


from .number_theory.prime_numbers import next_prime





class       _a							:




	def __init__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int | None = None	,      _SCREAMING_SNAKE_CASE		:	float | None = None	,      )-> None:
			lowerCAmelCase__     :    Any					  =      size_table
			lowerCAmelCase__     :    List[Any]					  =      [None] * self.size_table
			lowerCAmelCase__     :    int					  =      0.75 if lim_charge is None else lim_charge
			lowerCAmelCase__     :    Tuple					  =      1 if charge_factor is None else charge_factor
			lowerCAmelCase__     :    list					  =      []
			lowerCAmelCase__     :    dict					  =      {}




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> str:
			return self._keys




	def 							UpperCAmelCase__(      self		:	Dict	)-> Tuple:
			return sum(1 for slot in self.values if slot is not None	) / (
			    self.size_table * self.charge_factor
			)




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	int	)-> Optional[Any]:
			return key % self.size_table




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			print(F'step {step_ord}'	)
			print(list(range(len(self.values	)	)	)	)
			print(self.values	)




	def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	str	)-> int:
			lowerCAmelCase__     :    Union[str, Any]					  =      1
			lowerCAmelCase__     :    int					  =      values
			for value in values:
					self.insert_data(_SCREAMING_SNAKE_CASE	)
					self._step_by_step(_SCREAMING_SNAKE_CASE	)
					i += 1




	def 							UpperCAmelCase__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> str:
			lowerCAmelCase__     :    Optional[Any]					  =      data
			lowerCAmelCase__     :    List[str]					  =      data




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	str=None	)-> Dict:
			lowerCAmelCase__     :    int					  =      self.hash_function(key + 1	)

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

			return new_key




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




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	Optional[Any]	)-> Tuple:
			lowerCAmelCase__     :    Optional[int]					  =      self.hash_function(_SCREAMING_SNAKE_CASE	)

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

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

			else:
					lowerCAmelCase__     :    List[str]					  =      self._collision_resolution(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					if collision_resolution is not None:
							self._set_value(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
					else:
							self.rehashing()
							self.insert_data(_SCREAMING_SNAKE_CASE	)








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class       _a							(       unittest.TestCase):




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    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''')	,      )
			return model




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      1E-2 if torch_device != '''mps''' else 3E-2
			assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance





@slow
@require_torch_gpu
class       _a							(       unittest.TestCase):




	def 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




