





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

lowercase_					     =	logging.get_logger(__name__)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  if isinstance(_SCREAMING_SNAKE_CASE	,		(list, tuple)	) and isinstance(videos[0]	,		(list, tuple)	) and is_valid_image(videos[0][0]	):
    return videos

  elif isinstance(_SCREAMING_SNAKE_CASE	,		(list, tuple)	) and is_valid_image(videos[0]	):
    return [videos]

  elif is_valid_image(_SCREAMING_SNAKE_CASE	):
    return [[videos]]

  raise ValueError(F"""Could not make batched video from {videos}"""	)


class        SCREAMING_SNAKE_CASE     (lowercase_   ):

   '''simple docstring'''





   _UpperCamelCase					:	Dict				      = ['''pixel_values''']


   def __init__(					self    :       Any    ,  a    :       bool = True    ,  a    :       Dict[str, int] = None    ,  a    :       PILImageResampling = PILImageResampling.BILINEAR    ,  a    :       bool = True    ,  a    :       Dict[str, int] = None    ,  a    :       bool = True    ,  a    :       Union[int, float] = 1 / 255    ,  a    :       bool = True    ,  a    :       bool = True    ,  a    :       Optional[Union[float, List[float]]] = None    ,  a    :       Optional[Union[float, List[float]]] = None    ,  **a    :       int    ,  )->     Any:





     """simple docstring"""







     super().__init__(**UpperCamelCase__ )
     lowercase__			    =						size if size is not None else {'shortest_edge': 256}
     lowercase__			    =						get_size_dict(UpperCamelCase__    ,  default_to_square=UpperCamelCase__ )
     lowercase__			    =						crop_size if crop_size is not None else {'height': 224, 'width': 224}
     lowercase__			    =						get_size_dict(UpperCamelCase__    ,  param_name='crop_size' )

     lowercase__			    =						do_resize
     lowercase__			    =						size
     lowercase__			    =						do_center_crop
     lowercase__			    =						crop_size
     lowercase__			    =						resample
     lowercase__			    =						do_rescale
     lowercase__			    =						rescale_factor
     lowercase__			    =						offset
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowercase__			    =						image_std if image_std is not None else IMAGENET_STANDARD_STD


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       np.ndarray    ,  a    :       Dict[str, int]    ,  a    :       PILImageResampling = PILImageResampling.BILINEAR    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       int    ,  )->     Dict:





     """simple docstring"""







     lowercase__			    =						get_size_dict(UpperCamelCase__    ,  default_to_square=UpperCamelCase__ )
     if "shortest_edge" in size:
       lowercase__			    =						get_resize_output_image_size(UpperCamelCase__    ,  size['shortest_edge']    ,  default_to_square=UpperCamelCase__ )
     elif "height" in size and "width" in size:
       lowercase__			    =						(size['height'], size['width'])
     else:
       raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}""" )
     return resize(UpperCamelCase__    ,  size=UpperCamelCase__    ,  resample=UpperCamelCase__    ,  data_format=UpperCamelCase__    ,  **UpperCamelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       np.ndarray    ,  a    :       Dict[str, int]    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       Union[str, Any]    ,  )->     Tuple:





     """simple docstring"""







     lowercase__			    =						get_size_dict(UpperCamelCase__ )
     if "height" not in size or "width" not in size:
       raise ValueError(f"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}""" )
     return center_crop(UpperCamelCase__    ,  size=(size['height'], size['width'])    ,  data_format=UpperCamelCase__    ,  **UpperCamelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       np.ndarray    ,  a    :       Union[int, float]    ,  a    :       bool = True    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       str    ,  )->     str:





     """simple docstring"""







     lowercase__			    =						image.astype(np.floataa )
     if offset:
       lowercase__			    =						image - (scale / 2)
     return rescale(UpperCamelCase__    ,  scale=UpperCamelCase__    ,  data_format=UpperCamelCase__    ,  **UpperCamelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  a    :       np.ndarray    ,  a    :       Union[float, List[float]]    ,  a    :       Union[float, List[float]]    ,  a    :       Optional[Union[str, ChannelDimension]] = None    ,  **a    :       List[str]    ,  )->     Tuple:





     """simple docstring"""







     return normalize(UpperCamelCase__    ,  mean=UpperCamelCase__    ,  std=UpperCamelCase__    ,  data_format=UpperCamelCase__    ,  **UpperCamelCase__ )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       ImageInput    ,  a    :       bool = None    ,  a    :       Dict[str, int] = None    ,  a    :       PILImageResampling = None    ,  a    :       bool = None    ,  a    :       Dict[str, int] = None    ,  a    :       bool = None    ,  a    :       float = None    ,  a    :       bool = None    ,  a    :       bool = None    ,  a    :       Optional[Union[float, List[float]]] = None    ,  a    :       Optional[Union[float, List[float]]] = None    ,  a    :       Optional[ChannelDimension] = ChannelDimension.FIRST    ,  )->     str:





