

import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase     					=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase     					=  []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
    ]
)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     state_dict.pop(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       str	   =     val







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               _lowerCAmelCase :       Any	   =     key.replace("backbone.0.body"		,				"backbone.conv_encoder.model"					)
               _lowerCAmelCase :       Dict	   =     value
          else:
               _lowerCAmelCase :       Optional[Any]	   =     value

     return new_state_dict







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Optional[Any]	   =     ""
     if is_panoptic:
          _lowerCAmelCase :       str	   =     "detr."

     # first: transformer encoder
     for i in range(6					):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          _lowerCAmelCase :       List[str]	   =     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''					)
          _lowerCAmelCase :       Optional[Any]	   =     state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''					)
          # next, add query, keys and values (in that order) to the state dict
          _lowerCAmelCase :       List[str]	   =     in_proj_weight[:256, :]
          _lowerCAmelCase :       Optional[int]	   =     in_proj_bias[:256]
          _lowerCAmelCase :       List[Any]	   =     in_proj_weight[256:512, :]
          _lowerCAmelCase :       Tuple	   =     in_proj_bias[256:512]
          _lowerCAmelCase :       Optional[Any]	   =     in_proj_weight[-256:, :]
          _lowerCAmelCase :       List[str]	   =     in_proj_bias[-256:]
     # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
     for i in range(6					):
          # read in weights + bias of input projection layer of self-attention
          _lowerCAmelCase :       Tuple	   =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''					)
          _lowerCAmelCase :       Optional[int]	   =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'''					)
          # next, add query, keys and values (in that order) to the state dict
          _lowerCAmelCase :       Union[str, Any]	   =     in_proj_weight[:256, :]
          _lowerCAmelCase :       Optional[Any]	   =     in_proj_bias[:256]
          _lowerCAmelCase :       Any	   =     in_proj_weight[256:512, :]
          _lowerCAmelCase :       Any	   =     in_proj_bias[256:512]
          _lowerCAmelCase :       List[Any]	   =     in_proj_weight[-256:, :]
          _lowerCAmelCase :       Tuple	   =     in_proj_bias[-256:]
          # read in weights + bias of input projection layer of cross-attention
          _lowerCAmelCase :       str	   =     state_dict.pop(
              f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''					)
          _lowerCAmelCase :       Union[str, Any]	   =     state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''					)
          # next, add query, keys and values (in that order) of cross-attention to the state dict
          _lowerCAmelCase :       str	   =     in_proj_weight_cross_attn[:256, :]
          _lowerCAmelCase :       Optional[int]	   =     in_proj_bias_cross_attn[:256]
          _lowerCAmelCase :       Any	   =     in_proj_weight_cross_attn[256:512, :]
          _lowerCAmelCase :       List[str]	   =     in_proj_bias_cross_attn[256:512]
          _lowerCAmelCase :       Tuple	   =     in_proj_weight_cross_attn[-256:, :]
          _lowerCAmelCase :       int	   =     in_proj_bias_cross_attn[-256:]







def 						_snake_case		(     )							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     "http://images.cocodataset.org/val2017/000000039769.jpg"
     _lowerCAmelCase :       Any	   =     Image.open(requests.get(SCREAMING_SNAKE_CASE		,				stream=SCREAMING_SNAKE_CASE					).raw					)

     return im







@torch.no_grad()
def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   Dict:

     """simple docstring"""







     _lowerCAmelCase :       int	   =     DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          _lowerCAmelCase :       Dict	   =     "resnet101"
     if "dc5" in model_name:
          _lowerCAmelCase :       Dict	   =     True
     _lowerCAmelCase :       List[Any]	   =     "panoptic" in model_name
     if is_panoptic:
          _lowerCAmelCase :       int	   =     250
     else:
          _lowerCAmelCase :       int	   =     91
          _lowerCAmelCase :       Optional[int]	   =     "huggingface/label-files"
          _lowerCAmelCase :       List[str]	   =     "coco-detection-id2label.json"
          _lowerCAmelCase :       List[Any]	   =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				repo_type="dataset"					)		,				"r"					)					)
          _lowerCAmelCase :       List[Any]	   =     {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
          _lowerCAmelCase :       Tuple	   =     idalabel
          _lowerCAmelCase :       List[str]	   =     {v: k for k, v in idalabel.items()}

     # load image processor
     _lowerCAmelCase :       Any	   =     "coco_panoptic" if is_panoptic else "coco_detection"
     _lowerCAmelCase :       Any	   =     DetrImageProcessor(format=SCREAMING_SNAKE_CASE					)

