

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__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	int			,     __lowerCamelCase		:	UNetaDModel			,     __lowerCamelCase		:	UNetaDModel			,     __lowerCamelCase		:	DDPMScheduler			,     __lowerCamelCase		:	List[str]			,     )	->							Union[str, Any]:
       super().__init__()
       SCREAMING_SNAKE_CASE__					=					value_function
       SCREAMING_SNAKE_CASE__					=					unet
       SCREAMING_SNAKE_CASE__					=					scheduler
       SCREAMING_SNAKE_CASE__					=					env
       SCREAMING_SNAKE_CASE__					=					env.get_dataset()
       SCREAMING_SNAKE_CASE__					=					{}
       for key in self.data.keys():
            try:
                 SCREAMING_SNAKE_CASE__					=					self.data[key].mean()
            except:  # noqa: E722
                 pass
       SCREAMING_SNAKE_CASE__					=					{}
       for key in self.data.keys():
            try:
                 SCREAMING_SNAKE_CASE__					=					self.data[key].std()
            except:  # noqa: E722
                 pass
       SCREAMING_SNAKE_CASE__					=					env.observation_space.shape[0]
       SCREAMING_SNAKE_CASE__					=					env.action_space.shape[0]






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	int      )	->							List[str]:
       return (x_in - self.means[key]) / self.stds[key]






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[Any]      )	->							Optional[int]:
       return x_in * self.stds[key] + self.means[key]






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[Any]      )	->							Dict:
       if type(__lowerCamelCase      ) is dict:
            return {k: self.to_torch(__lowerCamelCase      ) for k, v in x_in.items()}
       elif torch.is_tensor(__lowerCamelCase      ):
            return x_in.to(self.unet.device      )
       return torch.tensor(__lowerCamelCase			,     device=self.unet.device      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	int      )	->							str:
       for key, val in cond.items():
            SCREAMING_SNAKE_CASE__					=					val.clone()
       return x_in






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str      )	->							int:
       SCREAMING_SNAKE_CASE__					=					x.shape[0]
       SCREAMING_SNAKE_CASE__					=					None
       for i in tqdm.tqdm(self.scheduler.timesteps      ):
            # create batch of timesteps to pass into model
            SCREAMING_SNAKE_CASE__					=					torch.full((batch_size,)			,     __lowerCamelCase			,     device=self.unet.device			,     dtype=torch.long      )
            for _ in range(__lowerCamelCase      ):
                 with torch.enable_grad():
                      x.requires_grad_()

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

                      SCREAMING_SNAKE_CASE__					=					self.scheduler._get_variance(__lowerCamelCase      )
                      SCREAMING_SNAKE_CASE__					=					torch.exp(0.5 * posterior_variance      )
                      SCREAMING_SNAKE_CASE__					=					model_std * grad

                 SCREAMING_SNAKE_CASE__					=					0
                 SCREAMING_SNAKE_CASE__					=					x.detach()
                 SCREAMING_SNAKE_CASE__					=					x + scale * grad
                 SCREAMING_SNAKE_CASE__					=					self.reset_xa(__lowerCamelCase			,     __lowerCamelCase			,     self.action_dim      )

            SCREAMING_SNAKE_CASE__					=					self.unet(x.permute(0			,     2			,     1      )			,     __lowerCamelCase      ).sample.permute(0			,     2			,     1      )

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

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







  def __call__(							self		:	Optional[int]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	List[str]=64			,     __lowerCamelCase		:	Union[str, Any]=32			,     __lowerCamelCase		:	Any=2			,     __lowerCamelCase		:	Optional[int]=0.1      )	->							List[str]:
       # normalize the observations and create  batch dimension
       SCREAMING_SNAKE_CASE__					=					self.normalize(__lowerCamelCase			,     '''observations'''      )
       SCREAMING_SNAKE_CASE__					=					obs[None].repeat(__lowerCamelCase			,     axis=0      )

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

       # run the diffusion process
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self.run_diffusion(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )

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

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

       SCREAMING_SNAKE_CASE__					=					denorm_actions[selected_index, 0]
       return denorm_actions





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


