




"""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 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       states.reshape(1   ,      -1   ,      model.config.state_dim		)
						snake_case__							:       str				      =       actions.reshape(1   ,      -1   ,      model.config.act_dim		)
						snake_case__							:       Union[str, Any]				      =       returns_to_go.reshape(1   ,      -1   ,      1		)
						snake_case__							:       Union[str, Any]				      =       timesteps.reshape(1   ,      -1		)

						if model.config.max_length is not None:
												snake_case__							:       Dict				      =       states[:, -model.config.max_length :]
												snake_case__							:       Optional[int]				      =       actions[:, -model.config.max_length :]
												snake_case__							:       Any				      =       returns_to_go[:, -model.config.max_length :]
												snake_case__							:       Union[str, Any]				      =       timesteps[:, -model.config.max_length :]

												# pad all tokens to sequence length
												snake_case__							:       Optional[Any]				      =       torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]		), torch.ones(states.shape[1]		)]		)
												snake_case__							:       Any				      =       attention_mask.to(dtype=torch.long   ,      device=states.device		).reshape(1   ,      -1		)
												snake_case__							:       Tuple				      =       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		)
												snake_case__							:       int				      =       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		)
												snake_case__							:       Tuple				      =       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		)
												snake_case__							:       str				      =       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:
												snake_case__							:       Dict				      =       None

						snake_case__      ,						snake_case__      ,						snake_case__							:       Any				      =       model(
						    states=lowerCamelCase   ,      actions=lowerCamelCase   ,      rewards=lowerCamelCase   ,      returns_to_go=lowerCamelCase   ,      timesteps=lowerCamelCase   ,      attention_mask=lowerCamelCase   ,      return_dict=lowerCamelCase   ,      )

						return action_preds[0, -1]


# build the environment

_lowercase						   =     gym.make("""Hopper-v3""")
_lowercase						   =     env.observation_space.shape[0]
_lowercase						   =     env.action_space.shape[0]
_lowercase						   =     10_00
_lowercase						   =     """cuda"""
_lowercase						   =     10_00.0  # normalization for rewards/returns
_lowercase						   =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowercase						   =     np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
_lowercase						   =     np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
_lowercase						   =     torch.from_numpy(state_mean).to(device=device)
_lowercase						   =     torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_lowercase						   =     DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-medium""")
_lowercase						   =     model.to(device)
model.eval()

for ep in range(10):
							_lowercase   ,_lowercase						   =     0, 0
							_lowercase						   =     env.reset()
							_lowercase						   =     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
							_lowercase						   =     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
							_lowercase						   =     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
							_lowercase						   =     torch.zeros(0, device=device, dtype=torch.floataa)

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

														_lowercase						   =     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),
														)
														_lowercase						   =     action
														_lowercase						   =     action.detach().cpu().numpy()

														_lowercase   ,_lowercase   ,_lowercase   ,_lowercase						   =     env.step(action)

														_lowercase						   =     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
														_lowercase						   =     torch.cat([states, cur_state], dim=0)
														_lowercase						   =     reward

														_lowercase						   =     target_return[0, -1] - (reward / scale)
														_lowercase						   =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
														_lowercase						   =     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








"""simple docstring"""





