

'''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,
)


__magic_name__   :   Optional[Any]  							=logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__   :   Any  							='\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n'




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				Tuple=8						):
				'''simple docstring'''




				__magic_name__	  = height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				__magic_name__	  = width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor




def      __snake_case       (       lowerCamelCase_ :				int	,  lowerCamelCase_ :				str=512	,  lowerCamelCase_ :				Any=512						):
				'''simple docstring'''




				__magic_name__	  = pil_image.resize((w, h)	,  resample=Image.BICUBIC	,  reducing_gap=1						)
				__magic_name__	  = np.array(pil_image.convert("RGB"						)						)
				__magic_name__	  = arr.astype(np.floataa						) / 127.5 - 1
				__magic_name__	  = np.transpose(lowerCamelCase_	,  [2, 0, 1]						)
				__magic_name__	  = torch.from_numpy(lowerCamelCase_						).unsqueeze(0						)
				return image






class 		UpperCamelCase_		(					lowerCAmelCase__   ):





						"""simple docstring"""




						def __init__( self				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				)							->					Any:
										super().__init__()

										self.register_modules(
										    unet=_lowerCamelCase	,				scheduler=_lowerCamelCase	,				movq=_lowerCamelCase	,				)
										__magic_name__	  = 2 ** (len(self.movq.config.block_out_channels  ) - 1)
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					int  )							->					Dict:
										__magic_name__	  = min(int(num_inference_steps * strength  )	,				_lowerCamelCase  )

										__magic_name__	  = max(num_inference_steps - init_timestep	,				0  )
										__magic_name__	  = self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Tuple=None  )							->					str:

										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  )}'  )

										__magic_name__	  = image.to(device=_lowerCamelCase	,				dtype=_lowerCamelCase  )

										__magic_name__	  = batch_size * num_images_per_prompt

										if image.shape[1] == 4:
														__magic_name__	  = 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  ):
																		__magic_name__	  = [
																		    self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(_lowerCamelCase  )
																		]
																		__magic_name__	  = torch.cat(_lowerCamelCase	,				dim=0  )
														else:
																		__magic_name__	  = self.movq.encode(_lowerCamelCase  ).latent_dist.sample(_lowerCamelCase  )

														__magic_name__	  = self.movq.config.scaling_factor * init_latents

										__magic_name__	  = torch.cat([init_latents]	,				dim=0  )

										__magic_name__	  = init_latents.shape
										__magic_name__	  = randn_tensor(_lowerCamelCase	,				generator=_lowerCamelCase	,				device=_lowerCamelCase	,				dtype=_lowerCamelCase  )

										# get latents
										__magic_name__	  = self.scheduler.add_noise(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )

										__magic_name__	  = init_latents

										return latents
						def 					__A					( self				:					str	,				_lowerCamelCase				:					Tuple=0  )							->					Tuple:

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

										__magic_name__	  = torch.device(f'cuda:{gpu_id}'  )

										__magic_name__	  = [
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																		cpu_offload(_lowerCamelCase	,				_lowerCamelCase  )
						def 					__A					( self				:					Dict	,				_lowerCamelCase				:					List[Any]=0  )							->					Dict:

										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."  )

										__magic_name__	  = 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)

										__magic_name__	  = None
										for cpu_offloaded_model in [self.unet, self.movq]:
														__magic_name__	  = cpu_offload_with_hook(_lowerCamelCase	,				_lowerCamelCase	,				prev_module_hook=_lowerCamelCase  )

										# We'll offload the last model manually.
										__magic_name__	  = hook
						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def 					__A					( self				:					List[Any]  )							->					List[str]:

										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				:					Union[str, Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any] = 5_12	,				_lowerCamelCase				:					List[str] = 5_12	,				_lowerCamelCase				:					Union[str, Any] = 1_00	,				_lowerCamelCase				:					Optional[int] = 4.0	,				_lowerCamelCase				:					int = 0.3	,				_lowerCamelCase				:					str = 1	,				_lowerCamelCase				:					Optional[Any] = None	,				_lowerCamelCase				:					Optional[int] = "pil"	,				_lowerCamelCase				:					Dict = True	,				)							->					Any:
										__magic_name__	  = self._execution_device

