
"""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"""






							SCREAMING_SNAKE_CASE_			   =     RePaintPipeline
							SCREAMING_SNAKE_CASE_			   =     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
							SCREAMING_SNAKE_CASE_			   =     PipelineTesterMixin.required_optional_params - {
							    """latents""",
							    """num_images_per_prompt""",
							    """callback""",
							    """callback_steps""",
							}
							SCREAMING_SNAKE_CASE_			   =     IMAGE_INPAINTING_BATCH_PARAMS
							SCREAMING_SNAKE_CASE_			   =     False





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										torch.manual_seed(0 )
										torch.manual_seed(0 )
										lowerCamelCase__			:  Tuple  =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__			:  int  =RePaintScheduler()
										lowerCamelCase__			:  List[Any]  ={'unet': unet, 'scheduler': scheduler}
										return components





							def    UpperCAmelCase__							(						self						:Union[str, Any]  ,							lowerCamelCase_						:Tuple  ,							lowerCamelCase_						:str=0 ):





										"""simple docstring"""

										if str(lowerCamelCase_ ).startswith('mps' ):
													lowerCamelCase__			:  Tuple  =torch.manual_seed(lowerCamelCase_ )
										else:
													lowerCamelCase__			:  Union[str, Any]  =torch.Generator(device=lowerCamelCase_ ).manual_seed(lowerCamelCase_ )
										lowerCamelCase__			:  Union[str, Any]  =np.random.RandomState(lowerCamelCase_ ).standard_normal((1, 3, 32, 32) )
										lowerCamelCase__			:  Optional[Any]  =torch.from_numpy(lowerCamelCase_ ).to(device=lowerCamelCase_  ,							dtype=torch.floataa )
										lowerCamelCase__			:  List[str]  =(image > 0).to(device=lowerCamelCase_  ,							dtype=torch.floataa )
										lowerCamelCase__			:  int  ={
										    '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						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  ='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__			:  Dict  =self.get_dummy_components()
										lowerCamelCase__			:  List[Any]  =RePaintPipeline(**lowerCamelCase_ )
										lowerCamelCase__			:  Tuple  =sd_pipe.to(lowerCamelCase_ )
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )

										lowerCamelCase__			:  List[str]  =self.get_dummy_inputs(lowerCamelCase_ )
										lowerCamelCase__			:  Dict  =sd_pipe(**lowerCamelCase_ ).images
										lowerCamelCase__			:  List[str]  =image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__			:  List[str]  =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						:Optional[int] ):





										"""simple docstring"""

										return super().test_save_load_local()





							@unittest.skip('non-deterministic pipeline' )
							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										return super().test_inference_batch_single_identical()





							@skip_mps
							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										return super().test_dict_tuple_outputs_equivalent()





							@skip_mps
							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										return super().test_save_load_optional_components()





							@skip_mps
							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										return super().test_attention_slicing_forward_pass()






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



							"""simple docstring"""






							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

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





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

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

										lowerCamelCase__			:  Any  ='google/ddpm-ema-celebahq-256'
										lowerCamelCase__			:  List[str]  =UNetaDModel.from_pretrained(lowerCamelCase_ )
										lowerCamelCase__			:  Optional[int]  =RePaintScheduler.from_pretrained(lowerCamelCase_ )

										lowerCamelCase__			:  int  =RePaintPipeline(unet=lowerCamelCase_  ,							scheduler=lowerCamelCase_ ).to(lowerCamelCase_ )
										repaint.set_progress_bar_config(disable=lowerCamelCase_ )
										repaint.enable_attention_slicing()

										lowerCamelCase__			:  Any  =torch.manual_seed(0 )
										lowerCamelCase__			:  int  =repaint(
										    lowerCamelCase_  ,							lowerCamelCase_  ,							num_inference_steps=250  ,							eta=0.0  ,							jump_length=10  ,							jump_n_sample=10  ,							generator=lowerCamelCase_  ,							output_type='np'  ,							)
										lowerCamelCase__			:  Any  =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, is_vision_available


lowerCAmelCase			=		{
    """configuration_donut_swin""": ["""DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DonutSwinConfig"""],
    """processing_donut""": ["""DonutProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		[
							    """DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DonutSwinModel""",
							    """DonutSwinPreTrainedModel""",
							]

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase			=		["""DonutFeatureExtractor"""]
							lowerCAmelCase			=		["""DonutImageProcessor"""]


if TYPE_CHECKING:
							from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
							from .processing_donut import DonutProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_donut_swin import (
														    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DonutSwinModel,
														    DonutSwinPreTrainedModel,
														)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_donut import DonutFeatureExtractor
														from .image_processing_donut import DonutImageProcessor

else:
							import sys

							lowerCAmelCase			=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)