
"""simple docstring"""






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    (   __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: int					      =      RePaintPipeline
							_lowerCamelCase				: List[Any]					      =      IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
							_lowerCamelCase				: Dict					      =      PipelineTesterMixin.required_optional_params - {
							    """latents""",
							    """num_images_per_prompt""",
							    """callback""",
							    """callback_steps""",
							}
							_lowerCamelCase				: List[str]					      =      IMAGE_INPAINTING_BATCH_PARAMS
							_lowerCamelCase				: Optional[Any]					      =      False
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										torch.manual_seed(0					)
										torch.manual_seed(0					)
										__lowercase						      =					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")     ,						)
										__lowercase						      =					RePaintScheduler()
										__lowercase						      =					{"unet": unet, "scheduler": scheduler}
										return components
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=0					) ->       Optional[int]:




										'''simple docstring'''
										if str(_lowerCamelCase					).startswith("mps"					):
													__lowercase						      =					torch.manual_seed(_lowerCamelCase					)
										else:
													__lowercase						      =					torch.Generator(device=_lowerCamelCase					).manual_seed(_lowerCamelCase					)
										__lowercase						      =					np.random.RandomState(_lowerCamelCase					).standard_normal((1, 3, 32, 32)					)
										__lowercase						      =					torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase     ,						dtype=torch.floataa					)
										__lowercase						      =					(image > 0).to(device=_lowerCamelCase     ,						dtype=torch.floataa					)
										__lowercase						      =					{
										    "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      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
										__lowercase						      =					self.get_dummy_components()
										__lowercase						      =					RePaintPipeline(**_lowerCamelCase					)
										__lowercase						      =					sd_pipe.to(_lowerCamelCase					)
										sd_pipe.set_progress_bar_config(disable=_lowerCamelCase					)

										__lowercase						      =					self.get_dummy_inputs(_lowerCamelCase					)
										__lowercase						      =					sd_pipe(**_lowerCamelCase					).images
										__lowercase						      =					image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										__lowercase						      =					np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3
							@skip_mps
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										return super().test_save_load_local()
							@unittest.skip("non-deterministic pipeline"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										return super().test_inference_batch_single_identical()
							@skip_mps
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Any:




										'''simple docstring'''
										return super().test_dict_tuple_outputs_equivalent()
							@skip_mps
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										return super().test_save_load_optional_components()





							@skip_mps
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										return super().test_attention_slicing_forward_pass()





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



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256.png"					)
										__lowercase						      =					load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"					)
										__lowercase						      =					load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
										    "repaint/celeba_hq_256_result.npy"					)

										__lowercase						      =					"google/ddpm-ema-celebahq-256"
										__lowercase						      =					UNetaDModel.from_pretrained(_lowerCamelCase					)
										__lowercase						      =					RePaintScheduler.from_pretrained(_lowerCamelCase					)

										__lowercase						      =					RePaintPipeline(unet=_lowerCamelCase     ,						scheduler=_lowerCamelCase					).to(_lowerCamelCase					)
										repaint.set_progress_bar_config(disable=_lowerCamelCase					)
										repaint.enable_attention_slicing()

										__lowercase						      =					torch.manual_seed(0					)
										__lowercase						      =					repaint(
										    _lowerCamelCase     ,						_lowerCamelCase     ,						num_inference_steps=250     ,						eta=0.0     ,						jump_length=10     ,						jump_n_sample=10     ,						generator=_lowerCamelCase     ,						output_type="np"     ,						)
										__lowercase						      =					output.images[0]

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







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

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





