code: 
  
  
  
  
  
  '''simple docstring'''
  
  # This model implementation is heavily based on:
  
  import gc
  import random
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      ControlNetModel,
      DDIMScheduler,
      StableDiffusionControlNetInpaintPipeline,
      UNetaDConditionModel,
  )
  from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
  from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
  
  from ..pipeline_params import (
      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
      TEXT_TO_IMAGE_IMAGE_PARAMS,
  )
  from ..test_pipelines_common import (
      PipelineKarrasSchedulerTesterMixin,
      PipelineLatentTesterMixin,
      PipelineTesterMixin,
  )
  
  
  enable_full_determinism()
  
  
  class       a_  (       snake_case_   ,					snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				StableDiffusionControlNetInpaintPipeline
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase									=				frozenset({'''control_image'''} )  # skip `image` and `mask` for now, only test for control_image
      UpperCamelCase									=				TEXT_TO_IMAGE_IMAGE_PARAMS
  
  
      def     snake_case_(	self )	->     Tuple:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    UNetaDConditionModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=9				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    ControlNetModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	in_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	cross_attention_dim=32				,	conditioning_embedding_out_channels=(16, 32)				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler(
              beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule="""scaled_linear"""				,	clip_sample=A				,	set_alpha_to_one=A				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    AutoencoderKL(
              block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    CLIPTextConfig(
              bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	intermediate_size=37				,	layer_norm_eps=1e-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1000				,	)
          _SCREAMING_SNAKE_CASE             =    CLIPTextModel(A )
          _SCREAMING_SNAKE_CASE             =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """controlnet""": controlnet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
  
  
      def     snake_case_(	self				,	A				,	A=0 )	->     List[Any]:
          if str(A ).startswith("""mps""" ):
              _SCREAMING_SNAKE_CASE             =    torch.manual_seed(A )
          else:
              _SCREAMING_SNAKE_CASE             =    torch.Generator(device=A ).manual_seed(A )
  
          _SCREAMING_SNAKE_CASE             =    2
          _SCREAMING_SNAKE_CASE             =    randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,	generator=A				,	device=torch.device(A )				,	)
          _SCREAMING_SNAKE_CASE             =    floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A ) ).to(A )
          _SCREAMING_SNAKE_CASE             =    init_image.cpu().permute(0				,	2				,	3				,	1 )[0]
  
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(A ) ).convert("""RGB""" ).resize((64, 64) )
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(init_image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
  
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """mask_image""": mask_image,
              """control_image""": control_image,
          }
  
          return inputs
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
  
  
      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
      def     snake_case_(	self )	->     Optional[int]:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
  
  
  
  
  
      def     snake_case_(	self )	->     Optional[int]:
          self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
  
  
  
  
  class       a_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				StableDiffusionControlNetInpaintPipeline
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase									=				frozenset([] )
  
  
      def     snake_case_(	self )	->     Tuple:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    UNetaDConditionModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=4				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    ControlNetModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	in_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	cross_attention_dim=32				,	conditioning_embedding_out_channels=(16, 32)				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler(
              beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule="""scaled_linear"""				,	clip_sample=A				,	set_alpha_to_one=A				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    AutoencoderKL(
              block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    CLIPTextConfig(
              bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	intermediate_size=37				,	layer_norm_eps=1e-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1000				,	)
          _SCREAMING_SNAKE_CASE             =    CLIPTextModel(A )
          _SCREAMING_SNAKE_CASE             =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """controlnet""": controlnet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
  
  
  
  
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				StableDiffusionControlNetInpaintPipeline
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCamelCase									=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  
  
      def     snake_case_(	self )	->     str:
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    UNetaDConditionModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	sample_size=32				,	in_channels=9				,	out_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,	cross_attention_dim=32				,	)
          torch.manual_seed(0 )
  
          def init_weights(A ):
              if isinstance(A				,	torch.nn.Convad ):
                  torch.nn.init.normal(m.weight )
                  m.bias.data.fill_(1.0 )
  
