




'''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


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

A					       ='\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      _a    (      __a					):
       __a							:	Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class      _a    (      __a					):
       def __init__(      self		:      str  ,							lowercase		:      PriorTransformer  ,							lowercase		:      CLIPTextModelWithProjection  ,							lowercase		:      CLIPTokenizer  ,							lowercase		:      HeunDiscreteScheduler  ,							lowercase		:      ShapERenderer  ,							):



           '''simple docstring'''

           super().__init__()

           self.register_modules(
               prior=lowercase  ,							text_encoder=lowercase  ,							tokenizer=lowercase  ,							scheduler=lowercase  ,							renderer=lowercase  ,							)
       def        A (      self		:      Optional[Any]  ,							lowercase		:      Optional[Any]  ,							lowercase		:      List[str]  ,							lowercase		:      Dict  ,							lowercase		:      List[str]  ,							lowercase		:      int  ,							lowercase		:      Tuple							):



           '''simple docstring'''

           if latents is None:
               UpperCAmelCase     			=						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}"							)
               UpperCAmelCase     			=						latents.to(lowercase							)

           UpperCAmelCase     			=						latents * scheduler.init_noise_sigma
           return latents
       def        A (      self		:      Union[str, Any]  ,							lowercase		:      List[Any]=0							):



           '''simple docstring'''

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

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

           UpperCAmelCase     			=						[self.text_encoder, self.prior]
           for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                   cpu_offload(lowercase  ,							lowercase							)
       def        A (      self		:      Any  ,							lowercase		:      List[Any]=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     			=						torch.device(f"cuda:{gpu_id}"							)

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

           UpperCAmelCase     			=						None
           for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               UpperCAmelCase			,	UpperCAmelCase     			=						cpu_offload_with_hook(lowercase  ,							lowercase  ,							prev_module_hook=lowercase							)

           if self.safety_checker is not None:
               UpperCAmelCase			,	UpperCAmelCase     			=						cpu_offload_with_hook(self.safety_checker  ,							lowercase  ,							prev_module_hook=lowercase							)

           # We'll offload the last model manually.
           UpperCAmelCase     			=						hook
       @property
       def        A (      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(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        A (      self		:      Union[str, Any]  ,							lowercase		:      Any  ,							lowercase		:      Tuple  ,							lowercase		:      List[str]  ,							lowercase		:      Any  ,							):



           '''simple docstring'''

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

           # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
           UpperCAmelCase     			=						0
           # get prompt text embeddings
           UpperCAmelCase     			=						self.tokenizer(
               lowercase  ,							padding='''max_length'''  ,							max_length=self.tokenizer.model_max_length  ,							truncation=lowercase  ,							return_tensors='''pt'''  ,							)
           UpperCAmelCase     			=						text_inputs.input_ids
           UpperCAmelCase     			=						self.tokenizer(lowercase  ,							padding='''longest'''  ,							return_tensors='''pt'''							).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowercase  ,							lowercase							):
               UpperCAmelCase     			=						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     			=						self.text_encoder(text_input_ids.to(lowercase							)							)
           UpperCAmelCase     			=						text_encoder_output.text_embeds

           UpperCAmelCase     			=						prompt_embeds.repeat_interleave(lowercase  ,							dim=0							)
           # in Shap-E it normalize the prompt_embeds and then later rescale it
           UpperCAmelCase     			=						prompt_embeds / torch.linalg.norm(lowercase  ,							dim=-1  ,							keepdim=lowercase							)

           if do_classifier_free_guidance:
               UpperCAmelCase     			=						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
               UpperCAmelCase     			=						torch.cat([negative_prompt_embeds, prompt_embeds]							)

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

           return prompt_embeds







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



           '''simple docstring'''

           if isinstance(lowercase  ,							lowercase							):
               UpperCAmelCase     			=						1
           elif isinstance(lowercase  ,							lowercase							):
               UpperCAmelCase     			=						len(lowercase							)
           else:
               raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(lowercase							)}"							)

           UpperCAmelCase     			=						self._execution_device

           UpperCAmelCase     			=						batch_size * num_images_per_prompt

           UpperCAmelCase     			=						guidance_scale > 1.0
           UpperCAmelCase     			=						self._encode_prompt(lowercase  ,							lowercase  ,							lowercase  ,							lowercase							)

           # prior

           self.scheduler.set_timesteps(lowercase  ,							device=lowercase							)
           UpperCAmelCase     			=						self.scheduler.timesteps

           UpperCAmelCase     			=						self.prior.config.num_embeddings
           UpperCAmelCase     			=						self.prior.config.embedding_dim

           UpperCAmelCase     			=						self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)  ,							prompt_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
           UpperCAmelCase     			=						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
               UpperCAmelCase     			=						torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
               UpperCAmelCase     			=						self.scheduler.scale_model_input(lowercase  ,							lowercase							)

               UpperCAmelCase     			=						self.prior(
                   lowercase  ,							timestep=lowercase  ,							proj_embedding=lowercase  ,							).predicted_image_embedding

               # remove the variance
               UpperCAmelCase			,	UpperCAmelCase     			=						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			,	UpperCAmelCase     			=						noise_pred.chunk(2							)
                   UpperCAmelCase     			=						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               UpperCAmelCase     			=						self.scheduler.step(
                   lowercase  ,							timestep=lowercase  ,							sample=lowercase  ,							).prev_sample

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

           UpperCAmelCase     			=						[]
           for i, latent in enumerate(lowercase							):
               UpperCAmelCase     			=						self.renderer.decode(
                   latent[None, :]  ,							lowercase  ,							size=lowercase  ,							ray_batch_size=4_096  ,							n_coarse_samples=64  ,							n_fine_samples=128  ,							)
               images.append(lowercase							)

           UpperCAmelCase     			=						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}"							)

           UpperCAmelCase     			=						images.cpu().numpy()

           if output_type == "pil":
               UpperCAmelCase     			=						[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 __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




