



"""simple docstring"""







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__snake_case	:				Any  					=			logging.get_logger(__name__)

__snake_case	:				Tuple  					=			{
    'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}

class       UpperCamelCase  (					a				):





     """simple docstring"""






     _lowerCamelCase					: List[str]				   ="blenderbot"
     _lowerCamelCase					: Optional[int]				   =["past_key_values"]
     _lowerCamelCase					: List[Any]				   ={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(					self :							Optional[Any]	,      _lowerCamelCase :							str=8_0_0_8	,      _lowerCamelCase :							Union[str, Any]=1_2_8	,      _lowerCamelCase :							List[Any]=2	,      _lowerCamelCase :							Optional[Any]=1_0_2_4_0	,      _lowerCamelCase :							str=3_2	,      _lowerCamelCase :							str=2_4	,      _lowerCamelCase :							Union[str, Any]=1_0_2_4_0	,      _lowerCamelCase :							Optional[Any]=3_2	,      _lowerCamelCase :							Optional[int]=0.0	,      _lowerCamelCase :							str=0.0	,      _lowerCamelCase :							Any=True	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							Union[str, Any]="gelu"	,      _lowerCamelCase :							Dict=2_5_6_0	,      _lowerCamelCase :							List[Any]=0.1	,      _lowerCamelCase :							str=0.0	,      _lowerCamelCase :							List[Any]=0.0	,      _lowerCamelCase :							List[str]=0.02	,      _lowerCamelCase :							Union[str, Any]=1	,      _lowerCamelCase :							List[str]=False	,      _lowerCamelCase :							Union[str, Any]=0	,      _lowerCamelCase :							Dict=1	,      _lowerCamelCase :							Union[str, Any]=2	,      _lowerCamelCase :							int=3	,      _lowerCamelCase :							Optional[int]=2	,      **_lowerCamelCase :							List[str]	,      ):
          A__				=   vocab_size
          A__				=   max_position_embeddings
          A__				=   d_model
          A__				=   encoder_ffn_dim
          A__				=   encoder_layers
          A__				=   encoder_attention_heads
          A__				=   decoder_ffn_dim
          A__				=   decoder_layers
          A__				=   decoder_attention_heads
          A__				=   dropout
          A__				=   attention_dropout
          A__				=   activation_dropout
          A__				=   activation_function
          A__				=   init_std
          A__				=   encoder_layerdrop
          A__				=   decoder_layerdrop
          A__				=   use_cache
          A__				=   encoder_layers
          A__				=   scale_embedding  # scale factor will be sqrt(d_model) if True

          super().__init__(
              pad_token_id=_lowerCamelCase	,      bos_token_id=_lowerCamelCase	,      eos_token_id=_lowerCamelCase	,      is_encoder_decoder=_lowerCamelCase	,      decoder_start_token_id=_lowerCamelCase	,      encoder_no_repeat_ngram_size=_lowerCamelCase	,      forced_eos_token_id=_lowerCamelCase	,      **_lowerCamelCase	,      )


class       UpperCamelCase  (					a				):





     """simple docstring"""






     @property
     def 						A__  (					self :							Optional[Any] ):
          if self.task in ["default", "seq2seq-lm"]:
               A__				=   OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )
               if self.use_past:
                    A__				=   {0: '''batch'''}
                    A__				=   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
               else:
                    A__				=   {0: '''batch''', 1: '''decoder_sequence'''}
                    A__				=   {0: '''batch''', 1: '''decoder_sequence'''}
               if self.use_past:
                    self.fill_with_past_key_values_(_lowerCamelCase	,      direction='''inputs''' )
          elif self.task == "causal-lm":
               A__				=   OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ] )
               if self.use_past:
                    A__	,     A__				=   self.num_layers
                    for i in range(_lowerCamelCase ):
                         A__				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
                         A__				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
          else:
               A__				=   OrderedDict(
                   [
                       ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                       ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                       ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ] )

          return common_inputs





     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 						A__  (					self :							str ):
          if self.task in ["default", "seq2seq-lm"]:
               A__				=   super().outputs
          else:
               A__				=   super(_lowerCamelCase	,      self ).outputs
               if self.use_past:
                    A__	,     A__				=   self.num_layers
                    for i in range(_lowerCamelCase ):
                         A__				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
                         A__				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
          return common_outputs





