


'''simple docstring'''



from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__UpperCAmelCase    :Optional[int]				       =     "\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\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], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Union[PIL.Image.Image, np.ndarray]



class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	Optional[int]							,				snake_case  :	PriorTransformer							,				snake_case  :	CLIPVisionModel							,				snake_case  :	CLIPImageProcessor							,				snake_case  :	HeunDiscreteScheduler							,				snake_case  :	ShapERenderer							,				)		->							List[str]:
          super().__init__()

          self.register_modules(
              prior=snake_case							,				image_encoder=snake_case							,				image_processor=snake_case							,				scheduler=snake_case							,				renderer=snake_case							,				)

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	List[Any]							,				snake_case  :	Dict							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]							,				snake_case  :	int    )		->							Any:
          if latents is None:
               __UpperCAmelCase			:				Any													=    randn_tensor(snake_case							,				generator=snake_case							,				device=snake_case							,				dtype=snake_case    )
          else:
               if latents.shape != shape:
                    raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'    )
               __UpperCAmelCase			:				Optional[Any]													=    latents.to(snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    latents * scheduler.init_noise_sigma
          return latents

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	str=0    )		->							Optional[Any]:
          if is_accelerate_available():
               from accelerate import cpu_offload
          else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''    )

          __UpperCAmelCase			:				Any													=    torch.device(f'cuda:{gpu_id}'    )

          __UpperCAmelCase			:				Union[str, Any]													=    [self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                    cpu_offload(snake_case							,				snake_case    )

     @property
     def 				lowerCamelCase__				(       self  :	str    )		->							Tuple:
          if self.device != torch.device('''meta'''    ) or not hasattr(self.image_encoder							,				'''_hf_hook'''    ):
               return self.device
          for module in self.image_encoder.modules():
               if (
                   hasattr(snake_case							,				'''_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  :	Any							,				snake_case  :	int							,				snake_case  :	List[str]							,				snake_case  :	Union[str, Any]							,				snake_case  :	Union[str, Any]							,				)		->							int:
          if isinstance(snake_case							,				snake_case    ) and isinstance(image[0]							,				torch.Tensor    ):
               __UpperCAmelCase			:				Tuple													=    torch.cat(snake_case							,				axis=0    ) if image[0].ndim == 4 else torch.stack(snake_case							,				axis=0    )

          if not isinstance(snake_case							,				torch.Tensor    ):
               __UpperCAmelCase			:				List[str]													=    self.image_processor(snake_case							,				return_tensors='''pt'''    ).pixel_values[0].unsqueeze(0    )

          __UpperCAmelCase			:				Union[str, Any]													=    image.to(dtype=self.image_encoder.dtype							,				device=snake_case    )

          __UpperCAmelCase			:				Any													=    self.image_encoder(snake_case    )['''last_hidden_state''']
          __UpperCAmelCase			:				Union[str, Any]													=    image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          __UpperCAmelCase			:				str													=    image_embeds.repeat_interleave(snake_case							,				dim=0    )

          if do_classifier_free_guidance:
               __UpperCAmelCase			:				Any													=    torch.zeros_like(snake_case    )

               # 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			:				Optional[Any]													=    torch.cat([negative_image_embeds, image_embeds]    )

          return image_embeds






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

          if isinstance(snake_case							,				PIL.Image.Image    ):
               __UpperCAmelCase			:				Optional[int]													=    1
          elif isinstance(snake_case							,				torch.Tensor    ):
               __UpperCAmelCase			:				int													=    image.shape[0]
          elif isinstance(snake_case							,				snake_case    ) and isinstance(image[0]							,				(torch.Tensor, PIL.Image.Image)    ):
               __UpperCAmelCase			:				int													=    len(snake_case    )
          else:
               raise ValueError(
                   f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(snake_case    )}'    )

          __UpperCAmelCase			:				Tuple													=    self._execution_device

          __UpperCAmelCase			:				List[Any]													=    batch_size * num_images_per_prompt

          __UpperCAmelCase			:				Union[str, Any]													=    guidance_scale > 1.0
          __UpperCAmelCase			:				str													=    self._encode_image(snake_case							,				snake_case							,				snake_case							,				snake_case    )

          # prior

          self.scheduler.set_timesteps(snake_case							,				device=snake_case    )
          __UpperCAmelCase			:				Optional[int]													=    self.scheduler.timesteps

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

          __UpperCAmelCase			:				Optional[Any]													=    self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,				image_embeds.dtype							,				snake_case							,				snake_case							,				snake_case							,				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			:				Tuple													=    latents.reshape(latents.shape[0]							,				snake_case							,				snake_case    )

          for i, t in enumerate(self.progress_bar(snake_case    )    ):
               # expand the latents if we are doing classifier free guidance
               __UpperCAmelCase			:				List[Any]													=    torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
               __UpperCAmelCase			:				List[str]													=    self.scheduler.scale_model_input(snake_case							,				snake_case    )

