






from collections import defaultdict







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	bool:
     UpperCamelCase__			:   List[Any]       				=  first_str.lower().strip()
     UpperCamelCase__			:   List[Any]       				=  second_str.lower().strip()

     # Remove whitespace
     UpperCamelCase__			:   int       				=  first_str.replace(" "					,    ""			)
     UpperCamelCase__			:   str       				=  second_str.replace(" "					,    ""			)

     # Strings of different lengths are not anagrams
     if len(__lowerCAmelCase			) != len(__lowerCAmelCase			):
          return False

     # Default values for count should be 0
     UpperCamelCase__			:   defaultdict[str, int]       				=  defaultdict(__lowerCAmelCase			)

     # For each character in input strings,
     # increment count in the corresponding
     for i in range(len(__lowerCAmelCase			)			):
          count[first_str[i]] += 1
          count[second_str[i]] -= 1

     return all(_count == 0 for _count in count.values()			)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 lowerCamelCase	:							Optional[Any]				=input('''Enter the first string ''').strip()
 lowerCamelCase	:							Any				=input('''Enter the second string ''').strip()

 lowerCamelCase	:							Dict				=check_anagrams(input_a, input_b)
 print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

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

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

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