






from collections import defaultdict


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    first_str.lower().strip()
  SCREAMING_SNAKE_CASE       :      Any							=    second_str.lower().strip()

  # Remove whitespace
  SCREAMING_SNAKE_CASE       :      int							=    first_str.replace(" "						,     ""				)
  SCREAMING_SNAKE_CASE       :      List[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
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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()
   snake_case    				=		input("Enter the first string ").strip()
   snake_case    				=		input("Enter the second string ").strip()

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














from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .multicontrolnet import MultiControlNetModel
   from .pipeline_controlnet import StableDiffusionControlNetPipeline
   from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
   from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
   from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






