


'''simple docstring'''






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from ..image_utils import load_image


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__A					:  int											=      logging.get_logger(__name__)


__A					:  Optional[Any]											=      Dict[str, Any]
__A					:  Union[str, Any]											=      List[Prediction]






@add_end_docstrings(_SCREAMING_SNAKE_CASE)
class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __init__(   self    :  Optional[int]    ,						*lowerCamelCase    :  List[str]    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      super().__init__(*__a    ,						**__a		)

      if self.framework == "tf":
        raise ValueError(F'The {self.__class__} is only available in PyTorch.'		)

      requires_backends(self    ,						"""vision"""		)
      self.check_model_type(
          dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)

    def __lowercase   (   self    :  Optional[int]    ,						**lowerCamelCase    :  Dict		)   ->  Tuple:
      lowerCAmelCase_			:      Union[str, Any]			=   {}
      if "threshold" in kwargs:
        lowerCAmelCase_			:      str			=   kwargs["""threshold"""]
      return {}, {}, postprocess_kwargs

    def __call__(   self    :  Optional[int]    ,						*lowerCamelCase    :  str    ,						**lowerCamelCase    :  Dict		)   ->  Union[Predictions, List[Prediction]]:
      return super().__call__(*__a    ,						**__a		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  Any		)   ->  Optional[int]:
      lowerCAmelCase_			:      List[str]			=   load_image(__a		)
      lowerCAmelCase_			:      int			=   torch.IntTensor([[image.height, image.width]]		)
      lowerCAmelCase_			:      str			=   self.image_processor(images=[image]    ,						return_tensors="""pt"""		)
      if self.tokenizer is not None:
        lowerCAmelCase_			:      Any			=   self.tokenizer(text=inputs["""words"""]    ,						boxes=inputs["""boxes"""]    ,						return_tensors="""pt"""		)
      lowerCAmelCase_			:      List[Any]			=   target_size
      return inputs

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  int		)   ->  List[str]:
      lowerCAmelCase_			:      str			=   model_inputs.pop("""target_size"""		)
      lowerCAmelCase_			:      Optional[Any]			=   self.model(**__a		)
      lowerCAmelCase_			:      int			=   outputs.__class__({"""target_size""": target_size, **outputs}		)
      if self.tokenizer is not None:
        lowerCAmelCase_			:      List[Any]			=   model_inputs["""bbox"""]
      return model_outputs

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Dict=0.9		)   ->  Tuple:
      lowerCAmelCase_			:      List[Any]			=   model_outputs["""target_size"""]
      if self.tokenizer is not None:
        # This is a LayoutLMForTokenClassification variant.
        # The OCR got the boxes and the model classified the words.
        lowerCAmelCase_,							lowerCAmelCase_			:      str			=   target_size[0].tolist()

        def unnormalize(lowerCamelCase    :  Any		):
          return self._get_bounding_box(
              torch.Tensor(
                  [
                      (width * bbox[0] / 10_00),
                      (height * bbox[1] / 10_00),
                      (width * bbox[2] / 10_00),
                      (height * bbox[3] / 10_00),
                  ]		)		)

        lowerCAmelCase_,							lowerCAmelCase_			:      Dict			=   model_outputs["""logits"""].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
        lowerCAmelCase_			:      int			=   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
        lowerCAmelCase_			:      Optional[Any]			=   [unnormalize(__a		) for bbox in model_outputs["""bbox"""].squeeze(0		)]
        lowerCAmelCase_			:      Union[str, Any]			=   ["""score""", """label""", """box"""]
        lowerCAmelCase_			:      Union[str, Any]			=   [dict(zip(__a    ,						__a		)		) for vals in zip(scores.tolist()    ,						__a    ,						__a		) if vals[0] > threshold]
      else:
        # This is a regular ForObjectDetectionModel
        lowerCAmelCase_			:      Tuple			=   self.image_processor.post_process_object_detection(__a    ,						__a    ,						__a		)
        lowerCAmelCase_			:      Dict			=   raw_annotations[0]
        lowerCAmelCase_			:      Optional[int]			=   raw_annotation["""scores"""]
        lowerCAmelCase_			:      Union[str, Any]			=   raw_annotation["""labels"""]
        lowerCAmelCase_			:      str			=   raw_annotation["""boxes"""]

