






import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCamelCase__							=     False




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''
       pass




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

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





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



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""			,						torch_dtype=torch.floataa    )
            pipe.to(lowerCamelCase__    )
            pipe.set_progress_bar_config(disable=lowerCamelCase__    )

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

            __snake_case			:str          =						torch.manual_seed(0    )
            __snake_case			:Optional[Any]          =						pipe.dual_guided(
                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			:int          =						VersatileDiffusionPipeline.from_pretrained(lowerCamelCase__			,						torch_dtype=torch.floataa    )
            pipe.to(lowerCamelCase__    )
            pipe.set_progress_bar_config(disable=lowerCamelCase__    )

            __snake_case			:Dict          =						generator.manual_seed(0    )
            __snake_case			:Tuple          =						pipe.dual_guided(
                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 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Dict          =						VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""			,						torch_dtype=torch.floataa    )
            pipe.to(lowerCamelCase__    )
            pipe.set_progress_bar_config(disable=lowerCamelCase__    )

            __snake_case			:List[Any]          =						'''cyberpunk 2077'''
            __snake_case			:Dict          =						load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""    )
            __snake_case			:Union[str, Any]          =						torch.manual_seed(0    )
            __snake_case			:int          =						pipe.dual_guided(
                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			:int          =						image[0, 2_53:2_56, 2_53:2_56, -1]

            assert image.shape == (1, 5_12, 5_12, 3)
            __snake_case			:Tuple          =						np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]    )

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

            __snake_case			:str          =						'''A painting of a squirrel eating a burger '''
            __snake_case			:Tuple          =						torch.manual_seed(0    )
            __snake_case			:Dict          =						pipe.text_to_image(
                prompt=lowerCamelCase__			,						generator=lowerCamelCase__			,						guidance_scale=7.5			,						num_inference_steps=50			,						output_type="""numpy"""    ).images

            __snake_case			:Union[str, Any]          =						image[0, 2_53:2_56, 2_53:2_56, -1]

            assert image.shape == (1, 5_12, 5_12, 3)
            __snake_case			:List[str]          =						np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]    )

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

            __snake_case			:Optional[int]          =						pipe.image_variation(lowerCamelCase__			,						generator=lowerCamelCase__			,						output_type="""numpy"""    ).images

            __snake_case			:List[Any]          =						image[0, 2_53:2_56, 2_53:2_56, -1]

            assert image.shape == (1, 5_12, 5_12, 3)
            __snake_case			:Union[str, Any]          =						np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]    )

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










def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]
     __snake_case			:Union[str, Any]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1 or len(snake_case__  ) <= key:
          return input_string

     for position, character in enumerate(snake_case__  ):
          __snake_case			:Any          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:Optional[int]          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append(snake_case__  )
     __snake_case			:List[Any]          =						["""""".join(snake_case__  ) for row in temp_grid]
     __snake_case			:Any          =						"""""".join(snake_case__  )

     return output_string






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     __snake_case			:Optional[int]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1:
          return input_string

     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]  # generates template
     for position in range(len(snake_case__  )  ):
          __snake_case			:List[str]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append("""*"""  )

     __snake_case			:str          =						0
     for row in temp_grid:  # fills in the characters
          __snake_case			:str          =						input_string[counter : counter + len(snake_case__  )]
          grid.append(list(snake_case__  )  )
          counter += len(snake_case__  )

     __snake_case			:Any          =						""""""  # reads as zigzag
     for position in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0  )
     return output_string






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						{}
     for key_guess in range(1	,len(snake_case__  )  ):  # tries every key
          __snake_case			:Dict          =						decrypt(snake_case__	,snake_case__  )
     return results


if __name__ == "__main__":
     import doctest

     doctest.testmod()