										__magic_name__	  = guidance_scale > 1.0

										if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = torch.cat(_lowerCamelCase	,				dim=0  )
										if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = torch.cat(_lowerCamelCase	,				dim=0  )
										if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = torch.cat(_lowerCamelCase	,				dim=0  )

										__magic_name__	  = image_embeds.shape[0]

										if do_classifier_free_guidance:
														__magic_name__	  = image_embeds.repeat_interleave(_lowerCamelCase	,				dim=0  )
														__magic_name__	  = negative_image_embeds.repeat_interleave(_lowerCamelCase	,				dim=0  )
														__magic_name__	  = hint.repeat_interleave(_lowerCamelCase	,				dim=0  )

										__magic_name__	  = torch.cat([negative_image_embeds, image_embeds]	,				dim=0  ).to(dtype=self.unet.dtype	,				device=_lowerCamelCase  )
										__magic_name__	  = torch.cat([hint, hint]	,				dim=0  ).to(dtype=self.unet.dtype	,				device=_lowerCamelCase  )

										if not isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = [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'  )

										__magic_name__	  = torch.cat([prepare_image(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  ) for i in image]	,				dim=0  )
										__magic_name__	  = image.to(dtype=image_embeds.dtype	,				device=_lowerCamelCase  )

										__magic_name__	  = self.movq.encode(_lowerCamelCase  )["""latents"""]
										__magic_name__	  = latents.repeat_interleave(_lowerCamelCase	,				dim=0  )
										self.scheduler.set_timesteps(_lowerCamelCase	,				device=_lowerCamelCase  )
										__magic_name__	  = self.get_timesteps(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )
										__magic_name__	  = timesteps[:1].repeat(batch_size * num_images_per_prompt  )
										__magic_name__	  = downscale_height_and_width(_lowerCamelCase	,				_lowerCamelCase	,				self.movq_scale_factor  )
										__magic_name__	  = 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
														__magic_name__	  = torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

														__magic_name__	  = {"""image_embeds""": image_embeds, """hint""": hint}
														__magic_name__	  = self.unet(
														    sample=_lowerCamelCase	,				timestep=_lowerCamelCase	,				encoder_hidden_states=_lowerCamelCase	,				added_cond_kwargs=_lowerCamelCase	,				return_dict=_lowerCamelCase	,				)[0]

														if do_classifier_free_guidance:
																		__magic_name__	  = noise_pred.split(latents.shape[1]	,				dim=1  )
																		__magic_name__	  = noise_pred.chunk(2  )
																		__magic_name__	  = variance_pred.chunk(2  )
																		__magic_name__	  = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																		__magic_name__	  = 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"]
														):
																		__magic_name__	  = noise_pred.split(latents.shape[1]	,				dim=1  )

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

														__magic_name__	  = self.scheduler.step(
														    _lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				generator=_lowerCamelCase	,				)[0]

										# post-processing
										__magic_name__	  = 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"]:
														__magic_name__	  = image * 0.5 + 0.5
														__magic_name__	  = image.clamp(0	,				1  )
														__magic_name__	  = image.cpu().permute(0	,				2	,				3	,				1  ).float().numpy()

										if output_type == "pil":
														__magic_name__	  = self.numpy_to_pil(_lowerCamelCase  )

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase  )







'''simple docstring'''
import os
import sys
import time

from . import transposition_cipher as trans_cipher




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = "Prehistoric Men.txt"
				__magic_name__	  = "Output.txt"
				__magic_name__	  = int(input("Enter key: "						)						)
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				if not os.path.exists(lowerCamelCase_						):
								print(F'File {input_file} does not exist. Quitting...'						)
								sys.exit()
				if os.path.exists(lowerCamelCase_						):
								print(F'Overwrite {output_file}? [y/n]'						)
								__magic_name__	  = input("> "						)
								if not response.lower().startswith("y"						):
												sys.exit()

				__magic_name__	  = time.time()
				if mode.lower().startswith("e"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif mode.lower().startswith("d"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

				with open(lowerCamelCase_	,  "w"						) as output_obj:
								output_obj.write(lowerCamelCase_						)

				__magic_name__	  = round(time.time() - start_time	,  2						)
				print(("Done (", total_time, "seconds )")						)


if __name__ == "__main__":
						main()



