



'''simple docstring'''




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCamelCase__	:     Tuple    =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__	:     Tuple    =			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any]	,				_lowerCamelCase:       Tuple	,				_lowerCamelCase:       Any=8 ):
		__SCREAMING_SNAKE_CASE	:		int   	=		height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


def     lowerCAmelCase_       (     _lowerCamelCase:       int	,				_lowerCamelCase:       Tuple=5_12	,				_lowerCamelCase:       Dict=5_12 ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		pil_image.resize((w, h)	,				resample=Image.BICUBIC	,				reducing_gap=1 )
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.array(pil_image.convert("""RGB""" ) )
		__SCREAMING_SNAKE_CASE	:		str   	=		arr.astype(np.floataa ) / 1_27.5 - 1
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		np.transpose(_lowerCamelCase	,				[2, 0, 1] )
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.from_numpy(_lowerCamelCase ).unsqueeze(0 )
		return image






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			int							,					lowerCAmelCase__					:			UNetaDConditionModel							,					lowerCAmelCase__					:			DDPMScheduler							,					lowerCAmelCase__					:			VQModel							,					):





					"""simple docstring"""






					super().__init__()

					self.register_modules(
					    unet=lowerCAmelCase__							,					scheduler=lowerCAmelCase__							,					movq=lowerCAmelCase__							,					)
					__SCREAMING_SNAKE_CASE	:		str   	=		2 ** (len(self.movq.config.block_out_channels						) - 1)

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		min(int(num_inference_steps * strength						)							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		max(num_inference_steps - init_timestep							,					0						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			str=None						):





					"""simple docstring"""






					if not isinstance(lowerCAmelCase__							,					(torch.Tensor, PIL.Image.Image, list)						):
							raise ValueError(
							    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__						)}"						)

					__SCREAMING_SNAKE_CASE	:		str   	=		image.to(device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		batch_size * num_images_per_prompt

					if image.shape[1] == 4:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image

					else:
							if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						) and len(lowerCAmelCase__						) != batch_size:
									raise ValueError(
									    F"You have passed a list of generators of length {len(lowerCAmelCase__						)}, but requested an effective batch"
									    F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

							elif isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE	:		int   	=		[
									    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(lowerCAmelCase__						)
									]
									__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
							else:
									__SCREAMING_SNAKE_CASE	:		Any   	=		self.movq.encode(lowerCAmelCase__						).latent_dist.sample(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		Dict   	=		self.movq.config.scaling_factor * init_latents

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.cat([init_latents]							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		init_latents.shape
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		randn_tensor(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)

					# get latents
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.scheduler.add_noise(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		init_latents

					return latents

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Union[str, Any]=0						):





					"""simple docstring"""






					if is_accelerate_available():
							from accelerate import cpu_offload
					else:
							raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.device(F"cuda:{gpu_id}"						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		[
					    self.unet,
					    self.movq,
					]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Optional[Any]=0						):





					"""simple docstring"""






					if is_accelerate_available() and is_accelerate_version(""">="""							,					"""0.17.0.dev0"""						):
							from accelerate import cpu_offload_with_hook
					else:
							raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.device(F"cuda:{gpu_id}"						)

					if self.device.type != "cpu":
							self.to("""cpu"""							,					silence_dtype_warnings=lowerCAmelCase__						)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					for cpu_offloaded_model in [self.unet, self.movq]:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		cpu_offload_with_hook(lowerCAmelCase__							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

					# We'll offload the last model manually.
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		hook

			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					if not hasattr(self.unet							,					"""_hf_hook"""						):
							return self.device
					for module in self.unet.modules():
							if (
							    hasattr(lowerCAmelCase__							,					"""_hf_hook"""						)
							    and hasattr(module._hf_hook							,					"""execution_device"""						)
							    and module._hf_hook.execution_device is not None
							):
									return torch.device(module._hf_hook.execution_device						)
					return self.device



