


"""simple docstring"""


from __future__ import annotations

from string import ascii_letters






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase = None )						->						str:
				_A       	=      alphabet or ascii_letters

				# The final result string
				_A       	=      ""

				for character in input_string:
								if character not in alpha:
												# Append without encryption if character is not in the alphabet
												result += character
								else:
												# Get the index of the new key and make sure it isn't too large
												_A       	=      (alpha.index(__lowercase ) + key) % len(__lowercase )

												# Append the encoded character to the alphabet
												result += alpha[new_key]

				return result






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase = None )						->						str:
				key *= -1

				return encrypt(__lowercase  ,      __lowercase  ,      __lowercase )






def   a__			(					__lowercase  ,      __lowercase = None )						->						dict[int, str]:
				_A       	=      alphabet or ascii_letters

				# To store data on all the combinations
				_A       	=      {}

				# Cycle through each combination
				for key in range(1  ,      len(__lowercase ) + 1 ):
								# Decrypt the message and store the result in the data
								_A       	=      decrypt(__lowercase  ,      __lowercase  ,      __lowercase )

				return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 10}\n Menu\n{"-" * 10}''')
				print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

				# get user input
				a_											=     input("\nWhat would you like to do?: ").strip() or "4"

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print("Invalid choice, please enter a valid choice")
				elif choice == "1":
						a_											=     input("Please enter the string to be encrypted: ")
						a_											=     int(input("Please enter off-set: ").strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						a_											=     input("Please enter the string to be decrypted: ")
						a_											=     int(input("Please enter off-set: ").strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						a_											=     input("Please enter the string to be decrypted: ")
						a_											=     brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print("Goodbye.")
						break



"""simple docstring"""


import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device






def   a__			(					__lowercase )						->						Optional[Any]:
				_A       	=      {}
				for name in unet.attn_processors.keys():
								_A       	=      None if name.endswith("attn1.processor" ) else unet.config.cross_attention_dim
								if name.startswith("mid_block" ):
												_A       	=      unet.config.block_out_channels[-1]
								elif name.startswith("up_blocks" ):
												_A       	=      int(name[len("up_blocks." )] )
												_A       	=      list(reversed(unet.config.block_out_channels ) )[block_id]
								elif name.startswith("down_blocks" ):
												_A       	=      int(name[len("down_blocks." )] )
												_A       	=      unet.config.block_out_channels[block_id]
								_A       	=      (
								    LoRAAttnProcessor2_0 if hasattr(__lowercase  ,      "scaled_dot_product_attention" ) else LoRAAttnProcessor
								)
								_A       	=      lora_attn_processor_class(
								    hidden_size=__lowercase  ,      cross_attention_dim=__lowercase )
				_A       	=      AttnProcsLayers(__lowercase )
				return lora_attn_procs, unet_lora_layers






def   a__			(					__lowercase )						->						Optional[Any]:
				_A       	=      {}
				_A       	=      (
				    LoRAAttnProcessor2_0 if hasattr(__lowercase  ,      "scaled_dot_product_attention" ) else LoRAAttnProcessor
				)
				for name, module in text_encoder.named_modules():
								if name.endswith(__lowercase ):
												_A       	=      lora_attn_processor_class(
												    hidden_size=module.out_proj.out_features  ,      cross_attention_dim=__lowercase )
				return text_lora_attn_procs






def   a__			(					__lowercase )						->						str:
				_A       	=      create_text_encoder_lora_attn_procs(__lowercase )
				_A       	=      AttnProcsLayers(__lowercase )
				return text_encoder_lora_layers






def   a__			(					__lowercase  ,      __lowercase=False )						->						Dict:
				for _, attn_proc in text_lora_attn_procs.items():
								# set up.weights
								for layer_name, layer_module in attn_proc.named_modules():
												if layer_name.endswith("_lora" ):
																_A       	=      (
																    torch.randn_like(layer_module.up.weight )
																    if randn_weight
																    else torch.zeros_like(layer_module.up.weight )
																)
																_A       	=      torch.nn.Parameter(__lowercase )




class snake_case   (	unittest.TestCase):







		def 			a_			(       self   :							Any		)							->    Any:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A       	=      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     , )
						_A       	=      DDIMScheduler(
						    beta_start=0.0_0_0_8_5     , beta_end=0.0_1_2     , beta_schedule="scaled_linear"     , clip_sample=a__     , set_alpha_to_one=a__     , steps_offset=1     , )
						torch.manual_seed(0		)
						_A       	=      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     , )
						_A       	=      CLIPTextConfig(
						    bos_token_id=0     , eos_token_id=2     , hidden_size=32     , intermediate_size=37     , layer_norm_eps=1E-0_5     , num_attention_heads=4     , num_hidden_layers=5     , pad_token_id=1     , vocab_size=10_00     , )
						_A       	=      CLIPTextModel(a__		)
						_A       	=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						_A	,		_A       	=      create_unet_lora_layers(a__		)
						_A       	=      create_text_encoder_lora_layers(a__		)

						_A       	=      {
						    "unet": unet,
						    "scheduler": scheduler,
						    "vae": vae,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "safety_checker": None,
						    "feature_extractor": None,
						}
						_A       	=      {
						    "unet_lora_layers": unet_lora_layers,
						    "text_encoder_lora_layers": text_encoder_lora_layers,
						    "unet_lora_attn_procs": unet_lora_attn_procs,
						}
						return pipeline_components, lora_components







		def 			a_			(       self   :							Any		)							->    Any:





						'''simple docstring'''