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

               # remove the variance
               __UpperCAmelCase							,     __UpperCAmelCase			:				str													=    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			:				Union[str, Any]													=    noise_pred.chunk(2    )
                    __UpperCAmelCase			:				Any													=    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               __UpperCAmelCase			:				Tuple													=    self.scheduler.step(
                   snake_case							,				timestep=snake_case							,				sample=snake_case							,				).prev_sample

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

          __UpperCAmelCase			:				str													=    []
          for i, latent in enumerate(snake_case    ):
               print()
               __UpperCAmelCase			:				Optional[int]													=    self.renderer.decode(
                   latent[None, :]							,				snake_case							,				size=snake_case							,				ray_batch_size=4096							,				n_coarse_samples=64							,				n_fine_samples=128							,				)

               images.append(snake_case    )

          __UpperCAmelCase			:				str													=    torch.stack(snake_case    )

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

          if output_type == "pil":
               __UpperCAmelCase			:				Optional[int]													=    [self.numpy_to_pil(snake_case    ) 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=snake_case    )



'''simple docstring'''



import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)




def 			_a			(		_lowercase      :			Any						):





     '''simple docstring'''






     if hor == 128:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Union[str, Any]													=    (32, 128, 256)
          __UpperCAmelCase			:				str													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''')

     elif hor == 32:
          __UpperCAmelCase			:				Optional[Any]													=    ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''')
          __UpperCAmelCase			:				Any													=    (32, 64, 128, 256)
          __UpperCAmelCase			:				Optional[int]													=    ('''UpResnetBlock1D''', '''UpResnetBlock1D''', '''UpResnetBlock1D''')
     __UpperCAmelCase			:				Any													=    torch.load(F'/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch'						)
     __UpperCAmelCase			:				str													=    model.state_dict()
     __UpperCAmelCase			:				int													=    {
         '''down_block_types''': down_block_types,
         '''block_out_channels''': block_out_channels,
         '''up_block_types''': up_block_types,
         '''layers_per_block''': 1,
         '''use_timestep_embedding''': True,
         '''out_block_type''': '''OutConv1DBlock''',
         '''norm_num_groups''': 8,
         '''downsample_each_block''': False,
         '''in_channels''': 14,
         '''out_channels''': 14,
         '''extra_in_channels''': 0,
         '''time_embedding_type''': '''positional''',
         '''flip_sin_to_cos''': False,
         '''freq_shift''': 1,
         '''sample_size''': 65536,
         '''mid_block_type''': '''MidResTemporalBlock1D''',
         '''act_fn''': '''mish''',
     }
     __UpperCAmelCase			:				Optional[Any]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)
     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(model.state_dict().keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				List[Any]													=    state_dict.pop(_lowercase						)
     hf_value_function.load_state_dict(_lowercase						)

     torch.save(hf_value_function.state_dict()    ,      F'hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin'						)
     with open(F'hub/hopper-medium-v2/unet/hor{hor}/config.json'    ,      '''w'''						) as f:
          json.dump(_lowercase    ,      _lowercase						)




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    {
         '''in_channels''': 14,
         '''down_block_types''': ('''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D''', '''DownResnetBlock1D'''),
         '''up_block_types''': (),
         '''out_block_type''': '''ValueFunction''',
         '''mid_block_type''': '''ValueFunctionMidBlock1D''',
         '''block_out_channels''': (32, 64, 128, 256),
         '''layers_per_block''': 1,
         '''downsample_each_block''': True,
         '''sample_size''': 65536,
         '''out_channels''': 14,
         '''extra_in_channels''': 0,
         '''time_embedding_type''': '''positional''',
         '''use_timestep_embedding''': True,
         '''flip_sin_to_cos''': False,
         '''freq_shift''': 1,
         '''norm_num_groups''': 8,
         '''act_fn''': '''mish''',
     }

     __UpperCAmelCase			:				str													=    torch.load('''/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch'''						)
     __UpperCAmelCase			:				Optional[Any]													=    model
     __UpperCAmelCase			:				Optional[int]													=    UNetaDModel(**_lowercase						)
     print(F'length of state dict: {len(state_dict.keys()						)}'						)
     print(F'length of value function dict: {len(hf_value_function.state_dict().keys()						)}'						)

     __UpperCAmelCase			:				Union[str, Any]													=    dict(zip(state_dict.keys()    ,      hf_value_function.state_dict().keys()						)						)
     for k, v in mapping.items():
          __UpperCAmelCase			:				Dict													=    state_dict.pop(_lowercase						)

     hf_value_function.load_state_dict(_lowercase						)

     torch.save(hf_value_function.state_dict()    ,      '''hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin'''						)
     with open('''hub/hopper-medium-v2/value_function/config.json'''    ,      '''w'''						) as f:
          json.dump(_lowercase    ,      _lowercase						)


if __name__ == "__main__":
      unet(3_2)
      # unet(128)
      value_function()