpperCAmelCase				:						str       						=			RePaintPipeline(unet=A_  ,    scheduler=A_		).to(A_		)
										repaint.set_progress_bar_config(disable=A_		)
										repaint.enable_attention_slicing()

										_UpperCAmelCase				:						List[str]       						=			torch.manual_seed(0		)
										_UpperCAmelCase				:						Any       						=			repaint(
										    A_  ,    A_  ,    num_inference_steps=250  ,    eta=0.0  ,    jump_length=10  ,    jump_n_sample=10  ,    generator=A_  ,    output_type="np"  ,    )
										_UpperCAmelCase				:						List[Any]       						=			output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image		).mean() < 1e-2





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE_						=					{'configuration_hubert': ['HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'HubertConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'HubertForCTC',
					    'HubertForSequenceClassification',
					    'HubertModel',
					    'HubertPreTrainedModel',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_						=					[
					    'TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFHubertForCTC',
					    'TFHubertModel',
					    'TFHubertPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