     # prepare image
     _lowerCAmelCase :       Tuple	   =     prepare_img()
     _lowerCAmelCase :       Tuple	   =     image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors="pt"					)
     _lowerCAmelCase :       int	   =     encoding["pixel_values"]

     logger.info(f'''Converting model {model_name}...'''					)

     # load original model from torch hub
     _lowerCAmelCase :       Dict	   =     torch.hub.load("facebookresearch/detr"		,				SCREAMING_SNAKE_CASE		,				pretrained=SCREAMING_SNAKE_CASE					).eval()
     _lowerCAmelCase :       Tuple	   =     detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               _lowerCAmelCase :       List[str]	   =     "detr." + src
          rename_key(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       int	   =     rename_backbone_keys(SCREAMING_SNAKE_CASE					)
     # query, key and value matrices need special treatment
     read_in_q_k_v(SCREAMING_SNAKE_CASE		,				is_panoptic=SCREAMING_SNAKE_CASE					)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     _lowerCAmelCase :       int	   =     "detr.model." if is_panoptic else "model."
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith("detr"					)
                   and not key.startswith("class_labels_classifier"					)
                   and not key.startswith("bbox_predictor"					)
               ):
                    _lowerCAmelCase :       List[str]	   =     state_dict.pop(SCREAMING_SNAKE_CASE					)
                    _lowerCAmelCase :       Tuple	   =     val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    _lowerCAmelCase :       Any	   =     state_dict.pop(SCREAMING_SNAKE_CASE					)
                    _lowerCAmelCase :       Optional[Any]	   =     val
               elif key.startswith("bbox_attention"					) or key.startswith("mask_head"					):
                    continue
               else:
                    _lowerCAmelCase :       Union[str, Any]	   =     state_dict.pop(SCREAMING_SNAKE_CASE					)
                    _lowerCAmelCase :       List[str]	   =     val
          else:
               if not key.startswith("class_labels_classifier"					) and not key.startswith("bbox_predictor"					):
                    _lowerCAmelCase :       Dict	   =     state_dict.pop(SCREAMING_SNAKE_CASE					)
                    _lowerCAmelCase :       int	   =     val
    # finally, create HuggingFace model and load state dict
     _lowerCAmelCase :       Tuple	   =     DetrForSegmentation(SCREAMING_SNAKE_CASE					) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE					)
     model.load_state_dict(SCREAMING_SNAKE_CASE					)
     model.eval()
     # verify our conversion
     _lowerCAmelCase :       List[Any]	   =     detr(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Dict	   =     model(SCREAMING_SNAKE_CASE					)
     assert torch.allclose(outputs.logits		,				original_outputs["pred_logits"]		,				atol=1e-4					)
     assert torch.allclose(outputs.pred_boxes		,				original_outputs["pred_boxes"]		,				atol=1e-4					)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks		,				original_outputs["pred_masks"]		,				atol=1e-4					)

     # Save model and image processor
     logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''					)
     Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
     model.save_pretrained(SCREAMING_SNAKE_CASE					)
     image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  __UpperCAmelCase     					=  argparse.ArgumentParser()

  parser.add_argument(
      '--model_name', default='detr_resnet50', type=str, help='Name of the DETR model you\'d like to convert.'
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  __UpperCAmelCase     					=  parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)









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     					=  '\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			):



 """simple docstring"""







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





class A__     (			A			):



 """simple docstring"""







 def __init__(		self			:						Dict  ,    A_			:						PriorTransformer  ,    A_			:						CLIPTextModelWithProjection  ,    A_			:						CLIPTokenizer  ,    A_			:						HeunDiscreteScheduler  ,    A_			:						ShapERenderer  ,    ):






      '''simple docstring'''




      super().__init__()

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

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						List[Any]  ,    A_			:						Optional[Any]  ,    A_			:						int  ,    A_			:						Optional[Any]  ,    A_			:						str  ,    A_			:						Dict							):






      '''simple docstring'''




      if latents is None:
           _lowerCAmelCase :       int	   =     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}'''							)
           _lowerCAmelCase :       Union[str, Any]	   =     latents.to(A_							)

      _lowerCAmelCase :       Union[str, Any]	   =     latents * scheduler.init_noise_sigma
      return latents

 def  __magic_name__							(		self			:						Any  ,    A_			:						List[Any]=0							):






      '''simple docstring'''




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

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

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

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						List[str]=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."							)

      _lowerCAmelCase :       Optional[int]	   =     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)