from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


_lowercase						   =     {
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """Constraint""",
							    """ConstraintListState""",
							    """DisjunctiveConstraint""",
							    """PhrasalConstraint""",
							]
							_lowercase						   =     [
							    """BeamHypotheses""",
							    """BeamScorer""",
							    """BeamSearchScorer""",
							    """ConstrainedBeamSearchScorer""",
							]
							_lowercase						   =     [
							    """EpsilonLogitsWarper""",
							    """EtaLogitsWarper""",
							    """ForcedBOSTokenLogitsProcessor""",
							    """ForcedEOSTokenLogitsProcessor""",
							    """HammingDiversityLogitsProcessor""",
							    """InfNanRemoveLogitsProcessor""",
							    """LogitsProcessor""",
							    """LogitsProcessorList""",
							    """LogitsWarper""",
							    """MinLengthLogitsProcessor""",
							    """MinNewTokensLengthLogitsProcessor""",
							    """NoBadWordsLogitsProcessor""",
							    """NoRepeatNGramLogitsProcessor""",
							    """PrefixConstrainedLogitsProcessor""",
							    """RepetitionPenaltyLogitsProcessor""",
							    """SequenceBiasLogitsProcessor""",
							    """EncoderRepetitionPenaltyLogitsProcessor""",
							    """TemperatureLogitsWarper""",
							    """TopKLogitsWarper""",
							    """TopPLogitsWarper""",
							    """TypicalLogitsWarper""",
							    """EncoderNoRepeatNGramLogitsProcessor""",
							    """ExponentialDecayLengthPenalty""",
							    """LogitNormalization""",
							]
							_lowercase						   =     [
							    """MaxNewTokensCriteria""",
							    """MaxLengthCriteria""",
							    """MaxTimeCriteria""",
							    """StoppingCriteria""",
							    """StoppingCriteriaList""",
							    """validate_stopping_criteria""",
							]
							_lowercase						   =     [
							    """GenerationMixin""",
							    """top_k_top_p_filtering""",
							    """GreedySearchEncoderDecoderOutput""",
							    """GreedySearchDecoderOnlyOutput""",
							    """SampleEncoderDecoderOutput""",
							    """SampleDecoderOnlyOutput""",
							    """BeamSearchEncoderDecoderOutput""",
							    """BeamSearchDecoderOnlyOutput""",
							    """BeamSampleEncoderDecoderOutput""",
							    """BeamSampleDecoderOnlyOutput""",
							    """ContrastiveSearchEncoderDecoderOutput""",
							    """ContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """TFForcedBOSTokenLogitsProcessor""",
							    """TFForcedEOSTokenLogitsProcessor""",
							    """TFLogitsProcessor""",
							    """TFLogitsProcessorList""",
							    """TFLogitsWarper""",
							    """TFMinLengthLogitsProcessor""",
							    """TFNoBadWordsLogitsProcessor""",
							    """TFNoRepeatNGramLogitsProcessor""",
							    """TFRepetitionPenaltyLogitsProcessor""",
							    """TFTemperatureLogitsWarper""",
							    """TFTopKLogitsWarper""",
							    """TFTopPLogitsWarper""",
							    """TFForceTokensLogitsProcessor""",
							    """TFSuppressTokensAtBeginLogitsProcessor""",
							    """TFSuppressTokensLogitsProcessor""",
							]
							_lowercase						   =     [
							    """TFGenerationMixin""",
							    """tf_top_k_top_p_filtering""",
							    """TFGreedySearchDecoderOnlyOutput""",
							    """TFGreedySearchEncoderDecoderOutput""",
							    """TFSampleEncoderDecoderOutput""",
							    """TFSampleDecoderOnlyOutput""",
							    """TFBeamSearchEncoderDecoderOutput""",
							    """TFBeamSearchDecoderOnlyOutput""",
							    """TFBeamSampleEncoderDecoderOutput""",
							    """TFBeamSampleDecoderOnlyOutput""",
							    """TFContrastiveSearchEncoderDecoderOutput""",
							    """TFContrastiveSearchDecoderOnlyOutput""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase						   =     [
							    """FlaxForcedBOSTokenLogitsProcessor""",
							    """FlaxForcedEOSTokenLogitsProcessor""",
							    """FlaxLogitsProcessor""",
							    """FlaxLogitsProcessorList""",
							    """FlaxLogitsWarper""",
							    """FlaxMinLengthLogitsProcessor""",
							    """FlaxTemperatureLogitsWarper""",
							    """FlaxTopKLogitsWarper""",
							    """FlaxTopPLogitsWarper""",
							]
							_lowercase						   =     [
							    """FlaxGenerationMixin""",
							    """FlaxGreedySearchOutput""",
							    """FlaxSampleOutput""",
							    """FlaxBeamSearchOutput""",
							]

