


'''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          =     """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          super().__init__()

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          if latents is None:
               __A  :					Optional[Any]						     =       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}"		)
               __A  :					Optional[Any]						     =       latents.to(lowerCamelCase		)

          __A  :					str						     =       latents * scheduler.init_noise_sigma
          return latents


     def lowerCAmelCase__     (      self ,    lowerCamelCase=0		):


          '''simple docstring'''





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

          __A  :					Optional[Any]						     =       torch.device(f"cuda:{gpu_id}"		)

          __A  :					Optional[int]						     =       [self.text_encoder, self.prior]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(lowerCamelCase ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase=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."		)

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

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

          __A  :					int						     =       None
          for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               __A     ,__A  :					Dict						     =       cpu_offload_with_hook(lowerCamelCase ,    lowerCamelCase ,    prev_module_hook=lowerCamelCase		)

          if self.safety_checker is not None:
               __A     ,__A  :					Union[str, Any]						     =       cpu_offload_with_hook(self.safety_checker ,    lowerCamelCase ,    prev_module_hook=lowerCamelCase		)

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


     @property
     def lowerCAmelCase__     (      self		):


          '''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(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 lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    ):


          '''simple docstring'''





          len(lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else 1

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

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

          __A  :					List[Any]						     =       self.text_encoder(text_input_ids.to(lowerCamelCase		)		)
          __A  :					Tuple						     =       text_encoder_output.text_embeds

          __A  :					Tuple						     =       prompt_embeds.repeat_interleave(lowerCamelCase ,    dim=0		)
          # in Shap-E it normalize the prompt_embeds and then later rescale it
          __A  :					str						     =       prompt_embeds / torch.linalg.norm(lowerCamelCase ,    dim=-1 ,    keepdim=lowerCamelCase		)

          if do_classifier_free_guidance:
               __A  :					Optional[int]						     =       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
               __A  :					List[str]						     =       torch.cat([negative_prompt_embeds, prompt_embeds]		)

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

          return prompt_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 ,    lowerCamelCase		):
               __A  :					Any						     =       1
          elif isinstance(lowerCamelCase ,    lowerCamelCase		):
               __A  :					Any						     =       len(lowerCamelCase		)
          else:
               raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase		)}"		)

          __A  :					int						     =       self._execution_device

          __A  :					Optional[Any]						     =       batch_size * num_images_per_prompt

          __A  :					Tuple						     =       guidance_scale > 1.0
          __A  :					str						     =       self._encode_prompt(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          # prior

          self.scheduler.set_timesteps(lowerCamelCase ,    device=lowerCamelCase		)
          __A  :					Optional[Any]						     =       self.scheduler.timesteps

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

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

               __A  :					Union[str, Any]						     =       self.prior(
                   lowerCamelCase ,    timestep=lowerCamelCase ,    proj_embedding=lowerCamelCase ,    ).predicted_image_embedding

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

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

               __A  :					Tuple						     =       self.scheduler.step(
                   lowerCamelCase ,    timestep=lowerCamelCase ,    sample=lowerCamelCase ,    ).prev_sample

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

          __A  :					Any						     =       []
          for i, latent in enumerate(lowerCamelCase		):
               __A  :					Dict						     =       self.renderer.decode(
                   latent[None, :] ,    lowerCamelCase ,    size=lowerCamelCase ,    ray_batch_size=4096 ,    n_coarse_samples=64 ,    n_fine_samples=128 ,    )
               images.append(lowerCamelCase		)

          __A  :					str						     =       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}"		)

          __A  :					Tuple						     =       images.cpu().numpy()

          if output_type == "pil":
               __A  :					int						     =       [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		)





'''simple docstring'''


from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
_UpperCamelCase          =     {
    """1""": ["""2""", """3""", """4""", """5"""],
    """2""": ["""1""", """3""", """4""", """5"""],
    """3""": ["""1""", """2""", """4""", """5""", """10"""],
    """4""": ["""1""", """2""", """3""", """5""", """6"""],
    """5""": ["""1""", """2""", """3""", """4""", """7"""],
    """6""": ["""7""", """8""", """9""", """10""", """4"""],
    """7""": ["""6""", """8""", """9""", """10""", """5"""],
    """8""": ["""6""", """7""", """9""", """10"""],
    """9""": ["""6""", """7""", """8""", """10"""],
    """10""": ["""6""", """7""", """8""", """9""", """3"""],
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Any						     =       {node: {node} for node in graph}

     __A  :					List[str]						     =       {node: graph[node][:] for node in graph}

     while len(SCREAMING_SNAKE_CASE       ) > 2:
          # Choose a random edge.
          __A  :					Tuple						     =       random.choice(list(graph_copy.keys()       )       )
          __A  :					Any						     =       random.choice(graph_copy[u]       )

          # Contract edge (u, v) to new node uv
          __A  :					str						     =       u + v
          __A  :					Any						     =       list(set(graph_copy[u] + graph_copy[v]       )       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          uv_neighbors.remove(SCREAMING_SNAKE_CASE       )
          __A  :					Any						     =       uv_neighbors
          for neighbor in uv_neighbors:
               graph_copy[neighbor].append(SCREAMING_SNAKE_CASE       )

          __A  :					str						     =       set(contracted_nodes[u].union(contracted_nodes[v]       )       )

          # Remove nodes u and v.
          del graph_copy[u]
          del graph_copy[v]
          for neighbor in uv_neighbors:
               if u in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )
               if v in graph_copy[neighbor]:
                    graph_copy[neighbor].remove(SCREAMING_SNAKE_CASE       )

    # Find cutset.
     __A  :					int						     =       [contracted_nodes[node] for node in graph_copy]
     return {
         (node, neighbor)
         for node in groups[0]
         for neighbor in graph[node]
         if neighbor in groups[1]
     }


if __name__ == "__main__":
      print(partition_graph(TEST_GRAPH))