						_A       	=      1
						_A       	=      10
						_A       	=      4
						_A       	=      (32, 32)

						_A       	=      torch.manual_seed(0		)
						_A       	=      floats_tensor((batch_size, num_channels) + sizes		)
						_A       	=      torch.randint(1     , a__     , size=(batch_size, sequence_length)     , generator=a__		)

						_A       	=      {
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}

						return noise, input_ids, pipeline_inputs

						# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb







		def 			a_			(       self   :							Dict		)							->    List[Any]:





						'''simple docstring'''







						_A       	=      77

						_A       	=      torch.randint(2     , 56     , size=(1, max_seq_length)     , generator=torch.manual_seed(0		)		)

						_A       	=      {}
						_A       	=      inputs
						return prepared_inputs







		def 			a_			(       self   :							Union[str, Any]     , a__   :							int		)							->    List[Any]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						LoraLoaderMixin.save_lora_weights(
						    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , )
						self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)







		def 			a_			(       self   :							List[Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , )
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Union[str, Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , safe_serialization=a__     , )
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.safetensors"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Optional[Any]		)							->    Tuple:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      lora_components["unet_lora_attn_procs"]
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										_A       	=      sd_pipe.unet
										unet.set_attn_processor(a__		)
										unet.save_attn_procs(a__		)
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







		def 			a_			(       self   :							Optional[Any]		)							->    Optional[int]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)

						_A       	=      self.get_dummy_tokens()

						# inference without lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with zeroed out up.weights
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert torch.allclose(
						    a__     , a__		), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

						# create lora_attn_procs with randn up.weights
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    a__     , a__		), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"







		def 			a_			(       self   :							Dict		)							->    int:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)

						_A       	=      self.get_dummy_tokens()

						# inference without lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with randn up.weights
						_A       	=      create_text_encoder_lora_attn_procs(pipe.text_encoder		)
						set_lora_up_weights(a__     , randn_weight=a__		)

						# monkey patch
						pipe._modify_text_encoder(a__		)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    a__     , a__		), "lora outputs should be different to without lora outputs"

						# remove monkey patch
						pipe._remove_text_encoder_monkey_patch()

						# inference with removed lora
						_A       	=      pipe.text_encoder(**a__		)[0]
						assert outputs_without_lora_removed.shape == (1, 77, 32)

						assert torch.allclose(
						    a__     , a__		), "remove lora monkey patch should restore the original outputs"







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





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , )
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						_A       	=      sd_pipe(**a__     , cross_attention_kwargs={"scale": 0.5}		).images
						_A       	=      lora_images_with_scale[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(
						    torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)







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





						'''simple docstring'''







						with tempfile.TemporaryDirectory() as tmpdirname:
										self.create_lora_weight_file(a__		)

										_A	,		_A       	=      self.get_dummy_components()
										_A       	=      StableDiffusionPipeline(**a__		)
										_A       	=      sd_pipe.to(a__		)
										sd_pipe.set_progress_bar_config(disable=a__		)

										# check if vanilla attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		self.assertIsInstance(module.processor     , (AttnProcessor, AttnProcessor2_0)		)

            # load LoRA weight file
										sd_pipe.load_lora_weights(a__		)

										# check if lora attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		_A       	=      (
																		    LoRAAttnProcessor2_0 if hasattr(a__     , "scaled_dot_product_attention"		) else LoRAAttnProcessor
																		)
																		self.assertIsInstance(module.processor     , a__		)







		@unittest.skipIf(torch_device != "cuda"     , "This test is supposed to run on GPU"		)
		def 			a_			(       self   :							Dict		)							->    List[str]:





						'''simple docstring'''







						with tempfile.TemporaryDirectory() as tmpdirname:
										self.create_lora_weight_file(a__		)

										_A	,		_A       	=      self.get_dummy_components()
										_A       	=      StableDiffusionPipeline(**a__		)
										_A       	=      sd_pipe.to(a__		)
										sd_pipe.set_progress_bar_config(disable=a__		)

										# enable XFormers
										sd_pipe.enable_xformers_memory_efficient_attention()

										# check if xFormers attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		self.assertIsInstance(module.processor     , a__		)

            # load LoRA weight file
										sd_pipe.load_lora_weights(a__		)

										# check if lora attention processors are used
										for _, module in sd_pipe.unet.named_modules():
														if isinstance(a__     , a__		):
																		self.assertIsInstance(module.processor     , a__		)







		@unittest.skipIf(torch_device != "cuda"     , "This test is supposed to run on GPU"		)
		def 			a_			(       self   :							List[str]		)							->    Union[str, Any]:





						'''simple docstring'''







						_A	,		_A       	=      self.get_dummy_components()
						_A       	=      StableDiffusionPipeline(**a__		)
						_A       	=      sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A	,		_A	,		_A       	=      self.get_dummy_inputs()

						# enable XFormers
						sd_pipe.enable_xformers_memory_efficient_attention()

						_A       	=      sd_pipe(**a__		).images
						_A       	=      original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
										LoraLoaderMixin.save_lora_weights(
										    save_directory=a__     , unet_lora_layers=lora_components["unet_lora_layers"]     , text_encoder_lora_layers=lora_components["text_encoder_lora_layers"]     , )
										self.assertTrue(os.path.isfile(os.path.join(a__     , "pytorch_lora_weights.bin"		)		)		)
										sd_pipe.load_lora_weights(a__		)

						_A       	=      sd_pipe(**a__		).images
						_A       	=      lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(a__		)     , torch.from_numpy(a__		)		)		)