


"""simple docstring"""







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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

lowerCamelCase_       :       Any      						=  """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""






@dataclass
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  42






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   ) ->					Union[str, Any]:
     super().__init__()

     self.register_modules(
         prior=__A			,   image_encoder=__A			,   image_processor=__A			,   scheduler=__A			,   renderer=__A			,   )


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     if latents is None:
         a					=randn_tensor(__A			,   generator=__A			,   device=__A			,   dtype=__A						)
     else:
         if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
         a					=latents.to(__A						)

     a					=latents * scheduler.init_noise_sigma
     return latents


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=0						) ->					Optional[Any]:
     if is_accelerate_available():
         from accelerate import cpu_offload
     else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

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

     a					=[self.image_encoder, self.prior]
     for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
             cpu_offload(__A			,   __A						)


 @property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     if self.device != torch.device('''meta'''						) or not hasattr(self.image_encoder			,   '''_hf_hook'''						):
         return self.device
     for module in self.image_encoder.modules():
         if (
             hasattr(__A			,   '''_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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A			,   __A			,   ) ->					Optional[Any]:
     if isinstance(__A			,   __A						) and isinstance(image[0]			,   torch.Tensor						):
         a					=torch.cat(__A			,   axis=0						) if image[0].ndim == 4 else torch.stack(__A			,   axis=0						)

     if not isinstance(__A			,   torch.Tensor						):
         a					=self.image_processor(__A			,   return_tensors='''pt'''						).pixel_values[0].unsqueeze(0						)

     a					=image.to(dtype=self.image_encoder.dtype			,   device=__A						)

     a					=self.image_encoder(__A						)['''last_hidden_state''']
     a					=image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

     a					=image_embeds.repeat_interleave(__A			,   dim=0						)

     if do_classifier_free_guidance:
         a					=torch.zeros_like(__A						)

         # 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
         a					=torch.cat([negative_image_embeds, image_embeds]						)

     return image_embeds





 @torch.no_grad()
 @replace_example_docstring(__A						)
 def __call__(		self			,   __A			,   __A = 1			,   __A = 25			,   __A = None			,   __A = None			,   __A = 4.0			,   __A = 64			,   __A = "pil"			,   __A = True			,   ) ->					Any:

     if isinstance(__A			,   PIL.Image.Image						):
         a					=1
     elif isinstance(__A			,   torch.Tensor						):
         a					=image.shape[0]
     elif isinstance(__A			,   __A						) and isinstance(image[0]			,   (torch.Tensor, PIL.Image.Image)						):
         a					=len(__A						)
     else:
         raise ValueError(
             f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__A						)}'''						)

     a					=self._execution_device

     a					=batch_size * num_images_per_prompt

     a					=guidance_scale > 1.0
     a					=self._encode_image(__A			,   __A			,   __A			,   __A						)

     # prior

     self.scheduler.set_timesteps(__A			,   device=__A						)
     a					=self.scheduler.timesteps

     a					=self.prior.config.num_embeddings
     a					=self.prior.config.embedding_dim

     a					=self.prepare_latents(
         (batch_size, num_embeddings * embedding_dim)			,   image_embeds.dtype			,   __A			,   __A			,   __A			,   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
     a					=latents.reshape(latents.shape[0]			,   __A			,   __A						)

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

         a					=self.prior(
             __A			,   timestep=__A			,   proj_embedding=__A			,   ).predicted_image_embedding

         # remove the variance
         a		,				a					=noise_pred.split(
             scaled_model_input.shape[2]			,   dim=2						)  # batch_size, num_embeddings, embedding_dim

         if do_classifier_free_guidance is not None:
             a		,				a					=noise_pred.chunk(2						)
             a					=noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

         a					=self.scheduler.step(
             __A			,   timestep=__A			,   sample=__A			,   ).prev_sample

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

     a					=[]
     for i, latent in enumerate(__A						):
         print()
         a					=self.renderer.decode(
             latent[None, :]			,   __A			,   size=__A			,   ray_batch_size=4096			,   n_coarse_samples=64			,   n_fine_samples=128			,   )

         images.append(__A						)

     a					=torch.stack(__A						)

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

     a					=images.cpu().numpy()

     if output_type == "pil":
         a					=[self.numpy_to_pil(__A						) 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=__A						)



"""simple docstring"""







from __future__ import annotations






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A = 0						) ->					Dict:
     a					=key


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					list[str]:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key is an appropriate size
     key %= 255

     return [chr(ord(__A						) ^ key						) for ch in content]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					str:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     a					=key or self.__key or 1

     # make sure key can be any size
     while key > 255:
         key -= 255

     # This will be returned
     a					=''''''

     for ch in content:
         ans += chr(ord(__A						) ^ key						)

     return ans


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = 0						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''encrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.encrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					bool:
     assert isinstance(__A			,   __A						) and isinstance(__A			,   __A						)

     try:
         with open(__A						) as fin, open('''decrypt.out'''			,   '''w+'''						) as fout:
             # actual encrypt-process
             for line in fin:
                 fout.write(self.decrypt_string(__A			,   __A						)						)

     except OSError:
         return False

     return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")