if TYPE_CHECKING:
							from .configuration_utils import GenerationConfig
							from .streamers import TextIteratorStreamer, TextStreamer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
														from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
														from .logits_process import (
														    EncoderNoRepeatNGramLogitsProcessor,
														    EncoderRepetitionPenaltyLogitsProcessor,
														    EpsilonLogitsWarper,
														    EtaLogitsWarper,
														    ExponentialDecayLengthPenalty,
														    ForcedBOSTokenLogitsProcessor,
														    ForcedEOSTokenLogitsProcessor,
														    HammingDiversityLogitsProcessor,
														    InfNanRemoveLogitsProcessor,
														    LogitNormalization,
														    LogitsProcessor,
														    LogitsProcessorList,
														    LogitsWarper,
														    MinLengthLogitsProcessor,
														    MinNewTokensLengthLogitsProcessor,
														    NoBadWordsLogitsProcessor,
														    NoRepeatNGramLogitsProcessor,
														    PrefixConstrainedLogitsProcessor,
														    RepetitionPenaltyLogitsProcessor,
														    SequenceBiasLogitsProcessor,
														    TemperatureLogitsWarper,
														    TopKLogitsWarper,
														    TopPLogitsWarper,
														    TypicalLogitsWarper,
														)
														from .stopping_criteria import (
														    MaxLengthCriteria,
														    MaxNewTokensCriteria,
														    MaxTimeCriteria,
														    StoppingCriteria,
														    StoppingCriteriaList,
														    validate_stopping_criteria,
														)
														from .utils import (
														    BeamSampleDecoderOnlyOutput,
														    BeamSampleEncoderDecoderOutput,
														    BeamSearchDecoderOnlyOutput,
														    BeamSearchEncoderDecoderOutput,
														    ContrastiveSearchDecoderOnlyOutput,
														    ContrastiveSearchEncoderDecoderOutput,
														    GenerationMixin,
														    GreedySearchDecoderOnlyOutput,
														    GreedySearchEncoderDecoderOutput,
														    SampleDecoderOnlyOutput,
														    SampleEncoderDecoderOutput,
														    top_k_top_p_filtering,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tf_logits_process import (
														    TFForcedBOSTokenLogitsProcessor,
														    TFForcedEOSTokenLogitsProcessor,
														    TFForceTokensLogitsProcessor,
														    TFLogitsProcessor,
														    TFLogitsProcessorList,
														    TFLogitsWarper,
														    TFMinLengthLogitsProcessor,
														    TFNoBadWordsLogitsProcessor,
														    TFNoRepeatNGramLogitsProcessor,
														    TFRepetitionPenaltyLogitsProcessor,
														    TFSuppressTokensAtBeginLogitsProcessor,
														    TFSuppressTokensLogitsProcessor,
														    TFTemperatureLogitsWarper,
														    TFTopKLogitsWarper,
														    TFTopPLogitsWarper,
														)
														from .tf_utils import (
														    TFBeamSampleDecoderOnlyOutput,
														    TFBeamSampleEncoderDecoderOutput,
														    TFBeamSearchDecoderOnlyOutput,
														    TFBeamSearchEncoderDecoderOutput,
														    TFContrastiveSearchDecoderOnlyOutput,
														    TFContrastiveSearchEncoderDecoderOutput,
														    TFGenerationMixin,
														    TFGreedySearchDecoderOnlyOutput,
														    TFGreedySearchEncoderDecoderOutput,
														    TFSampleDecoderOnlyOutput,
														    TFSampleEncoderDecoderOutput,
														    tf_top_k_top_p_filtering,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .flax_logits_process import (
														    FlaxForcedBOSTokenLogitsProcessor,
														    FlaxForcedEOSTokenLogitsProcessor,
														    FlaxLogitsProcessor,
														    FlaxLogitsProcessorList,
														    FlaxLogitsWarper,
														    FlaxMinLengthLogitsProcessor,
														    FlaxTemperatureLogitsWarper,
														    FlaxTopKLogitsWarper,
														    FlaxTopPLogitsWarper,
														)
														from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
							import sys

							_lowercase						   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


