




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

snake_case_ : Tuple    		=       "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def    A						(__A   :    Any   ,				__A   :    List[str]   ,				__A   :    Optional[int]=8       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase_          =							width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor







class __snake_case			(			a		):







   def __init__(      self		:						List[str]					,						_snake_case		:						UNetaDConditionModel					,						_snake_case		:						DDPMScheduler					,						_snake_case		:						VQModel					,						):

        """simple docstring"""


        super().__init__()

        self.register_modules(
            unet=_snake_case					,						scheduler=_snake_case					,						movq=_snake_case					,						)
        UpperCAmelCase_          =							2 ** (len(self.movq.config.block_out_channels) - 1)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						List[str]					,						_snake_case		:						Any					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        if latents is None:
             UpperCAmelCase_          =							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_          =							latents.to(_snake_case)

        UpperCAmelCase_          =							latents * scheduler.init_noise_sigma
        return latents







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Union[str, 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.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                  cpu_offload(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						Optional[int]=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=_snake_case)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        UpperCAmelCase_          =							None
        for cpu_offloaded_model in [self.unet, self.movq]:
             UpperCAmelCase_     ,       UpperCAmelCase_          =							cpu_offload_with_hook(_snake_case					,						_snake_case					,						prev_module_hook=_snake_case)

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







   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        if not hasattr(self.unet					,						'''_hf_hook'''):
             return self.device
        for module in self.unet.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







   @torch.no_grad()
   @replace_example_docstring(_snake_case)
   def __call__(      self		:						List[Any]					,						_snake_case		:						Union[torch.FloatTensor, List[torch.FloatTensor]]					,						_snake_case		:						Union[torch.FloatTensor, List[torch.FloatTensor]]					,						_snake_case		:						int = 512					,						_snake_case		:						int = 512					,						_snake_case		:						int = 100					,						_snake_case		:						float = 4.0					,						_snake_case		:						int = 1					,						_snake_case		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None					,						_snake_case		:						Optional[torch.FloatTensor] = None					,						_snake_case		:						Optional[str] = "pil"					,						_snake_case		:						bool = True					,						):

        """simple docstring"""


        UpperCAmelCase_          =							self._execution_device

        UpperCAmelCase_          =							guidance_scale > 1.0

        if isinstance(_snake_case					,						_snake_case):
             UpperCAmelCase_          =							torch.cat(_snake_case					,						dim=0)
        UpperCAmelCase_          =							image_embeds.shape[0] * num_images_per_prompt
        if isinstance(_snake_case					,						_snake_case):
             UpperCAmelCase_          =							torch.cat(_snake_case					,						dim=0)

        if do_classifier_free_guidance:
             UpperCAmelCase_          =							image_embeds.repeat_interleave(_snake_case					,						dim=0)
             UpperCAmelCase_          =							negative_image_embeds.repeat_interleave(_snake_case					,						dim=0)

        UpperCAmelCase_          =							torch.cat([negative_image_embeds, image_embeds]					,						dim=0).to(dtype=self.unet.dtype					,						device=_snake_case)

        self.scheduler.set_timesteps(_snake_case					,						device=_snake_case)
        UpperCAmelCase_          =							self.scheduler.timesteps

        UpperCAmelCase_          =							self.unet.config.in_channels

        UpperCAmelCase_     ,       UpperCAmelCase_          =							downscale_height_and_width(_snake_case					,						_snake_case					,						self.movq_scale_factor)

        # create initial latent
        UpperCAmelCase_          =							self.prepare_latents(
            (batch_size, num_channels_latents, height, width)					,						image_embeds.dtype					,						_snake_case					,						_snake_case					,						_snake_case					,						self.scheduler					,						)

        for i, t in enumerate(self.progress_bar(_snake_case)):
             # expand the latents if we are doing classifier free guidance
             UpperCAmelCase_          =							torch.cat([latents] * 2) if do_classifier_free_guidance else latents

             UpperCAmelCase_          =							{'''image_embeds''': image_embeds}
             UpperCAmelCase_          =							self.unet(
                 sample=_snake_case					,						timestep=_snake_case					,						encoder_hidden_states=_snake_case					,						added_cond_kwargs=_snake_case					,						return_dict=_snake_case					,						)[0]

             if do_classifier_free_guidance:
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							noise_pred.split(latents.shape[1]					,						dim=1)
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							noise_pred.chunk(2)
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							variance_pred.chunk(2)
                  UpperCAmelCase_          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  UpperCAmelCase_          =							torch.cat([noise_pred, variance_pred_text]					,						dim=1)

             if not (
                 hasattr(self.scheduler.config					,						'''variance_type''')
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                  UpperCAmelCase_     ,       UpperCAmelCase_          =							noise_pred.split(latents.shape[1]					,						dim=1)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCAmelCase_          =							self.scheduler.step(
                 _snake_case					,						_snake_case					,						_snake_case					,						generator=_snake_case					,						)[0]
        # post-processing
        UpperCAmelCase_          =							self.movq.decode(_snake_case					,						force_not_quantize=_snake_case)['''sample''']

        if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""")

        if output_type in ["np", "pil"]:
             UpperCAmelCase_          =							image * 0.5 + 0.5
             UpperCAmelCase_          =							image.clamp(0					,						1)
             UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1).float().numpy()

        if output_type == "pil":
             UpperCAmelCase_          =							self.numpy_to_pil(_snake_case)

        if not return_dict:
             return (image,)

        return ImagePipelineOutput(images=_snake_case)











import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


A__  :  Union[str, Any]								=					{
    '''n_samples''': 64,
    '''horizon''': 32,
    '''num_inference_steps''': 20,
    '''n_guide_steps''': 2,  # can set to 0 for faster sampling, does not use value network
    '''scale_grad_by_std''': True,
    '''scale''': 0.1,
    '''eta''': 0.0,
    '''t_grad_cutoff''': 2,
    '''device''': '''cpu''',
}


if __name__ == "__main__":
						A__  :  Optional[int]								=					'''hopper-medium-v2'''
						A__  :  int								=					gym.make(env_name)

						A__  :  Optional[int]								=					ValueGuidedRLPipeline.from_pretrained(
						    '''bglick13/hopper-medium-v2-value-function-hor32''',
						    env=env,
						)

						env.seed(0)
						A__  :  int								=					env.reset()
						A__  :  Optional[int]								=					0
						A__  :  Union[str, Any]								=					0
						A__  :  Union[str, Any]								=					1000
						A__  :  Optional[Any]								=					[obs.copy()]
						try:
												for t in tqdm.tqdm(range(T)):
																		# call the policy
																		A__  :  Union[str, Any]								=					pipeline(obs, planning_horizon=32)

																		# execute action in environment
																		A__					,     A__					,     A__					,     A__  :  str								=					env.step(denorm_actions)
																		A__  :  Dict								=					env.get_normalized_score(total_reward)

																		# update return
																		total_reward += reward
																		total_score += score
																		print(
																		    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
																		    F''' {total_score}'''
																		)

																		# save observations for rendering
																		rollout.append(next_observation.copy())

																		A__  :  List[str]								=					next_observation
						except KeyboardInterrupt:
												pass

						print(F'''Total reward: {total_reward}''')





