






"""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 	__UpperCamelCase    (    lowerCamelCase_      ,    unittest.TestCase	):
					lowercase_						:							Optional[Any]			=     RePaintPipeline
					lowercase_						:							List[Any]			=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					lowercase_						:							List[Any]			=     PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					lowercase_						:							List[Any]			=     IMAGE_INPAINTING_BATCH_PARAMS
					lowercase_						:							List[str]			=     False
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					List[Any]:
											torch.manual_seed(0      )
											torch.manual_seed(0      )
											lowerCAmelCase      :Union[str, 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')    ,   )
											lowerCAmelCase      :Union[str, Any]				  = RePaintScheduler()
											lowerCAmelCase      :Optional[Any]				  = {"unet": unet, "scheduler": scheduler}
											return components
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  List[str]    ,   UpperCAmelCase       :  List[str]=0      )  ->					Union[str, Any]:







											if str(UpperCAmelCase      ).startswith('mps'      ):
																	lowerCAmelCase      :str				  = torch.manual_seed(UpperCAmelCase      )
											else:
																	lowerCAmelCase      :List[Any]				  = torch.Generator(device=UpperCAmelCase      ).manual_seed(UpperCAmelCase      )
											lowerCAmelCase      :Any				  = np.random.RandomState(UpperCAmelCase      ).standard_normal((1, 3, 32, 32)      )
											lowerCAmelCase      :Any				  = torch.from_numpy(UpperCAmelCase      ).to(device=UpperCAmelCase    ,   dtype=torch.floataa      )
											lowerCAmelCase      :List[str]				  = (image > 0).to(device=UpperCAmelCase    ,   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]      )  ->					str:
											lowerCAmelCase      :List[Any]				  = "cpu"  # ensure determinism for the device-dependent torch.Generator
											lowerCAmelCase      :Any				  = self.get_dummy_components()
											lowerCAmelCase      :Optional[int]				  = RePaintPipeline(**UpperCAmelCase      )
											lowerCAmelCase      :Optional[Any]				  = sd_pipe.to(UpperCAmelCase      )
											sd_pipe.set_progress_bar_config(disable=UpperCAmelCase      )

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

											assert image.shape == (1, 32, 32, 3)
											lowerCAmelCase      :List[str]				  = np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]      )

											assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3
					@skip_mps
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Any:
											return super().test_save_load_local()
					@unittest.skip('non-deterministic pipeline'      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					List[Any]:
											return super().test_inference_batch_single_identical()
					@skip_mps
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					int:
											return super().test_dict_tuple_outputs_equivalent()
					@skip_mps
					def 					UpperCAmelCase__   (       self       :  List[str]      )  ->					Tuple:
											return super().test_save_load_optional_components()
					@skip_mps
					def 					UpperCAmelCase__   (       self       :  str      )  ->					List[Any]:
											return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					List[str]:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Tuple:
											lowerCAmelCase      :Optional[int]				  = load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
											    'repaint/celeba_hq_256.png'      )
											lowerCAmelCase      :Any				  = load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
											lowerCAmelCase      :List[Any]				  = load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
											    'repaint/celeba_hq_256_result.npy'      )

											lowerCAmelCase      :str				  = "google/ddpm-ema-celebahq-256"
											lowerCAmelCase      :Optional[Any]				  = UNetaDModel.from_pretrained(UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = RePaintScheduler.from_pretrained(UpperCAmelCase      )

											lowerCAmelCase      :str				  = RePaintPipeline(unet=UpperCAmelCase    ,   scheduler=UpperCAmelCase      ).to(UpperCAmelCase      )
											repaint.set_progress_bar_config(disable=UpperCAmelCase      )
											repaint.enable_attention_slicing()

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

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












"""simple docstring"""


from .number_theory.prime_numbers import next_prime




class 	__UpperCamelCase    :
					def __init__(       self       :  List[str]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  int | None = None    ,   UpperCAmelCase       :  float | None = None    ,   )  ->					None:
											lowerCAmelCase      :Tuple				  = size_table
											lowerCAmelCase      :List[Any]				  = [None] * self.size_table
											lowerCAmelCase      :Tuple				  = 0.7_5 if lim_charge is None else lim_charge
											lowerCAmelCase      :str				  = 1 if charge_factor is None else charge_factor
											lowerCAmelCase      :list				  = []
											lowerCAmelCase      :dict				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[int]      )  ->					Union[str, Any]:
											return self._keys
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					Optional[Any]:
											return sum(1 for slot in self.values if slot is not None      ) / (
											    self.size_table * self.charge_factor
											)
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  List[str]      )  ->					int:
											return key % self.size_table
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					int:
											print(f"""step {step_ord}"""      )
											print(list(range(len(self.values      )      )      )      )
											print(self.values      )
					def 					UpperCAmelCase__   (       self       :  Any    ,   UpperCAmelCase       :  Dict      )  ->					Any:
											lowerCAmelCase      :List[str]				  = 1
											lowerCAmelCase      :Union[str, Any]				  = values
											for value in values:
																	self.insert_data(UpperCAmelCase      )
																	self._step_by_step(UpperCAmelCase      )
																	i += 1
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :List[Any]				  = data
											lowerCAmelCase      :str				  = data
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str=None      )  ->					Tuple:
											lowerCAmelCase      :Optional[Any]				  = self.hash_function(key + 1      )

											while self.values[new_key] is not None and self.values[new_key] != key:
																	if self.values.count(UpperCAmelCase      ) > 0:
																							lowerCAmelCase      :Tuple				  = self.hash_function(new_key + 1      )
																	else:
																							lowerCAmelCase      :Union[str, Any]				  = None
																							break

											return new_key
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					Any:
											lowerCAmelCase      :Optional[Any]				  = [value for value in self.values if value is not None]
											lowerCAmelCase      :List[str]				  = next_prime(self.size_table    ,   factor=2      )
											self._keys.clear()
											lowerCAmelCase      :int				  = [None] * self.size_table  # hell's pointers D: don't DRY ;/
											for value in survivor_values:
																	self.insert_data(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  Tuple      )  ->					int:
											lowerCAmelCase      :Union[str, Any]				  = self.hash_function(UpperCAmelCase      )

											if self.values[key] is None:
																	self._set_value(UpperCAmelCase    ,   UpperCAmelCase      )

											elif self.values[key] == data:
																	pass

											else:
																	lowerCAmelCase      :Tuple				  = self._collision_resolution(UpperCAmelCase    ,   UpperCAmelCase      )
																	if collision_resolution is not None:
																							self._set_value(UpperCAmelCase    ,   UpperCAmelCase      )
																	else:
																							self.rehashing()
																							self.insert_data(UpperCAmelCase      )