


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


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

_SCREAMING_SNAKE_CASE      :			List[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   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		42






class   A__ (   snake_case__     ):







    """simple docstring"""

    def __init__(	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         super().__init__()

         self.register_modules(
             prior=__A      , image_encoder=__A      , image_processor=__A      , scheduler=__A      , renderer=__A      , )
    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         if latents is None:
              snake_case					=    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}'''   )
              snake_case					=    latents.to(__A   )

         snake_case					=    latents * scheduler.init_noise_sigma
         return latents
    def 		a_      (	self      , __snake_case=0   ):
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

         snake_case					=    torch.device(F'''cuda:{gpu_id}'''   )

         snake_case					=    [self.image_encoder, self.prior]
         for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                   cpu_offload(__A      , __A   )
    @property
    def 		a_      (	self   ):
         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 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case      , ):
         if isinstance(__A      , __A   ) and isinstance(image[0]      , torch.Tensor   ):
              snake_case					=    torch.cat(__A      , axis=0   ) if image[0].ndim == 4 else torch.stack(__A      , axis=0   )

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

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

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

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

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

         return image_embeds




    @torch.no_grad()
    @replace_example_docstring(__A   )
    def __call__(	self      , __snake_case      , __snake_case = 1      , __snake_case = 2_5      , __snake_case = None      , __snake_case = None      , __snake_case = 4.0      , __snake_case = 6_4      , __snake_case = "pil"      , __snake_case = True      , ):

         if isinstance(__A      , PIL.Image.Image   ):
              snake_case					=    1
         elif isinstance(__A      , torch.Tensor   ):
              snake_case					=    image.shape[0]
         elif isinstance(__A      , __A   ) and isinstance(image[0]      , (torch.Tensor, PIL.Image.Image)   ):
              snake_case					=    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   )}'''   )

         snake_case					=    self._execution_device

         snake_case					=    batch_size * num_images_per_prompt

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

         # prior

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

         snake_case					=    self.prior.config.num_embeddings
         snake_case					=    self.prior.config.embedding_dim

         snake_case					=    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
         snake_case					=    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
              snake_case					=    torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
              snake_case					=    self.scheduler.scale_model_input(__A      , __A   )

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

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

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

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

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

         snake_case					=    []
         for i, latent in enumerate(__A   ):
              print()
              snake_case					=    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   )

         snake_case					=    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}'''   )

         snake_case					=    images.cpu().numpy()

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








import math

class   A__ :







    """simple docstring"""

    def 		a_      (	self      , __snake_case      , __snake_case   ):
         snake_case					=    0.0
         snake_case					=    0.0
         for i in range(len(__snake_case   )   ):
              da += math.pow((sample[i] - weights[0][i])      , 2   )
              da += math.pow((sample[i] - weights[1][i])      , 2   )
              return 0 if da > da else 1
         return 0




    def 		a_      (	self      , __snake_case      , __snake_case      , __snake_case      , __snake_case   ):
         for i in range(len(__snake_case   )   ):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights






def       UpperCAmelCase__						():



     """simple docstring"""






     snake_case					=    [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

     # weight initialization ( n, C )
     snake_case					=    [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

     # training
     snake_case					=    SelfOrganizingMap()
     snake_case					=    3
     snake_case					=    0.5

     for _ in range(UpperCamelCase_    ):
          for j in range(len(UpperCamelCase_    )    ):
               # training sample
               snake_case					=    training_samples[j]

               # Compute the winning vector
               snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

               # Update the winning vector
               snake_case					=    self_organizing_map.update(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    )

    # classify test sample
     snake_case					=    [0, 0, 0, 1]
     snake_case					=    self_organizing_map.get_winner(UpperCamelCase_							,UpperCamelCase_    )

     # results
     print(F'''Clusters that the test sample belongs to : {winner}'''    )
     print(F'''Weights that have been trained : {weights}'''    )


# running the main() function
if __name__ == "__main__":
 main()