      _lowerCAmelCase :       Union[str, Any]	   =     None
      for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[int]	   =     cpu_offload_with_hook(A_  ,    A_  ,    prev_module_hook=A_							)

      if self.safety_checker is not None:
           _lowerCAmelCase   ,      _lowerCAmelCase :       Optional[Any]	   =     cpu_offload_with_hook(self.safety_checker  ,    A_  ,    prev_module_hook=A_							)

      # We'll offload the last model manually.
      _lowerCAmelCase :       Any	   =     hook

 @property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''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  __magic_name__							(		self			:						str  ,    A_			:						List[str]  ,    A_			:						Optional[int]  ,    A_			:						List[str]  ,    A_			:						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
      _lowerCAmelCase :       Dict	   =     0
      # get prompt text embeddings
      _lowerCAmelCase :       Any	   =     self.tokenizer(
          A_  ,    padding="max_length"  ,    max_length=self.tokenizer.model_max_length  ,    truncation=A_  ,    return_tensors="pt"  ,    )
      _lowerCAmelCase :       List[Any]	   =     text_inputs.input_ids
      _lowerCAmelCase :       Any	   =     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_							):
           _lowerCAmelCase :       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}'''							)

      _lowerCAmelCase :       Any	   =     self.text_encoder(text_input_ids.to(A_							)							)
      _lowerCAmelCase :       Union[str, Any]	   =     text_encoder_output.text_embeds

      _lowerCAmelCase :       Optional[Any]	   =     prompt_embeds.repeat_interleave(A_  ,    dim=0							)
      # in Shap-E it normalize the prompt_embeds and then later rescale it
      _lowerCAmelCase :       Any	   =     prompt_embeds / torch.linalg.norm(A_  ,    dim=-1  ,    keepdim=A_							)

      if do_classifier_free_guidance:
           _lowerCAmelCase :       Union[str, Any]	   =     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
           _lowerCAmelCase :       Union[str, Any]	   =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

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

      return prompt_embeds


 @torch.no_grad()
 @replace_example_docstring(A_							)
 def __call__(		self			:						Tuple  ,    A_			:						str  ,    A_			:						int = 1  ,    A_			:						int = 2_5  ,    A_			:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    A_			:						Optional[torch.FloatTensor] = None  ,    A_			:						float = 4.0  ,    A_			:						int = 6_4  ,    A_			:						Optional[str] = "pil"  ,    A_			:						bool = True  ,    ):






      '''simple docstring'''




      if isinstance(A_  ,    A_							):
           _lowerCAmelCase :       Tuple	   =     1
      elif isinstance(A_  ,    A_							):
           _lowerCAmelCase :       List[Any]	   =     len(A_							)
      else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_							)}'''							)

      _lowerCAmelCase :       int	   =     self._execution_device

      _lowerCAmelCase :       int	   =     batch_size * num_images_per_prompt

      _lowerCAmelCase :       List[Any]	   =     guidance_scale > 1.0
      _lowerCAmelCase :       Tuple	   =     self._encode_prompt(A_  ,    A_  ,    A_  ,    A_							)

      # prior

      self.scheduler.set_timesteps(A_  ,    device=A_							)
      _lowerCAmelCase :       List[str]	   =     self.scheduler.timesteps

      _lowerCAmelCase :       Dict	   =     self.prior.config.num_embeddings
      _lowerCAmelCase :       int	   =     self.prior.config.embedding_dim

      _lowerCAmelCase :       Optional[Any]	   =     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
      _lowerCAmelCase :       int	   =     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
           _lowerCAmelCase :       Any	   =     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
           _lowerCAmelCase :       Optional[int]	   =     self.scheduler.scale_model_input(A_  ,    A_							)

           _lowerCAmelCase :       Optional[int]	   =     self.prior(
               A_  ,    timestep=A_  ,    proj_embedding=A_  ,    ).predicted_image_embedding

           # remove the variance
           _lowerCAmelCase   ,      _lowerCAmelCase :       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:
                _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     noise_pred.chunk(2							)
                _lowerCAmelCase :       Any	   =     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           _lowerCAmelCase :       str	   =     self.scheduler.step(
               A_  ,    timestep=A_  ,    sample=A_  ,    ).prev_sample

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

      _lowerCAmelCase :       Optional[Any]	   =     []
      for i, latent in enumerate(A_							):
           _lowerCAmelCase :       Optional[int]	   =     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_							)

      _lowerCAmelCase :       Any	   =     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}'''							)

      _lowerCAmelCase :       List[Any]	   =     images.cpu().numpy()

      if output_type == "pil":
           _lowerCAmelCase :       Optional[Any]	   =     [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_							)






