'''simple docstring'''




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__lowercase        =    3






def 							snake_case__				(     _A:   int					)       ->      int:

		'''simple docstring'''


		print("""Generating primitive root of p"""					)
		while True:
				lowerCAmelCase     =    random.randrange(3 ,  _A					)
				if pow(_A ,  2 ,  _A					) == 1:
						continue
				if pow(_A ,  _A ,  _A					) == 1:
						continue
				return g






def 							snake_case__				(     _A:   int					)       ->      tuple[tuple[int, int, int, int], tuple[int, int]]:

		'''simple docstring'''


		print("""Generating prime p..."""					)
		lowerCAmelCase     =    rabin_miller.generate_large_prime(_A					)  # select large prime number.
		lowerCAmelCase     =    primitive_root(_A					)  # one primitive root on modulo p.
		lowerCAmelCase     =    random.randrange(3 ,  _A					)  # private_key -> have to be greater than 2 for safety.
		lowerCAmelCase     =    cryptomath.find_mod_inverse(pow(_A ,  _A ,  _A					) ,  _A					)

		lowerCAmelCase     =    (key_size, e_a, e_a, p)
		lowerCAmelCase     =    (key_size, d)

		return public_key, private_key






def 							snake_case__				(     _A:   str ,  _A:   int					)       ->      None:

		'''simple docstring'''


		if os.path.exists(f"{name}_pubkey.txt"					) or os.path.exists(f"{name}_privkey.txt"					):
				print("""\nWARNING:"""					)
				print(
				    f"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
				    """Use a different name or delete these files and re-run this program."""					)
				sys.exit()

		lowerCAmelCase   ,	lowerCAmelCase     =    generate_key(_A					)
		print(f"\nWriting public key to file {name}_pubkey.txt..."					)
		with open(f"{name}_pubkey.txt" ,  """w"""					) as fo:
				fo.write(f"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"					)

		print(f"Writing private key to file {name}_privkey.txt..."					)
		with open(f"{name}_privkey.txt" ,  """w"""					) as fo:
				fo.write(f"{private_key[0]},{private_key[1]}"					)






def 							snake_case__				(     )       ->      None:

		'''simple docstring'''


		print("""Making key files..."""					)
		make_key_files("""elgamal""" ,  2048					)
		print("""Key files generation successful"""					)


if __name__ == "__main__":
						main()





'''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


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

__lowercase        =    '''
    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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Union[PIL.Image.Image, np.ndarray]







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__()

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					if latents is None:
							lowerCAmelCase     =    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     =    latents.to(__lowerCAmelCase)

					lowerCAmelCase     =    latents * scheduler.init_noise_sigma
					return latents



			def 				a_			(  self			,       __lowerCAmelCase=0):




					"""simple docstring"""


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

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

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



			@property
			def 				a_			(  self):




					"""simple docstring"""


					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(__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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       torch.Tensor):
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       axis=0) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase			,       axis=0)

					if not isinstance(__lowerCAmelCase			,       torch.Tensor):
							lowerCAmelCase     =    self.image_processor(__lowerCAmelCase			,       return_tensors="""pt""").pixel_values[0].unsqueeze(0)

					lowerCAmelCase     =    image.to(dtype=self.image_encoder.dtype			,       device=__lowerCAmelCase)

					lowerCAmelCase     =    self.image_encoder(__lowerCAmelCase)["""last_hidden_state"""]
					lowerCAmelCase     =    image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCAmelCase     =    image_embeds.repeat_interleave(__lowerCAmelCase			,       dim=0)

					if do_classifier_free_guidance:
							lowerCAmelCase     =    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     =    torch.cat([negative_image_embeds, image_embeds])

					return image_embeds







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




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       PIL.Image.Image):
							lowerCAmelCase     =    1
					elif isinstance(__lowerCAmelCase			,       torch.Tensor):
							lowerCAmelCase     =    image.shape[0]
					elif isinstance(__lowerCAmelCase			,       __lowerCAmelCase) and isinstance(image[0]			,       (torch.Tensor, PIL.Image.Image)):
							lowerCAmelCase     =    len(__lowerCAmelCase)
					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(__lowerCAmelCase)}")

					lowerCAmelCase     =    self._execution_device

					lowerCAmelCase     =    batch_size * num_images_per_prompt

					lowerCAmelCase     =    guidance_scale > 1.0
					lowerCAmelCase     =    self._encode_image(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)

					# prior

					self.scheduler.set_timesteps(__lowerCAmelCase			,       device=__lowerCAmelCase)
					lowerCAmelCase     =    self.scheduler.timesteps

					lowerCAmelCase     =    self.prior.config.num_embeddings
					lowerCAmelCase     =    self.prior.config.embedding_dim

					lowerCAmelCase     =    self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)			,       image_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     =    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     =    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
							lowerCAmelCase     =    self.scheduler.scale_model_input(__lowerCAmelCase			,       __lowerCAmelCase)

							lowerCAmelCase     =    self.prior(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       proj_embedding=__lowerCAmelCase			,       ).predicted_image_embedding

							# remove the variance
							lowerCAmelCase   ,	lowerCAmelCase     =    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     =    noise_pred.chunk(2)
									lowerCAmelCase     =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							lowerCAmelCase     =    self.scheduler.step(
							    __lowerCAmelCase			,       timestep=__lowerCAmelCase			,       sample=__lowerCAmelCase			,       ).prev_sample

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

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

							images.append(__lowerCAmelCase)

					lowerCAmelCase     =    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     =    images.cpu().numpy()

					if output_type == "pil":
							lowerCAmelCase     =    [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)




