"""simple docstring"""




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


_a      :		int           =   False
class 						__A							( unittest.TestCase		):
     pass







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






     def     __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

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

       _lowerCAmelCase						:       Any  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Union[str, Any]  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=2				,     output_type="""numpy"""				,     ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(a__  )
         _lowerCAmelCase						:       str  			=     VersatileDiffusionPipeline.from_pretrained(a__				,     torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Optional[int]  			=     generator.manual_seed(0  )
       _lowerCAmelCase						:       Dict  			=     pipe.dual_guided(
           prompt="""first prompt"""				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     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  ):
       _lowerCAmelCase						:       List[Any]  			=     VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""				,     torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:       Dict  			=     """cyberpunk 2077"""
       _lowerCAmelCase						:       int  			=     load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""  )
       _lowerCAmelCase						:       Optional[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       Tuple  			=     pipe.dual_guided(
           prompt=a__				,     image=a__				,     text_to_image_strength=0.7_5				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""				,     ).images

       _lowerCAmelCase						:       List[str]  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Optional[int]  			=     np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]  )

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

       _lowerCAmelCase						:       str  			=     """A painting of a squirrel eating a burger """
       _lowerCAmelCase						:       List[Any]  			=     torch.manual_seed(0  )
       _lowerCAmelCase						:       str  			=     pipe.text_to_image(
           prompt=a__				,     generator=a__				,     guidance_scale=7.5				,     num_inference_steps=50				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       Dict  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Dict  			=     np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]  )

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

       _lowerCAmelCase						:       str  			=     pipe.image_variation(a__				,     generator=a__				,     output_type="""numpy"""  ).images

       _lowerCAmelCase						:       Tuple  			=     image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:       Optional[int]  			=     np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]  )

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





'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

