
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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Union[str, Any]					     =      StableDiffusionControlNetInpaintPipeline
	_UpperCAmelCase :	Optional[int]					     =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCAmelCase :	Optional[Any]					     =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCAmelCase :	List[Any]					     =      frozenset({"control_image"}      )  # skip `image` and `mask` for now, only test for control_image
	_UpperCAmelCase :	int					     =      TEXT_TO_IMAGE_IMAGE_PARAMS



	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_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			)
				_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			)
				_snake_case						=    DDIMScheduler(
				    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='scaled_linear'    ,						clip_sample=__UpperCamelCase    ,						set_alpha_to_one=__UpperCamelCase    ,						)
				torch.manual_seed(0			)
				_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			)
				_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=1_000    ,						)
				_snake_case						=    CLIPTextModel(__UpperCamelCase			)
				_snake_case						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

				_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 				A							(						self			:					Optional[int]    ,						lowercase			:					List[Any]    ,						lowercase			:					List[Any]=0			):


				'''simple docstring'''


				if str(__UpperCamelCase			).startswith('mps'			):
							_snake_case						=    torch.manual_seed(__UpperCamelCase			)
				else:
							_snake_case						=    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

				_snake_case						=    2
				_snake_case						=    randn_tensor(
				    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,						generator=__UpperCamelCase    ,						device=torch.device(__UpperCamelCase			)    ,						)
				_snake_case						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)
				_snake_case						=    init_image.cpu().permute(0    ,						2    ,						3    ,						1			)[0]

				_snake_case						=    Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			).resize((64, 64)			)
				_snake_case						=    Image.fromarray(np.uinta(init_image + 4			)			).convert('RGB'			).resize((64, 64)			)

				_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 				A							(						self			:					List[str]			):


				'''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 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)





	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				self._test_inference_batch_single_identical(expected_max_diff=2E-3			)




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase_      ):





	'''simple docstring'''



	_UpperCAmelCase :	Dict					     =      StableDiffusionControlNetInpaintPipeline
	_UpperCAmelCase :	Union[str, Any]					     =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCAmelCase :	Optional[int]					     =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCAmelCase :	str					     =      frozenset([]      )



	def 				A							(						self			:					int			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_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			)
				_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			)
				_snake_case						=    DDIMScheduler(
				    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='scaled_linear'    ,						clip_sample=__UpperCamelCase    ,						set_alpha_to_one=__UpperCamelCase    ,						)
				torch.manual_seed(0			)
				_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			)
				_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=1_000    ,						)
				_snake_case						=    CLIPTextModel(__UpperCamelCase			)
				_snake_case						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

				_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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase_							,UpperCAmelCase_							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Any					     =      StableDiffusionControlNetInpaintPipeline
	_UpperCAmelCase :	Optional[int]					     =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCAmelCase :	Any					     =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



	def 				A							(						self			:					int			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_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(lowercase			:					Optional[Any]			):
							if isinstance(__UpperCamelCase    ,						torch.nn.Convad			):
										torch.nn.init.normal(m.weight			)
										m.bias.data.fill_(1.0			)

				_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(__UpperCamelCase			)

				torch.manual_seed(0			)
				_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(__UpperCamelCase			)

				torch.manual_seed(0			)
				_snake_case						=    DDIMScheduler(
				    beta_start=0.00085    ,						beta_end=0.012    ,						beta_schedule='scaled_linear'    ,						clip_sample=__UpperCamelCase    ,						set_alpha_to_one=__UpperCamelCase    ,						)
				torch.manual_seed(0			)
				_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			)
				_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=1_000    ,						)
				_snake_case						=    CLIPTextModel(__UpperCamelCase			)
				_snake_case						=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

				_snake_case						=    MultiControlNetModel([controlneta, controlneta]			)

				_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 				A							(						self			:					Union[str, Any]    ,						lowercase			:					List[Any]    ,						lowercase			:					int=0			):


				'''simple docstring'''


				if str(__UpperCamelCase			).startswith('mps'			):
							_snake_case						=    torch.manual_seed(__UpperCamelCase			)
				else:
							_snake_case						=    torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

				_snake_case						=    2

				_snake_case						=    [
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,						generator=__UpperCamelCase    ,						device=torch.device(__UpperCamelCase			)    ,						),
				    randn_tensor(
				        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,						generator=__UpperCamelCase    ,						device=torch.device(__UpperCamelCase			)    ,						),
				]
				_snake_case						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)
				_snake_case						=    init_image.cpu().permute(0    ,						2    ,						3    ,						1			)[0]

				_snake_case						=    Image.fromarray(np.uinta(__UpperCamelCase			)			).convert('RGB'			).resize((64, 64)			)
				_snake_case						=    Image.fromarray(np.uinta(init_image + 4			)			).convert('RGB'			).resize((64, 64)			)

				_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 				A							(						self			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    self.get_dummy_components()
				_snake_case						=    self.pipeline_class(**__UpperCamelCase			)
				pipe.to(__UpperCamelCase			)

				_snake_case						=    10.0
				_snake_case						=    4

				_snake_case						=    self.get_dummy_inputs(__UpperCamelCase			)
				_snake_case						=    steps
				_snake_case						=    scale
				_snake_case						=    pipe(**__UpperCamelCase			)[0]

				_snake_case						=    self.get_dummy_inputs(__UpperCamelCase			)
				_snake_case						=    steps
				_snake_case						=    scale
				_snake_case						=    pipe(**__UpperCamelCase    ,						control_guidance_start=0.1    ,						control_guidance_end=0.2			)[0]

