
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 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: Union[str, Any] ,    snake_case_			: UNetaDModel ,    snake_case_			: UNetaDModel ,    snake_case_			: DDPMScheduler ,    snake_case_			: Optional[int] ,    ):
          super().__init__()
          __a			:  Any		    =  value_function
          __a			:  str		    =  unet
          __a			:  List[str]		    =  scheduler
          __a			:  str		    =  env
          __a			:  str		    =  env.get_dataset()
          __a			:  List[str]		    =  {}
          for key in self.data.keys():
                 try:
                        __a			:  Optional[Any]		    =  self.data[key].mean()
                 except:  # noqa: E722
                        pass
          __a			:  Dict		    =  {}
          for key in self.data.keys():
                 try:
                        __a			:  Optional[int]		    =  self.data[key].std()
                 except:  # noqa: E722
                        pass
          __a			:  Dict		    =  env.observation_space.shape[0]
          __a			:  List[str]		    =  env.action_space.shape[0]
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: List[str] ,    snake_case_			: Optional[int]       ):
          return (x_in - self.means[key]) / self.stds[key]
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: Dict ,    snake_case_			: Dict       ):
          return x_in * self.stds[key] + self.means[key]
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: int       ):


          if type(__UpperCAmelCase       ) is dict:
                 return {k: self.to_torch(__UpperCAmelCase       ) for k, v in x_in.items()}
          elif torch.is_tensor(__UpperCAmelCase       ):
                 return x_in.to(self.unet.device       )
          return torch.tensor(__UpperCAmelCase ,    device=self.unet.device       )
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: int ,    snake_case_			: Union[str, Any] ,    snake_case_			: Tuple       ):


          for key, val in cond.items():
                 __a			:  List[str]		    =  val.clone()
          return x_in
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[str] ,    snake_case_			: str ,    snake_case_			: List[str] ,    snake_case_			: Optional[Any]       ):
          __a			:  Dict		    =  x.shape[0]
          __a			:  List[str]		    =  None
          for i in tqdm.tqdm(self.scheduler.timesteps       ):
                 # create batch of timesteps to pass into model
                 __a			:  Union[str, Any]		    =  torch.full((batch_size,) ,    __UpperCAmelCase ,    device=self.unet.device ,    dtype=torch.long       )
                 for _ in range(__UpperCAmelCase       ):
                        with torch.enable_grad():
                               x.requires_grad_()

                               # permute to match dimension for pre-trained models
                               __a			:  Optional[int]		    =  self.value_function(x.permute(0 ,    2 ,    1       ) ,    __UpperCAmelCase       ).sample
                               __a			:  Any		    =  torch.autograd.grad([y.sum()] ,    [x]       )[0]

                               __a			:  Union[str, Any]		    =  self.scheduler._get_variance(__UpperCAmelCase       )
                               __a			:  Tuple		    =  torch.exp(0.5 * posterior_variance       )
                               __a			:  Dict		    =  model_std * grad

                        __a			:  Optional[Any]		    =  0
                        __a			:  str		    =  x.detach()
                        __a			:  Tuple		    =  x + scale * grad
                        __a			:  Optional[Any]		    =  self.reset_xa(__UpperCAmelCase ,    __UpperCAmelCase ,    self.action_dim       )

                 __a			:  Optional[int]		    =  self.unet(x.permute(0 ,    2 ,    1       ) ,    __UpperCAmelCase       ).sample.permute(0 ,    2 ,    1       )

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

                 # apply conditions to the trajectory (set the initial state)
                 __a			:  List[str]		    =  self.reset_xa(__UpperCAmelCase ,    __UpperCAmelCase ,    self.action_dim       )
                 __a			:  Optional[Any]		    =  self.to_torch(__UpperCAmelCase       )
          return x, y







   def __call__(self			: Union[str, Any] ,    snake_case_			: Dict ,    snake_case_			: Optional[int]=6_4 ,    snake_case_			: int=3_2 ,    snake_case_			: Optional[Any]=2 ,    snake_case_			: str=0.1       ):
          __a			:  List[str]		    =  self.normalize(__UpperCAmelCase ,    '''observations'''       )
          __a			:  Any		    =  obs[None].repeat(__UpperCAmelCase ,    axis=0       )

          __a			:  Optional[Any]		    =  {0: self.to_torch(__UpperCAmelCase       )}
          __a			:  Optional[Any]		    =  (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)
          __a			:  Union[str, Any]		    =  randn_tensor(__UpperCAmelCase ,    device=self.unet.device       )
          __a			:  Dict		    =  self.reset_xa(__UpperCAmelCase ,    __UpperCAmelCase ,    self.action_dim       )
          __a			:  Tuple		    =  self.to_torch(__UpperCAmelCase       )

          # run the diffusion process
          __a    ,	__a			:  Optional[int]		    =  self.run_diffusion(__UpperCAmelCase ,    __UpperCAmelCase ,    __UpperCAmelCase ,    __UpperCAmelCase       )

          # sort output trajectories by value
          __a			:  Tuple		    =  y.argsort(0 ,    descending=__UpperCAmelCase       ).squeeze()
          __a			:  int		    =  x[sorted_idx]
          __a			:  int		    =  sorted_values[:, :, : self.action_dim]
          __a			:  List[Any]		    =  actions.detach().cpu().numpy()
          __a			:  Optional[int]		    =  self.de_normalize(__UpperCAmelCase ,    key='''actions'''       )

          # select the action with the highest value
          if y is not None:
                 __a			:  List[str]		    =  0
          else:
                 # if we didn't run value guiding, select a random action
                 __a			:  List[Any]		    =  np.random.randint(0 ,    __UpperCAmelCase       )

          __a			:  Tuple		    =  denorm_actions[selected_index, 0]
          return denorm_actions


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
