




'''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 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







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

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

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

		_snake_case	,   _snake_case	,   _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

_lowerCamelCase							:						int							     =  gym.make('Hopper-v3')
_lowerCamelCase							:						int							     =  env.observation_space.shape[0]
_lowerCamelCase							:						Any							     =  env.action_space.shape[0]
_lowerCamelCase							:						List[Any]							     =  1_000
_lowerCamelCase							:						Dict							     =  'cuda'
_lowerCamelCase							:						str							     =  1_000.0  # normalization for rewards/returns
_lowerCamelCase							:						Tuple							     =  3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCamelCase							:						List[Any]							     =  np.array(
    [
        1.311279,
        -0.08469521,
        -0.5382719,
        -0.07201576,
        0.04932366,
        2.1066856,
        -0.15017354,
        0.00878345,
        -0.2848186,
        -0.18540096,
        -0.28461286,
    ]
)
_lowerCamelCase							:						Tuple							     =  np.array(
    [
        0.17790751,
        0.05444621,
        0.21297139,
        0.14530419,
        0.6124444,
        0.85174465,
        1.4515252,
        0.6751696,
        1.536239,
        1.6160746,
        5.6072536,
    ]
)
_lowerCamelCase							:						List[str]							     =  torch.from_numpy(state_mean).to(device=device)
_lowerCamelCase							:						List[str]							     =  torch.from_numpy(state_std).to(device=device)

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

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

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

												_lowerCamelCase							:						Dict							     =  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),
												)
												_lowerCamelCase							:						str							     =  action
												_lowerCamelCase							:						List[str]							     =  action.detach().cpu().numpy()

												_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase							:						List[Any]							     =  env.step(action)

												_lowerCamelCase							:						List[str]							     =  torch.from_numpy(state).to(device=device).reshape(1, state_dim)
												_lowerCamelCase							:						List[Any]							     =  torch.cat([states, cur_state], dim=0)
												_lowerCamelCase							:						Union[str, Any]							     =  reward

												_lowerCamelCase							:						Dict							     =  target_return[0, -1] - (reward / scale)
												_lowerCamelCase							:						Union[str, Any]							     =  torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_lowerCamelCase							:						Dict							     =  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'''







import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_lowerCamelCase							:						Tuple							     =  logging.getLogger(__name__)





@dataclass
class 					lowerCamelCase_							:




					'''simple docstring'''






					lowercase__   =			field(
					    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
					lowercase__   =			field(default=UpperCAmelCase_,	metadata={"""help""": """Set this flag to use fast tokenization."""}							)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					lowercase__   =			field(
					    default=UpperCAmelCase_,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},	)






def 		_lowercase		(     ):



		'''simple docstring'''







		_snake_case     	=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )

		if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				_snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				_snake_case	,   _snake_case	,   _snake_case     	=	parser.parse_args_into_dataclasses()

		if (
		    os.path.exists(training_args.output_dir  )
		    and os.listdir(training_args.output_dir  )
		    and training_args.do_train
		    and not training_args.overwrite_output_dir
		):
				raise ValueError(
				    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
				    " --overwrite_output_dir to overcome."  )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,							datefmt="%m/%d/%Y %H:%M:%S"						,							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,							)
		logger.warning(
		    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"						,							training_args.local_rank						,							training_args.device						,							training_args.n_gpu						,							bool(training_args.local_rank != -1  )						,							training_args.fpaa						,							)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank  ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info("Training/evaluation parameters %s"						,							lowercase_  )

		# Prepare Question-Answering task
		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		_snake_case     	=	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							)
		_snake_case     	=	AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,							cache_dir=model_args.cache_dir						,							use_fast=lowercase_						,							)
		_snake_case     	=	AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path						,							from_tf=bool(".ckpt" in model_args.model_name_or_path  )						,							config=lowercase_						,							cache_dir=model_args.cache_dir						,							)

		# Get datasets
		_snake_case     	=	hasattr(model.config						,							"lang2id"  )
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir  )
		    if training_args.do_train
		    else None
		)
		_snake_case     	=	(
		    SquadDataset(
		        lowercase_						,							tokenizer=lowercase_						,							mode="dev"						,							is_language_sensitive=lowercase_						,							cache_dir=model_args.cache_dir						,							)
		    if training_args.do_eval
		    else None
		)

		# Data collator
		_snake_case     	=	DataCollatorWithPadding(lowercase_						,							pad_to_multiple_of=8  ) if training_args.fpaa else None

		# Initialize our Trainer
		_snake_case     	=	Trainer(
		    model=lowercase_						,							args=lowercase_						,							train_dataset=lowercase_						,							eval_dataset=lowercase_						,							data_collator=lowercase_						,							)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir  )






def 		_lowercase		(     lowercase_  ):



		'''simple docstring'''







		main()


if __name__ == "__main__":
						main()