     def 						A__  (					self :							str	,      _lowerCamelCase :							PreTrainedTokenizer	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[TensorType] = None	,      ):
          A__				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
          # Generate decoder inputs
          A__				=   seq_length if not self.use_past else 1
          A__				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
          A__				=   {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
          A__				=   dict(**_lowerCamelCase	,      **_lowerCamelCase )

          if self.use_past:
               if not is_torch_available():
                    raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                    import torch
               A__	,     A__				=   common_inputs['''input_ids'''].shape
               A__				=   common_inputs['''decoder_input_ids'''].shape[1]
               A__	,     A__				=   self.num_attention_heads
               A__				=   (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               A__				=   decoder_seq_length
               A__				=   (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )
               A__				=   torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase	,      _lowerCamelCase )]	,      dim=1 )
               A__				=   []
               A__	,     A__				=   self.num_layers

               for _ in range(_lowerCamelCase ):
                    common_inputs["past_key_values"].append(
                        (
                            torch.zeros(_lowerCamelCase ),
                            torch.zeros(_lowerCamelCase ),
                            torch.zeros(_lowerCamelCase ),
                            torch.zeros(_lowerCamelCase ),
                        ) )
          return common_inputs





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							PreTrainedTokenizer	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[TensorType] = None	,      ):
          A__				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )

          if self.use_past:
               if not is_torch_available():
                    raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
               else:
                    import torch
               A__	,     A__				=   common_inputs['''input_ids'''].shape
               A__				=   seqlen
               A__	,     A__				=   self.num_layers
               A__	,     A__				=   self.num_attention_heads
               A__				=   (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               A__				=   common_inputs['''attention_mask'''].dtype
               A__				=   torch.cat(
                   [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase	,      _lowerCamelCase	,      dtype=_lowerCamelCase )]	,      dim=1 )
               A__				=   [
                   (torch.zeros(_lowerCamelCase ), torch.zeros(_lowerCamelCase )) for _ in range(_lowerCamelCase )
               ]
          return common_inputs





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							PreTrainedTokenizer	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[TensorType] = None	,      ):
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          A__				=   compute_effective_axis_dimension(
              _lowerCamelCase	,      fixed_dimension=OnnxConfig.default_fixed_batch	,      num_token_to_add=0 )

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          A__				=   tokenizer.num_special_tokens_to_add(_lowerCamelCase )
          A__				=   compute_effective_axis_dimension(
              _lowerCamelCase	,      fixed_dimension=OnnxConfig.default_fixed_sequence	,      num_token_to_add=_lowerCamelCase )

          # Generate dummy inputs according to compute batch and sequence
          A__				=   [''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
          A__				=   dict(tokenizer(_lowerCamelCase	,      return_tensors=_lowerCamelCase ) )
          return common_inputs





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							PreTrainedTokenizer	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							int = -1	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Optional[TensorType] = None	,      ):
          if self.task in ["default", "seq2seq-lm"]:
               A__				=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   _lowerCamelCase	,      batch_size=_lowerCamelCase	,      seq_length=_lowerCamelCase	,      is_pair=_lowerCamelCase	,      framework=_lowerCamelCase )

          elif self.task == "causal-lm":
               A__				=   self._generate_dummy_inputs_for_causal_lm(
                   _lowerCamelCase	,      batch_size=_lowerCamelCase	,      seq_length=_lowerCamelCase	,      is_pair=_lowerCamelCase	,      framework=_lowerCamelCase )
          else:
               A__				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   _lowerCamelCase	,      batch_size=_lowerCamelCase	,      seq_length=_lowerCamelCase	,      is_pair=_lowerCamelCase	,      framework=_lowerCamelCase )

          return common_inputs





     def 						A__  (					self :							int	,      _lowerCamelCase :							int	,      _lowerCamelCase :							Dict	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          if self.task in ["default", "seq2seq-lm"]:
               A__				=   super()._flatten_past_key_values_(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
          else:
               A__				=   super(_lowerCamelCase	,      self )._flatten_past_key_values_(
                   _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )





     def 						A__  (					self :							Dict	,      _lowerCamelCase :							Mapping[str, Mapping[int, str]]	,      _lowerCamelCase :							str ):
          if direction not in ["inputs", "outputs"]:
               raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given''' )

