

'''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


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

lowerCamelCase				:   int      				=  "\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__					=       42







class 							A__					( A__						):





  def __init__(	self   :			Dict							,      _a   :			PriorTransformer							,      _a   :			CLIPTextModelWithProjection							,      _a   :			CLIPTokenizer							,      _a   :			HeunDiscreteScheduler							,      _a   :			ShapERenderer							,      )			->					Tuple:





       '''simple docstring'''






       super().__init__()

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





  def 		A						(	self   :			Optional[int]							,      _a   :			List[Any]							,      _a   :			List[str]							,      _a   :			str							,      _a   :			Dict							,      _a   :			List[str]							,      _a   :			Any				)			->					int:





       '''simple docstring'''






       if latents is None:
            _SCREAMING_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}"				)
            _SCREAMING_SNAKE_CASE												=latents.to(_a				)

       _SCREAMING_SNAKE_CASE												=latents * scheduler.init_noise_sigma
       return latents





  def 		A						(	self   :			List[Any]							,      _a   :			Tuple=0				)			->					Dict:





       '''simple docstring'''






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

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

       _SCREAMING_SNAKE_CASE												=[self.text_encoder, self.prior]
       for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                 cpu_offload(_a							,      _a				)





  def 		A						(	self   :			str							,      _a   :			List[Any]=0				)			->					Optional[Any]:





       '''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.'				)

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

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

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

       if self.safety_checker is not None:
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=cpu_offload_with_hook(self.safety_checker							,      _a							,      prev_module_hook=_a				)

       # We'll offload the last model manually.
       _SCREAMING_SNAKE_CASE												=hook





  @property
  def 		A						(	self   :			Tuple				)			->					str:





       '''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(_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   :			List[str]							,      _a   :			List[Any]							,      _a   :			List[str]							,      _a   :			List[str]							,      _a   :			Union[str, Any]							,      )			->					str:





       '''simple docstring'''






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

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

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

       _SCREAMING_SNAKE_CASE												=self.text_encoder(text_input_ids.to(_a				)				)
       _SCREAMING_SNAKE_CASE												=text_encoder_output.text_embeds

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

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

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

       return prompt_embeds





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





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE												=self._execution_device

       _SCREAMING_SNAKE_CASE												=batch_size * num_images_per_prompt

       _SCREAMING_SNAKE_CASE												=guidance_scale > 1.0
       _SCREAMING_SNAKE_CASE												=self._encode_prompt(_a							,      _a							,      _a							,      _a				)

       # prior

       self.scheduler.set_timesteps(_a							,      device=_a				)
       _SCREAMING_SNAKE_CASE												=self.scheduler.timesteps

       _SCREAMING_SNAKE_CASE												=self.prior.config.num_embeddings
       _SCREAMING_SNAKE_CASE												=self.prior.config.embedding_dim

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

            _SCREAMING_SNAKE_CASE												=self.prior(
                _a							,      timestep=_a							,      proj_embedding=_a							,      ).predicted_image_embedding

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

            _SCREAMING_SNAKE_CASE												=self.scheduler.step(
                _a							,      timestep=_a							,      sample=_a							,      ).prev_sample

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

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

       _SCREAMING_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}"				)

       _SCREAMING_SNAKE_CASE												=images.cpu().numpy()

       if output_type == "pil":
            _SCREAMING_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				)



'''simple docstring'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
