

'''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			:	Optional[Any]								=  logging.get_logger(__name__)  # pylint: disable=invalid-name

_a			:	str								=  '\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     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]




class     lowercase_		(						a	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_				,	a_				,	a_				,	a_				,	)							->			Dict:


    """simple docstring"""
    super().__init__()

    self.register_modules(
        prior=a_				,	text_encoder=a_				,	tokenizer=a_				,	scheduler=a_				,	renderer=a_				,	)
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	a_				,	a_       )							->			Optional[int]:


    """simple docstring"""
    if latents is None:
      UpperCAmelCase  							=		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}'''       )
      UpperCAmelCase  							=		latents.to(a_       )

    UpperCAmelCase  							=		latents * scheduler.init_noise_sigma
    return latents
  def   snake_case_     (       self				,	a_=0       )							->			Optional[int]:


    """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(a_				,	a_       )
  def   snake_case_     (       self				,	a_=0       )							->			List[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.'       )

    UpperCAmelCase  							=		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)

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

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

    # We'll offload the last model manually.
    UpperCAmelCase  							=		hook
  @property
  def   snake_case_     (       self       )							->			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   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_				,	)							->			Any:


    """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
    UpperCAmelCase  							=		0
    # get prompt text embeddings
    UpperCAmelCase  							=		self.tokenizer(
        a_				,	padding='max_length'				,	max_length=self.tokenizer.model_max_length				,	truncation=a_				,	return_tensors='pt'				,	)
    UpperCAmelCase  							=		text_inputs.input_ids
    UpperCAmelCase  							=		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_       ):
      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(a_       )       )
    UpperCAmelCase  							=		text_encoder_output.text_embeds

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

    if do_classifier_free_guidance:
      UpperCAmelCase  							=		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
      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(a_       )
  def __call__(       self				,	a_				,	a_ = 1				,	a_ = 2_5				,	a_ = None				,	a_ = None				,	a_ = 4.0				,	a_ = 6_4				,	a_ = "pil"				,	a_ = True				,	)							->			Optional[Any]:


    """simple docstring"""

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

    UpperCAmelCase  							=		self._execution_device

    UpperCAmelCase  							=		batch_size * num_images_per_prompt

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

    # prior

    self.scheduler.set_timesteps(a_				,	device=a_       )
    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				,	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
    UpperCAmelCase  							=		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
      UpperCAmelCase  							=		torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
      UpperCAmelCase  							=		self.scheduler.scale_model_input(a_				,	a_       )

      UpperCAmelCase  							=		self.prior(
          a_				,	timestep=a_				,	proj_embedding=a_				,	).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(
          a_				,	timestep=a_				,	sample=a_				,	).prev_sample

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

    UpperCAmelCase  							=		[]
    for i, latent in enumerate(a_       ):
      UpperCAmelCase  							=		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_       )

    UpperCAmelCase  							=		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}'''       )

    UpperCAmelCase  							=		images.cpu().numpy()

    if output_type == "pil":
      UpperCAmelCase  							=		[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'''




import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_a			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=sys.argv[1:]       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		arg

    assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
    assert outputfile is not None and not os.path.isfile(a_       ), F'''{outputfile}'''
    if subset_list is not None:
      with open(os.path.realpath(a_       )       ) as f:
        UpperCAmelCase  							=		{self._vqa_file_split()[0] for x in tryload(a_       )}
    else:
      UpperCAmelCase  							=		None

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    for i, file in enumerate(os.listdir(self.inputdir       )       ):
      if self.subset_list is not None and i not in self.subset_list:
        continue
      batch.append(self._vqa_file_split(a_       )       )
      if len(a_       ) == self.batch:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

    for i in range(1       ):
      yield list(map(a_				,	zip(*a_       )       )       )





  def __call__(       self       )							->			str:


    """simple docstring"""
    # make writer
    if not TEST:
      UpperCAmelCase  							=		datasets.ArrowWriter(features=self.schema				,	path=self.outputfile       )
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
      UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		self.preprocess(a_       )
      UpperCAmelCase  							=		self.model(
          a_				,	a_				,	scales_yx=a_				,	padding='max_detections'				,	max_detections=self.config.MAX_DETECTIONS				,	pad_value=0				,	return_tensors='np'				,	location='cpu'				,	)
      UpperCAmelCase  							=		output_dict.pop('normalized_boxes'       )
      if not TEST:
        UpperCAmelCase  							=		np.array(a_       )
        UpperCAmelCase  							=		self.schema.encode_batch(a_       )
        writer.write_batch(a_       )
      if TEST:
        break
      # finalizer the writer
    if not TEST:
      UpperCAmelCase							, UpperCAmelCase  							=		writer.finalize()
      print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


if __name__ == "__main__":
  _a			:	Optional[Any]								=  Extract(sys.argv[1:])
  extract()
  if not TEST:
    _a			:	Tuple								=  datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)


