



'''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__				(   snake_case_     ,			unittest.TestCase					):
	"""simple docstring"""


	A__    :    Optional[int]						=				RePaintPipeline
	A__    :    Any						=				IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
	A__    :    Optional[int]						=				PipelineTesterMixin.required_optional_params - {
	    '''latents''',
	    '''num_images_per_prompt''',
	    '''callback''',
	    '''callback_steps''',
	}
	A__    :    Optional[Any]						=				IMAGE_INPAINTING_BATCH_PARAMS
	A__    :    int						=				False


	def      a__ (		self			) ->							Dict:
				torch.manual_seed(0			)
				torch.manual_seed(0			)
				A:      Optional[int]														=   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""")					,   )
				A:      Dict														=   RePaintScheduler()
				A:      str														=   {"""unet""": unet, """scheduler""": scheduler}
				return components


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

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

				assert image.shape == (1, 32, 32, 3)
				A:      Optional[Any]														=   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      a__ (		self			) ->							Dict:
				return super().test_save_load_local()


	@unittest.skip("""non-deterministic pipeline"""			)
	def      a__ (		self			) ->							Union[str, Any]:
				return super().test_inference_batch_single_identical()


	@skip_mps
	def      a__ (		self			) ->							Dict:
				return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def      a__ (		self			) ->							Optional[Any]:
				return super().test_save_load_optional_components()





	@skip_mps
	def      a__ (		self			) ->							str:
				return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class   SCREAMING_SNAKE_CASE__				(   unittest.TestCase					):
	"""simple docstring"""


	def      a__ (		self			) ->							Union[str, Any]:
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def      a__ (		self			) ->							int:
				A:      List[Any]														=   load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
				    """repaint/celeba_hq_256.png"""			)
				A:      str														=   load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""			)
				A:      List[Any]														=   load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
				    """repaint/celeba_hq_256_result.npy"""			)

				A:      Tuple														=   """google/ddpm-ema-celebahq-256"""
				A:      Dict														=   UNetaDModel.from_pretrained(A			)
				A:      Tuple														=   RePaintScheduler.from_pretrained(A			)

				A:      Any														=   RePaintPipeline(unet=A					,   scheduler=A			).to(A			)
				repaint.set_progress_bar_config(disable=A			)
				repaint.enable_attention_slicing()

				A:      int														=   torch.manual_seed(0			)
				A:      str														=   repaint(
				    A					,   A					,   num_inference_steps=2_50					,   eta=0.0					,   jump_length=10					,   jump_n_sample=10					,   generator=A					,   output_type="""np"""					,   )
				A:      Optional[Any]														=   output.images[0]

				assert image.shape == (2_56, 2_56, 3)
				assert np.abs(expected_image - image			).mean() < 1e-2










'''simple docstring'''
from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime


class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	def __init__(		self					,   *A					,   **A			) ->							Dict:
				super().__init__(*A					,   **A			)


	def      a__ (		self					,   A					,   A			) ->							Dict:
				A:      int														=   (
				    next_prime(value % self.size_table			)
				    if not is_prime(value % self.size_table			)
				    else value % self.size_table
				)  # gt = bigger than
				return next_prime_gt - (data % next_prime_gt)


	def      a__ (		self					,   A					,   A					,   A			) ->							int:
				return (increment * self.__hash_function_a(A					,   A			)) % self.size_table





	def      a__ (		self					,   A					,   A=None			) ->							str:
				A:      Tuple														=   1
				A:      str														=   self.hash_function(A			)

				while self.values[new_key] is not None and self.values[new_key] != key:
							A:      int														=   (
							    self.__hash_double_function(A					,   A					,   A			)
							    if self.balanced_factor() >= self.lim_charge
							    else None
							)
							if new_key is None:
										break
							else:
										i += 1

				return new_key