          _SCREAMING_SNAKE_CASE             =    ControlNetModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	in_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	cross_attention_dim=32				,	conditioning_embedding_out_channels=(16, 32)				,	)
          controlneta.controlnet_down_blocks.apply(A )
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    ControlNetModel(
              block_out_channels=(32, 64)				,	layers_per_block=2				,	in_channels=4				,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,	cross_attention_dim=32				,	conditioning_embedding_out_channels=(16, 32)				,	)
          controlneta.controlnet_down_blocks.apply(A )
  
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler(
              beta_start=0.0_0085				,	beta_end=0.012				,	beta_schedule="""scaled_linear"""				,	clip_sample=A				,	set_alpha_to_one=A				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    AutoencoderKL(
              block_out_channels=[32, 64]				,	in_channels=3				,	out_channels=3				,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,	latent_channels=4				,	)
          torch.manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    CLIPTextConfig(
              bos_token_id=0				,	eos_token_id=2				,	hidden_size=32				,	intermediate_size=37				,	layer_norm_eps=1e-05				,	num_attention_heads=4				,	num_hidden_layers=5				,	pad_token_id=1				,	vocab_size=1000				,	)
          _SCREAMING_SNAKE_CASE             =    CLIPTextModel(A )
          _SCREAMING_SNAKE_CASE             =    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
  
          _SCREAMING_SNAKE_CASE             =    MultiControlNetModel([controlneta, controlneta] )
  
          _SCREAMING_SNAKE_CASE             =    {
              """unet""": unet,
              """controlnet""": controlnet,
              """scheduler""": scheduler,
              """vae""": vae,
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """safety_checker""": None,
              """feature_extractor""": None,
          }
          return components
  
  
      def     snake_case_(	self				,	A				,	A=0 )	->     Dict:
          if str(A ).startswith("""mps""" ):
              _SCREAMING_SNAKE_CASE             =    torch.manual_seed(A )
          else:
              _SCREAMING_SNAKE_CASE             =    torch.Generator(device=A ).manual_seed(A )
  
          _SCREAMING_SNAKE_CASE             =    2
  
          _SCREAMING_SNAKE_CASE             =    [
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,	generator=A				,	device=torch.device(A )				,	),
              randn_tensor(
                  (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,	generator=A				,	device=torch.device(A )				,	),
          ]
          _SCREAMING_SNAKE_CASE             =    floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A ) ).to(A )
          _SCREAMING_SNAKE_CASE             =    init_image.cpu().permute(0				,	2				,	3				,	1 )[0]
  
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(A ) ).convert("""RGB""" ).resize((64, 64) )
          _SCREAMING_SNAKE_CASE             =    Image.fromarray(np.uinta(init_image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
  
          _SCREAMING_SNAKE_CASE             =    {
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """guidance_scale""": 6.0,
              """output_type""": """numpy""",
              """image""": image,
              """mask_image""": mask_image,
              """control_image""": control_image,
          }
  
          return inputs
  
  
      def     snake_case_(	self )	->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    self.pipeline_class(**A )
          pipe.to(A )
  
          _SCREAMING_SNAKE_CASE             =    10.0
          _SCREAMING_SNAKE_CASE             =    4
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    steps
          _SCREAMING_SNAKE_CASE             =    scale
          _SCREAMING_SNAKE_CASE             =    pipe(**A )[0]
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    steps
          _SCREAMING_SNAKE_CASE             =    scale
          _SCREAMING_SNAKE_CASE             =    pipe(**A				,	control_guidance_start=0.1				,	control_guidance_end=0.2 )[0]
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    steps
          _SCREAMING_SNAKE_CASE             =    scale
          _SCREAMING_SNAKE_CASE             =    pipe(**A				,	control_guidance_start=[0.1, 0.3]				,	control_guidance_end=[0.2, 0.7] )[0]
  
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_inputs(A )
          _SCREAMING_SNAKE_CASE             =    steps
          _SCREAMING_SNAKE_CASE             =    scale
          _SCREAMING_SNAKE_CASE             =    pipe(**A				,	control_guidance_start=0.4				,	control_guidance_end=[0.5, 0.8] )[0]
  
          # make sure that all outputs are different
          assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
          assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
          assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
  
  
      def     snake_case_(	self )	->     Any:
          return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
  
