


import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


A				:   List[str]		= logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:   Tuple		= "\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = \"a shark\"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")\n        ```\n"







@dataclass
class       _lowercase			(							lowercase__):


       """simple docstring"""




       A__ 						=      42







class       _lowercase			(							lowercase__):


       """simple docstring"""




       def __init__(       self							:      Dict ,				__lowerCamelCase							:      PriorTransformer ,				__lowerCamelCase							:      CLIPTextModelWithProjection ,				__lowerCamelCase							:      CLIPTokenizer ,				__lowerCamelCase							:      HeunDiscreteScheduler ,				__lowerCamelCase							:      ShapERenderer ,				):


             '''simple docstring'''



             super().__init__()

             self.register_modules(
                 prior=__lowerCamelCase ,				text_encoder=__lowerCamelCase ,				tokenizer=__lowerCamelCase ,				scheduler=__lowerCamelCase ,				renderer=__lowerCamelCase ,				)





       def 						lowerCAmelCase					(       self							:      Dict ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      List[Any]	):


             '''simple docstring'''



             if latents is None:
                   lowerCamelCase__		:    int   =  randn_tensor(__lowerCamelCase ,				generator=__lowerCamelCase ,				device=__lowerCamelCase ,				dtype=__lowerCamelCase	)
             else:
                   if latents.shape != shape:
                         raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"	)
                   lowerCamelCase__		:    Tuple   =  latents.to(__lowerCamelCase	)

             lowerCamelCase__		:    Optional[int]   =  latents * scheduler.init_noise_sigma
             return latents





       def 						lowerCAmelCase					(       self							:      Optional[int] ,				__lowerCamelCase							:      int=0	):


             '''simple docstring'''



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

             lowerCamelCase__		:    str   =  torch.device(f"cuda:{gpu_id}"	)

             lowerCamelCase__		:    str   =  [self.text_encoder, self.prior]
             for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                         cpu_offload(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Optional[Any] ,				__lowerCamelCase							:      Tuple=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."	)

             lowerCamelCase__		:    Optional[int]   =  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)

             lowerCamelCase__		:    Dict   =  None
             for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                   lowerCamelCase__   ,       lowerCamelCase__		:    Dict   =  cpu_offload_with_hook(__lowerCamelCase ,				__lowerCamelCase ,				prev_module_hook=__lowerCamelCase	)

             if self.safety_checker is not None:
                   lowerCamelCase__   ,       lowerCamelCase__		:    Optional[int]   =  cpu_offload_with_hook(self.safety_checker ,				__lowerCamelCase ,				prev_module_hook=__lowerCamelCase	)

             # We'll offload the last model manually.
             lowerCamelCase__		:    Any   =  hook





       @property
       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             if self.device != torch.device("meta"	) or not hasattr(self.text_encoder ,				"_hf_hook"	):
                   return self.device
             for module in self.text_encoder.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





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      int ,				):


             '''simple docstring'''



             len(__lowerCamelCase	) if isinstance(__lowerCamelCase ,				__lowerCamelCase	) else 1

             # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
             lowerCamelCase__		:    Optional[Any]   =  0
             # get prompt text embeddings
             lowerCamelCase__		:    Optional[Any]   =  self.tokenizer(
                 __lowerCamelCase ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				truncation=__lowerCamelCase ,				return_tensors="pt" ,				)
             lowerCamelCase__		:    Optional[Any]   =  text_inputs.input_ids
             lowerCamelCase__		:    List[str]   =  self.tokenizer(__lowerCamelCase ,				padding="longest" ,				return_tensors="pt"	).input_ids

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Union[str, Any]   =  self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]	)
                   logger.warning(
                       "The following part of your input was truncated because CLIP can only handle sequences up to"
                       f" {self.tokenizer.model_max_length} tokens: {removed_text}"	)

             lowerCamelCase__		:    Union[str, Any]   =  self.text_encoder(text_input_ids.to(__lowerCamelCase	)	)
             lowerCamelCase__		:    Dict   =  text_encoder_output.text_embeds

             lowerCamelCase__		:    Any   =  prompt_embeds.repeat_interleave(__lowerCamelCase ,				dim=0	)
             # in Shap-E it normalize the prompt_embeds and then later rescale it
             lowerCamelCase__		:    int   =  prompt_embeds / torch.linalg.norm(__lowerCamelCase ,				dim=-1 ,				keepdim=__lowerCamelCase	)

             if do_classifier_free_guidance:
                   lowerCamelCase__		:    str   =  torch.zeros_like(__lowerCamelCase	)

                   # For classifier free guidance, we need to do two forward passes.
                   # Here we concatenate the unconditional and text embeddings into a single batch
                   # to avoid doing two forward passes
                   lowerCamelCase__		:    Optional[Any]   =  torch.cat([negative_prompt_embeds, prompt_embeds]	)

