
'''simple docstring'''





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


A							     = False



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



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def 		_lowerCamelCase						(self	:			Optional[Any]     ) ->							Dict:
        snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"     )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

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

        snake_case_    							=				torch.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt="first prompt"						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			guidance_scale=7.5						,			num_inference_steps=2						,			output_type="numpy"						,			).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(lowerCAmelCase_     )
              snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained(lowerCAmelCase_     )

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

        snake_case_    							=				generator.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt="first prompt"						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			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 		_lowerCamelCase						(self	:			str     ) ->							Optional[int]:
        snake_case_    							=				VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"     )
        pipe.remove_unused_weights()
        pipe.to(lowerCAmelCase_     )
        pipe.set_progress_bar_config(disable=lowerCAmelCase_     )

        snake_case_    							=				"cyberpunk 2077"
        snake_case_    							=				load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"     )
        snake_case_    							=				torch.manual_seed(0     )
        snake_case_    							=				pipe(
            prompt=lowerCAmelCase_						,			image=lowerCAmelCase_						,			text_to_image_strength=0.75						,			generator=lowerCAmelCase_						,			guidance_scale=7.5						,			num_inference_steps=50						,			output_type="numpy"						,			).images

        snake_case_    							=				image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        snake_case_    							=				np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779]     )

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


'''simple docstring'''

from hashlib import mda, shaaaa

lowerCAmelCase_			:			Union[str, Any]    	=   (shaaaa, mda)





class 				lowercase    :


   def __init__( self					:						Optional[Any] ,		__lowerCAmelCase					:						int = 8)   ->       None:
         lowercase_     			=   0B0
         lowercase_     			=   size


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						str)   ->       None:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         self.bitarray |= h


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       bool:
         lowercase_     			=   self.hash_(__lowerCAmelCase)
         return (h & self.bitarray) == h


   def __contains__( self					:						str ,		__lowerCAmelCase					:						str)   ->       bool:
         return self.exists(__lowerCAmelCase)


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						int)   ->       str:
         lowercase_     			=   bin(__lowerCAmelCase)[2:]
         return res.zfill(self.size)


   @property
   def __UpperCAmelCase							( self					:						Dict)   ->       str:
         return self.format_bin(self.bitarray)


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						str)   ->       int:
         lowercase_     			=   0B0
         for func in HASH_FUNCTIONS:
               lowercase_     			=   (
                   int.from_bytes(func(value.encode()).digest() ,		"little") % self.size
               )
               res |= 2**position
         return res


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       str:
         return self.format_bin(self.hash_(__lowerCAmelCase))


   @property
   def __UpperCAmelCase							( self					:						str)   ->       float:
         lowercase_     			=   bin(self.bitarray).count("1")
         return (n_ones / self.size) ** len(__lowerCAmelCase)






