




def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				int:
      # The longer word should come first
      if len(__UpperCAmelCase							) < len(__UpperCAmelCase							):
            return levenshtein_distance(__UpperCAmelCase    ,    __UpperCAmelCase							)

      if len(__UpperCAmelCase							) == 0:
            return len(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    list(range(len(__UpperCAmelCase							) + 1							)							)

      for i, ca in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [i + 1]

            for j, ca in enumerate(__UpperCAmelCase							):
                  # Calculate insertions, deletions and substitutions
                  SCREAMING_SNAKE_CASE_					=    previous_row[j + 1] + 1
                  SCREAMING_SNAKE_CASE_					=    current_row[j] + 1
                  SCREAMING_SNAKE_CASE_					=    previous_row[j] + (ca != ca)

                  # Get the minimum to append to the current row
                  current_row.append(min(__UpperCAmelCase    ,    __UpperCAmelCase    ,    __UpperCAmelCase							)							)

            # Store the previous row
            SCREAMING_SNAKE_CASE_					=    current_row

      # Returns the last element (distance)
      return previous_row[-1]


if __name__ == "__main__":
   lowerCamelCase__				:     Union[str, Any]			       =							input('Enter the first word:\n').strip()
   lowerCamelCase__				:     Union[str, Any]			       =							input('Enter the second word:\n').strip()

   lowerCamelCase__				:     List[str]			       =							levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    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')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 3, 32, 32)		)
       SCREAMING_SNAKE_CASE_					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    (image > 0).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs



 def    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    self.get_dummy_inputs(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe(**_lowerCAmelCase		).images
       SCREAMING_SNAKE_CASE_					=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-3



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





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


 '''simple docstring'''







 def    lowerCAmelCase_						(							self						:	List[str]		):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def    lowerCAmelCase_						(							self						:	Dict		):
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
       SCREAMING_SNAKE_CASE_					=    load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'		)

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_					=    torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    repaint(
           _lowerCAmelCase       ,       _lowerCAmelCase       ,       num_inference_steps=250       ,       eta=0.0       ,       jump_length=10       ,       jump_n_sample=10       ,       generator=_lowerCAmelCase       ,       output_type='np'       ,       )
       SCREAMING_SNAKE_CASE_					=    output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image		).mean() < 1E-2