





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        __lowerCAmelCase (					lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       unittest.TestCase):
      _lowercase       :      Dict        =    StableDiffusionControlNetInpaintPipeline
      _lowercase       :      List[Any]        =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      _lowercase       :      List[Any]        =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _lowercase       :      List[Any]        =    frozenset({"""control_image"""})  # skip `image` and `mask` for now, only test for control_image
      _lowercase       :      Optional[int]        =    TEXT_TO_IMAGE_IMAGE_PARAMS







      def      _lowercase						(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__			:      int            =UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   sample_size=3_2	,   in_channels=9	,   out_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,   cross_attention_dim=3_2	,   )
        torch.manual_seed(0							)
        a__			:      Union[str, Any]            =ControlNetModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   in_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   cross_attention_dim=3_2	,   conditioning_embedding_out_channels=(1_6, 3_2)	,   )
        torch.manual_seed(0							)
        a__			:      Optional[Any]            =DDIMScheduler(
            beta_start=0.0_00_85	,   beta_end=0.0_12	,   beta_schedule="scaled_linear"	,   clip_sample=lowerCAmelCase__	,   set_alpha_to_one=lowerCAmelCase__	,   )
        torch.manual_seed(0							)
        a__			:      Optional[int]            =AutoencoderKL(
            block_out_channels=[3_2, 6_4]	,   in_channels=3	,   out_channels=3	,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,   latent_channels=4	,   )
        torch.manual_seed(0							)
        a__			:      Optional[Any]            =CLIPTextConfig(
            bos_token_id=0	,   eos_token_id=2	,   hidden_size=3_2	,   intermediate_size=3_7	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1_0_0_0	,   )
        a__			:      int            =CLIPTextModel(lowerCAmelCase__							)
        a__			:      Optional[int]            =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

        a__			:      List[Any]            ={
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=0							)     ->		Union[str, Any]:



        '''simple docstring'''




        if str(lowerCAmelCase__							).startswith("mps"							):
          a__			:      Optional[int]            =torch.manual_seed(lowerCAmelCase__							)
        else:
          a__			:      str            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)

        a__			:      Dict            =2
        a__			:      List[Any]            =randn_tensor(
            (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,   generator=lowerCAmelCase__	,   device=torch.device(lowerCAmelCase__							)	,   )
        a__			:      Optional[int]            =floats_tensor((1, 3, 3_2, 3_2)	,   rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
        a__			:      str            =init_image.cpu().permute(0	,   2	,   3	,   1							)[0]

        a__			:      Union[str, Any]            =Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert("RGB"							).resize((6_4, 6_4)							)
        a__			:      Union[str, Any]            =Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((6_4, 6_4)							)

        a__			:      Tuple            ={
            "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      _lowercase						(					self							)     ->		List[Any]:



        '''simple docstring'''




        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      _lowercase						(					self							)     ->		Optional[Any]:



        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)







      def      _lowercase						(					self							)     ->		Tuple:



        '''simple docstring'''




        self._test_inference_batch_single_identical(expected_max_diff=2E-3							)







class        __lowerCAmelCase (					lowerCamelCase_):
      _lowercase       :      Any        =    StableDiffusionControlNetInpaintPipeline
      _lowercase       :      Optional[int]        =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      _lowercase       :      int        =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _lowercase       :      List[str]        =    frozenset([])







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__			:      Optional[int]            =UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   sample_size=3_2	,   in_channels=4	,   out_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,   cross_attention_dim=3_2	,   )
        torch.manual_seed(0							)
        a__			:      Optional[int]            =ControlNetModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   in_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   cross_attention_dim=3_2	,   conditioning_embedding_out_channels=(1_6, 3_2)	,   )
        torch.manual_seed(0							)
        a__			:      Optional[int]            =DDIMScheduler(
            beta_start=0.0_00_85	,   beta_end=0.0_12	,   beta_schedule="scaled_linear"	,   clip_sample=lowerCAmelCase__	,   set_alpha_to_one=lowerCAmelCase__	,   )
        torch.manual_seed(0							)
        a__			:      Dict            =AutoencoderKL(
            block_out_channels=[3_2, 6_4]	,   in_channels=3	,   out_channels=3	,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,   latent_channels=4	,   )
        torch.manual_seed(0							)
        a__			:      int            =CLIPTextConfig(
            bos_token_id=0	,   eos_token_id=2	,   hidden_size=3_2	,   intermediate_size=3_7	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1_0_0_0	,   )
        a__			:      Any            =CLIPTextModel(lowerCAmelCase__							)
        a__			:      str            =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

