'''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


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

_A								=   '\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\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], "corgi_3d.gif")\n        ```\n'


@dataclass
class       UpperCAmelCase__  (       _UpperCAmelCase			):






   """simple docstring"""
   A							:       Union[PIL.Image.Image, np.ndarray]



class       UpperCAmelCase__  (       _UpperCAmelCase			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     str:
        super().__init__()

        self.register_modules(
            prior=lowercase__       ,				image_encoder=lowercase__       ,				image_processor=lowercase__       ,				scheduler=lowercase__       ,				renderer=lowercase__       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a       ,				_a				)    ->     Optional[Any]:
        if latents is None:
             lowercase_					:       Any     				=					randn_tensor(lowercase__       ,				generator=lowercase__       ,				device=lowercase__       ,				dtype=lowercase__				)
        else:
             if latents.shape != shape:
                  raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
             lowercase_					:       Tuple     				=					latents.to(lowercase__				)

        lowercase_					:       Any     				=					latents * scheduler.init_noise_sigma
        return latents
   def 		_lowerCamelCase (self       ,				_a=0				)    ->     Dict:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('Please install accelerate via `pip install accelerate`'				)

        lowercase_					:       List[Any]     				=					torch.device(f'''cuda:{gpu_id}'''				)

        lowercase_					:       List[str]     				=					[self.image_encoder, self.prior]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(lowercase__       ,				lowercase__				)
   @property
   def 		_lowerCamelCase (self				)    ->     List[str]:
        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(lowercase__       ,				'_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       ,				_a       ,				_a       ,				_a       ,				_a       ,				)    ->     List[Any]:
        if isinstance(lowercase__       ,				lowercase__				) and isinstance(image[0]       ,				torch.Tensor				):
             lowercase_					:       Tuple     				=					torch.cat(lowercase__       ,				axis=0				) if image[0].ndim == 4 else torch.stack(lowercase__       ,				axis=0				)

        if not isinstance(lowercase__       ,				torch.Tensor				):
             lowercase_					:       List[str]     				=					self.image_processor(lowercase__       ,				return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

        lowercase_					:       List[Any]     				=					image.to(dtype=self.image_encoder.dtype       ,				device=lowercase__				)

        lowercase_					:       int     				=					self.image_encoder(lowercase__				)["""last_hidden_state"""]
        lowercase_					:       Any     				=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        lowercase_					:       List[str]     				=					image_embeds.repeat_interleave(lowercase__       ,				dim=0				)

        if do_classifier_free_guidance:
             lowercase_					:       Any     				=					torch.zeros_like(lowercase__				)

             # 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
             lowercase_					:       List[str]     				=					torch.cat([negative_image_embeds, image_embeds]				)

        return image_embeds





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

        if isinstance(lowercase__       ,				PIL.Image.Image				):
             lowercase_					:       Dict     				=					1
        elif isinstance(lowercase__       ,				torch.Tensor				):
             lowercase_					:       str     				=					image.shape[0]
        elif isinstance(lowercase__       ,				lowercase__				) and isinstance(image[0]       ,				(torch.Tensor, PIL.Image.Image)				):
             lowercase_					:       Union[str, Any]     				=					len(lowercase__				)
        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(lowercase__				)}'''				)

        lowercase_					:       Tuple     				=					self._execution_device

        lowercase_					:       int     				=					batch_size * num_images_per_prompt

        lowercase_					:       Optional[int]     				=					guidance_scale > 1.0
        lowercase_					:       List[str]     				=					self._encode_image(lowercase__       ,				lowercase__       ,				lowercase__       ,				lowercase__				)

        # prior

        self.scheduler.set_timesteps(lowercase__       ,				device=lowercase__				)
        lowercase_					:       Any     				=					self.scheduler.timesteps

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

        lowercase_					:       Tuple     				=					self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)       ,				image_embeds.dtype       ,				lowercase__       ,				lowercase__       ,				lowercase__       ,				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
        lowercase_					:       Optional[int]     				=					latents.reshape(latents.shape[0]       ,				lowercase__       ,				lowercase__				)

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

             lowercase_					:       List[Any]     				=					self.prior(
                 lowercase__       ,				timestep=lowercase__       ,				proj_embedding=lowercase__       ,				).predicted_image_embedding

             # remove the variance
             lowercase_					:       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:
                  lowercase_					:       List[Any]     				=					noise_pred.chunk(2				)
                  lowercase_					:       Tuple     				=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

             lowercase_					:       int     				=					self.scheduler.step(
                 lowercase__       ,				timestep=lowercase__       ,				sample=lowercase__       ,				).prev_sample

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

        lowercase_					:       List[str]     				=					[]
        for i, latent in enumerate(lowercase__				):
             print()
             lowercase_					:       int     				=					self.renderer.decode(
                 latent[None, :]       ,				lowercase__       ,				size=lowercase__       ,				ray_batch_size=4_096       ,				n_coarse_samples=64       ,				n_fine_samples=128       ,				)

             images.append(lowercase__				)

        lowercase_					:       Any     				=					torch.stack(lowercase__				)

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

        lowercase_					:       Optional[Any]     				=					images.cpu().numpy()

        if output_type == "pil":
             lowercase_					:       List[str]     				=					[self.numpy_to_pil(lowercase__				) 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=lowercase__				)







'''simple docstring'''




from decimal import Decimal, getcontext
from math import ceil, factorial

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     if not isinstance(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							):
          raise TypeError('Undefined for non-integers'							)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'							)

     lowercase_					:       str     				=					precision
     lowercase_					:       List[str]     				=					ceil(precision / 14							)
     lowercase_					:       Union[str, Any]     				=					426_880 * Decimal(10_005							).sqrt()
     lowercase_					:       List[Any]     				=					1
     lowercase_					:       Optional[int]     				=					13_591_409
     lowercase_					:       Dict     				=					Decimal(SCREAMING_SNAKE_CASE_							)
     for k in range(1  ,       SCREAMING_SNAKE_CASE_							):
          lowercase_					:       List[str]     				=					factorial(6 * k							) // (factorial(3 * k							) * factorial(SCREAMING_SNAKE_CASE_							) ** 3)
          linear_term += 545_140_134
          exponential_term *= -262_537_412_640_768_000
          partial_sum += Decimal(multinomial_term * linear_term							) / exponential_term
     return str(constant_term / partial_sum							)[:-1]


if __name__ == "__main__":
 _A								=   5_0
 print(F"""The first {n} digits of pi is: {pi(n)}""")




