"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 						__A							(			UpperCAmelCase_		):






					def __init__(		self    ,					a__    ,					a__    ,					a__    ,					a__ = None    ,					):
							super().__init__()
							self.register_modules(transformer=__lowercase    ,					vae=__lowercase    ,					scheduler=__lowercase  )

							# create a imagenet -> id dictionary for easier use
							_lowerCAmelCase						:	Tuple        =					{}
							if idalabel is not None:
									for key, value in idalabel.items():
											for label in value.split(""","""  ):
													_lowerCAmelCase						:	List[Any]        =					int(__lowercase  )
									_lowerCAmelCase						:	List[str]        =					dict(sorted(self.labels.items()  )  )






					def    __A  (		self    ,					a__  ):

							if not isinstance(__lowercase    ,					__lowercase  ):
									_lowerCAmelCase						:	Tuple        =					list(__lowercase  )

							for l in label:
									if l not in self.labels:
											raise ValueError(
											    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."  )

							return [self.labels[l] for l in label]






					@torch.no_grad()
					def __call__(		self    ,					a__    ,					a__ = 4.0    ,					a__ = None    ,					a__ = 50    ,					a__ = "pil"    ,					a__ = True    ,					):
							_lowerCAmelCase						:	str        =					len(__lowercase  )
							_lowerCAmelCase						:	List[Any]        =					self.transformer.config.sample_size
							_lowerCAmelCase						:	Dict        =					self.transformer.config.in_channels

							_lowerCAmelCase						:	List[Any]        =					randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)    ,					generator=__lowercase    ,					device=self.device    ,					dtype=self.transformer.dtype    ,					)
							_lowerCAmelCase						:	List[Any]        =					torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

							_lowerCAmelCase						:	List[Any]        =					torch.tensor(__lowercase    ,					device=self.device  ).reshape(-1  )
							_lowerCAmelCase						:	Optional[int]        =					torch.tensor([1000] * batch_size    ,					device=self.device  )
							_lowerCAmelCase						:	Any        =					torch.cat([class_labels, class_null]    ,					0  ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__lowercase  )

							for t in self.progress_bar(self.scheduler.timesteps  ):
									if guidance_scale > 1:
											_lowerCAmelCase						:	Tuple        =					latent_model_input[: len(__lowercase  ) // 2]
											_lowerCAmelCase						:	int        =					torch.cat([half, half]    ,					dim=0  )
									_lowerCAmelCase						:	List[Any]        =					self.scheduler.scale_model_input(__lowercase    ,					__lowercase  )

									_lowerCAmelCase						:	Any        =					t
									if not torch.is_tensor(__lowercase  ):
											# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
											# This would be a good case for the `match` statement (Python 3.10+)
											_lowerCAmelCase						:	int        =					latent_model_input.device.type == '''mps'''
											if isinstance(__lowercase    ,					__lowercase  ):
													_lowerCAmelCase						:	int        =					torch.floataa if is_mps else torch.floataa
											else:
													_lowerCAmelCase						:	Optional[int]        =					torch.intaa if is_mps else torch.intaa
											_lowerCAmelCase						:	Tuple        =					torch.tensor([timesteps]    ,					dtype=__lowercase    ,					device=latent_model_input.device  )
									elif len(timesteps.shape  ) == 0:
											_lowerCAmelCase						:	str        =					timesteps[None].to(latent_model_input.device  )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									_lowerCAmelCase						:	Optional[Any]        =					timesteps.expand(latent_model_input.shape[0]  )
									# predict noise model_output
									_lowerCAmelCase						:	Tuple        =					self.transformer(
									    __lowercase    ,					timestep=__lowercase    ,					class_labels=__lowercase  ).sample

									# perform guidance
									if guidance_scale > 1:
											_lowerCAmelCase						:	Dict        =					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
											_lowerCAmelCase						:	List[Any]        =					torch.split(__lowercase    ,					len(__lowercase  ) // 2    ,					dim=0  )

											_lowerCAmelCase						:	Union[str, Any]        =					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
											_lowerCAmelCase						:	Any        =					torch.cat([half_eps, half_eps]    ,					dim=0  )

											_lowerCAmelCase						:	List[str]        =					torch.cat([eps, rest]    ,					dim=1  )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
											_lowerCAmelCase						:	List[Any]        =					torch.split(__lowercase    ,					__lowercase    ,					dim=1  )
									else:
											_lowerCAmelCase						:	str        =					noise_pred

									# compute previous image: x_t -> x_t-1
									_lowerCAmelCase						:	List[Any]        =					self.scheduler.step(__lowercase    ,					__lowercase    ,					__lowercase  ).prev_sample

							if guidance_scale > 1:
									_lowerCAmelCase						:	Any        =					latent_model_input.chunk(2    ,					dim=0  )
							else:
									_lowerCAmelCase						:	Optional[int]        =					latent_model_input

							_lowerCAmelCase						:	List[str]        =					1 / self.vae.config.scaling_factor * latents
							_lowerCAmelCase						:	Dict        =					self.vae.decode(__lowercase  ).sample

							_lowerCAmelCase						:	Any        =					(samples / 2 + 0.5).clamp(0    ,					1  )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_lowerCAmelCase						:	Any        =					samples.cpu().permute(0    ,					2    ,					3    ,					1  ).float().numpy()

							if output_type == "pil":
									_lowerCAmelCase						:	Optional[Any]        =					self.numpy_to_pil(__lowercase  )

							if not return_dict:
									return (samples,)

							return ImagePipelineOutput(images=__lowercase  )





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