        a__			:      int            ={
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components







class        __lowerCAmelCase (					lowerCamelCase_		,       lowerCamelCase_		,       unittest.TestCase):
      _lowercase       :      str        =    StableDiffusionControlNetInpaintPipeline
      _lowercase       :      List[Any]        =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      _lowercase       :      Any        =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







      def      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        torch.manual_seed(0							)
        a__			:      Any            =UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   sample_size=3_2	,   in_channels=9	,   out_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,   cross_attention_dim=3_2	,   )
        torch.manual_seed(0							)

        def init_weights(lowerCAmelCase__							):
          if isinstance(lowerCAmelCase__	,   torch.nn.Convad							):
            torch.nn.init.normal(m.weight							)
            m.bias.data.fill_(1.0							)

        a__			:      Tuple            =ControlNetModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   in_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   cross_attention_dim=3_2	,   conditioning_embedding_out_channels=(1_6, 3_2)	,   )
        controlneta.controlnet_down_blocks.apply(lowerCAmelCase__							)

        torch.manual_seed(0							)
        a__			:      Any            =ControlNetModel(
            block_out_channels=(3_2, 6_4)	,   layers_per_block=2	,   in_channels=4	,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,   cross_attention_dim=3_2	,   conditioning_embedding_out_channels=(1_6, 3_2)	,   )
        controlneta.controlnet_down_blocks.apply(lowerCAmelCase__							)

        torch.manual_seed(0							)
        a__			:      str            =DDIMScheduler(
            beta_start=0.0_00_85	,   beta_end=0.0_12	,   beta_schedule="scaled_linear"	,   clip_sample=lowerCAmelCase__	,   set_alpha_to_one=lowerCAmelCase__	,   )
        torch.manual_seed(0							)
        a__			:      int            =AutoencoderKL(
            block_out_channels=[3_2, 6_4]	,   in_channels=3	,   out_channels=3	,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]	,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]	,   latent_channels=4	,   )
        torch.manual_seed(0							)
        a__			:      Optional[Any]            =CLIPTextConfig(
            bos_token_id=0	,   eos_token_id=2	,   hidden_size=3_2	,   intermediate_size=3_7	,   layer_norm_eps=1E-05	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1_0_0_0	,   )
        a__			:      List[Any]            =CLIPTextModel(lowerCAmelCase__							)
        a__			:      int            =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

        a__			:      Tuple            =MultiControlNetModel([controlneta, controlneta]							)

        a__			:      List[str]            ={
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components







      def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=0							)     ->		List[str]:



        '''simple docstring'''




        if str(lowerCAmelCase__							).startswith("mps"							):
          a__			:      int            =torch.manual_seed(lowerCAmelCase__							)
        else:
          a__			:      List[str]            =torch.Generator(device=lowerCAmelCase__							).manual_seed(lowerCAmelCase__							)

        a__			:      Optional[Any]            =2

        a__			:      Optional[Any]            =[
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,   generator=lowerCAmelCase__	,   device=torch.device(lowerCAmelCase__							)	,   ),
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)	,   generator=lowerCAmelCase__	,   device=torch.device(lowerCAmelCase__							)	,   ),
        ]
        a__			:      Optional[int]            =floats_tensor((1, 3, 3_2, 3_2)	,   rng=random.Random(lowerCAmelCase__							)							).to(lowerCAmelCase__							)
        a__			:      List[Any]            =init_image.cpu().permute(0	,   2	,   3	,   1							)[0]

        a__			:      List[Any]            =Image.fromarray(np.uinta(lowerCAmelCase__							)							).convert("RGB"							).resize((6_4, 6_4)							)
        a__			:      str            =Image.fromarray(np.uinta(init_image + 4							)							).convert("RGB"							).resize((6_4, 6_4)							)

        a__			:      str            ={
            "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      _lowercase						(					self							)     ->		Tuple:



        '''simple docstring'''




        a__			:      Optional[Any]            =self.get_dummy_components()
        a__			:      int            =self.pipeline_class(**lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)

        a__			:      List[Any]            =10.0
        a__			:      str            =4

        a__			:      Tuple            =self.get_dummy_inputs(lowerCAmelCase__							)
        a__			:      Optional[int]            =steps
        a__			:      Any            =scale
        a__			:      Union[str, Any]            =pipe(**lowerCAmelCase__							)[0]

        a__			:      str            =self.get_dummy_inputs(lowerCAmelCase__							)
        a__			:      Optional[Any]            =steps
        a__			:      Optional[Any]            =scale
        a__			:      Dict            =pipe(**lowerCAmelCase__	,   control_guidance_start=0.1	,   control_guidance_end=0.2							)[0]

        a__			:      Union[str, Any]            =self.get_dummy_inputs(lowerCAmelCase__							)
        a__			:      Optional[int]            =steps
        a__			:      Optional[int]            =scale
        a__			:      List[str]            =pipe(**lowerCAmelCase__	,   control_guidance_start=[0.1, 0.3]	,   control_guidance_end=[0.2, 0.7]							)[0]

        a__			:      Union[str, Any]            =self.get_dummy_inputs(lowerCAmelCase__							)
        a__			:      Dict            =steps
        a__			:      Optional[int]            =scale
        a__			:      Union[str, Any]            =pipe(**lowerCAmelCase__	,   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      _lowercase						(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




        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      _lowercase						(					self							)     ->		Dict:



        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3							)







      def      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        self._test_inference_batch_single_identical(expected_max_diff=2E-3							)







      def      _lowercase						(					self							)     ->		List[str]:



        '''simple docstring'''




        a__			:      List[str]            =self.get_dummy_components()
        a__			:      Union[str, Any]            =self.pipeline_class(**lowerCAmelCase__							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)
        with tempfile.TemporaryDirectory() as tmpdir:
          try:
            # save_pretrained is not implemented for Multi-ControlNet
            pipe.save_pretrained(lowerCAmelCase__							)
          except NotImplementedError:
            pass







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







      def      _lowercase						(					self							)     ->		Optional[int]:



        '''simple docstring'''




        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def      _lowercase						(					self							)     ->		Union[str, Any]:



        '''simple docstring'''




        a__			:      Union[str, Any]            =ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"							)

        a__			:      List[Any]            =StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-inpainting"	,   safety_checker=lowerCAmelCase__	,   controlnet=lowerCAmelCase__							)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__			:      Optional[int]            =torch.Generator(device="cpu"							).manual_seed(0							)
        a__			:      int            =load_image(
            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"							).resize((5_1_2, 5_1_2)							)

        a__			:      int            =load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
            "/stable_diffusion_inpaint/input_bench_mask.png"							).resize((5_1_2, 5_1_2)							)

        a__			:      Tuple            ="pitch black hole"

        a__			:      Optional[int]            =load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"							).resize((5_1_2, 5_1_2)							)

        a__			:      Optional[Any]            =pipe(
            lowerCAmelCase__	,   image=lowerCAmelCase__	,   mask_image=lowerCAmelCase__	,   control_image=lowerCAmelCase__	,   generator=lowerCAmelCase__	,   output_type="np"	,   num_inference_steps=3	,   )

        a__			:      Optional[Any]            =output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        a__			:      Union[str, Any]            =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      _lowercase						(					self							)     ->		str:



        '''simple docstring'''




        a__			:      int            =ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"							)

        a__			:      Tuple            =StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5"	,   safety_checker=lowerCAmelCase__	,   controlnet=lowerCAmelCase__							)
        a__			:      Tuple            =DDIMScheduler.from_config(pipe.scheduler.config							)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__			:      List[str]            =torch.Generator(device="cpu"							).manual_seed(3_3							)

        a__			:      Any            =load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"							)
        a__			:      List[str]            =init_image.resize((5_1_2, 5_1_2)							)

        a__			:      Optional[Any]            =load_image(
            "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"							)
        a__			:      Any            =mask_image.resize((5_1_2, 5_1_2)							)

        a__			:      int            ="a handsome man with ray-ban sunglasses"

        def make_inpaint_condition(lowerCAmelCase__	,   lowerCAmelCase__							):
          a__			:      Optional[int]            =np.array(image.convert("RGB"							)							).astype(np.floataa							) / 2_5_5.0
          a__			:      int            =np.array(image_mask.convert("L"							)							).astype(np.floataa							) / 2_5_5.0

          assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
          a__			:      Union[str, Any]            =-1.0  # set as masked pixel
          a__			:      List[str]            =np.expand_dims(lowerCAmelCase__	,   0							).transpose(0	,   3	,   1	,   2							)
          a__			:      Optional[Any]            =torch.from_numpy(lowerCAmelCase__							)
          return image

        a__			:      Any            =make_inpaint_condition(lowerCAmelCase__	,   lowerCAmelCase__							)

        a__			:      List[Any]            =pipe(
            lowerCAmelCase__	,   image=lowerCAmelCase__	,   mask_image=lowerCAmelCase__	,   control_image=lowerCAmelCase__	,   guidance_scale=9.0	,   eta=1.0	,   generator=lowerCAmelCase__	,   num_inference_steps=2_0	,   output_type="np"	,   )
        a__			:      Optional[Any]            =output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        a__			:      Tuple            =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













from __future__ import annotations
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):





  """simple docstring"""



  a__      ,							a__      ,							a__			:      Optional[int]            ="", "", ""
  a__			:      str            =[]

  for character in message_part:
    tmp.append(character_to_number[character]		)

  for each in tmp:
    one += each[0]
    two += each[1]
    three += each[2]

  return one + two + three
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     dict[str, str]		):





  """simple docstring"""



  a__      ,							a__			:      Any            ="", ""
  a__			:      Optional[int]            =[]

  for character in message_part:
    this_part += character_to_number[character]

  for digit in this_part:
    tmp += digit
    if len(SCREAMING_SNAKE_CASE		) == len(SCREAMING_SNAKE_CASE		):
      result.append(SCREAMING_SNAKE_CASE		)
      a__			:      str            =""

  return result[0], result[1], result[2]
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str		):





  """simple docstring"""



  a__			:      Optional[Any]            =alphabet.replace(" " , ""		).upper()
  a__			:      List[str]            =message.replace(" " , ""		).upper()

  # Check length and characters
  if len(SCREAMING_SNAKE_CASE		) != 27:
    raise KeyError("Length of alphabet has to be 27."		)
  for each in message:
    if each not in alphabet:
      raise ValueError("Each message character has to be included in alphabet!"		)

    # Generate dictionares
  a__			:      Any            =(
      "111",
      "112",
      "113",
      "121",
      "122",
      "123",
      "131",
      "132",
      "133",
      "211",
      "212",
      "213",
      "221",
      "222",
      "223",
      "231",
      "232",
      "233",
      "311",
      "312",
      "313",
      "321",
      "322",
      "323",
      "331",
      "332",
      "333",
  )
  a__			:      Tuple            ={}
  a__			:      List[str]            ={}
  for letter, number in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		):
    a__			:      Dict            =number
    a__			:      Union[str, Any]            =letter

  return message, alphabet, character_to_number, number_to_character
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):





  """simple docstring"""



  a__      ,							a__      ,							a__      ,							a__			:      Union[str, Any]            =__prepare(
      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  a__      ,							a__			:      Union[str, Any]            ="", ""

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
    encrypted_numeric += __encrypt_part(
        message[i : i + period] , SCREAMING_SNAKE_CASE		)

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) , 3		):
    encrypted += number_to_character[encrypted_numeric[i : i + 3]]

  return encrypted
def   _A (	SCREAMING_SNAKE_CASE  :     str , SCREAMING_SNAKE_CASE  :     str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." , SCREAMING_SNAKE_CASE  :     int = 5		):





  """simple docstring"""



  a__      ,							a__      ,							a__      ,							a__			:      Dict            =__prepare(
      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE		)
  a__			:      Optional[Any]            =[]
  a__			:      Any            =""

  for i in range(0 , len(SCREAMING_SNAKE_CASE		) + 1 , SCREAMING_SNAKE_CASE		):
    a__      ,							a__      ,							a__			:      Union[str, Any]            =__decrypt_part(message[i : i + period] , SCREAMING_SNAKE_CASE		)

    for j in range(0 , len(SCREAMING_SNAKE_CASE		)		):
      decrypted_numeric.append(a[j] + b[j] + c[j]		)

  for each in decrypted_numeric:
    decrypted += number_to_character[each]

  return decrypted


if __name__ == "__main__":
     UpperCAmelCase			:			List[str]	  = """DEFEND THE EAST WALL OF THE CASTLE."""
     UpperCAmelCase			:			Optional[Any]	  = encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
     UpperCAmelCase			:			Optional[int]	  = decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
     print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")