				_snake_case						=    self.get_dummy_inputs(__UpperCamelCase			)
				_snake_case						=    steps
				_snake_case						=    scale
				_snake_case						=    pipe(**__UpperCamelCase    ,						control_guidance_start=[0.1, 0.3]    ,						control_guidance_end=[0.2, 0.7]			)[0]

				_snake_case						=    self.get_dummy_inputs(__UpperCamelCase			)
				_snake_case						=    steps
				_snake_case						=    scale
				_snake_case						=    pipe(**__UpperCamelCase    ,						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 				A							(						self			:					Optional[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 				A							(						self			:					Tuple			):


				'''simple docstring'''


				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)



	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				self._test_inference_batch_single_identical(expected_max_diff=2E-3			)





	def 				A							(						self			:					Any			):


				'''simple docstring'''


				_snake_case						=    self.get_dummy_components()
				_snake_case						=    self.pipeline_class(**__UpperCamelCase			)
				pipe.to(__UpperCamelCase			)
				pipe.set_progress_bar_config(disable=__UpperCamelCase			)
				with tempfile.TemporaryDirectory() as tmpdir:
							try:
										# save_pretrained is not implemented for Multi-ControlNet
										pipe.save_pretrained(__UpperCamelCase			)
							except NotImplementedError:
										pass




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





	'''simple docstring'''



	def 				A							(						self			:					Optional[Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				_snake_case						=    ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'			)

				_snake_case						=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    'runwayml/stable-diffusion-inpainting'    ,						safety_checker=__UpperCamelCase    ,						controlnet=__UpperCamelCase			)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase			)

				_snake_case						=    torch.Generator(device='cpu'			).manual_seed(0			)
				_snake_case						=    load_image(
				    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'			).resize((512, 512)			)

				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
				    '/stable_diffusion_inpaint/input_bench_mask.png'			).resize((512, 512)			)

				_snake_case						=    'pitch black hole'

				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'			).resize((512, 512)			)

				_snake_case						=    pipe(
				    __UpperCamelCase    ,						image=__UpperCamelCase    ,						mask_image=__UpperCamelCase    ,						control_image=__UpperCamelCase    ,						generator=__UpperCamelCase    ,						output_type='np'    ,						num_inference_steps=3    ,						)

				_snake_case						=    output.images[0]

				assert image.shape == (512, 512, 3)

				_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 				A							(						self			:					str			):


				'''simple docstring'''


				_snake_case						=    ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'			)

				_snake_case						=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
				    'runwayml/stable-diffusion-v1-5'    ,						safety_checker=__UpperCamelCase    ,						controlnet=__UpperCamelCase			)
				_snake_case						=    DDIMScheduler.from_config(pipe.scheduler.config			)
				pipe.enable_model_cpu_offload()
				pipe.set_progress_bar_config(disable=__UpperCamelCase			)

				_snake_case						=    torch.Generator(device='cpu'			).manual_seed(33			)

				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'			)
				_snake_case						=    init_image.resize((512, 512)			)

				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'			)
				_snake_case						=    mask_image.resize((512, 512)			)

				_snake_case						=    'a handsome man with ray-ban sunglasses'

				def make_inpaint_condition(lowercase			:					Tuple    ,						lowercase			:					str			):
							_snake_case						=    np.array(image.convert('RGB'			)			).astype(np.floataa			) / 255.0
							_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"
							_snake_case						=    -1.0  # set as masked pixel
							_snake_case						=    np.expand_dims(__UpperCamelCase    ,						0			).transpose(0    ,						3    ,						1    ,						2			)
							_snake_case						=    torch.from_numpy(__UpperCamelCase			)
							return image

				_snake_case						=    make_inpaint_condition(__UpperCamelCase    ,						__UpperCamelCase			)

				_snake_case						=    pipe(
				    __UpperCamelCase    ,						image=__UpperCamelCase    ,						mask_image=__UpperCamelCase    ,						control_image=__UpperCamelCase    ,						guidance_scale=9.0    ,						eta=1.0    ,						generator=__UpperCamelCase    ,						num_inference_steps=20    ,						output_type='np'    ,						)
				_snake_case						=    output.images[0]

				assert image.shape == (512, 512, 3)

				_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

import random
import sys

_lowerCamelCase :       Tuple     					=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def  a_	( )				->						None:
			_snake_case						=    input('Enter message: '				)
			_snake_case						=    'LFWOAYUISVKMNXPBDCRJTQEGHZ'
			_snake_case						=    input('Encrypt/Decrypt [e/d]: '				)

			check_valid_key(__lowercase				)

			if resp.lower().startswith('e'				):
						_snake_case						=    'encrypt'
						_snake_case						=    encrypt_message(__lowercase ,      __lowercase				)
			elif resp.lower().startswith('d'				):
						_snake_case						=    'decrypt'
						_snake_case						=    decrypt_message(__lowercase ,      __lowercase				)

			print(f'''\n{mode.title()}ion: \n{translated}'''				)






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    list(__lowercase				)
			_snake_case						=    list(__lowercase				)
			key_list.sort()
			letters_list.sort()

			if key_list != letters_list:
						sys.exit('Error in the key or symbol set.'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'encrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'decrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			_snake_case						=    ''
			_snake_case						=    LETTERS
			_snake_case						=    key

			if mode == "decrypt":
						_snake_case			,      _snake_case						=    chars_b, chars_a

			for symbol in message:
						if symbol.upper() in chars_a:
									_snake_case						=    chars_a.find(symbol.upper()				)
									if symbol.isupper():
												translated += chars_b[sym_index].upper()
									else:
												translated += chars_b[sym_index].lower()
						else:
									translated += symbol

			return translated






def  a_	( )				->						str:
			_snake_case						=    list(__lowercase				)
			random.shuffle(__lowercase				)
			return "".join(__lowercase				)


if __name__ == "__main__":
			main()