
"""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  :str	):





										"""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__     :					Union[str, Any]		=RePaintScheduler()
										lowerCamelCase__     :					Any		={'unet': unet, 'scheduler': scheduler}
										return components





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





										"""simple docstring"""

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





										"""simple docstring"""

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

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__     :					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[Any]	):





										"""simple docstring"""

										return super().test_save_load_local()





							@unittest.skip('non-deterministic pipeline'	)
							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""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  :int	):





										"""simple docstring"""

										return super().test_save_load_optional_components()





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





										"""simple docstring"""

										return super().test_attention_slicing_forward_pass()






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



							"""simple docstring"""






							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

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

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

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

										lowerCamelCase__     :					Optional[int]		=torch.manual_seed(0	)
										lowerCamelCase__     :					List[Any]		=repaint(
										    lowerCamelCase_					,							lowerCamelCase_					,							num_inference_steps=250					,							eta=0.0					,							jump_length=10					,							jump_n_sample=10					,							generator=lowerCamelCase_					,							output_type='np'					,							)
										lowerCamelCase__     :					List[str]		=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_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""MobileNetV1FeatureExtractor"""]
							lowerCAmelCase								=						["""MobileNetV1ImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MobileNetV1ForImageClassification""",
							    """MobileNetV1Model""",
							    """MobileNetV1PreTrainedModel""",
							    """load_tf_weights_in_mobilenet_v1""",
							]


if TYPE_CHECKING:
							from .configuration_mobilenet_va import (
							    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileNetVaConfig,
							    MobileNetVaOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
														from .image_processing_mobilenet_va import MobileNetVaImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilenet_va import (
														    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileNetVaForImageClassification,
														    MobileNetVaModel,
														    MobileNetVaPreTrainedModel,
														    load_tf_weights_in_mobilenet_va,
														)


else:
							import sys

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