
'''simple docstring'''






from __future__ import annotations

import typing
from collections import Counter







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      _lowerCamelCase				:   typing.Counter[int]								=   Counter()
      for base in range(1				,	max_perimeter + 1							):
            for perpendicular in range(lowerCAmelCase_				,	max_perimeter + 1							):
                  _lowerCamelCase				:   List[str]								=   (base * base + perpendicular * perpendicular) ** 0.5
                  if hypotenuse == int(lowerCAmelCase_							):
                        _lowerCamelCase				:   Optional[Any]								=   int(base + perpendicular + hypotenuse							)
                        if perimeter > max_perimeter:
                              continue
                        triplets[perimeter] += 1
      return triplets







def 		A_       (	_lowerCAmelCase   :    Union[str, Any] = 1000							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   pythagorean_triple(lowerCAmelCase_							)
      return triplets.most_common(1							)[0][0]


if __name__ == "__main__":
 print(f'''Perimeter {solution()} has maximum solutions''')

'''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


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

UpperCAmelCase_       :   str   =       '\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__						(       A ):
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       str,__A      :       PriorTransformer,__A      :       CLIPVisionModel,__A      :       CLIPImageProcessor,__A      :       HeunDiscreteScheduler,__A      :       ShapERenderer,):
          super().__init__()

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



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       str,__A      :       Tuple,__A      :       Any,__A      :       Optional[int],__A      :       Tuple,__A      :       Union[str, Any]     ):
          if latents is None:
                _lowerCamelCase				:   int								=   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}'     )
                _lowerCamelCase				:   Union[str, Any]								=   latents.to(__A     )

          _lowerCamelCase				:   Tuple								=   latents * scheduler.init_noise_sigma
          return latents



    def   lowerCamelCase_  (	self      :       List[str],__A      :       List[Any]=0     ):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError("Please install accelerate via `pip install accelerate`"     )

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

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



    @property
    def   lowerCamelCase_  (	self      :       Optional[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   lowerCamelCase_  (	self      :       Tuple,__A      :       int,__A      :       Any,__A      :       str,__A      :       int,):
          if isinstance(__A,__A     ) and isinstance(image[0],torch.Tensor     ):
                _lowerCamelCase				:   Dict								=   torch.cat(__A,axis=0     ) if image[0].ndim == 4 else torch.stack(__A,axis=0     )

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

          _lowerCamelCase				:   Any								=   image.to(dtype=self.image_encoder.dtype,device=__A     )

          _lowerCamelCase				:   List[str]								=   self.image_encoder(__A     )["last_hidden_state"]
          _lowerCamelCase				:   Optional[Any]								=   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          _lowerCamelCase				:   Any								=   image_embeds.repeat_interleave(__A,dim=0     )

          if do_classifier_free_guidance:
                _lowerCamelCase				:   int								=   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
                _lowerCamelCase				:   Union[str, Any]								=   torch.cat([negative_image_embeds, image_embeds]     )

          return image_embeds



    @torch.no_grad()
    @replace_example_docstring(__A     )
    def __call__(	self      :       Tuple,__A      :       Union[PIL.Image.Image, List[PIL.Image.Image]],__A      :       int = 1,__A      :       int = 2_5,__A      :       Optional[Union[torch.Generator, List[torch.Generator]]] = None,__A      :       Optional[torch.FloatTensor] = None,__A      :       float = 4.0,__A      :       int = 6_4,__A      :       Optional[str] = "pil",__A      :       bool = True,):

          if isinstance(__A,PIL.Image.Image     ):
                _lowerCamelCase				:   Optional[int]								=   1
          elif isinstance(__A,torch.Tensor     ):
                _lowerCamelCase				:   Optional[Any]								=   image.shape[0]
          elif isinstance(__A,__A     ) and isinstance(image[0],(torch.Tensor, PIL.Image.Image)     ):
                _lowerCamelCase				:   str								=   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     )}'     )

          _lowerCamelCase				:   Optional[int]								=   self._execution_device

          _lowerCamelCase				:   int								=   batch_size * num_images_per_prompt

          _lowerCamelCase				:   Union[str, Any]								=   guidance_scale > 1.0
          _lowerCamelCase				:   Tuple								=   self._encode_image(__A,__A,__A,__A     )

          # prior

          self.scheduler.set_timesteps(__A,device=__A     )
          _lowerCamelCase				:   List[Any]								=   self.scheduler.timesteps

          _lowerCamelCase				:   Dict								=   self.prior.config.num_embeddings
          _lowerCamelCase				:   Tuple								=   self.prior.config.embedding_dim

          _lowerCamelCase				:   List[str]								=   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
          _lowerCamelCase				:   Tuple								=   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
                _lowerCamelCase				:   Optional[int]								=   torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
                _lowerCamelCase				:   Tuple								=   self.scheduler.scale_model_input(__A,__A     )

                _lowerCamelCase				:   List[str]								=   self.prior(
                    __A,timestep=__A,proj_embedding=__A,).predicted_image_embedding

                # remove the variance
                _lowerCamelCase						,		_lowerCamelCase				:   Optional[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				:   int								=   noise_pred.chunk(2     )
                      _lowerCamelCase				:   Optional[int]								=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                _lowerCamelCase				:   Any								=   self.scheduler.step(
                    __A,timestep=__A,sample=__A,).prev_sample

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

          _lowerCamelCase				:   Any								=   []
          for i, latent in enumerate(__A     ):
                print()
                _lowerCamelCase				:   int								=   self.renderer.decode(
                    latent[None, :],__A,size=__A,ray_batch_size=4_0_9_6,n_coarse_samples=6_4,n_fine_samples=1_2_8,)

                images.append(__A     )

          _lowerCamelCase				:   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}'     )

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

          if output_type == "pil":
                _lowerCamelCase				:   List[str]								=   [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     )