			@torch.no_grad()
			@replace_example_docstring(lowerCAmelCase__						)
			def __call__(							self					:			Any							,					lowerCAmelCase__					:			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					lowerCAmelCase__					:			Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,					lowerCAmelCase__					:			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			int = 5_1_2							,					lowerCAmelCase__					:			int = 5_1_2							,					lowerCAmelCase__					:			int = 1_0_0							,					lowerCAmelCase__					:			float = 4.0							,					lowerCAmelCase__					:			float = 0.3							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self._execution_device

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		guidance_scale > 1.0

					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		int   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat(lowerCAmelCase__							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		image_embeds.shape[0]

					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		negative_image_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
							__SCREAMING_SNAKE_CASE	:		int   	=		hint.repeat_interleave(lowerCAmelCase__							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat([negative_image_embeds, image_embeds]							,					dim=0						).to(dtype=self.unet.dtype							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		str   	=		torch.cat([hint, hint]							,					dim=0						).to(dtype=self.unet.dtype							,					device=lowerCAmelCase__						)

					if not isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[image]
					if not all(isinstance(lowerCAmelCase__							,					(PIL.Image.Image, torch.Tensor)						) for i in image						):
							raise ValueError(
							    F"Input is in incorrect format: {[type(lowerCAmelCase__						) for i in image]}. Currently, we only support  PIL image and pytorch tensor"						)

					__SCREAMING_SNAKE_CASE	:		str   	=		torch.cat([prepare_image(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						) for i in image]							,					dim=0						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		image.to(dtype=image_embeds.dtype							,					device=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.movq.encode(lowerCAmelCase__						)["""latents"""]
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		latents.repeat_interleave(lowerCAmelCase__							,					dim=0						)
					self.scheduler.set_timesteps(lowerCAmelCase__							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_timesteps(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		timesteps[:1].repeat(batch_size * num_images_per_prompt						)
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		downscale_height_and_width(lowerCAmelCase__							,					lowerCAmelCase__							,					self.movq_scale_factor						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.prepare_latents(
					    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					image_embeds.dtype							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					for i, t in enumerate(self.progress_bar(lowerCAmelCase__						)						):
							# expand the latents if we are doing classifier free guidance
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

							__SCREAMING_SNAKE_CASE	:		str   	=		{"""image_embeds""": image_embeds, """hint""": hint}
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.unet(
							    sample=lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					encoder_hidden_states=lowerCAmelCase__							,					added_cond_kwargs=lowerCAmelCase__							,					return_dict=lowerCAmelCase__							,					)[0]

							if do_classifier_free_guidance:
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		noise_pred.split(latents.shape[1]							,					dim=1						)
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		str   	=		noise_pred.chunk(2						)
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		variance_pred.chunk(2						)
									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
									__SCREAMING_SNAKE_CASE	:		Tuple   	=		torch.cat([noise_pred, variance_pred_text]							,					dim=1						)

							if not (
							    hasattr(self.scheduler.config							,					"""variance_type"""						)
							    and self.scheduler.config.variance_type in ["learned", "learned_range"]
							):
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[Any]   	=		noise_pred.split(latents.shape[1]							,					dim=1						)

							# compute the previous noisy sample x_t -> x_t-1

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.scheduler.step(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=lowerCAmelCase__							,					)[0]

					# post-processing
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.movq.decode(lowerCAmelCase__							,					force_not_quantize=lowerCAmelCase__						)["""sample"""]

					if output_type not in ["pt", "np", "pil"]:
							raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"						)

					if output_type in ["np", "pil"]:
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		image * 0.5 + 0.5
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		image.clamp(0							,					1						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image.cpu().permute(0							,					2							,					3							,					1						).float().numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.numpy_to_pil(lowerCAmelCase__						)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__						)




'''simple docstring'''




import os
import sys

from . import rsa_key_generator as rkg

UpperCamelCase__	:     Any    =			1_28
UpperCamelCase__	:     Optional[int]    =			2_56


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		message.encode("""ascii""" )

		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[]
		for block_start in range(0	,				len(_lowerCamelCase )	,				_lowerCamelCase ):
				__SCREAMING_SNAKE_CASE	:		str   	=		0
				for i in range(_lowerCamelCase	,				min(block_start + block_size	,				len(_lowerCamelCase ) ) ):
						block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
				block_ints.append(_lowerCamelCase )
		return block_ints


