

"""simple docstring"""




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


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

__UpperCAmelCase	=			'\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     (      __lowerCamelCase   ):
		snake_case_			    =    4_2







class        __lowercase     (      __lowerCamelCase   ):

		def __init__(					self						:     Optional[int]   ,A						:     PriorTransformer   ,A						:     CLIPTextModelWithProjection   ,A						:     CLIPTokenizer   ,A						:     HeunDiscreteScheduler   ,A						:     ShapERenderer   ,):






			'''simple docstring'''
			super().__init__()

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

		def 				__lowercase    (					self						:     Optional[Any]   ,A						:     Optional[int]   ,A						:     str   ,A						:     List[str]   ,A						:     Any   ,A						:     List[str]   ,A						:     Any      ):






			'''simple docstring'''
			if latents is None:
				UpperCAmelCase__   :					Union[str, Any]   				=   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}"      )
				UpperCAmelCase__   :					int   				=   latents.to(A      )

			UpperCAmelCase__   :					int   				=   latents * scheduler.init_noise_sigma
			return latents

		def 				__lowercase    (					self						:     Tuple   ,A						:     Any=0      ):






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

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

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

		def 				__lowercase    (					self						:     Dict   ,A						:     List[str]=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."""      )

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

			if self.device.type != "cpu":
				self.to("""cpu"""   ,silence_dtype_warnings=A      )
				torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			UpperCAmelCase__   :					List[str]   				=   None
			for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
				UpperCAmelCase__   :					Optional[Any]   				=   cpu_offload_with_hook(A   ,A   ,prev_module_hook=A      )

			if self.safety_checker is not None:
				UpperCAmelCase__   :					Tuple   				=   cpu_offload_with_hook(self.safety_checker   ,A   ,prev_module_hook=A      )

			# We'll offload the last model manually.
			UpperCAmelCase__   :					List[Any]   				=   hook

		@property
		def 				__lowercase    (					self						:     Dict      ):






			'''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(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 				__lowercase    (					self						:     Any   ,A						:     int   ,A						:     Optional[int]   ,A						:     Dict   ,A						:     Any   ,):






			'''simple docstring'''
			len(A      ) if isinstance(A   ,A      ) else 1

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

			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A   ,A      ):
				UpperCAmelCase__   :					Optional[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}"      )

			UpperCAmelCase__   :					Optional[Any]   				=   self.text_encoder(text_input_ids.to(A      )      )
			UpperCAmelCase__   :					Tuple   				=   text_encoder_output.text_embeds

			UpperCAmelCase__   :					Union[str, Any]   				=   prompt_embeds.repeat_interleave(A   ,dim=0      )
			# in Shap-E it normalize the prompt_embeds and then later rescale it
			UpperCAmelCase__   :					List[Any]   				=   prompt_embeds / torch.linalg.norm(A   ,dim=-1   ,keepdim=A      )

			if do_classifier_free_guidance:
				UpperCAmelCase__   :					str   				=   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
				UpperCAmelCase__   :					Tuple   				=   torch.cat([negative_prompt_embeds, prompt_embeds]      )

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

			return prompt_embeds




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






			'''simple docstring'''

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

			UpperCAmelCase__   :					List[str]   				=   self._execution_device

			UpperCAmelCase__   :					Any   				=   batch_size * num_images_per_prompt

			UpperCAmelCase__   :					str   				=   guidance_scale > 1.0
			UpperCAmelCase__   :					List[str]   				=   self._encode_prompt(A   ,A   ,A   ,A      )

			# prior

			self.scheduler.set_timesteps(A   ,device=A      )
			UpperCAmelCase__   :					str   				=   self.scheduler.timesteps

			UpperCAmelCase__   :					Optional[Any]   				=   self.prior.config.num_embeddings
			UpperCAmelCase__   :					Optional[int]   				=   self.prior.config.embedding_dim

			UpperCAmelCase__   :					List[Any]   				=   self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim)   ,prompt_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
			UpperCAmelCase__   :					Optional[int]   				=   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
				UpperCAmelCase__   :					List[str]   				=   torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
				UpperCAmelCase__   :					List[str]   				=   self.scheduler.scale_model_input(A   ,A      )

				UpperCAmelCase__   :					Optional[Any]   				=   self.prior(
				    A   ,timestep=A   ,proj_embedding=A   ,).predicted_image_embedding

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

				if do_classifier_free_guidance is not None:
					UpperCAmelCase__   :					List[str]   				=   noise_pred.chunk(2      )
					UpperCAmelCase__   :					Optional[int]   				=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

				UpperCAmelCase__   :					Optional[Any]   				=   self.scheduler.step(
				    A   ,timestep=A   ,sample=A   ,).prev_sample

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

			UpperCAmelCase__   :					Union[str, Any]   				=   []
			for i, latent in enumerate(A      ):
				UpperCAmelCase__   :					Tuple   				=   self.renderer.decode(
				    latent[None, :]   ,A   ,size=A   ,ray_batch_size=4_096   ,n_coarse_samples=64   ,n_fine_samples=128   ,)
				images.append(A      )

			UpperCAmelCase__   :					List[Any]   				=   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}"      )

			UpperCAmelCase__   :					Any   				=   images.cpu().numpy()

			if output_type == "pil":
				UpperCAmelCase__   :					Tuple   				=   [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"""




def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   1
 UpperCAmelCase__   :					Union[str, Any]   				=   0
 UpperCAmelCase__   :					List[str]   				=   1
 UpperCAmelCase__   :					Any   				=   1
 UpperCAmelCase__   :					Optional[int]   				=   3
 UpperCAmelCase__   :					Any   				=   3
 UpperCAmelCase__   :					Optional[Any]   				=   limit
 UpperCAmelCase__   :					Dict   				=   0

 UpperCAmelCase__   :					int   				=   """"""
 while counter != decimal + 1:
  if 4 * q + r - t < n * t:
   result += str(__UpperCamelCase )
   if counter == 0:
    result += "."

   if decimal == counter:
    break

   counter += 1
   UpperCAmelCase__   :					Union[str, Any]   				=   10 * (r - n * t)
   UpperCAmelCase__   :					int   				=   ((10 * (3 * q + r)) // t) - 10 * n
   q *= 10
   UpperCAmelCase__   :					str   				=   nr
  else:
   UpperCAmelCase__   :					List[str]   				=   (2 * q + r) * l
   UpperCAmelCase__   :					Union[str, Any]   				=   (q * (7 * k) + 2 + (r * l)) // (t * l)
   q *= k
   t *= l
   l += 2
   k += 1
   UpperCAmelCase__   :					Optional[Any]   				=   nn
   UpperCAmelCase__   :					str   				=   nr
 return result
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 print(F"{calculate_pi(50 ) = }" )
 import doctest

 doctest.testmod()


if __name__ == "__main__":
      main()