     """simple docstring"""







     if do_resize and size is None or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.' )

     if do_center_crop and crop_size is None:
       raise ValueError('Crop size must be specified if do_center_crop is True.' )

     if do_rescale and rescale_factor is None:
       raise ValueError('Rescale factor must be specified if do_rescale is True.' )

     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('Image mean and std must be specified if do_normalize is True.' )

     if offset and not do_rescale:
       raise ValueError('For offset, do_rescale must also be set to True.' )

     # All transformations expect numpy arrays.
     lowercase__			    =						to_numpy_array(UpperCamelCase__ )

     if do_resize:
       lowercase__			    =						self.resize(image=UpperCamelCase__    ,  size=UpperCamelCase__    ,  resample=UpperCamelCase__ )

     if do_center_crop:
       lowercase__			    =						self.center_crop(UpperCamelCase__    ,  size=UpperCamelCase__ )

     if do_rescale:
       lowercase__			    =						self.rescale(image=UpperCamelCase__    ,  scale=UpperCamelCase__    ,  offset=UpperCamelCase__ )

     if do_normalize:
       lowercase__			    =						self.normalize(image=UpperCamelCase__    ,  mean=UpperCamelCase__    ,  std=UpperCamelCase__ )

     lowercase__			    =						to_channel_dimension_format(UpperCamelCase__    ,  UpperCamelCase__ )
     return image


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  a    :       ImageInput    ,  a    :       bool = None    ,  a    :       Dict[str, int] = None    ,  a    :       PILImageResampling = None    ,  a    :       bool = None    ,  a    :       Dict[str, int] = None    ,  a    :       bool = None    ,  a    :       float = None    ,  a    :       bool = None    ,  a    :       bool = None    ,  a    :       Optional[Union[float, List[float]]] = None    ,  a    :       Optional[Union[float, List[float]]] = None    ,  a    :       Optional[Union[str, TensorType]] = None    ,  a    :       ChannelDimension = ChannelDimension.FIRST    ,  **a    :       Tuple    ,  )->     str:





     """simple docstring"""







     lowercase__			    =						do_resize if do_resize is not None else self.do_resize
     lowercase__			    =						resample if resample is not None else self.resample
     lowercase__			    =						do_center_crop if do_center_crop is not None else self.do_center_crop
     lowercase__			    =						do_rescale if do_rescale is not None else self.do_rescale
     lowercase__			    =						rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__			    =						offset if offset is not None else self.offset
     lowercase__			    =						do_normalize if do_normalize is not None else self.do_normalize
     lowercase__			    =						image_mean if image_mean is not None else self.image_mean
     lowercase__			    =						image_std if image_std is not None else self.image_std

     lowercase__			    =						size if size is not None else self.size
     lowercase__			    =						get_size_dict(UpperCamelCase__    ,  default_to_square=UpperCamelCase__ )
     lowercase__			    =						crop_size if crop_size is not None else self.crop_size
     lowercase__			    =						get_size_dict(UpperCamelCase__    ,  param_name='crop_size' )

     if not valid_images(UpperCamelCase__ ):
       raise ValueError(
           'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
           'torch.Tensor, tf.Tensor or jax.ndarray.' )

     lowercase__			    =						make_batched(UpperCamelCase__ )

     lowercase__			    =						[
         [
             self._preprocess_image(
                 image=UpperCamelCase__    ,  do_resize=UpperCamelCase__    ,  size=UpperCamelCase__    ,  resample=UpperCamelCase__    ,  do_center_crop=UpperCamelCase__    ,  crop_size=UpperCamelCase__    ,  do_rescale=UpperCamelCase__    ,  rescale_factor=UpperCamelCase__    ,  offset=UpperCamelCase__    ,  do_normalize=UpperCamelCase__    ,  image_mean=UpperCamelCase__    ,  image_std=UpperCamelCase__    ,  data_format=UpperCamelCase__    ,  )
             for img in video
         ]
         for video in videos
     ]

     lowercase__			    =						{'pixel_values': videos}
     return BatchFeature(data=UpperCamelCase__    ,  tensor_type=UpperCamelCase__ )









import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def __init__(					self    :       Optional[Any]    ,  a    :       UNetaDModel    ,  a    :       UNetaDModel    ,  a    :       DDPMScheduler    ,  a    :       Any    ,  )->     Dict:





     """simple docstring"""







     super().__init__()
     lowercase__			    =						value_function
     lowercase__			    =						unet
     lowercase__			    =						scheduler
     lowercase__			    =						env
     lowercase__			    =						env.get_dataset()
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].mean()
       except:  # noqa: E722
         pass
     lowercase__			    =						{}
     for key in self.data.keys():
       try:
         lowercase__			    =						self.data[key].std()
       except:  # noqa: E722
         pass
     lowercase__			    =						env.observation_space.shape[0]
     lowercase__			    =						env.action_space.shape[0]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Any    ,  a    :       int )->     Dict:





     """simple docstring"""







     return (x_in - self.means[key]) / self.stds[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       str    ,  a    :       List[str] )->     str:





     """simple docstring"""







     return x_in * self.stds[key] + self.means[key]


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str]    ,  a    :       Tuple )->     Tuple:





     """simple docstring"""







     if type(a ) is dict:
       return {k: self.to_torch(a ) for k, v in x_in.items()}
     elif torch.is_tensor(a ):
       return x_in.to(self.unet.device )
     return torch.tensor(a    ,  device=self.unet.device )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any]    ,  a    :       Optional[int]    ,  a    :       Dict    ,  a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     for key, val in cond.items():
       lowercase__			    =						val.clone()
     return x_in


   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple    ,  a    :       Optional[Any]    ,  a    :       Any    ,  a    :       Optional[Any]    ,  a    :       Optional[int] )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						x.shape[0]
     lowercase__			    =						None
     for i in tqdm.tqdm(self.scheduler.timesteps ):
       # create batch of timesteps to pass into model
       lowercase__			    =						torch.full((batch_size,)    ,  a    ,  device=self.unet.device    ,  dtype=torch.long )
       for _ in range(a ):
         with torch.enable_grad():
           x.requires_grad_()

           # permute to match dimension for pre-trained models
           lowercase__			    =						self.value_function(x.permute(0    ,  2    ,  1 )    ,  a ).sample
           lowercase__			    =						torch.autograd.grad([y.sum()]    ,  [x] )[0]

           lowercase__			    =						self.scheduler._get_variance(a )
           lowercase__			    =						torch.exp(0.5 * posterior_variance )
           lowercase__			    =						model_std * grad

         lowercase__			    =						0
         lowercase__			    =						x.detach()
         lowercase__			    =						x + scale * grad
         lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )

       lowercase__			    =						self.unet(x.permute(0    ,  2    ,  1 )    ,  a ).sample.permute(0    ,  2    ,  1 )

       # TODO: verify deprecation of this kwarg
       lowercase__			    =						self.scheduler.step(a    ,  a    ,  a    ,  predict_epsilon=a )['prev_sample']

       # apply conditions to the trajectory (set the initial state)
       lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
       lowercase__			    =						self.to_torch(a )
     return x, y


   def __call__(					self    :       Any    ,  a    :       Tuple    ,  a    :       int=64    ,  a    :       Tuple=32    ,  a    :       List[Any]=2    ,  a    :       List[str]=0.1 )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						self.normalize(a    ,  'observations' )
     lowercase__			    =						obs[None].repeat(a    ,  axis=0 )

     lowercase__			    =						{0: self.to_torch(a )}
     lowercase__			    =						(batch_size, planning_horizon, self.state_dim + self.action_dim)

     # generate initial noise and apply our conditions (to make the trajectories start at current state)
     lowercase__			    =						randn_tensor(a    ,  device=self.unet.device )
     lowercase__			    =						self.reset_xa(a    ,  a    ,  self.action_dim )
     lowercase__			    =						self.to_torch(a )

     # run the diffusion process
     lowercase__    ,					lowercase__			    =						self.run_diffusion(a    ,  a    ,  a    ,  a )

     # sort output trajectories by value
     lowercase__			    =						y.argsort(0    ,  descending=a ).squeeze()
     lowercase__			    =						x[sorted_idx]
     lowercase__			    =						sorted_values[:, :, : self.action_dim]
     lowercase__			    =						actions.detach().cpu().numpy()
     lowercase__			    =						self.de_normalize(a    ,  key='actions' )

     # select the action with the highest value
     if y is not None:
       lowercase__			    =						0
     else:
       # if we didn't run value guiding, select a random action
       lowercase__			    =						np.random.randint(0    ,  a )

     lowercase__			    =						denorm_actions[selected_index, 0]
     return denorm_actions