          A__				=   '''past_key_values''' if direction == '''inputs''' else '''present'''
          A__	,     A__				=   self.num_layers

          A__				=   '''past_encoder_sequence'''
          A__				=   '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

          for i in range(_lowerCamelCase ):
               A__				=   {0: '''batch''', 2: decoder_sequence}
               A__				=   {0: '''batch''', 2: decoder_sequence}
               A__				=   {0: '''batch''', 2: encoder_sequence}
               A__				=   {0: '''batch''', 2: encoder_sequence}








"""simple docstring"""







import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.



def   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

     A__				=   states.reshape(1		,							-1		,							model.config.state_dim      )
     A__				=   actions.reshape(1		,							-1		,							model.config.act_dim      )
     A__				=   returns_to_go.reshape(1		,							-1		,							1      )
     A__				=   timesteps.reshape(1		,							-1      )

     if model.config.max_length is not None:
          A__				=   states[:, -model.config.max_length :]
          A__				=   actions[:, -model.config.max_length :]
          A__				=   returns_to_go[:, -model.config.max_length :]
          A__				=   timesteps[:, -model.config.max_length :]

          # pad all tokens to sequence length
          A__				=   torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
          A__				=   attention_mask.to(dtype=torch.long		,							device=states.device      ).reshape(1		,							-1      )
          A__				=   torch.cat(
              [
                  torch.zeros(
                      (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)		,							device=states.device		,							),
                  states,
              ]		,							dim=1		,							).to(dtype=torch.floataa      )
          A__				=   torch.cat(
              [
                  torch.zeros(
                      (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)		,							device=actions.device		,							),
                  actions,
              ]		,							dim=1		,							).to(dtype=torch.floataa      )
          A__				=   torch.cat(
              [
                  torch.zeros(
                      (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)		,							device=returns_to_go.device		,							),
                  returns_to_go,
              ]		,							dim=1		,							).to(dtype=torch.floataa      )
          A__				=   torch.cat(
              [
                  torch.zeros(
                      (timesteps.shape[0], model.config.max_length - timesteps.shape[1])		,							device=timesteps.device      ),
                  timesteps,
              ]		,							dim=1		,							).to(dtype=torch.long      )
     else:
          A__				=   None

     A__	,     A__	,     A__				=   model(
         states=__a		,							actions=__a		,							rewards=__a		,							returns_to_go=__a		,							timesteps=__a		,							attention_mask=__a		,							return_dict=__a		,							)

     return action_preds[0, -1]


# build the environment

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
__snake_case	:				str  					=			np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
__snake_case	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case	:				str  					=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
__snake_case	:				str  					=			model.to(device)
model.eval()

for ep in range(10):
 __snake_case					,  __snake_case	:				Union[str, Any]  					=			0, 0
 __snake_case	:				List[Any]  					=			env.reset()
 __snake_case	:				List[str]  					=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __snake_case	:				Union[str, Any]  					=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __snake_case	:				List[str]  					=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __snake_case	:				str  					=			torch.zeros(0, device=device, dtype=torch.floataa)

 __snake_case	:				int  					=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 for t in range(max_ep_len):
  env.render()
  # add padding
  __snake_case	:				Dict  					=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  __snake_case	:				Dict  					=			torch.cat([rewards, torch.zeros(1, device=device)])

  __snake_case	:				Any  					=			get_action(
      model,
      (states.to(dtype=torch.floataa) - state_mean) / state_std,
      actions.to(dtype=torch.floataa),
      rewards.to(dtype=torch.floataa),
      target_return.to(dtype=torch.floataa),
      timesteps.to(dtype=torch.long),
  )
  __snake_case	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

  __snake_case	:				Tuple  					=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __snake_case	:				Dict  					=			torch.cat([states, cur_state], dim=0)
  __snake_case	:				Optional[Any]  					=			reward

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				Any  					=			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

  episode_return += reward
  episode_length += 1

  if done:
   break



