'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      _lowerCamelCase :				List[str]														= None

_lowerCamelCase :				Tuple														= logging.get_logger(__name__)

_lowerCamelCase :				List[Any]														= {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase :				List[str]														= {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

_lowerCamelCase :				Optional[Any]														= {
    """microsoft/deberta-v2-xlarge""": 5_12,
    """microsoft/deberta-v2-xxlarge""": 5_12,
    """microsoft/deberta-v2-xlarge-mnli""": 5_12,
    """microsoft/deberta-v2-xxlarge-mnli""": 5_12,
}

_lowerCamelCase :				Optional[int]														= {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						VOCAB_FILES_NAMES
   __UpperCAmelCase           =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase           =						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase           =						DebertaVaTokenizer



   def __init__(    self					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[UNK]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[PAD]"					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[MASK]"					,      **lowerCAmelCase__					,      )				->     None:
        """simple docstring"""





        super().__init__(
            lowerCAmelCase__					,      tokenizer_file=lowerCAmelCase__					,      do_lower_case=lowerCAmelCase__					,      bos_token=lowerCAmelCase__					,      eos_token=lowerCAmelCase__					,      unk_token=lowerCAmelCase__					,      sep_token=lowerCAmelCase__					,      pad_token=lowerCAmelCase__					,      cls_token=lowerCAmelCase__					,      mask_token=lowerCAmelCase__					,      split_by_punct=lowerCAmelCase__					,      **lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =do_lower_case
        _UpperCamelCase  :Dict       =split_by_punct
        _UpperCamelCase  :Any       =vocab_file
        _UpperCamelCase  :Any       =False if not self.vocab_file else True



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     int:
        """simple docstring"""





        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCamelCase  :List[Any]       =[self.cls_token_id]
        _UpperCamelCase  :List[str]       =[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False     )				->     Optional[Any]:
        """simple docstring"""





        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCAmelCase__					,      token_ids_a=lowerCAmelCase__					,      already_has_special_tokens=lowerCAmelCase__     )

        if token_ids_a is not None:
             return [1] + ([0] * len(lowerCAmelCase__     )) + [1] + ([0] * len(lowerCAmelCase__     )) + [1]
        return [1] + ([0] * len(lowerCAmelCase__     )) + [1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =[self.sep_token_id]
        _UpperCamelCase  :List[str]       =[self.cls_token_id]
        if token_ids_a is None:
             return len(cls + token_ids_a + sep     ) * [0]
        return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__ = None     )				->     Tuple[str]:
        """simple docstring"""





        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""     )

        if not os.path.isdir(lowerCAmelCase__     ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
             return
        _UpperCamelCase  :str       =os.path.join(
            lowerCAmelCase__					,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCAmelCase__     ):
             copyfile(self.vocab_file					,      lowerCAmelCase__     )

        return (out_vocab_file,)

"""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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

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

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

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

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					return action_preds[0, -1]


# build the environment

snake_case					  =						gym.make('Hopper-v3')
snake_case					  =						env.observation_space.shape[0]
snake_case					  =						env.action_space.shape[0]
snake_case					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case					  =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case					  =						torch.from_numpy(state_mean).to(device=device)
snake_case					  =						torch.from_numpy(state_std).to(device=device)

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

for ep in range(1_0):
	snake_case  ,     snake_case					  =						0, 0
	snake_case					  =						env.reset()
	snake_case					  =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case					  =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros(0, device=device, dtype=torch.floataa)

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

		snake_case					  =						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					  =						action
		snake_case					  =						action.detach().cpu().numpy()

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

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

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