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
		for block_int in block_ints:
				__SCREAMING_SNAKE_CASE	:		list[str]   	=		[]
				for i in range(block_size - 1	,				-1	,				-1 ):
						if len(_lowerCamelCase ) + i < message_length:
								__SCREAMING_SNAKE_CASE	:		List[Any]   	=		block_int // (BYTE_SIZE**i)
								__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		block_int % (BYTE_SIZE**i)
								block_message.insert(0	,				chr(_lowerCamelCase ) )
				message.extend(_lowerCamelCase )
		return "".join(_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE ):
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key

		for block in get_blocks_from_text(_lowerCamelCase	,				_lowerCamelCase ):
				encrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return encrypted_blocks


def     lowerCAmelCase_       (     _lowerCamelCase:       list[int]	,				_lowerCamelCase:       int	,				_lowerCamelCase:       tuple[int, int]	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		key
		for block in encrypted_blocks:
				decrypted_blocks.append(pow(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase ) )
		return get_text_from_blocks(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )


def     lowerCAmelCase_       (     _lowerCamelCase:       str ):
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		content.split(""",""" )
		return (int(_lowerCamelCase ), int(_lowerCamelCase ), int(_lowerCamelCase ))


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       str	,				_lowerCamelCase:       int = DEFAULT_BLOCK_SIZE	,				):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Either decrease the block size or use different keys.""".format(
				        block_size * 8	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[str(_lowerCamelCase ) for i in encrypt_message(_lowerCamelCase	,				(n, e)	,				_lowerCamelCase )]

		__SCREAMING_SNAKE_CASE	:		int   	=		""",""".join(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		F"{len(_lowerCamelCase )}_{block_size}_{encrypted_content}"
		with open(_lowerCamelCase	,				"""w""" ) as fo:
				fo.write(_lowerCamelCase )
		return encrypted_content


def     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       str ):
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		read_key_file(_lowerCamelCase )
		with open(_lowerCamelCase ) as fo:
				__SCREAMING_SNAKE_CASE	:		Any   	=		fo.read()
		__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		content.split("""_""" )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )
		__SCREAMING_SNAKE_CASE	:		int   	=		int(_lowerCamelCase )

		if key_size < block_size * 8:
				sys.exit(
				    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
				    """requires the block size to be equal to or greater than the key size. """
				    """Did you specify the correct key file and encrypted file?""".format(
				        block_size * 8	,				_lowerCamelCase ) )

		__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
		for block in encrypted_message.split(""",""" ):
				encrypted_blocks.append(int(_lowerCamelCase ) )

		return decrypt_message(_lowerCamelCase	,				_lowerCamelCase	,				(n, d)	,				_lowerCamelCase )


def     lowerCAmelCase_       (     ):
		__SCREAMING_SNAKE_CASE	:		Dict   	=		"""encrypted_file.txt"""
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		input(r"""Encrypt\Decrypt [e\d]: """ )

		if response.lower().startswith("""e""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		"""encrypt"""
		elif response.lower().startswith("""d""" ):
				__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""decrypt"""

		if mode == "encrypt":
				if not os.path.exists("""rsa_pubkey.txt""" ):
						rkg.make_key_files("""rsa"""	,				10_24 )

				__SCREAMING_SNAKE_CASE	:		int   	=		input("""\nEnter message: """ )
				__SCREAMING_SNAKE_CASE	:		str   	=		"""rsa_pubkey.txt"""
				print(F"Encrypting and writing to {filename}..." )
				__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		encrypt_and_write_to_file(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase )

				print("""\nEncrypted text:""" )
				print(_lowerCamelCase )

		elif mode == "decrypt":
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""rsa_privkey.txt"""
				print(F"Reading from {filename} and decrypting..." )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		read_from_file_and_decrypt(_lowerCamelCase	,				_lowerCamelCase )
				print("""writing decryption to rsa_decryption.txt...""" )
				with open("""rsa_decryption.txt"""	,				"""w""" ) as dec:
						dec.write(_lowerCamelCase )

				print("""\nDecryption:""" )
				print(_lowerCamelCase )


if __name__ == "__main__":
						main()