from .number_theory.prime_numbers import next_prime




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int | None = None							,      lowerCamelCase							:		float | None = None							,      )			->				None:
        _UpperCamelCase  :							Tuple	    =	size_table
        _UpperCamelCase  :							Tuple	    =	[None] * self.size_table
        _UpperCamelCase  :							int	    =	0.7_5 if lim_charge is None else lim_charge
        _UpperCamelCase  :							Tuple	    =	1 if charge_factor is None else charge_factor
        _UpperCamelCase  :							list	    =	[]
        _UpperCamelCase  :							dict	    =	{}

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

 def        __snake_case       (      self							:		List[Any]	)			->				Optional[int]:
        return sum(1 for slot in self.values if slot is not None	) / (
            self.size_table * self.charge_factor
        )

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		int	)			->				Any:
        return key % self.size_table

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				Optional[Any]:
        print(f"step {step_ord}"	)
        print(list(range(len(self.values	)	)	)	)
        print(self.values	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	1
        _UpperCamelCase  :							Union[str, Any]	    =	values
        for value in values:
               self.insert_data(lowerCamelCase	)
               self._step_by_step(lowerCamelCase	)
               i += 1

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Optional[int]	)			->				Any:
        _UpperCamelCase  :							int	    =	data
        _UpperCamelCase  :							Optional[int]	    =	data

 def        __snake_case       (      self							:		Optional[int]							,      lowerCamelCase							:		Optional[int]							,      lowerCamelCase							:		str=None	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	self.hash_function(key + 1	)

        while self.values[new_key] is not None and self.values[new_key] != key:
               if self.values.count(lowerCamelCase	) > 0:
                      _UpperCamelCase  :							Optional[int]	    =	self.hash_function(new_key + 1	)
               else:
                      _UpperCamelCase  :							Tuple	    =	None
                      break

        return new_key

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

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str	)			->				Union[str, Any]:
        _UpperCamelCase  :							Dict	    =	self.hash_function(lowerCamelCase	)

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

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

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





import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__				:				List[str]						=      False






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

							"""simple docstring"""






							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													# remove text_unet
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													with tempfile.TemporaryDirectory() as tmpdirname:
																			pipe.save_pretrained(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE_      )

													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      generator.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt='''first prompt'''  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=2  ,						output_type='''numpy'''  ,						).images

													assert np.abs(image - new_image      ).sum() < 1E-5, "Models don't have the same forward pass"



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase  	=      VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion'''      )
													pipe.remove_unused_weights()
													pipe.to(SCREAMING_SNAKE_CASE_      )
													pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      '''cyberpunk 2077'''
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''      )
													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      pipe(
													    prompt=SCREAMING_SNAKE_CASE_  ,						image=SCREAMING_SNAKE_CASE_  ,						text_to_image_strength=0.7_5  ,						generator=SCREAMING_SNAKE_CASE_  ,						guidance_scale=7.5  ,						num_inference_steps=50  ,						output_type='''numpy'''  ,						).images

													__UpperCamelCase  	=      image[0, 253:256, 253:256, -1]

													assert image.shape == (1, 512, 512, 3)
													__UpperCamelCase  	=      np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]      )

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