        lowerCAmelCase_			:      Tuple			=   scores.tolist()
        lowerCAmelCase_			:      Optional[int]			=   [self.model.config.idalabel[label.item()] for label in labels]
        lowerCAmelCase_			:      Optional[Any]			=   [self._get_bounding_box(__a		) for box in boxes]

        # {"scores": [...], ...} --> [{"score":x, ...}, ...]
        lowerCAmelCase_			:      Any			=   ["""score""", """label""", """box"""]
        lowerCAmelCase_			:      Dict			=   [
            dict(zip(__a    ,						__a		)		)
            for vals in zip(raw_annotation["""scores"""]    ,						raw_annotation["""labels"""]    ,						raw_annotation["""boxes"""]		)
        ]

      return annotation





    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  List[Any]		)   ->  Dict[str, int]:
      if self.framework != "pt":
        raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""		)
      lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_			:      Tuple			=   box.int().tolist()
      lowerCAmelCase_			:      Dict			=   {
          """xmin""": xmin,
          """ymin""": ymin,
          """xmax""": xmax,
          """ymax""": ymax,
      }
      return bbox





"""simple docstring"""





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(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a				,		__a				,		__a				,		)       ->       Optional[int]:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       int:






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



		def 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							return x_in * self.stds[key] + self.means[key]



		def 						UpperCAmelCase							(					self				,		__a)       ->       Union[str, Any]:






							'''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 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a)       ->       str:






							'''simple docstring'''
							for key, val in cond.items():
												_UpperCamelCase							 =  val.clone()
							return x_in



		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a				,		__a)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  x.shape[0]
							_UpperCamelCase							 =  None
							for i in tqdm.tqdm(self.scheduler.timesteps):
												# create batch of timesteps to pass into model
												_UpperCamelCase							 =  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
																						_UpperCamelCase							 =  self.value_function(x.permute(0				,		2				,		1)				,		__a).sample
																						_UpperCamelCase							 =  torch.autograd.grad([y.sum()]				,		[x])[0]

																						_UpperCamelCase							 =  self.scheduler._get_variance(__a)
																						_UpperCamelCase							 =  torch.exp(0.5 * posterior_variance)
																						_UpperCamelCase							 =  model_std * grad

																	_UpperCamelCase							 =  0
																	_UpperCamelCase							 =  x.detach()
																	_UpperCamelCase							 =  x + scale * grad
																	_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)

												_UpperCamelCase							 =  self.unet(x.permute(0				,		2				,		1)				,		__a).sample.permute(0				,		2				,		1)

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

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




		def __call__(					self				,		__a				,		__a=64				,		__a=32				,		__a=2				,		__a=0.1)       ->       Optional[Any]:






							'''simple docstring'''
							# normalize the observations and create  batch dimension
							_UpperCamelCase							 =  self.normalize(__a				,		'''observations''')
							_UpperCamelCase							 =  obs[None].repeat(__a				,		axis=0)

							_UpperCamelCase							 =  {0: self.to_torch(__a)}
							_UpperCamelCase							 =  (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)
							_UpperCamelCase							 =  randn_tensor(__a				,		device=self.unet.device)
							_UpperCamelCase							 =  self.reset_xa(__a				,		__a				,		self.action_dim)
							_UpperCamelCase							 =  self.to_torch(__a)

							# run the diffusion process
							_UpperCamelCase ,  _UpperCamelCase							 =  self.run_diffusion(__a				,		__a				,		__a				,		__a)

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

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

							_UpperCamelCase							 =  denorm_actions[selected_index, 0]
							return denorm_actions