             # Rescale the features to have unit variance
             lowerCamelCase__		:    Dict   =  math.sqrt(prompt_embeds.shape[1]	) * prompt_embeds

             return prompt_embeds





       @torch.no_grad()
       @replace_example_docstring(__lowerCamelCase	)
       def __call__(       self							:      List[str] ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      int = 1 ,				__lowerCamelCase							:      int = 25 ,				__lowerCamelCase							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None ,				__lowerCamelCase							:      Optional[torch.FloatTensor] = None ,				__lowerCamelCase							:      float = 4.0 ,				__lowerCamelCase							:      int = 64 ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Union[str, Any]   =  1
             elif isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Optional[Any]   =  len(__lowerCamelCase	)
             else:
                   raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase	)}"	)

             lowerCamelCase__		:    Union[str, Any]   =  self._execution_device

             lowerCamelCase__		:    Dict   =  batch_size * num_images_per_prompt

             lowerCamelCase__		:    List[Any]   =  guidance_scale > 1.0
             lowerCamelCase__		:    int   =  self._encode_prompt(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)

             # prior

             self.scheduler.set_timesteps(__lowerCamelCase ,				device=__lowerCamelCase	)
             lowerCamelCase__		:    str   =  self.scheduler.timesteps

             lowerCamelCase__		:    Dict   =  self.prior.config.num_embeddings
             lowerCamelCase__		:    List[Any]   =  self.prior.config.embedding_dim

             lowerCamelCase__		:    Dict   =  self.prepare_latents(
                 (batch_size, num_embeddings * embedding_dim) ,				prompt_embeds.dtype ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				self.scheduler ,				)

             # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
             lowerCamelCase__		:    str   =  latents.reshape(latents.shape[0] ,				__lowerCamelCase ,				__lowerCamelCase	)

             for i, t in enumerate(self.progress_bar(__lowerCamelCase	)	):
                   # expand the latents if we are doing classifier free guidance
                   lowerCamelCase__		:    int   =  torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
                   lowerCamelCase__		:    str   =  self.scheduler.scale_model_input(__lowerCamelCase ,				__lowerCamelCase	)

                   lowerCamelCase__		:    str   =  self.prior(
                       __lowerCamelCase ,				timestep=__lowerCamelCase ,				proj_embedding=__lowerCamelCase ,				).predicted_image_embedding

                   # remove the variance
                   lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  noise_pred.split(
                       scaled_model_input.shape[2] ,				dim=2	)  # batch_size, num_embeddings, embedding_dim

                   if do_classifier_free_guidance is not None:
                         lowerCamelCase__   ,       lowerCamelCase__		:    List[Any]   =  noise_pred.chunk(2	)
                         lowerCamelCase__		:    str   =  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                   lowerCamelCase__		:    Union[str, Any]   =  self.scheduler.step(
                       __lowerCamelCase ,				timestep=__lowerCamelCase ,				sample=__lowerCamelCase ,				).prev_sample

             if output_type == "latent":
                   return ShapEPipelineOutput(images=__lowerCamelCase	)

             lowerCamelCase__		:    List[Any]   =  []
             for i, latent in enumerate(__lowerCamelCase	):
                   lowerCamelCase__		:    List[str]   =  self.renderer.decode(
                       latent[None, :] ,				__lowerCamelCase ,				size=__lowerCamelCase ,				ray_batch_size=4096 ,				n_coarse_samples=64 ,				n_fine_samples=128 ,				)
                   images.append(__lowerCamelCase	)

             lowerCamelCase__		:    int   =  torch.stack(__lowerCamelCase	)

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

             lowerCamelCase__		:    Union[str, Any]   =  images.cpu().numpy()

             if output_type == "pil":
                   lowerCamelCase__		:    str   =  [self.numpy_to_pil(__lowerCamelCase	) for image in images]

             # Offload last model to CPU
             if hasattr(self ,				"final_offload_hook"	) and self.final_offload_hook is not None:
                   self.final_offload_hook.offload()

             if not return_dict:
                   return (images,)

             return ShapEPipelineOutput(images=__lowerCamelCase	)






A				:   Union[str, Any]		= {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def 				lowercase_		(		_A      :   str						):

      """simple docstring"""




      return [alphabet[char] for char in key.upper()]

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[str]   =  ""
      lowerCamelCase__		:    Union[str, Any]   =  0
      lowerCamelCase__		:    Any   =  generate_table(_A						)
      for char in words.upper():
            cipher += get_opponent(table[count]   , _A						)
            lowerCamelCase__		:    Optional[int]   =  (count + 1) % len(_A						)
      return cipher

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      return encrypt(_A   , _A						)

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[Any]   =  0 if char in table[0] else 1
      lowerCamelCase__		:    List[Any]   =  table[row].index(_A						)
      return row, col

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  get_position(_A   , char.upper()						)
      if row == 1:
            return table[0][col]
      else:
            return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A				:   List[str]		= input("Enter key: ").strip()
    A				:   Tuple		= input("Enter text to encrypt: ").strip()
    A				:   Dict		= encrypt(key, text)

    print(f'Encrypted: {cipher_text}')
    print(f'Decrypted with key: {decrypt(key, cipher_text)}')


