


"""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 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      unittest.TestCase):




							'''simple docstring'''





							__magic_name__					:     Any				  =  RePaintPipeline
							__magic_name__					:     str				  =  IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
							__magic_name__					:     int				  =  PipelineTesterMixin.required_optional_params - {
							    '''latents''',
							    '''num_images_per_prompt''',
							    '''callback''',
							    '''callback_steps''',
							}
							__magic_name__					:     Dict				  =  IMAGE_INPAINTING_BATCH_PARAMS
							__magic_name__					:     str				  =  False




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								torch.manual_seed(0)
								torch.manual_seed(0)
								snake_case__  :						Dict		=      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")	,					)
								snake_case__  :						int		=      RePaintScheduler()
								snake_case__  :						Dict		=      {"unet": unet, "scheduler": scheduler}
								return components




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=0)   ->	Tuple:







								'''simple docstring'''




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







								'''simple docstring'''




								snake_case__  :						int		=      "cpu"  # ensure determinism for the device-dependent torch.Generator
								snake_case__  :						Dict		=      self.get_dummy_components()
								snake_case__  :						Tuple		=      RePaintPipeline(**lowerCamelCase__)
								snake_case__  :						Tuple		=      sd_pipe.to(lowerCamelCase__)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase__)

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

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, 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)   ->	Optional[Any]:







								'''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)   ->	int:







								'''simple docstring'''




								return super().test_dict_tuple_outputs_equivalent()




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







								'''simple docstring'''




								return super().test_save_load_optional_components()







							@skip_mps
							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return super().test_attention_slicing_forward_pass()


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




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




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







							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Any		=      load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
								    "repaint/celeba_hq_256.png")
								snake_case__  :						Dict		=      load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png")
								snake_case__  :						Optional[Any]		=      load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
								    "repaint/celeba_hq_256_result.npy")

								snake_case__  :						Tuple		=      "google/ddpm-ema-celebahq-256"
								snake_case__  :						List[Any]		=      UNetaDModel.from_pretrained(lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      RePaintScheduler.from_pretrained(lowerCamelCase__)

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

								snake_case__  :						int		=      torch.manual_seed(0)
								snake_case__  :						Any		=      repaint(
								    lowerCamelCase__	,					lowerCamelCase__	,					num_inference_steps=250	,					eta=0.0	,					jump_length=10	,					jump_n_sample=10	,					generator=lowerCamelCase__	,					output_type="np"	,					)
								snake_case__  :						Union[str, Any]		=      output.images[0]

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









"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