  
      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
      def     snake_case_(	self )	->     int:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
  
  
      def     snake_case_(	self )	->     str:
          self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
  
  
  
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    self.get_dummy_components()
          _SCREAMING_SNAKE_CASE             =    self.pipeline_class(**A )
          pipe.to(A )
          pipe.set_progress_bar_config(disable=A )
          with tempfile.TemporaryDirectory() as tmpdir:
              try:
                  # save_pretrained is not implemented for Multi-ControlNet
                  pipe.save_pretrained(A )
              except NotImplementedError:
                  pass
  
  
  
  
  @slow
  @require_torch_gpu
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
  
  
      def     snake_case_(	self )	->     Dict:
          _SCREAMING_SNAKE_CASE             =    ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
  
          _SCREAMING_SNAKE_CASE             =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
              """runwayml/stable-diffusion-inpainting"""				,	safety_checker=A				,	controlnet=A )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    torch.Generator(device="""cpu""" ).manual_seed(0 )
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
  
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
              """/stable_diffusion_inpaint/input_bench_mask.png""" ).resize((512, 512) )
  
          _SCREAMING_SNAKE_CASE             =    """pitch black hole"""
  
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
  
          _SCREAMING_SNAKE_CASE             =    pipe(
              A				,	image=A				,	mask_image=A				,	control_image=A				,	generator=A				,	output_type="""np"""				,	num_inference_steps=3				,	)
  
          _SCREAMING_SNAKE_CASE             =    output.images[0]
  
          assert image.shape == (512, 512, 3)
  
          _SCREAMING_SNAKE_CASE             =    load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy""" )
  
          assert np.abs(expected_image - image ).max() < 9e-2
  
  
  
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint""" )
  
          _SCREAMING_SNAKE_CASE             =    StableDiffusionControlNetInpaintPipeline.from_pretrained(
              """runwayml/stable-diffusion-v1-5"""				,	safety_checker=A				,	controlnet=A )
          _SCREAMING_SNAKE_CASE             =    DDIMScheduler.from_config(pipe.scheduler.config )
          pipe.enable_model_cpu_offload()
          pipe.set_progress_bar_config(disable=A )
  
          _SCREAMING_SNAKE_CASE             =    torch.Generator(device="""cpu""" ).manual_seed(33 )
  
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png""" )
          _SCREAMING_SNAKE_CASE             =    init_image.resize((512, 512) )
  
          _SCREAMING_SNAKE_CASE             =    load_image(
              """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png""" )
          _SCREAMING_SNAKE_CASE             =    mask_image.resize((512, 512) )
  
          _SCREAMING_SNAKE_CASE             =    """a handsome man with ray-ban sunglasses"""
  
          def make_inpaint_condition(A				,	A ):
              _SCREAMING_SNAKE_CASE             =    np.array(image.convert("""RGB""" ) ).astype(np.floataa ) / 255.0
              _SCREAMING_SNAKE_CASE             =    np.array(image_mask.convert("""L""" ) ).astype(np.floataa ) / 255.0
  
              assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
              _SCREAMING_SNAKE_CASE             =    -1.0  # set as masked pixel
              _SCREAMING_SNAKE_CASE             =    np.expand_dims(A				,	0 ).transpose(0				,	3				,	1				,	2 )
              _SCREAMING_SNAKE_CASE             =    torch.from_numpy(A )
              return image
  
          _SCREAMING_SNAKE_CASE             =    make_inpaint_condition(A				,	A )
  
          _SCREAMING_SNAKE_CASE             =    pipe(
              A				,	image=A				,	mask_image=A				,	control_image=A				,	guidance_scale=9.0				,	eta=1.0				,	generator=A				,	num_inference_steps=20				,	output_type="""np"""				,	)
          _SCREAMING_SNAKE_CASE             =    output.images[0]
  
          assert image.shape == (512, 512, 3)
  
          _SCREAMING_SNAKE_CASE             =    load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy""" )
  
          assert np.abs(expected_image - image ).max() < 9e-2
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				round(time.time() - start_time, 2)
        print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
        # Done!  9,581 word patterns found in 0.58 seconds.
  
  
  
  